Introduction
IBM Watson™ Language Translator translates text from one language to another. The service offers multiple IBM-provided translation models that you can customize based on your unique terminology and language. Use Language Translator to take news from across the globe and present it in your language, communicate with your customers in their own language, and more.
This documentation describes Java SDK major version 9. For more information about how to update your code from the previous version, see the migration guide.
This documentation describes Node SDK major version 6. For more information about how to update your code from the previous version, see the migration guide.
This documentation describes Python SDK major version 5. For more information about how to update your code from the previous version, see the migration guide.
This documentation describes Ruby SDK major version 2. For more information about how to update your code from the previous version, see the migration guide.
This documentation describes .NET Standard SDK major version 5. For more information about how to update your code from the previous version, see the migration guide.
This documentation describes Go SDK major version 2. For more information about how to update your code from the previous version, see the migration guide.
This documentation describes Swift SDK major version 4. For more information about how to update your code from the previous version, see the migration guide.
This documentation describes Unity SDK major version 5. For more information about how to update your code from the previous version, see the migration guide.
The IBM Watson Unity SDK has the following requirements.
- The SDK requires Unity version 2018.2 or later to support Transport Layer Security (TLS) 1.2.
- Set the project settings for both the Scripting Runtime Version and the Api Compatibility Level to
.NET 4.x Equivalent
. - For more information, see TLS 1.0 support.
- Set the project settings for both the Scripting Runtime Version and the Api Compatibility Level to
- The SDK doesn't support the WebGL projects. Change your build settings to any platform except
WebGL
.
For more information about how to install and configure the SDK and SDK Core, see https://github.com/watson-developer-cloud/unity-sdk.
The code examples on this tab use the client library that is provided for Java.
Maven
<dependency>
<groupId>com.ibm.watson</groupId>
<artifactId>ibm-watson</artifactId>
<version>10.0.1</version>
</dependency>
Gradle
compile 'com.ibm.watson:ibm-watson:10.0.1'
GitHub
The code examples on this tab use the client library that is provided for Node.js.
Installation
npm install ibm-watson@^7.0.0
GitHub
The code examples on this tab use the client library that is provided for Python.
Installation
pip install --upgrade "ibm-watson>=6.0.0"
GitHub
The code examples on this tab use the client library that is provided for Ruby.
Installation
gem install ibm_watson
GitHub
The code examples on this tab use the client library that is provided for Go.
go get -u github.com/watson-developer-cloud/go-sdk/v2@v3.0.0
GitHub
The code examples on this tab use the client library that is provided for Swift.
Cocoapods
pod 'IBMWatsonLanguageTranslatorV3', '~> 5.0.0'
Carthage
github "watson-developer-cloud/swift-sdk" ~> 5.0.0
Swift Package Manager
.package(url: "https://github.com/watson-developer-cloud/swift-sdk", from: "5.0.0")
GitHub
The code examples on this tab use the client library that is provided for .NET Standard.
Package Manager
Install-Package IBM.Watson.LanguageTranslator.v3 -Version 6.0.0
.NET CLI
dotnet add package IBM.Watson.LanguageTranslator.v3 --version 6.0.0
PackageReference
<PackageReference Include="IBM.Watson.LanguageTranslator.v3" Version="6.0.0" />
GitHub
The code examples on this tab use the client library that is provided for Unity.
GitHub
IBM Cloud URLs
The base URLs come from the service instance. To find the URL, view the service credentials by clicking the name of the service in the Resource list. Use the value of the URL. Add the method to form the complete API endpoint for your request.
The following example URL represents a Language Translator instance that is hosted in Washington DC:
https://api.us-east.language-translator.watson.cloud.ibm.com/instances/6bbda3b3-d572-45e1-8c54-22d6ed9e52c2
The following URLs represent the base URLs for Language Translator. When you call the API, use the URL that corresponds to the location of your service instance.
- Dallas:
https://api.us-south.language-translator.watson.cloud.ibm.com
- Washington DC:
https://api.us-east.language-translator.watson.cloud.ibm.com
- Frankfurt:
https://api.eu-de.language-translator.watson.cloud.ibm.com
- Sydney:
https://api.au-syd.language-translator.watson.cloud.ibm.com
- Tokyo:
https://api.jp-tok.language-translator.watson.cloud.ibm.com
- London:
https://api.eu-gb.language-translator.watson.cloud.ibm.com
- Seoul:
https://api.kr-seo.language-translator.watson.cloud.ibm.com
Set the correct service URL by calling the setServiceUrl()
method of the service instance.
Set the correct service URL by specifying the serviceUrl
parameter when you create the service instance.
Set the correct service URL by calling the set_service_url()
method of the service instance.
Set the correct service URL by specifying the service_url
property of the service instance.
Set the correct service URL by calling the SetServiceURL()
method of the service instance.
Set the correct service URL by setting the serviceURL
property of the service instance.
Set the correct service URL by calling the SetServiceUrl()
method of the service instance.
Set the correct service URL by calling the SetServiceUrl()
method of the service instance.
Dallas API endpoint example for services managed on IBM Cloud
curl -X {request_method} -u "apikey:{apikey}" "https://api.us-south.language-translator.watson.cloud.ibm.com/instances/{instance_id}"
Your service instance might not use this URL
Default URL
https://api.us-south.language-translator.watson.cloud.ibm.com
Example for the Washington DC location
IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
LanguageTranslator languageTranslator = new LanguageTranslator("{version}", authenticator);
languageTranslator.setServiceUrl("https://api.us-east.language-translator.watson.cloud.ibm.com");
Default URL
https://api.us-south.language-translator.watson.cloud.ibm.com
Example for the Washington DC location
const LanguageTranslatorV3 = require('ibm-watson/language-translator/v3');
const { IamAuthenticator } = require('ibm-watson/auth');
const languageTranslator = new LanguageTranslatorV3({
version: '{version}',
authenticator: new IamAuthenticator({
apikey: '{apikey}',
}),
serviceUrl: 'https://api.us-east.language-translator.watson.cloud.ibm.com',
});
Default URL
https://api.us-south.language-translator.watson.cloud.ibm.com
Example for the Washington DC location
from ibm_watson import LanguageTranslatorV3
from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
authenticator = IAMAuthenticator('{apikey}')
language_translator = LanguageTranslatorV3(
version='{version}',
authenticator=authenticator
)
language_translator.set_service_url('https://api.us-east.language-translator.watson.cloud.ibm.com')
Default URL
https://api.us-south.language-translator.watson.cloud.ibm.com
Example for the Washington DC location
require "ibm_watson/authenticators"
require "ibm_watson/language_translator_v3"
include IBMWatson
authenticator = Authenticators::IamAuthenticator.new(
apikey: "{apikey}"
)
language_translator = LanguageTranslatorV3.new(
version: "{version}",
authenticator: authenticator
)
language_translator.service_url = "https://api.us-east.language-translator.watson.cloud.ibm.com"
Default URL
https://api.us-south.language-translator.watson.cloud.ibm.com
Example for the Washington DC location
languageTranslator, languageTranslatorErr := languagetranslatorv3.NewLanguageTranslatorV3(options)
if languageTranslatorErr != nil {
panic(languageTranslatorErr)
}
languageTranslator.SetServiceURL("https://api.us-east.language-translator.watson.cloud.ibm.com")
Default URL
https://api.us-south.language-translator.watson.cloud.ibm.com
Example for the Washington DC location
let authenticator = WatsonIAMAuthenticator(apiKey: "{apikey}")
let languageTranslator = LanguageTranslator(version: "{version}", authenticator: authenticator)
languageTranslator.serviceURL = "https://api.us-east.language-translator.watson.cloud.ibm.com"
Default URL
https://api.us-south.language-translator.watson.cloud.ibm.com
Example for the Washington DC location
IamAuthenticator authenticator = new IamAuthenticator(
apikey: "{apikey}"
);
LanguageTranslatorService languageTranslator = new LanguageTranslatorService("{version}", authenticator);
languageTranslator.SetServiceUrl("https://api.us-east.language-translator.watson.cloud.ibm.com");
Default URL
https://api.us-south.language-translator.watson.cloud.ibm.com
Example for the Washington DC location
var authenticator = new IamAuthenticator(
apikey: "{apikey}"
);
while (!authenticator.CanAuthenticate())
yield return null;
var languageTranslator = new LanguageTranslatorService("{version}", authenticator);
languageTranslator.SetServiceUrl("https://api.us-east.language-translator.watson.cloud.ibm.com");
Disabling SSL verification
All Watson services use Secure Sockets Layer (SSL) (or Transport Layer Security (TLS)) for secure connections between the client and server. The connection is verified against the local certificate store to ensure authentication, integrity, and confidentiality.
If you use a self-signed certificate, you need to disable SSL verification to make a successful connection.
Enabling SSL verification is highly recommended. Disabling SSL jeopardizes the security of the connection and data. Disable SSL only if necessary, and take steps to enable SSL as soon as possible.
To disable SSL verification for a curl request, use the --insecure
(-k
) option with the request.
To disable SSL verification, create an HttpConfigOptions
object and set the disableSslVerification
property to true
. Then, pass the object to the service instance by using the configureClient
method.
To disable SSL verification, set the disableSslVerification
parameter to true
when you create the service instance.
To disable SSL verification, specify True
on the set_disable_ssl_verification
method for the service instance.
To disable SSL verification, set the disable_ssl_verification
parameter to true
in the configure_http_client()
method for the service instance.
To disable SSL verification, call the DisableSSLVerification
method on the service instance.
To disable SSL verification, call the disableSSLVerification()
method on the service instance. You cannot disable SSL verification on Linux.
To disable SSL verification, set the DisableSslVerification
method to true
on the service instance.
To disable SSL verification, set the DisableSslVerification
method to true
on the service instance.
Example to disable SSL verification. Replace {apikey}
and {url}
with your service credentials.
curl -k -X {request_method} -u "apikey:{apikey}" "{url}/{method}"
Example to disable SSL verification
IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
LanguageTranslator languageTranslator = new LanguageTranslator("{version}", authenticator);
languageTranslator.setServiceUrl("{url}");
HttpConfigOptions configOptions = new HttpConfigOptions.Builder()
.disableSslVerification(true)
.build();
languageTranslator.configureClient(configOptions);
Example to disable SSL verification
const LanguageTranslatorV3 = require('ibm-watson/language-translator/v3');
const { IamAuthenticator } = require('ibm-watson/auth');
const languageTranslator = new LanguageTranslatorV3({
version: '{version}',
authenticator: new IamAuthenticator({
apikey: '{apikey}',
}),
serviceUrl: '{url}',
disableSslVerification: true,
});
Example to disable SSL verification
from ibm_watson import LanguageTranslatorV3
from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
authenticator = IAMAuthenticator('{apikey}')
language_translator = LanguageTranslatorV3(
version='{version}',
authenticator=authenticator
)
language_translator.set_service_url('{url}')
language_translator.set_disable_ssl_verification(True)
Example to disable SSL verification
require "ibm_watson/authenticators"
require "ibm_watson/language_translator_v3"
include IBMWatson
authenticator = Authenticators::IamAuthenticator.new(
apikey: "{apikey}"
)
language_translator = LanguageTranslatorV3.new(
version: "{version}",
authenticator: authenticator
)
language_translator.service_url = "{url}"
language_translator.configure_http_client(disable_ssl_verification: true)
Example to disable SSL verification
languageTranslator, languageTranslatorErr := languagetranslatorv3.NewLanguageTranslatorV3(options)
if languageTranslatorErr != nil {
panic(languageTranslatorErr)
}
languageTranslator.SetServiceURL("{url}")
languageTranslator.DisableSSLVerification()
Example to disable SSL verification
let authenticator = WatsonIAMAuthenticator(apiKey: "{apikey}")
let languageTranslator = LanguageTranslator(version: "{version}", authenticator: authenticator)
languageTranslator.serviceURL = "{url}"
languageTranslator.disableSSLVerification()
Example to disable SSL verification
IamAuthenticator authenticator = new IamAuthenticator(
apikey: "{apikey}"
);
LanguageTranslatorService languageTranslator = new LanguageTranslatorService("{version}", authenticator);
languageTranslator.SetServiceUrl("{url}");
languageTranslator.DisableSslVerification(true);
Example to disable SSL verification
var authenticator = new IamAuthenticator(
apikey: "{apikey}"
);
while (!authenticator.CanAuthenticate())
yield return null;
var languageTranslator = new LanguageTranslatorService("{version}", authenticator);
languageTranslator.SetServiceUrl("{url}");
languageTranslator.DisableSslVerification = true;
Authentication
You authenticate to the API by using IBM Cloud Identity and Access Management (IAM).
You can pass either a bearer token in an authorization header or an API key. Tokens support authenticated requests without embedding service credentials in every call. API keys use basic authentication. For more information, see Authenticating to Watson services.
- For testing and development, you can pass an API key directly.
- For production use, unless you use the Watson SDKs, use an IAM token.
If you pass in an API key, use apikey
for the username and the value of the API key as the password. For example, if the API key is f5sAznhrKQyvBFFaZbtF60m5tzLbqWhyALQawBg5TjRI
in the service credentials, include the credentials in your call like this:
curl -u "apikey:f5sAznhrKQyvBFFaZbtF60m5tzLbqWhyALQawBg5TjRI"
For IBM Cloud instances, the SDK provides initialization methods for each form of authentication.
- Use the API key to have the SDK manage the lifecycle of the access token. The SDK requests an access token, ensures that the access token is valid, and refreshes it if necessary.
- Use the access token to manage the lifecycle yourself. You must periodically refresh the token.
For more information, see IAM authentication with the SDK.For more information, see IAM authentication with the SDK.For more information, see IAM authentication with the SDK.For more information, see IAM authentication with the SDK.For more information, see IAM authentication with the SDK.For more information, see IAM authentication with the SDK.For more information, see IAM authentication with the SDK.For more information, see IAM authentication with the SDK.
Replace {apikey}
and {url}
with your service credentials.
curl -X {request_method} -u "apikey:{apikey}" "{url}/v3/{method}"
SDK managing the IAM token. Replace {apikey}
, {version}
, and {url}
.
IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
LanguageTranslator languageTranslator = new LanguageTranslator("{version}", authenticator);
languageTranslator.setServiceUrl("{url}");
SDK managing the IAM token. Replace {apikey}
, {version}
, and {url}
.
const LanguageTranslatorV3 = require('ibm-watson/language-translator/v3');
const { IamAuthenticator } = require('ibm-watson/auth');
const languageTranslator = new LanguageTranslatorV3({
version: '{version}',
authenticator: new IamAuthenticator({
apikey: '{apikey}',
}),
serviceUrl: '{url}',
});
SDK managing the IAM token. Replace {apikey}
, {version}
, and {url}
.
from ibm_watson import LanguageTranslatorV3
from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
authenticator = IAMAuthenticator('{apikey}')
language_translator = LanguageTranslatorV3(
version='{version}',
authenticator=authenticator
)
language_translator.set_service_url('{url}')
SDK managing the IAM token. Replace {apikey}
, {version}
, and {url}
.
require "ibm_watson/authenticators"
require "ibm_watson/language_translator_v3"
include IBMWatson
authenticator = Authenticators::IamAuthenticator.new(
apikey: "{apikey}"
)
language_translator = LanguageTranslatorV3.new(
version: "{version}",
authenticator: authenticator
)
language_translator.service_url = "{url}"
SDK managing the IAM token. Replace {apikey}
, {version}
, and {url}
.
import (
"github.com/IBM/go-sdk-core/core"
"github.com/watson-developer-cloud/go-sdk/languagetranslatorv3"
)
func main() {
authenticator := &core.IamAuthenticator{
ApiKey: "{apikey}",
}
options := &languagetranslatorv3.LanguageTranslatorV3Options{
Version: "{version}",
Authenticator: authenticator,
}
languageTranslator, languageTranslatorErr := languagetranslatorv3.NewLanguageTranslatorV3(options)
if languageTranslatorErr != nil {
panic(languageTranslatorErr)
}
languageTranslator.SetServiceURL("{url}")
}
SDK managing the IAM token. Replace {apikey}
, {version}
, and {url}
.
let authenticator = WatsonIAMAuthenticator(apiKey: "{apikey}")
let languageTranslator = LanguageTranslator(version: "{version}", authenticator: authenticator)
languageTranslator.serviceURL = "{url}"
SDK managing the IAM token. Replace {apikey}
, {version}
, and {url}
.
IamAuthenticator authenticator = new IamAuthenticator(
apikey: "{apikey}"
);
LanguageTranslatorService languageTranslator = new LanguageTranslatorService("{version}", authenticator);
languageTranslator.SetServiceUrl("{url}");
SDK managing the IAM token. Replace {apikey}
, {version}
, and {url}
.
var authenticator = new IamAuthenticator(
apikey: "{apikey}"
);
while (!authenticator.CanAuthenticate())
yield return null;
var languageTranslator = new LanguageTranslatorService("{version}", authenticator);
languageTranslator.SetServiceUrl("{url}");
Access between services
Your application might use more than one Watson service. You can grant access between services and you can grant access to more than one service for your applications.
For IBM Cloud services, the method to grant access between Watson services varies depending on the type of API key. For more information, see IAM access.
- To grant access between IBM Cloud services, create an authorization between the services. For more information, see Granting access between services.
- To grant access to your services by applications without using user credentials, create a service ID, add an API key, and assign access policies. For more information, see Creating and working with service IDs.
When you give a user ID access to multiple services, use an endpoint URL that includes the service instance ID (for example, https://api.us-south.language-translator.watson.cloud.ibm.com/instances/6bbda3b3-d572-45e1-8c54-22d6ed9e52c2
). You can find the instance ID in two places:
-
By clicking the service instance row in the Resource list. The instance ID is the GUID in the details pane.
-
By clicking the name of the service instance in the list and looking at the credentials URL.
If you don't see the instance ID in the URL, the credentials predate service IDs. Add new credentials from the Service credentials page and use those credentials.
Versioning
API requests require a version parameter that takes a date in the format version=YYYY-MM-DD
. When the API is updated with any breaking changes, the service introduces a new version date for the API.
Send the version parameter with every API request. The service uses the API version for the date you specify, or the most recent version before that date. Don't default to the current date. Instead, specify a date that matches a version that is compatible with your app, and don't change it until your app is ready for a later version.
Specify the version to use on API requests with the version parameter when you create the service instance. The service uses the API version for the date you specify, or the most recent version before that date. Don't default to the current date. Instead, specify a date that matches a version that is compatible with your app, and don't change it until your app is ready for a later version.
This documentation describes the current version of Language Translator, 2018-05-01
. In some cases, differences in earlier versions are noted in the descriptions of parameters and response models.
Error handling
Language Translator uses standard HTTP response codes to indicate whether a method completed successfully. HTTP response codes in the 2xx range indicate success. A response in the 4xx range is some sort of failure, and a response in the 5xx range usually indicates an internal system error that cannot be resolved by the user. Response codes are listed with the method.
ErrorResponse
Name | Description |
---|---|
code integer |
The HTTP response code. |
error string |
General description of an error. |
The Java SDK generates an exception for any unsuccessful method invocation. All methods that accept an argument can also throw an IllegalArgumentException
.
Exception | Description |
---|---|
IllegalArgumentException | An invalid argument was passed to the method. |
When the Java SDK receives an error response from the Language Translator service, it generates an exception from the com.ibm.watson.developer_cloud.service.exception
package. All service exceptions contain the following fields.
Field | Description |
---|---|
statusCode | The HTTP response code that is returned. |
message | A message that describes the error. |
When the Node SDK receives an error response from the Language Translator service, it creates an Error
object with information that describes the error that occurred. This error object is passed as the first parameter to the callback function for the method. The contents of the error object are as shown in the following table.
Error
Field | Description |
---|---|
code | The HTTP response code that is returned. |
message | A message that describes the error. |
The Python SDK generates an exception for any unsuccessful method invocation. When the Python SDK receives an error response from the Language Translator service, it generates an ApiException
with the following fields.
Field | Description |
---|---|
code | The HTTP response code that is returned. |
message | A message that describes the error. |
info | A dictionary of additional information about the error. |
When the Ruby SDK receives an error response from the Language Translator service, it generates an ApiException
with the following fields.
Field | Description |
---|---|
code | The HTTP response code that is returned. |
message | A message that describes the error. |
info | A dictionary of additional information about the error. |
The Go SDK generates an error for any unsuccessful service instantiation and method invocation. You can check for the error immediately. The contents of the error object are as shown in the following table.
Error
Field | Description |
---|---|
code | The HTTP response code that is returned. |
message | A message that describes the error. |
The Swift SDK returns a WatsonError
in the completionHandler
any unsuccessful method invocation. This error type is an enum that conforms to LocalizedError
and contains an errorDescription
property that returns an error message. Some of the WatsonError
cases contain associated values that reveal more information about the error.
Field | Description |
---|---|
errorDescription | A message that describes the error. |
When the .NET Standard SDK receives an error response from the Language Translator service, it generates a ServiceResponseException
with the following fields.
Field | Description |
---|---|
Message | A message that describes the error. |
CodeDescription | The HTTP response code that is returned. |
When the Unity SDK receives an error response from the Language Translator service, it generates an IBMError
with the following fields.
Field | Description |
---|---|
Url | The URL that generated the error. |
StatusCode | The HTTP response code returned. |
ErrorMessage | A message that describes the error. |
Response | The contents of the response from the server. |
ResponseHeaders | A dictionary of headers returned by the request. |
Example error handling
try {
// Invoke a method
} catch (NotFoundException e) {
// Handle Not Found (404) exception
} catch (RequestTooLargeException e) {
// Handle Request Too Large (413) exception
} catch (ServiceResponseException e) {
// Base class for all exceptions caused by error responses from the service
System.out.println("Service returned status code "
+ e.getStatusCode() + ": " + e.getMessage());
}
Example error handling
languageTranslator.method(params)
.catch(err => {
console.log('error:', err);
});
Example error handling
from ibm_watson import ApiException
try:
# Invoke a method
except ApiException as ex:
print "Method failed with status code " + str(ex.code) + ": " + ex.message
Example error handling
require "ibm_watson"
begin
# Invoke a method
rescue IBMWatson::ApiException => ex
print "Method failed with status code #{ex.code}: #{ex.error}"
end
Example error handling
import "github.com/watson-developer-cloud/go-sdk/languagetranslatorv3"
// Instantiate a service
languageTranslator, languageTranslatorErr := languagetranslatorv3.NewLanguageTranslatorV3(options)
// Check for errors
if languageTranslatorErr != nil {
panic(languageTranslatorErr)
}
// Call a method
result, _, responseErr := languageTranslator.MethodName(&methodOptions)
// Check for errors
if responseErr != nil {
panic(responseErr)
}
Example error handling
languageTranslator.method() {
response, error in
if let error = error {
switch error {
case let .http(statusCode, message, metadata):
switch statusCode {
case .some(404):
// Handle Not Found (404) exception
print("Not found")
case .some(413):
// Handle Request Too Large (413) exception
print("Payload too large")
default:
if let statusCode = statusCode {
print("Error - code: \(statusCode), \(message ?? "")")
}
}
default:
print(error.localizedDescription)
}
return
}
guard let result = response?.result else {
print(error?.localizedDescription ?? "unknown error")
return
}
print(result)
}
Example error handling
try
{
// Invoke a method
}
catch(ServiceResponseException e)
{
Console.WriteLine("Error: " + e.Message);
}
catch (Exception e)
{
Console.WriteLine("Error: " + e.Message);
}
Example error handling
// Invoke a method
languageTranslator.MethodName(Callback, Parameters);
// Check for errors
private void Callback(DetailedResponse<ExampleResponse> response, IBMError error)
{
if (error == null)
{
Log.Debug("ExampleCallback", "Response received: {0}", response.Response);
}
else
{
Log.Debug("ExampleCallback", "Error received: {0}, {1}, {3}", error.StatusCode, error.ErrorMessage, error.Response);
}
}
Additional headers
Some Watson services accept special parameters in headers that are passed with the request.
You can pass request header parameters in all requests or in a single request to the service.
To pass a request header, use the --header
(-H
) option with a curl request.
To pass header parameters with every request, use the setDefaultHeaders
method of the service object. See Data collection for an example use of this method.
To pass header parameters in a single request, use the addHeader
method as a modifier on the request before you execute it.
To pass header parameters with every request, specify the headers
parameter when you create the service object. See Data collection for an example use of this method.
To pass header parameters in a single request, use the headers
method as a modifier on the request before you execute it.
To pass header parameters with every request, specify the set_default_headers
method of the service object. See Data collection for an example use of this method.
To pass header parameters in a single request, include headers
as a dict
in the request.
To pass header parameters with every request, specify the add_default_headers
method of the service object. See Data collection for an example use of this method.
To pass header parameters in a single request, specify the headers
method as a chainable method in the request.
To pass header parameters with every request, specify the SetDefaultHeaders
method of the service object. See Data collection for an example use of this method.
To pass header parameters in a single request, specify the Headers
as a map
in the request.
To pass header parameters with every request, add them to the defaultHeaders
property of the service object. See Data collection for an example use of this method.
To pass header parameters in a single request, pass the headers
parameter to the request method.
To pass header parameters in a single request, use the WithHeader()
method as a modifier on the request before you execute it. See Data collection for an example use of this method.
To pass header parameters in a single request, use the WithHeader()
method as a modifier on the request before you execute it.
Example header parameter in a request
curl -X {request_method} -H "Request-Header: {header_value}" "{url}/v3/{method}"
Example header parameter in a request
ReturnType returnValue = languageTranslator.methodName(parameters)
.addHeader("Custom-Header", "{header_value}")
.execute();
Example header parameter in a request
const parameters = {
{parameters}
};
languageTranslator.methodName(
parameters,
headers: {
'Custom-Header': '{header_value}'
})
.then(result => {
console.log(response);
})
.catch(err => {
console.log('error:', err);
});
Example header parameter in a request
response = language_translator.methodName(
parameters,
headers = {
'Custom-Header': '{header_value}'
})
Example header parameter in a request
response = language_translator.headers(
"Custom-Header" => "{header_value}"
).methodName(parameters)
Example header parameter in a request
result, _, responseErr := languageTranslator.MethodName(
&methodOptions{
Headers: map[string]string{
"Accept": "application/json",
},
},
)
Example header parameter in a request
let customHeader: [String: String] = ["Custom-Header": "{header_value}"]
languageTranslator.methodName(parameters, headers: customHeader) {
response, error in
}
Example header parameter in a request
IamAuthenticator authenticator = new IamAuthenticator(
apikey: "{apikey}"
);
LanguageTranslatorService languageTranslator = new LanguageTranslatorService("{version}", authenticator);
languageTranslator.SetServiceUrl("{url}");
languageTranslator.WithHeader("Custom-Header", "header_value");
Example header parameter in a request
var authenticator = new IamAuthenticator(
apikey: "{apikey}"
);
while (!authenticator.CanAuthenticate())
yield return null;
var languageTranslator = new LanguageTranslatorService("{version}", authenticator);
languageTranslator.SetServiceUrl("{url}");
languageTranslator.WithHeader("Custom-Header", "header_value");
Response details
The Language Translator service might return information to the application in response headers.
To access all response headers that the service returns, include the --include
(-i
) option with a curl request. To see detailed response data for the request, including request headers, response headers, and extra debugging information, include the --verbose
(-v
) option with the request.
Example request to access response headers
curl -X {request_method} {authentication_method} --include "{url}/v3/{method}"
To access information in the response headers, use one of the request methods that returns details with the response: executeWithDetails()
, enqueueWithDetails()
, or rxWithDetails()
. These methods return a Response<T>
object, where T
is the expected response model. Use the getResult()
method to access the response object for the method, and use the getHeaders()
method to access information in response headers.
Example request to access response headers
Response<ReturnType> response = languageTranslator.methodName(parameters)
.executeWithDetails();
// Access response from methodName
ReturnType returnValue = response.getResult();
// Access information in response headers
Headers responseHeaders = response.getHeaders();
All response data is available in the Response<T>
object that is returned by each method. To access information in the response
object, use the following properties.
Property | Description |
---|---|
result |
Returns the response for the service-specific method. |
headers |
Returns the response header information. |
status |
Returns the HTTP status code. |
Example request to access response headers
languageTranslator.methodName(parameters)
.then(response => {
console.log(response.headers);
})
.catch(err => {
console.log('error:', err);
});
The return value from all service methods is a DetailedResponse
object. To access information in the result object or response headers, use the following methods.
DetailedResponse
Method | Description |
---|---|
get_result() |
Returns the response for the service-specific method. |
get_headers() |
Returns the response header information. |
get_status_code() |
Returns the HTTP status code. |
Example request to access response headers
language_translator.set_detailed_response(True)
response = language_translator.methodName(parameters)
# Access response from methodName
print(json.dumps(response.get_result(), indent=2))
# Access information in response headers
print(response.get_headers())
# Access HTTP response status
print(response.get_status_code())
The return value from all service methods is a DetailedResponse
object. To access information in the response
object, use the following properties.
DetailedResponse
Property | Description |
---|---|
result |
Returns the response for the service-specific method. |
headers |
Returns the response header information. |
status |
Returns the HTTP status code. |
Example request to access response headers
response = language_translator.methodName(parameters)
# Access response from methodName
print response.result
# Access information in response headers
print response.headers
# Access HTTP response status
print response.status
The return value from all service methods is a DetailedResponse
object. To access information in the response
object or response headers, use the following methods.
DetailedResponse
Method | Description |
---|---|
GetResult() |
Returns the response for the service-specific method. |
GetHeaders() |
Returns the response header information. |
GetStatusCode() |
Returns the HTTP status code. |
Example request to access response headers
import (
"github.com/IBM/go-sdk-core/core"
"github.com/watson-developer-cloud/go-sdk/languagetranslatorv3"
)
result, response, responseErr := languageTranslator.MethodName(
&methodOptions{})
// Access result
core.PrettyPrint(response.GetResult(), "Result ")
// Access response headers
core.PrettyPrint(response.GetHeaders(), "Headers ")
// Access status code
core.PrettyPrint(response.GetStatusCode(), "Status Code ")
All response data is available in the WatsonResponse<T>
object that is returned in each method's completionHandler
.
Example request to access response headers
languageTranslator.methodName(parameters) {
response, error in
guard let result = response?.result else {
print(error?.localizedDescription ?? "unknown error")
return
}
print(result) // The data returned by the service
print(response?.statusCode)
print(response?.headers)
}
The response contains fields for response headers, response JSON, and the status code.
DetailedResponse
Property | Description |
---|---|
Result |
Returns the result for the service-specific method. |
Response |
Returns the raw JSON response for the service-specific method. |
Headers |
Returns the response header information. |
StatusCode |
Returns the HTTP status code. |
Example request to access response headers
var results = languageTranslator.MethodName(parameters);
var result = results.Result; // The result object
var responseHeaders = results.Headers; // The response headers
var responseJson = results.Response; // The raw response JSON
var statusCode = results.StatusCode; // The response status code
The response contains fields for response headers, response JSON, and the status code.
DetailedResponse
Property | Description |
---|---|
Result |
Returns the result for the service-specific method. |
Response |
Returns the raw JSON response for the service-specific method. |
Headers |
Returns the response header information. |
StatusCode |
Returns the HTTP status code. |
Example request to access response headers
private void Example()
{
languageTranslator.MethodName(Callback, Parameters);
}
private void Callback(DetailedResponse<ResponseType> response, IBMError error)
{
var result = response.Result; // The result object
var responseHeaders = response.Headers; // The response headers
var responseJson = reresponsesults.Response; // The raw response JSON
var statusCode = response.StatusCode; // The response status code
}
Data collection
By default, Language Translator service instances that are not part of Premium plans collect data about API requests and their results. This data is collected only to improve the services for future users. The collected data is not shared or made public. Data is not collected for services that are part of Premium plans.
To prevent IBM usage of your data for an API request, set the X-Watson-Learning-Opt-Out header parameter to true
.
You must set the header on each request that you do not want IBM to access for general service improvements.
You can set the header by using the setDefaultHeaders
method of the service object.
You can set the header by using the headers
parameter when you create the service object.
You can set the header by using the set_default_headers
method of the service object.
You can set the header by using the add_default_headers
method of the service object.
You can set the header by using the SetDefaultHeaders
method of the service object.
You can set the header by adding it to the defaultHeaders
property of the service object.
You can set the header by using the WithHeader()
method of the service object.
Example request
curl -u "apikey:{apikey}" -H "X-Watson-Learning-Opt-Out: true" "{url}/{method}"
Example request
Map<String, String> headers = new HashMap<String, String>();
headers.put("X-Watson-Learning-Opt-Out", "true");
languageTranslator.setDefaultHeaders(headers);
Example request
const LanguageTranslatorV3 = require('ibm-watson/language-translator/v3');
const { IamAuthenticator } = require('ibm-watson/auth');
const languageTranslator = new LanguageTranslatorV3({
version: '{version}',
authenticator: new IamAuthenticator({
apikey: '{apikey}',
}),
serviceUrl: '{url}',
headers: {
'X-Watson-Learning-Opt-Out': 'true'
}
});
Example request
language_translator.set_default_headers({'x-watson-learning-opt-out': "true"})
Example request
language_translator.add_default_headers(headers: {"x-watson-learning-opt-out" => "true"})
Example request
import "net/http"
headers := http.Header{}
headers.Add("x-watson-learning-opt-out", "true")
languageTranslator.SetDefaultHeaders(headers)
Example request
languageTranslator.defaultHeaders["X-Watson-Learning-Opt-Out"] = "true"
Example request
IamAuthenticator authenticator = new IamAuthenticator(
apikey: "{apikey}"
);
LanguageTranslatorService languageTranslator = new LanguageTranslatorService("{version}", authenticator);
languageTranslator.SetServiceUrl("{url}");
languageTranslator.WithHeader("X-Watson-Learning-Opt-Out", "true");
Example request
var authenticator = new IamAuthenticator(
apikey: "{apikey}"
);
while (!authenticator.CanAuthenticate())
yield return null;
var languageTranslator = new LanguageTranslatorService("{version}", authenticator);
languageTranslator.SetServiceUrl("{url}");
languageTranslator.WithHeader("X-Watson-Learning-Opt-Out", "true");
Synchronous and asynchronous requests
The Java SDK supports both synchronous (blocking) and asynchronous (non-blocking) execution of service methods. All service methods implement the ServiceCall interface.
- To call a method synchronously, use the
execute
method of theServiceCall
interface. You can call theexecute
method directly from an instance of the service. - To call a method asynchronously, use the
enqueue
method of theServiceCall
interface to receive a callback when the response arrives. The ServiceCallback interface of the method's argument providesonResponse
andonFailure
methods that you override to handle the callback.
The Ruby SDK supports both synchronous (blocking) and asynchronous (non-blocking) execution of service methods. All service methods implement the Concurrent::Async module. When you use the synchronous or asynchronous methods, an IVar object is returned. You access the DetailedResponse
object by calling ivar_object.value
.
For more information about the Ivar object, see the IVar class docs.
-
To call a method synchronously, either call the method directly or use the
.await
chainable method of theConcurrent::Async
module.Calling a method directly (without
.await
) returns aDetailedResponse
object. -
To call a method asynchronously, use the
.async
chainable method of theConcurrent::Async
module.
You can call the .await
and .async
methods directly from an instance of the service.
Example synchronous request
ReturnType returnValue = languageTranslator.method(parameters).execute();
Example asynchronous request
languageTranslator.method(parameters).enqueue(new ServiceCallback<ReturnType>() {
@Override public void onResponse(ReturnType response) {
. . .
}
@Override public void onFailure(Exception e) {
. . .
}
});
Example synchronous request
response = language_translator.method_name(parameters)
or
response = language_translator.await.method_name(parameters)
Example asynchronous request
response = language_translator.async.method_name(parameters)
Related information
- Language Translator docs
- Release notes
- Javadoc for LanguageTranslator
- Javadoc for sdk-core
Methods
List supported languages
Lists all supported languages for translation. The method returns an array of supported languages with information about each language. Languages are listed in alphabetical order by language code (for example, af
, ar
). In addition to basic information about each language, the response indicates whether the language is supported_as_source
for translation and supported_as_target
for translation. It also lists whether the language is identifiable
.
Lists all supported languages for translation. The method returns an array of supported languages with information about each language. Languages are listed in alphabetical order by language code (for example, af
, ar
). In addition to basic information about each language, the response indicates whether the language is supported_as_source
for translation and supported_as_target
for translation. It also lists whether the language is identifiable
.
Lists all supported languages for translation. The method returns an array of supported languages with information about each language. Languages are listed in alphabetical order by language code (for example, af
, ar
). In addition to basic information about each language, the response indicates whether the language is supported_as_source
for translation and supported_as_target
for translation. It also lists whether the language is identifiable
.
Lists all supported languages for translation. The method returns an array of supported languages with information about each language. Languages are listed in alphabetical order by language code (for example, af
, ar
). In addition to basic information about each language, the response indicates whether the language is supported_as_source
for translation and supported_as_target
for translation. It also lists whether the language is identifiable
.
Lists all supported languages for translation. The method returns an array of supported languages with information about each language. Languages are listed in alphabetical order by language code (for example, af
, ar
). In addition to basic information about each language, the response indicates whether the language is supported_as_source
for translation and supported_as_target
for translation. It also lists whether the language is identifiable
.
Lists all supported languages for translation. The method returns an array of supported languages with information about each language. Languages are listed in alphabetical order by language code (for example, af
, ar
). In addition to basic information about each language, the response indicates whether the language is supported_as_source
for translation and supported_as_target
for translation. It also lists whether the language is identifiable
.
Lists all supported languages for translation. The method returns an array of supported languages with information about each language. Languages are listed in alphabetical order by language code (for example, af
, ar
). In addition to basic information about each language, the response indicates whether the language is supported_as_source
for translation and supported_as_target
for translation. It also lists whether the language is identifiable
.
Lists all supported languages for translation. The method returns an array of supported languages with information about each language. Languages are listed in alphabetical order by language code (for example, af
, ar
). In addition to basic information about each language, the response indicates whether the language is supported_as_source
for translation and supported_as_target
for translation. It also lists whether the language is identifiable
.
Lists all supported languages for translation. The method returns an array of supported languages with information about each language. Languages are listed in alphabetical order by language code (for example, af
, ar
). In addition to basic information about each language, the response indicates whether the language is supported_as_source
for translation and supported_as_target
for translation. It also lists whether the language is identifiable
.
GET /v3/languages
(languageTranslator *LanguageTranslatorV3) ListLanguages(listLanguagesOptions *ListLanguagesOptions) (result *Languages, response *core.DetailedResponse, err error)
(languageTranslator *LanguageTranslatorV3) ListLanguagesWithContext(ctx context.Context, listLanguagesOptions *ListLanguagesOptions) (result *Languages, response *core.DetailedResponse, err error)
ServiceCall<Languages> listLanguages(ListLanguagesOptions listLanguagesOptions)
listLanguages(params)
list_languages(self,
**kwargs
) -> DetailedResponse
list_languages
func listLanguages(
headers: [String: String]? = nil,
completionHandler: @escaping (WatsonResponse<Languages>?, WatsonError?) -> Void)
ListLanguages()
ListLanguages(Callback<Languages> callback)
Request
Instantiate the ListLanguagesOptions
struct and set the fields to provide parameter values for the ListLanguages
method.
Use the ListLanguagesOptions.Builder
to create a ListLanguagesOptions
object that contains the parameter values for the listLanguages
method.
Query Parameters
Release date of the version of the API you want to use. Specify dates in YYYY-MM-DD format. The current version is
2018-05-01
.
WithContext method only
A context.Context instance that you can use to specify a timeout for the operation or to cancel an in-flight request.
parameters
parameters
parameters
parameters
parameters
parameters
curl -X GET --user apikey:{apikey} "{url}/v3/languages?version=2018-05-01"
IamAuthenticator authenticator = new IamAuthenticator( apikey: "{apikey}" ); LanguageTranslatorService languageTranslator = new LanguageTranslatorService("2018-05-01", authenticator); languageTranslator.SetServiceUrl("{url}"); var result = service.ListLanguages(); Console.WriteLine(result.Response);
package main import ( "encoding/json" "fmt" "github.com/IBM/go-sdk-core/v5/core" "github.com/watson-developer-cloud/go-sdk/v2/languagetranslatorv3" ) func main() { authenticator := &core.IamAuthenticator{ ApiKey: "{apikey}", } options := &languagetranslatorv3.LanguageTranslatorV3Options{ Version: "2018-05-01", Authenticator: authenticator, } languageTranslator, languageTranslatorErr := languagetranslatorv3. NewLanguageTranslatorV3(options) if languageTranslatorErr != nil { panic(languageTranslatorErr) } languageTranslator.SetServiceURL("{url}") result, _, responseErr := languageTranslator.ListLanguages( &languagetranslatorv3.ListLanguagesOptions{}, ) if responseErr != nil { panic(responseErr) } b, _ := json.MarshalIndent(result, "", " ") fmt.Println(string(b)) }
IamAuthenticator authenticator = new IamAuthenticator("{apikey}"); LanguageTranslator languageTranslator = new LanguageTranslator("2018-05-01", authenticator); languageTranslator.setServiceUrl("{url}"); Languages languages = languageTranslator.listLanguages() .execute().getResult(); System.out.println(languages);
const LanguageTranslatorV3 = require('ibm-watson/language-translator/v3'); const { IamAuthenticator } = require('ibm-watson/auth'); const languageTranslator = new LanguageTranslatorV3({ version: '2018-05-01', authenticator: new IamAuthenticator({ apikey: '{apikey}', }), serviceUrl: '{url}', }); languageTranslator.listLanguages() .then(languages => { console.log(JSON.stringify(languages, null, 2)); }) .catch(err => { console.log('error:', err); });
import json from ibm_watson import LanguageTranslatorV3 from ibm_cloud_sdk_core.authenticators import IAMAuthenticator authenticator = IAMAuthenticator('{apikey}') language_translator = LanguageTranslatorV3( version='2018-05-01', authenticator=authenticator ) language_translator.set_service_url('{url}') languages = language_translator.list_languages().get_result() print(json.dumps(languages, indent=2))
require "json" require "ibm_watson/authenticators" require "ibm_watson/language_translator_v3" include IBMWatson authenticator = Authenticators::IamAuthenticator.new( apikey: "{apikey}" ) language_translator = LanguageTranslatorV3.new( version: "2018-05-01", authenticator: authenticator ) language_translator.service_url = "{url}" languages = language_translator.list_languages puts JSON.pretty_generate(languages.result)
let authenticator = WatsonIAMAuthenticator(apiKey: "{apikey}") let languageTranslator = LanguageTranslator(version: "2018-05-01", authenticator: authenticator) languageTranslator.serviceURL = "{url}" languageTranslator.listLanguages() { response, error in guard let languages = response?.result else { print(error?.localizedDescription ?? "unknown error") return } print(languages) }
var authenticator = new IamAuthenticator( apikey: "{apikey}" ); while (!authenticator.CanAuthenticate()) yield return null; var languageTranslator = new LanguageTranslatorService("2018-05-01", authenticator); languageTranslator.SetServiceUrl("{url}"); Languages listLanguagesResponse = null; service.ListLanguages( callback: (DetailedResponse<Languages> response, IBMError error) => { Log.Debug("LanguageTranslatorServiceV3", "ListLanguages result: {0}", response.Response); listLanguagesResponse = response.Result; } ); while (listLanguagesResponse == null) yield return null;
Response
The response type for listing supported languages.
An array of supported languages with information about each language.
The response type for listing supported languages.
An array of supported languages with information about each language.
The language code for the language (for example,
af
).The name of the language in English (for example,
Afrikaans
).The native name of the language (for example,
Afrikaans
).The country code for the language (for example,
ZA
for South Africa).Indicates whether words of the language are separated by whitespace:
true
if the words are separated;false
otherwise.Indicates the direction of the language:
right_to_left
orleft_to_right
.Indicates whether the language can be used as the source for translation:
true
if the language can be used as the source;false
otherwise.Indicates whether the language can be used as the target for translation:
true
if the language can be used as the target;false
otherwise.Indicates whether the language supports automatic detection:
true
if the language can be detected automatically;false
otherwise.
Languages
The response type for listing supported languages.
An array of supported languages with information about each language.
The language code for the language (for example,
af
).The name of the language in English (for example,
Afrikaans
).The native name of the language (for example,
Afrikaans
).The country code for the language (for example,
ZA
for South Africa).Indicates whether words of the language are separated by whitespace:
true
if the words are separated;false
otherwise.Indicates the direction of the language:
right_to_left
orleft_to_right
.Indicates whether the language can be used as the source for translation:
true
if the language can be used as the source;false
otherwise.Indicates whether the language can be used as the target for translation:
true
if the language can be used as the target;false
otherwise.Indicates whether the language supports automatic detection:
true
if the language can be detected automatically;false
otherwise.
languages
The response type for listing supported languages.
An array of supported languages with information about each language.
The language code for the language (for example,
af
).The name of the language in English (for example,
Afrikaans
).The native name of the language (for example,
Afrikaans
).The country code for the language (for example,
ZA
for South Africa).Indicates whether words of the language are separated by whitespace:
true
if the words are separated;false
otherwise.Indicates the direction of the language:
right_to_left
orleft_to_right
.Indicates whether the language can be used as the source for translation:
true
if the language can be used as the source;false
otherwise.Indicates whether the language can be used as the target for translation:
true
if the language can be used as the target;false
otherwise.Indicates whether the language supports automatic detection:
true
if the language can be detected automatically;false
otherwise.
languages
The response type for listing supported languages.
An array of supported languages with information about each language.
The language code for the language (for example,
af
).The name of the language in English (for example,
Afrikaans
).The native name of the language (for example,
Afrikaans
).The country code for the language (for example,
ZA
for South Africa).Indicates whether words of the language are separated by whitespace:
true
if the words are separated;false
otherwise.Indicates the direction of the language:
right_to_left
orleft_to_right
.Indicates whether the language can be used as the source for translation:
true
if the language can be used as the source;false
otherwise.Indicates whether the language can be used as the target for translation:
true
if the language can be used as the target;false
otherwise.Indicates whether the language supports automatic detection:
true
if the language can be detected automatically;false
otherwise.
languages
The response type for listing supported languages.
An array of supported languages with information about each language.
The language code for the language (for example,
af
).The name of the language in English (for example,
Afrikaans
).The native name of the language (for example,
Afrikaans
).The country code for the language (for example,
ZA
for South Africa).Indicates whether words of the language are separated by whitespace:
true
if the words are separated;false
otherwise.Indicates the direction of the language:
right_to_left
orleft_to_right
.Indicates whether the language can be used as the source for translation:
true
if the language can be used as the source;false
otherwise.Indicates whether the language can be used as the target for translation:
true
if the language can be used as the target;false
otherwise.Indicates whether the language supports automatic detection:
true
if the language can be detected automatically;false
otherwise.
languages
The response type for listing supported languages.
An array of supported languages with information about each language.
The language code for the language (for example,
af
).The name of the language in English (for example,
Afrikaans
).The native name of the language (for example,
Afrikaans
).The country code for the language (for example,
ZA
for South Africa).Indicates whether words of the language are separated by whitespace:
true
if the words are separated;false
otherwise.Indicates the direction of the language:
right_to_left
orleft_to_right
.Indicates whether the language can be used as the source for translation:
true
if the language can be used as the source;false
otherwise.Indicates whether the language can be used as the target for translation:
true
if the language can be used as the target;false
otherwise.Indicates whether the language supports automatic detection:
true
if the language can be detected automatically;false
otherwise.
languages
The response type for listing supported languages.
An array of supported languages with information about each language.
The language code for the language (for example,
af
).The name of the language in English (for example,
Afrikaans
).The native name of the language (for example,
Afrikaans
).The country code for the language (for example,
ZA
for South Africa).Indicates whether words of the language are separated by whitespace:
true
if the words are separated;false
otherwise.Indicates the direction of the language:
right_to_left
orleft_to_right
.Indicates whether the language can be used as the source for translation:
true
if the language can be used as the source;false
otherwise.Indicates whether the language can be used as the target for translation:
true
if the language can be used as the target;false
otherwise.Indicates whether the language supports automatic detection:
true
if the language can be detected automatically;false
otherwise.
_Languages
The response type for listing supported languages.
An array of supported languages with information about each language.
The language code for the language (for example,
af
).The name of the language in English (for example,
Afrikaans
).The native name of the language (for example,
Afrikaans
).The country code for the language (for example,
ZA
for South Africa).Indicates whether words of the language are separated by whitespace:
true
if the words are separated;false
otherwise.Indicates the direction of the language:
right_to_left
orleft_to_right
.Indicates whether the language can be used as the source for translation:
true
if the language can be used as the source;false
otherwise.Indicates whether the language can be used as the target for translation:
true
if the language can be used as the target;false
otherwise.Indicates whether the language supports automatic detection:
true
if the language can be detected automatically;false
otherwise.
_Languages
Status Code
Success
Invalid request. See the response body for details.
{ "languages": [ { "language": "af", "language_name": "Afrikaans", "native_language_name": "Afrikaans", "country_code": "ZA", "words_separated": true, "direction": "left_to_right", "supported_as_source": false, "supported_as_target": false, "identifiable": true }, { "language": "ar", "language_name": "Arabic", "native_language_name": "العربية", "country_code": "AR", "words_separated": true, "direction": "right_to_left", "supported_as_source": true, "supported_as_target": true, "identifiable": true } ] }
{ "languages": [ { "language": "af", "language_name": "Afrikaans", "native_language_name": "Afrikaans", "country_code": "ZA", "words_separated": true, "direction": "left_to_right", "supported_as_source": false, "supported_as_target": false, "identifiable": true }, { "language": "ar", "language_name": "Arabic", "native_language_name": "العربية", "country_code": "AR", "words_separated": true, "direction": "right_to_left", "supported_as_source": true, "supported_as_target": true, "identifiable": true } ] }
Translate
Translates the input text from the source language to the target language. Specify a model ID that indicates the source and target languages, or specify the source and target languages individually. You can omit the source language to have the service attempt to detect the language from the input text. If you omit the source language, the request must contain sufficient input text for the service to identify the source language.
You can translate a maximum of 50 KB (51,200 bytes) of text with a single request. All input text must be encoded in UTF-8 format.
Translates the input text from the source language to the target language. Specify a model ID that indicates the source and target languages, or specify the source and target languages individually. You can omit the source language to have the service attempt to detect the language from the input text. If you omit the source language, the request must contain sufficient input text for the service to identify the source language.
You can translate a maximum of 50 KB (51,200 bytes) of text with a single request. All input text must be encoded in UTF-8 format.
Translates the input text from the source language to the target language. Specify a model ID that indicates the source and target languages, or specify the source and target languages individually. You can omit the source language to have the service attempt to detect the language from the input text. If you omit the source language, the request must contain sufficient input text for the service to identify the source language.
You can translate a maximum of 50 KB (51,200 bytes) of text with a single request. All input text must be encoded in UTF-8 format.
Translates the input text from the source language to the target language. Specify a model ID that indicates the source and target languages, or specify the source and target languages individually. You can omit the source language to have the service attempt to detect the language from the input text. If you omit the source language, the request must contain sufficient input text for the service to identify the source language.
You can translate a maximum of 50 KB (51,200 bytes) of text with a single request. All input text must be encoded in UTF-8 format.
Translates the input text from the source language to the target language. Specify a model ID that indicates the source and target languages, or specify the source and target languages individually. You can omit the source language to have the service attempt to detect the language from the input text. If you omit the source language, the request must contain sufficient input text for the service to identify the source language.
You can translate a maximum of 50 KB (51,200 bytes) of text with a single request. All input text must be encoded in UTF-8 format.
Translates the input text from the source language to the target language. Specify a model ID that indicates the source and target languages, or specify the source and target languages individually. You can omit the source language to have the service attempt to detect the language from the input text. If you omit the source language, the request must contain sufficient input text for the service to identify the source language.
You can translate a maximum of 50 KB (51,200 bytes) of text with a single request. All input text must be encoded in UTF-8 format.
Translates the input text from the source language to the target language. Specify a model ID that indicates the source and target languages, or specify the source and target languages individually. You can omit the source language to have the service attempt to detect the language from the input text. If you omit the source language, the request must contain sufficient input text for the service to identify the source language.
You can translate a maximum of 50 KB (51,200 bytes) of text with a single request. All input text must be encoded in UTF-8 format.
Translates the input text from the source language to the target language. Specify a model ID that indicates the source and target languages, or specify the source and target languages individually. You can omit the source language to have the service attempt to detect the language from the input text. If you omit the source language, the request must contain sufficient input text for the service to identify the source language.
You can translate a maximum of 50 KB (51,200 bytes) of text with a single request. All input text must be encoded in UTF-8 format.
Translates the input text from the source language to the target language. Specify a model ID that indicates the source and target languages, or specify the source and target languages individually. You can omit the source language to have the service attempt to detect the language from the input text. If you omit the source language, the request must contain sufficient input text for the service to identify the source language.
You can translate a maximum of 50 KB (51,200 bytes) of text with a single request. All input text must be encoded in UTF-8 format.
POST /v3/translate
(languageTranslator *LanguageTranslatorV3) Translate(translateOptions *TranslateOptions) (result *TranslationResult, response *core.DetailedResponse, err error)
(languageTranslator *LanguageTranslatorV3) TranslateWithContext(ctx context.Context, translateOptions *TranslateOptions) (result *TranslationResult, response *core.DetailedResponse, err error)
ServiceCall<TranslationResult> translate(TranslateOptions translateOptions)
translate(params)
translate(self,
text: List[str],
*,
model_id: str = None,
source: str = None,
target: str = None,
**kwargs
) -> DetailedResponse
translate(text:, model_id: nil, source: nil, target: nil)
func translate(
text: [String],
modelID: String? = nil,
source: String? = nil,
target: String? = nil,
headers: [String: String]? = nil,
completionHandler: @escaping (WatsonResponse<TranslationResult>?, WatsonError?) -> Void)
Translate(List<string> text, string modelId = null, string source = null, string target = null)
Translate(Callback<TranslationResult> callback, List<string> text, string modelId = null, string source = null, string target = null)
Request
Instantiate the TranslateOptions
struct and set the fields to provide parameter values for the Translate
method.
Use the TranslateOptions.Builder
to create a TranslateOptions
object that contains the parameter values for the translate
method.
Query Parameters
Release date of the version of the API you want to use. Specify dates in YYYY-MM-DD format. The current version is
2018-05-01
.
The translate request containing the text, and either a model ID or source and target language pair.
Input text in UTF-8 encoding. Submit a maximum of 50 KB (51,200 bytes) of text with a single request. Multiple elements result in multiple translations in the response.
The model to use for translation. For example,
en-de
selects the IBM-provided base model for English-to-German translation. A model ID overrides thesource
andtarget
parameters and is required if you use a custom model. If no model ID is specified, you must specify at least a target language.Language code that specifies the language of the input text. If omitted, the service derives the source language from the input text. The input must contain sufficient text for the service to identify the language reliably.
Language code that specifies the target language for translation. Required if model ID is not specified.
WithContext method only
A context.Context instance that you can use to specify a timeout for the operation or to cancel an in-flight request.
The Translate options.
Input text in UTF-8 encoding. Submit a maximum of 50 KB (51,200 bytes) of text with a single request. Multiple elements result in multiple translations in the response.
The model to use for translation. For example,
en-de
selects the IBM-provided base model for English-to-German translation. A model ID overrides thesource
andtarget
parameters and is required if you use a custom model. If no model ID is specified, you must specify at least a target language.Language code that specifies the language of the input text. If omitted, the service derives the source language from the input text. The input must contain sufficient text for the service to identify the language reliably.
Language code that specifies the target language for translation. Required if model ID is not specified.
The translate options.
Input text in UTF-8 encoding. Submit a maximum of 50 KB (51,200 bytes) of text with a single request. Multiple elements result in multiple translations in the response.
The model to use for translation. For example,
en-de
selects the IBM-provided base model for English-to-German translation. A model ID overrides thesource
andtarget
parameters and is required if you use a custom model. If no model ID is specified, you must specify at least a target language.Language code that specifies the language of the input text. If omitted, the service derives the source language from the input text. The input must contain sufficient text for the service to identify the language reliably.
Language code that specifies the target language for translation. Required if model ID is not specified.
parameters
Input text in UTF-8 encoding. Submit a maximum of 50 KB (51,200 bytes) of text with a single request. Multiple elements result in multiple translations in the response.
The model to use for translation. For example,
en-de
selects the IBM-provided base model for English-to-German translation. A model ID overrides thesource
andtarget
parameters and is required if you use a custom model. If no model ID is specified, you must specify at least a target language.Language code that specifies the language of the input text. If omitted, the service derives the source language from the input text. The input must contain sufficient text for the service to identify the language reliably.
Language code that specifies the target language for translation. Required if model ID is not specified.
parameters
Input text in UTF-8 encoding. Submit a maximum of 50 KB (51,200 bytes) of text with a single request. Multiple elements result in multiple translations in the response.
The model to use for translation. For example,
en-de
selects the IBM-provided base model for English-to-German translation. A model ID overrides thesource
andtarget
parameters and is required if you use a custom model. If no model ID is specified, you must specify at least a target language.Language code that specifies the language of the input text. If omitted, the service derives the source language from the input text. The input must contain sufficient text for the service to identify the language reliably.
Language code that specifies the target language for translation. Required if model ID is not specified.
parameters
Input text in UTF-8 encoding. Submit a maximum of 50 KB (51,200 bytes) of text with a single request. Multiple elements result in multiple translations in the response.
The model to use for translation. For example,
en-de
selects the IBM-provided base model for English-to-German translation. A model ID overrides thesource
andtarget
parameters and is required if you use a custom model. If no model ID is specified, you must specify at least a target language.Language code that specifies the language of the input text. If omitted, the service derives the source language from the input text. The input must contain sufficient text for the service to identify the language reliably.
Language code that specifies the target language for translation. Required if model ID is not specified.
parameters
Input text in UTF-8 encoding. Submit a maximum of 50 KB (51,200 bytes) of text with a single request. Multiple elements result in multiple translations in the response.
The model to use for translation. For example,
en-de
selects the IBM-provided base model for English-to-German translation. A model ID overrides thesource
andtarget
parameters and is required if you use a custom model. If no model ID is specified, you must specify at least a target language.Language code that specifies the language of the input text. If omitted, the service derives the source language from the input text. The input must contain sufficient text for the service to identify the language reliably.
Language code that specifies the target language for translation. Required if model ID is not specified.
parameters
Input text in UTF-8 encoding. Submit a maximum of 50 KB (51,200 bytes) of text with a single request. Multiple elements result in multiple translations in the response.
The model to use for translation. For example,
en-de
selects the IBM-provided base model for English-to-German translation. A model ID overrides thesource
andtarget
parameters and is required if you use a custom model. If no model ID is specified, you must specify at least a target language.Language code that specifies the language of the input text. If omitted, the service derives the source language from the input text. The input must contain sufficient text for the service to identify the language reliably.
Language code that specifies the target language for translation. Required if model ID is not specified.
parameters
Input text in UTF-8 encoding. Submit a maximum of 50 KB (51,200 bytes) of text with a single request. Multiple elements result in multiple translations in the response.
The model to use for translation. For example,
en-de
selects the IBM-provided base model for English-to-German translation. A model ID overrides thesource
andtarget
parameters and is required if you use a custom model. If no model ID is specified, you must specify at least a target language.Language code that specifies the language of the input text. If omitted, the service derives the source language from the input text. The input must contain sufficient text for the service to identify the language reliably.
Language code that specifies the target language for translation. Required if model ID is not specified.
curl -X POST --user apikey:{apikey} --header "Content-Type: application/json" --data "{\"text\":[\"Hello, how are you today?\"],\"model_id\":\"en-es\"}" "{url}/v3/translate?version=2018-05-01"
IamAuthenticator authenticator = new IamAuthenticator( apikey: "{apikey}" ); LanguageTranslatorService languageTranslator = new LanguageTranslatorService("2018-05-01", authenticator); languageTranslator.SetServiceUrl("{url}"); var result = service.Translate( text: new List<string>() { "Hello, how are you today?" }, modelId: "en-es" ); Console.WriteLine(result.Response);
package main import ( "encoding/json" "fmt" "github.com/IBM/go-sdk-core/v5/core" "github.com/watson-developer-cloud/go-sdk/v2/languagetranslatorv3" ) func main() { authenticator := &core.IamAuthenticator{ ApiKey: "{apikey}", } options := &languagetranslatorv3.LanguageTranslatorV3Options{ Version: "2018-05-01", Authenticator: authenticator, } languageTranslator, languageTranslatorErr := languagetranslatorv3. NewLanguageTranslatorV3(options) if languageTranslatorErr != nil { panic(languageTranslatorErr) } languageTranslator.SetServiceURL("{url}") result, _, responseErr := languageTranslator.Translate( &languagetranslatorv3.TranslateOptions{ Text: []string{"Hello, how are you today?"}, ModelID: core.StringPtr("en-es"), }, ) if responseErr != nil { panic(responseErr) } b, _ := json.MarshalIndent(result, "", " ") fmt.Println(string(b)) }
IamAuthenticator authenticator = new IamAuthenticator("{apikey}"); LanguageTranslator languageTranslator = new LanguageTranslator("2018-05-01", authenticator); languageTranslator.setServiceUrl("{url}"); TranslateOptions translateOptions = new TranslateOptions.Builder() .addText("Hello, how are you today?") .modelId("en-es") .build(); TranslationResult result = languageTranslator.translate(translateOptions) .execute().getResult(); System.out.println(result);
const LanguageTranslatorV3 = require('ibm-watson/language-translator/v3'); const { IamAuthenticator } = require('ibm-watson/auth'); const languageTranslator = new LanguageTranslatorV3({ version: '2018-05-01', authenticator: new IamAuthenticator({ apikey: '{apikey}', }), serviceUrl: '{url}', }); const translateParams = { text: 'Hello, how are you today?', modelId: 'en-es', }; languageTranslator.translate(translateParams) .then(translationResult => { console.log(JSON.stringify(translationResult, null, 2)); }) .catch(err => { console.log('error:', err); });
import json from ibm_watson import LanguageTranslatorV3 from ibm_cloud_sdk_core.authenticators import IAMAuthenticator authenticator = IAMAuthenticator('{apikey}') language_translator = LanguageTranslatorV3( version='2018-05-01', authenticator=authenticator ) language_translator.set_service_url('{url}') translation = language_translator.translate( text='Hello, how are you today?', model_id='en-es').get_result() print(json.dumps(translation, indent=2, ensure_ascii=False))
require "json" require "ibm_watson/authenticators" require "ibm_watson/language_translator_v3" include IBMWatson authenticator = Authenticators::IamAuthenticator.new( apikey: "{apikey}" ) language_translator = LanguageTranslatorV3.new( version: "2018-05-01", authenticator: authenticator ) language_translator.service_url = "{url}" translation = language_translator.translate( text: "Hello, how are you today?", model_id: "en-es" ) puts JSON.pretty_generate(translation.result)
let authenticator = WatsonIAMAuthenticator(apiKey: "{apikey}") let languageTranslator = LanguageTranslator(version: "2018-05-01", authenticator: authenticator) languageTranslator.serviceURL = "{url}" languageTranslator.translate( text: ["Hello, how are you today?"], modelID: "en-es") { response, error in guard let translation = response?.result else { print(error?.localizedDescription ?? "unknown error") return } print(translation) }
var authenticator = new IamAuthenticator( apikey: "{apikey}" ); while (!authenticator.CanAuthenticate()) yield return null; var languageTranslator = new LanguageTranslatorService("2018-05-01", authenticator); languageTranslator.SetServiceUrl("{url}"); TranslationResult translateResponse = null; service.Translate( callback: (DetailedResponse<TranslationResult> response, IBMError error) => { Log.Debug("LanguageTranslatorServiceV3", "Translate result: {0}", response.Response); translateResponse = response.Result; }, text: new List<string>() { "Hello, how are you today?" }, modelId: "en-es" ); while (translateResponse == null) yield return null;
Response
An estimate of the number of words in the input text.
Number of characters in the input text.
List of translation output in UTF-8, corresponding to the input text entries.
The language code of the source text if the source language was automatically detected.
A score between 0 and 1 indicating the confidence of source language detection. A higher value indicates greater confidence. This is returned only when the service automatically detects the source language.
Possible values: 0 ≤ value ≤ 1
An estimate of the number of words in the input text.
Number of characters in the input text.
The language code of the source text if the source language was automatically detected.
A score between 0 and 1 indicating the confidence of source language detection. A higher value indicates greater confidence. This is returned only when the service automatically detects the source language.
Possible values: 0 ≤ value ≤ 1
List of translation output in UTF-8, corresponding to the input text entries.
Translation output in UTF-8.
Translations
An estimate of the number of words in the input text.
Number of characters in the input text.
The language code of the source text if the source language was automatically detected.
A score between 0 and 1 indicating the confidence of source language detection. A higher value indicates greater confidence. This is returned only when the service automatically detects the source language.
Possible values: 0 ≤ value ≤ 1
List of translation output in UTF-8, corresponding to the input text entries.
Translation output in UTF-8.
translations
An estimate of the number of words in the input text.
Number of characters in the input text.
The language code of the source text if the source language was automatically detected.
A score between 0 and 1 indicating the confidence of source language detection. A higher value indicates greater confidence. This is returned only when the service automatically detects the source language.
Possible values: 0 ≤ value ≤ 1
List of translation output in UTF-8, corresponding to the input text entries.
Translation output in UTF-8.
translations
An estimate of the number of words in the input text.
Number of characters in the input text.
The language code of the source text if the source language was automatically detected.
A score between 0 and 1 indicating the confidence of source language detection. A higher value indicates greater confidence. This is returned only when the service automatically detects the source language.
Possible values: 0 ≤ value ≤ 1
List of translation output in UTF-8, corresponding to the input text entries.
Translation output in UTF-8.
translations
An estimate of the number of words in the input text.
Number of characters in the input text.
The language code of the source text if the source language was automatically detected.
A score between 0 and 1 indicating the confidence of source language detection. A higher value indicates greater confidence. This is returned only when the service automatically detects the source language.
Possible values: 0 ≤ value ≤ 1
List of translation output in UTF-8, corresponding to the input text entries.
Translation output in UTF-8.
translations
An estimate of the number of words in the input text.
Number of characters in the input text.
The language code of the source text if the source language was automatically detected.
A score between 0 and 1 indicating the confidence of source language detection. A higher value indicates greater confidence. This is returned only when the service automatically detects the source language.
Possible values: 0 ≤ value ≤ 1
List of translation output in UTF-8, corresponding to the input text entries.
Translation output in UTF-8.
translations
An estimate of the number of words in the input text.
Number of characters in the input text.
The language code of the source text if the source language was automatically detected.
A score between 0 and 1 indicating the confidence of source language detection. A higher value indicates greater confidence. This is returned only when the service automatically detects the source language.
Possible values: 0 ≤ value ≤ 1
List of translation output in UTF-8, corresponding to the input text entries.
Translation output in UTF-8.
Translations
An estimate of the number of words in the input text.
Number of characters in the input text.
The language code of the source text if the source language was automatically detected.
A score between 0 and 1 indicating the confidence of source language detection. A higher value indicates greater confidence. This is returned only when the service automatically detects the source language.
Possible values: 0 ≤ value ≤ 1
List of translation output in UTF-8, corresponding to the input text entries.
Translation output in UTF-8.
Translations
Status Code
Success
Invalid request. See the response body for details.
Model Not Found: the specified model_id does not exist or specified source - target language pair is not supported.
Unable to automatically detect the source language, confidence too low: the request contains insufficient input text for the service to identify the source language reliably.
Request Entity Too Large: You sent a request that exceeds the maximum translation size (50 KB) supported by the service.
Too Many Requests: The service is currently processing too many requests. Wait a short time and try the request again. If you are submitting multiple concurrent requests, consider throttling the rate at which you submit requests.
{ "translations": [ { "translation": "Hola, ¿cómo estás hoy?" } ], "word_count": 7, "character_count": 25 }
{ "translations": [ { "translation": "Hola, ¿cómo estás hoy?" } ], "word_count": 7, "character_count": 25 }
List identifiable languages
Lists the languages that the service can identify. Returns the language code (for example, en
for English or es
for Spanish) and name of each language.
Lists the languages that the service can identify. Returns the language code (for example, en
for English or es
for Spanish) and name of each language.
Lists the languages that the service can identify. Returns the language code (for example, en
for English or es
for Spanish) and name of each language.
Lists the languages that the service can identify. Returns the language code (for example, en
for English or es
for Spanish) and name of each language.
Lists the languages that the service can identify. Returns the language code (for example, en
for English or es
for Spanish) and name of each language.
Lists the languages that the service can identify. Returns the language code (for example, en
for English or es
for Spanish) and name of each language.
Lists the languages that the service can identify. Returns the language code (for example, en
for English or es
for Spanish) and name of each language.
Lists the languages that the service can identify. Returns the language code (for example, en
for English or es
for Spanish) and name of each language.
Lists the languages that the service can identify. Returns the language code (for example, en
for English or es
for Spanish) and name of each language.
GET /v3/identifiable_languages
(languageTranslator *LanguageTranslatorV3) ListIdentifiableLanguages(listIdentifiableLanguagesOptions *ListIdentifiableLanguagesOptions) (result *IdentifiableLanguages, response *core.DetailedResponse, err error)
(languageTranslator *LanguageTranslatorV3) ListIdentifiableLanguagesWithContext(ctx context.Context, listIdentifiableLanguagesOptions *ListIdentifiableLanguagesOptions) (result *IdentifiableLanguages, response *core.DetailedResponse, err error)
ServiceCall<IdentifiableLanguages> listIdentifiableLanguages(ListIdentifiableLanguagesOptions listIdentifiableLanguagesOptions)
listIdentifiableLanguages(params)
list_identifiable_languages(self,
**kwargs
) -> DetailedResponse
list_identifiable_languages
func listIdentifiableLanguages(
headers: [String: String]? = nil,
completionHandler: @escaping (WatsonResponse<IdentifiableLanguages>?, WatsonError?) -> Void)
ListIdentifiableLanguages()
ListIdentifiableLanguages(Callback<IdentifiableLanguages> callback)
Request
Instantiate the ListIdentifiableLanguagesOptions
struct and set the fields to provide parameter values for the ListIdentifiableLanguages
method.
Use the ListIdentifiableLanguagesOptions.Builder
to create a ListIdentifiableLanguagesOptions
object that contains the parameter values for the listIdentifiableLanguages
method.
Query Parameters
Release date of the version of the API you want to use. Specify dates in YYYY-MM-DD format. The current version is
2018-05-01
.
WithContext method only
A context.Context instance that you can use to specify a timeout for the operation or to cancel an in-flight request.
parameters
parameters
parameters
parameters
parameters
parameters
curl -X GET --user apikey:{apikey} "{url}/v3/identifiable_languages?version=2018-05-01"
IamAuthenticator authenticator = new IamAuthenticator( apikey: "{apikey}" ); LanguageTranslatorService languageTranslator = new LanguageTranslatorService("2018-05-01", authenticator); languageTranslator.SetServiceUrl("{url}"); var result = service.ListIdentifiableLanguages(); Console.WriteLine(result.Response);
package main import ( "encoding/json" "fmt" "github.com/IBM/go-sdk-core/v5/core" "github.com/watson-developer-cloud/go-sdk/v2/languagetranslatorv3" ) func main() { authenticator := &core.IamAuthenticator{ ApiKey: "{apikey}", } options := &languagetranslatorv3.LanguageTranslatorV3Options{ Version: "2018-05-01", Authenticator: authenticator, } languageTranslator, languageTranslatorErr := languagetranslatorv3. NewLanguageTranslatorV3(options) if languageTranslatorErr != nil { panic(languageTranslatorErr) } languageTranslator.SetServiceURL("{url}") result, _, responseErr := languageTranslator.ListIdentifiableLanguages( &languagetranslatorv3.ListIdentifiableLanguagesOptions{}, ) if responseErr != nil { panic(responseErr) } b, _ := json.MarshalIndent(result, "", " ") fmt.Println(string(b)) }
IamAuthenticator authenticator = new IamAuthenticator("{apikey}"); LanguageTranslator languageTranslator = new LanguageTranslator("2018-05-01", authenticator); languageTranslator.setServiceUrl("{url}"); IdentifiableLanguages languages = languageTranslator.listIdentifiableLanguages() .execute().getResult(); System.out.println(languages);
const LanguageTranslatorV3 = require('ibm-watson/language-translator/v3'); const { IamAuthenticator } = require('ibm-watson/auth'); const languageTranslator = new LanguageTranslatorV3({ version: '2018-05-01', authenticator: new IamAuthenticator({ apikey: '{apikey}', }), serviceUrl: '{url}', }); languageTranslator.listIdentifiableLanguages() .then(identifiedLanguages => { console.log(JSON.stringify(identifiedLanguages, null, 2)); }) .catch(err => { console.log('error:', err); });
import json from ibm_watson import LanguageTranslatorV3 from ibm_cloud_sdk_core.authenticators import IAMAuthenticator authenticator = IAMAuthenticator('{apikey}') language_translator = LanguageTranslatorV3( version='2018-05-01', authenticator=authenticator ) language_translator.set_service_url('{url}') languages = language_translator.list_identifiable_languages().get_result() print(json.dumps(languages, indent=2))
require "json" require "ibm_watson/authenticators" require "ibm_watson/language_translator_v3" include IBMWatson authenticator = Authenticators::IamAuthenticator.new( apikey: "{apikey}" ) language_translator = LanguageTranslatorV3.new( version: "2018-05-01", authenticator: authenticator ) language_translator.service_url = "{url}" languages = language_translator.list_identifiable_languages puts JSON.pretty_generate(languages.result)
let authenticator = WatsonIAMAuthenticator(apiKey: "{apikey}") let languageTranslator = LanguageTranslator(version: "2018-05-01", authenticator: authenticator) languageTranslator.serviceURL = "{url}" languageTranslator.listIdentifiableLanguages() { response, error in guard let languagesList = response?.result else { print(error?.localizedDescription ?? "unknown error") return } print(languagesList) }
var authenticator = new IamAuthenticator( apikey: "{apikey}" ); while (!authenticator.CanAuthenticate()) yield return null; var languageTranslator = new LanguageTranslatorService("2018-05-01", authenticator); languageTranslator.SetServiceUrl("{url}"); IdentifiableLanguages listIdentifiableLanguagesResponse = null; service.ListIdentifiableLanguages( callback: (DetailedResponse<IdentifiableLanguages> response, IBMError error) => { Log.Debug("LanguageTranslatorServiceV3", "ListIdentifiableLanguages result: {0}", response.Response); listIdentifiableLanguagesResponse = response.Result; } ); while (listIdentifiableLanguagesResponse == null) yield return null;
Response
A list of all languages that the service can identify.
A list of all languages that the service can identify.
The language code for an identifiable language.
The name of the identifiable language.
Languages
A list of all languages that the service can identify.
The language code for an identifiable language.
The name of the identifiable language.
languages
A list of all languages that the service can identify.
The language code for an identifiable language.
The name of the identifiable language.
languages
A list of all languages that the service can identify.
The language code for an identifiable language.
The name of the identifiable language.
languages
A list of all languages that the service can identify.
The language code for an identifiable language.
The name of the identifiable language.
languages
A list of all languages that the service can identify.
The language code for an identifiable language.
The name of the identifiable language.
languages
A list of all languages that the service can identify.
The language code for an identifiable language.
The name of the identifiable language.
Languages
A list of all languages that the service can identify.
The language code for an identifiable language.
The name of the identifiable language.
Languages
Status Code
Success
Invalid request. See the response body for details.
{ "languages": [ { "language": "af", "name": "Afrikaans" }, { "language": "ar", "name": "Arabic" }, { "language": "az", "name": "Azerbaijani" }, { "language": "ba", "name": "Bashkir" } ] }
{ "languages": [ { "language": "af", "name": "Afrikaans" }, { "language": "ar", "name": "Arabic" }, { "language": "az", "name": "Azerbaijani" }, { "language": "ba", "name": "Bashkir" } ] }
Identify language
Identifies the language of the input text.
Identifies the language of the input text.
Identifies the language of the input text.
Identifies the language of the input text.
Identifies the language of the input text.
Identifies the language of the input text.
Identifies the language of the input text.
Identifies the language of the input text.
Identifies the language of the input text.
POST /v3/identify
(languageTranslator *LanguageTranslatorV3) Identify(identifyOptions *IdentifyOptions) (result *IdentifiedLanguages, response *core.DetailedResponse, err error)
(languageTranslator *LanguageTranslatorV3) IdentifyWithContext(ctx context.Context, identifyOptions *IdentifyOptions) (result *IdentifiedLanguages, response *core.DetailedResponse, err error)
ServiceCall<IdentifiedLanguages> identify(IdentifyOptions identifyOptions)
identify(params)
identify(self,
text: Union[str, TextIO],
**kwargs
) -> DetailedResponse
identify(text:)
func identify(
text: String,
headers: [String: String]? = nil,
completionHandler: @escaping (WatsonResponse<IdentifiedLanguages>?, WatsonError?) -> Void)
Identify(string text)
Identify(Callback<IdentifiedLanguages> callback, string text)
Request
Instantiate the IdentifyOptions
struct and set the fields to provide parameter values for the Identify
method.
Use the IdentifyOptions.Builder
to create a IdentifyOptions
object that contains the parameter values for the identify
method.
Query Parameters
Release date of the version of the API you want to use. Specify dates in YYYY-MM-DD format. The current version is
2018-05-01
.
Input text in UTF-8 format.
WithContext method only
A context.Context instance that you can use to specify a timeout for the operation or to cancel an in-flight request.
The Identify options.
Input text in UTF-8 format.
The identify options.
Input text in UTF-8 format.
parameters
Input text in UTF-8 format.
parameters
Input text in UTF-8 format.
parameters
Input text in UTF-8 format.
parameters
Input text in UTF-8 format.
parameters
Input text in UTF-8 format.
parameters
Input text in UTF-8 format.
curl -X POST --user apikey:{apikey} --header "Content-Type: text/plain" --data "Language translator translates text from one language to another" "{url}/v3/identify?version=2018-05-01"
IamAuthenticator authenticator = new IamAuthenticator( apikey: "{apikey}" ); LanguageTranslatorService languageTranslator = new LanguageTranslatorService("2018-05-01", authenticator); languageTranslator.SetServiceUrl("{url}"); var result = service.Identify( text: "Language translator translates text from one language to another" ); Console.WriteLine(result.Response);
package main import ( "encoding/json" "fmt" "github.com/IBM/go-sdk-core/v5/core" "github.com/watson-developer-cloud/go-sdk/v2/languagetranslatorv3" ) func main() { authenticator := &core.IamAuthenticator{ ApiKey: "{apikey}", } options := &languagetranslatorv3.LanguageTranslatorV3Options{ Version: "2018-05-01", Authenticator: authenticator, } languageTranslator, languageTranslatorErr := languagetranslatorv3. NewLanguageTranslatorV3(options) if languageTranslatorErr != nil { panic(languageTranslatorErr) } languageTranslator.SetServiceURL("{url}") result, _, responseErr := languageTranslator.Identify( &languagetranslatorv3.IdentifyOptions{ Text: core.StringPtr("Language translator translates text from one language to another"), }, ) if responseErr != nil { panic(responseErr) } b, _ := json.MarshalIndent(result, "", " ") fmt.Println(string(b)) }
IamAuthenticator authenticator = new IamAuthenticator("{apikey}"); LanguageTranslator languageTranslator = new LanguageTranslator("2018-05-01", authenticator); languageTranslator.setServiceUrl("{url}"); IdentifyOptions identifyOptions = new IdentifyOptions.Builder() .text("Language translator translates text from one language to another") .build(); IdentifiedLanguages languages = languageTranslator.identify(identifyOptions) .execute().getResult(); System.out.println(languages);
const LanguageTranslatorV3 = require('ibm-watson/language-translator/v3'); const { IamAuthenticator } = require('ibm-watson/auth'); const languageTranslator = new LanguageTranslatorV3({ version: '2018-05-01', authenticator: new IamAuthenticator({ apikey: '{apikey}', }), serviceUrl: '{url}', }); const identifyParams = { text: 'Language translator translates text from one language to another' }; languageTranslator.identify(identifyParams) .then(identifiedLanguages => { console.log(JSON.stringify(identifiedLanguages, null, 2)); }) .catch(err => { console.log('error:', err); });
import json from ibm_watson import LanguageTranslatorV3 from ibm_cloud_sdk_core.authenticators import IAMAuthenticator authenticator = IAMAuthenticator('{apikey}') language_translator = LanguageTranslatorV3( version='2018-05-01', authenticator=authenticator ) language_translator.set_service_url('{url}') language = language_translator.identify( 'Language translator translates text from one language to another').get_result() print(json.dumps(language, indent=2))
require "json" require "ibm_watson/authenticators" require "ibm_watson/language_translator_v3" include IBMWatson authenticator = Authenticators::IamAuthenticator.new( apikey: "{apikey}" ) language_translator = LanguageTranslatorV3.new( version: "2018-05-01", authenticator: authenticator ) language_translator.service_url = "{url}" language = language_translator.identify( text: "Language translator translates text from one language to another" ) puts JSON.pretty_generate(language.result)
let authenticator = WatsonIAMAuthenticator(apiKey: "{apikey}") let languageTranslator = LanguageTranslator(version: "2018-05-01", authenticator: authenticator) languageTranslator.serviceURL = "{url}" languageTranslator.identify( text: "Language translator translates text from one language to another") { response, error in guard let languages = response?.result else { print(error?.localizedDescription ?? "unknown error") return } print(languages) }
var authenticator = new IamAuthenticator( apikey: "{apikey}" ); while (!authenticator.CanAuthenticate()) yield return null; var languageTranslator = new LanguageTranslatorService("2018-05-01", authenticator); languageTranslator.SetServiceUrl("{url}"); IdentifiedLanguages identifyResponse = null; service.Identify( callback: (DetailedResponse<IdentifiedLanguages> response, IBMError error) => { Log.Debug("LanguageTranslatorServiceV3", "Identify language result: {0}", response.Response); identifyResponse = response.Result; }, text: "Language translator translates text from one language to another" ); while (identifyResponse == null) yield return null;
Response
A ranking of identified languages with confidence scores.
A ranking of identified languages with confidence scores.
The language code for an identified language.
The confidence score for the identified language.
Possible values: 0 ≤ value ≤ 1
Languages
A ranking of identified languages with confidence scores.
The language code for an identified language.
The confidence score for the identified language.
Possible values: 0 ≤ value ≤ 1
languages
A ranking of identified languages with confidence scores.
The language code for an identified language.
The confidence score for the identified language.
Possible values: 0 ≤ value ≤ 1
languages
A ranking of identified languages with confidence scores.
The language code for an identified language.
The confidence score for the identified language.
Possible values: 0 ≤ value ≤ 1
languages
A ranking of identified languages with confidence scores.
The language code for an identified language.
The confidence score for the identified language.
Possible values: 0 ≤ value ≤ 1
languages
A ranking of identified languages with confidence scores.
The language code for an identified language.
The confidence score for the identified language.
Possible values: 0 ≤ value ≤ 1
languages
A ranking of identified languages with confidence scores.
The language code for an identified language.
The confidence score for the identified language.
Possible values: 0 ≤ value ≤ 1
Languages
A ranking of identified languages with confidence scores.
The language code for an identified language.
The confidence score for the identified language.
Possible values: 0 ≤ value ≤ 1
Languages
Status Code
Success
Invalid request. See the response body for details.
{ "languages": [ { "language": "en", "confidence": 0.9804833843796723 }, { "language": "nn", "confidence": 0.005988721319786277 }, { "language": "sq", "confidence": 0.0036927759389060203 }, { "language": "nb", "confidence": 0.0035802051870239037 } ] }
{ "languages": [ { "language": "en", "confidence": 0.9804833843796723 }, { "language": "nn", "confidence": 0.005988721319786277 }, { "language": "sq", "confidence": 0.0036927759389060203 }, { "language": "nb", "confidence": 0.0035802051870239037 } ] }
List models
Lists available translation models.
Lists available translation models.
Lists available translation models.
Lists available translation models.
Lists available translation models.
Lists available translation models.
Lists available translation models.
Lists available translation models.
Lists available translation models.
GET /v3/models
(languageTranslator *LanguageTranslatorV3) ListModels(listModelsOptions *ListModelsOptions) (result *TranslationModels, response *core.DetailedResponse, err error)
(languageTranslator *LanguageTranslatorV3) ListModelsWithContext(ctx context.Context, listModelsOptions *ListModelsOptions) (result *TranslationModels, response *core.DetailedResponse, err error)
ServiceCall<TranslationModels> listModels(ListModelsOptions listModelsOptions)
listModels(params)
list_models(self,
*,
source: str = None,
target: str = None,
default: bool = None,
**kwargs
) -> DetailedResponse
list_models(source: nil, target: nil, default: nil)
func listModels(
source: String? = nil,
target: String? = nil,
`default`: Bool? = nil,
headers: [String: String]? = nil,
completionHandler: @escaping (WatsonResponse<TranslationModels>?, WatsonError?) -> Void)
ListModels(string source = null, string target = null, bool? _default = null)
ListModels(Callback<TranslationModels> callback, string source = null, string target = null, bool? _default = null)
Request
Instantiate the ListModelsOptions
struct and set the fields to provide parameter values for the ListModels
method.
Use the ListModelsOptions.Builder
to create a ListModelsOptions
object that contains the parameter values for the listModels
method.
Query Parameters
Release date of the version of the API you want to use. Specify dates in YYYY-MM-DD format. The current version is
2018-05-01
.Specify a language code to filter results by source language.
Specify a language code to filter results by target language.
If the
default
parameter isn't specified, the service returns all models (default and non-default) for each language pair. To return only default models, set this parameter totrue
. To return only non-default models, set this parameter tofalse
. There is exactly one default model, the IBM-provided base model, per language pair.
WithContext method only
A context.Context instance that you can use to specify a timeout for the operation or to cancel an in-flight request.
The ListModels options.
Specify a language code to filter results by source language.
Specify a language code to filter results by target language.
If the
default
parameter isn't specified, the service returns all models (default and non-default) for each language pair. To return only default models, set this parameter totrue
. To return only non-default models, set this parameter tofalse
. There is exactly one default model, the IBM-provided base model, per language pair.
The listModels options.
Specify a language code to filter results by source language.
Specify a language code to filter results by target language.
If the
default
parameter isn't specified, the service returns all models (default and non-default) for each language pair. To return only default models, set this parameter totrue
. To return only non-default models, set this parameter tofalse
. There is exactly one default model, the IBM-provided base model, per language pair.
parameters
Specify a language code to filter results by source language.
Specify a language code to filter results by target language.
If the
default
parameter isn't specified, the service returns all models (default and non-default) for each language pair. To return only default models, set this parameter totrue
. To return only non-default models, set this parameter tofalse
. There is exactly one default model, the IBM-provided base model, per language pair.
parameters
Specify a language code to filter results by source language.
Specify a language code to filter results by target language.
If the
default
parameter isn't specified, the service returns all models (default and non-default) for each language pair. To return only default models, set this parameter totrue
. To return only non-default models, set this parameter tofalse
. There is exactly one default model, the IBM-provided base model, per language pair.
parameters
Specify a language code to filter results by source language.
Specify a language code to filter results by target language.
If the
default
parameter isn't specified, the service returns all models (default and non-default) for each language pair. To return only default models, set this parameter totrue
. To return only non-default models, set this parameter tofalse
. There is exactly one default model, the IBM-provided base model, per language pair.
parameters
Specify a language code to filter results by source language.
Specify a language code to filter results by target language.
If the
default
parameter isn't specified, the service returns all models (default and non-default) for each language pair. To return only default models, set this parameter totrue
. To return only non-default models, set this parameter tofalse
. There is exactly one default model, the IBM-provided base model, per language pair.
parameters
Specify a language code to filter results by source language.
Specify a language code to filter results by target language.
If the
default
parameter isn't specified, the service returns all models (default and non-default) for each language pair. To return only default models, set this parameter totrue
. To return only non-default models, set this parameter tofalse
. There is exactly one default model, the IBM-provided base model, per language pair.
parameters
Specify a language code to filter results by source language.
Specify a language code to filter results by target language.
If the
default
parameter isn't specified, the service returns all models (default and non-default) for each language pair. To return only default models, set this parameter totrue
. To return only non-default models, set this parameter tofalse
. There is exactly one default model, the IBM-provided base model, per language pair.
curl -X GET --user apikey:{apikey} "{url}/v3/models?version=2018-05-01"
IamAuthenticator authenticator = new IamAuthenticator( apikey: "{apikey}" ); LanguageTranslatorService languageTranslator = new LanguageTranslatorService("2018-05-01", authenticator); languageTranslator.SetServiceUrl("{url}"); var result = service.ListModels(); Console.WriteLine(result.Response);
package main import ( "encoding/json" "fmt" "github.com/IBM/go-sdk-core/v5/core" "github.com/watson-developer-cloud/go-sdk/v2/languagetranslatorv3" ) func main() { authenticator := &core.IamAuthenticator{ ApiKey: "{apikey}", } options := &languagetranslatorv3.LanguageTranslatorV3Options{ Version: "2018-05-01", Authenticator: authenticator, } languageTranslator, languageTranslatorErr := languagetranslatorv3. NewLanguageTranslatorV3(options) if languageTranslatorErr != nil { panic(languageTranslatorErr) } languageTranslator.SetServiceURL("{url}") result, _, responseErr := languageTranslator.ListModels( &languagetranslatorv3.ListModelsOptions{}, ) if responseErr != nil { panic(responseErr) } b, _ := json.MarshalIndent(result, "", " ") fmt.Println(string(b)) }
IamAuthenticator authenticator = new IamAuthenticator("{apikey}"); LanguageTranslator languageTranslator = new LanguageTranslator("2018-05-01", authenticator); languageTranslator.setServiceUrl("{url}"); TranslationModels models = languageTranslator.listModels() .execute().getResult(); System.out.println(models);
const LanguageTranslatorV3 = require('ibm-watson/language-translator/v3'); const { IamAuthenticator } = require('ibm-watson/auth'); const languageTranslator = new LanguageTranslatorV3({ version: '2018-05-01', authenticator: new IamAuthenticator({ apikey: '{apikey}', }), serviceUrl: '{url}', }); languageTranslator.listModels() .then(translationModels => { console.log(JSON.stringify(translationModels, null, 2)); }) .catch(err => { console.log('error:', err); });
import json from ibm_watson import LanguageTranslatorV3 from ibm_cloud_sdk_core.authenticators import IAMAuthenticator authenticator = IAMAuthenticator('{apikey}') language_translator = LanguageTranslatorV3( version='2018-05-01', authenticator=authenticator ) language_translator.set_service_url('{url}') models = language_translator.list_models().get_result() print(json.dumps(models, indent=2))
require "json" require "ibm_watson/authenticators" require "ibm_watson/language_translator_v3" include IBMWatson authenticator = Authenticators::IamAuthenticator.new( apikey: "{apikey}" ) language_translator = LanguageTranslatorV3.new( version: "2018-05-01", authenticator: authenticator ) language_translator.service_url = "{url}" models = language_translator.list_models puts JSON.pretty_generate(models.result)
let authenticator = WatsonIAMAuthenticator(apiKey: "{apikey}") let languageTranslator = LanguageTranslator(version: "2018-05-01", authenticator: authenticator) languageTranslator.serviceURL = "{url}" languageTranslator.listModels() { response, error in guard let models = response?.result else { print(error?.localizedDescription ?? "unknown error") return } print(models) }
var authenticator = new IamAuthenticator( apikey: "{apikey}" ); while (!authenticator.CanAuthenticate()) yield return null; var languageTranslator = new LanguageTranslatorService("2018-05-01", authenticator); languageTranslator.SetServiceUrl("{url}"); TranslationModels listModelsResponse = null; service.ListModels( callback: (DetailedResponse<TranslationModels> response, IBMError error) => { Log.Debug("LanguageTranslatorServiceV3", "ListModels result: {0}", response.Response); listModelsResponse = response.Result; } ); while (listModelsResponse == null) yield return null;
Response
The response type for listing existing translation models.
An array of available models.
The response type for listing existing translation models.
An array of available models.
A globally unique string that identifies the underlying model that is used for translation.
Optional name that can be specified when the model is created.
Translation source language code.
Translation target language code.
Model ID of the base model that was used to customize the model. If the model is not a custom model, this will be an empty string.
The domain of the translation model.
Whether this model can be used as a base for customization. Customized models are not further customizable, and some base models are not customizable.
Whether or not the model is a default model. A default model is the model for a given language pair that will be used when that language pair is specified in the source and target parameters.
Either an empty string, indicating the model is not a custom model, or the ID of the service instance that created the model.
Availability of a model.
Possible values: [
uploading
,uploaded
,dispatching
,queued
,training
,trained
,publishing
,available
,deleted
,error
]
Models
The response type for listing existing translation models.
An array of available models.
A globally unique string that identifies the underlying model that is used for translation.
Optional name that can be specified when the model is created.
Translation source language code.
Translation target language code.
Model ID of the base model that was used to customize the model. If the model is not a custom model, this will be an empty string.
The domain of the translation model.
Whether this model can be used as a base for customization. Customized models are not further customizable, and some base models are not customizable.
Whether or not the model is a default model. A default model is the model for a given language pair that will be used when that language pair is specified in the source and target parameters.
Either an empty string, indicating the model is not a custom model, or the ID of the service instance that created the model.
Availability of a model.
Possible values: [
uploading
,uploaded
,dispatching
,queued
,training
,trained
,publishing
,available
,deleted
,error
]
models
The response type for listing existing translation models.
An array of available models.
A globally unique string that identifies the underlying model that is used for translation.
Optional name that can be specified when the model is created.
Translation source language code.
Translation target language code.
Model ID of the base model that was used to customize the model. If the model is not a custom model, this will be an empty string.
The domain of the translation model.
Whether this model can be used as a base for customization. Customized models are not further customizable, and some base models are not customizable.
Whether or not the model is a default model. A default model is the model for a given language pair that will be used when that language pair is specified in the source and target parameters.
Either an empty string, indicating the model is not a custom model, or the ID of the service instance that created the model.
Availability of a model.
Possible values: [
uploading
,uploaded
,dispatching
,queued
,training
,trained
,publishing
,available
,deleted
,error
]
models
The response type for listing existing translation models.
An array of available models.
A globally unique string that identifies the underlying model that is used for translation.
Optional name that can be specified when the model is created.
Translation source language code.
Translation target language code.
Model ID of the base model that was used to customize the model. If the model is not a custom model, this will be an empty string.
The domain of the translation model.
Whether this model can be used as a base for customization. Customized models are not further customizable, and some base models are not customizable.
Whether or not the model is a default model. A default model is the model for a given language pair that will be used when that language pair is specified in the source and target parameters.
Either an empty string, indicating the model is not a custom model, or the ID of the service instance that created the model.
Availability of a model.
Possible values: [
uploading
,uploaded
,dispatching
,queued
,training
,trained
,publishing
,available
,deleted
,error
]
models
The response type for listing existing translation models.
An array of available models.
A globally unique string that identifies the underlying model that is used for translation.
Optional name that can be specified when the model is created.
Translation source language code.
Translation target language code.
Model ID of the base model that was used to customize the model. If the model is not a custom model, this will be an empty string.
The domain of the translation model.
Whether this model can be used as a base for customization. Customized models are not further customizable, and some base models are not customizable.
Whether or not the model is a default model. A default model is the model for a given language pair that will be used when that language pair is specified in the source and target parameters.
Either an empty string, indicating the model is not a custom model, or the ID of the service instance that created the model.
Availability of a model.
Possible values: [
uploading
,uploaded
,dispatching
,queued
,training
,trained
,publishing
,available
,deleted
,error
]
models
The response type for listing existing translation models.
An array of available models.
A globally unique string that identifies the underlying model that is used for translation.
Optional name that can be specified when the model is created.
Translation source language code.
Translation target language code.
Model ID of the base model that was used to customize the model. If the model is not a custom model, this will be an empty string.
The domain of the translation model.
Whether this model can be used as a base for customization. Customized models are not further customizable, and some base models are not customizable.
Whether or not the model is a default model. A default model is the model for a given language pair that will be used when that language pair is specified in the source and target parameters.
Either an empty string, indicating the model is not a custom model, or the ID of the service instance that created the model.
Availability of a model.
Possible values: [
uploading
,uploaded
,dispatching
,queued
,training
,trained
,publishing
,available
,deleted
,error
]
models
The response type for listing existing translation models.
An array of available models.
A globally unique string that identifies the underlying model that is used for translation.
Optional name that can be specified when the model is created.
Translation source language code.
Translation target language code.
Model ID of the base model that was used to customize the model. If the model is not a custom model, this will be an empty string.
The domain of the translation model.
Whether this model can be used as a base for customization. Customized models are not further customizable, and some base models are not customizable.
Whether or not the model is a default model. A default model is the model for a given language pair that will be used when that language pair is specified in the source and target parameters.
Either an empty string, indicating the model is not a custom model, or the ID of the service instance that created the model.
Availability of a model.
Possible values: [
uploading
,uploaded
,dispatching
,queued
,training
,trained
,publishing
,available
,deleted
,error
]
Models
The response type for listing existing translation models.
An array of available models.
A globally unique string that identifies the underlying model that is used for translation.
Optional name that can be specified when the model is created.
Translation source language code.
Translation target language code.
Model ID of the base model that was used to customize the model. If the model is not a custom model, this will be an empty string.
The domain of the translation model.
Whether this model can be used as a base for customization. Customized models are not further customizable, and some base models are not customizable.
Whether or not the model is a default model. A default model is the model for a given language pair that will be used when that language pair is specified in the source and target parameters.
Either an empty string, indicating the model is not a custom model, or the ID of the service instance that created the model.
Availability of a model.
Possible values: [
uploading
,uploaded
,dispatching
,queued
,training
,trained
,publishing
,available
,deleted
,error
]
Models
Status Code
Success
Invalid request. See the response body for details.
{ "models": [ { "model_id": "en-nl", "source": "en", "target": "nl", "base_model_id": "", "domain": "general", "customizable": true, "default_model": true, "owner": "", "status": "available", "name": "en-nl" }, { "model_id": "en-tr", "source": "en", "target": "tr", "base_model_id": "", "domain": "general", "customizable": true, "default_model": true, "owner": "", "status": "available", "name": "en-tr" } ] }
{ "models": [ { "model_id": "en-nl", "source": "en", "target": "nl", "base_model_id": "", "domain": "general", "customizable": true, "default_model": true, "owner": "", "status": "available", "name": "en-nl" }, { "model_id": "en-tr", "source": "en", "target": "tr", "base_model_id": "", "domain": "general", "customizable": true, "default_model": true, "owner": "", "status": "available", "name": "en-tr" } ] }
Create model
Uploads training files to customize a translation model. You can customize a model with a forced glossary or with a parallel corpus:
- Use a forced glossary to force certain terms and phrases to be translated in a specific way. You can upload only a single forced glossary file for a model. The size of a forced glossary file for a custom model is limited to 10 MB.
- Use a parallel corpus when you want your custom model to learn from general translation patterns in parallel sentences in your samples. What your model learns from a parallel corpus can improve translation results for input text that the model has not been trained on. You can upload multiple parallel corpora files with a request. To successfully train with parallel corpora, the corpora files must contain a cumulative total of at least 5000 parallel sentences. The cumulative size of all uploaded corpus files for a custom model is limited to 250 MB.
Depending on the type of customization and the size of the uploaded files, training time can range from minutes for a glossary to several hours for a large parallel corpus. To create a model that is customized with a parallel corpus and a forced glossary, customize the model with a parallel corpus first and then customize the resulting model with a forced glossary.
You can create a maximum of 10 custom models per language pair. For more information about customizing a translation model, including the formatting and character restrictions for data files, see Customizing your model.
Supported file formats
You can provide your training data for customization in the following document formats:
- TMX (
.tmx
) - Translation Memory eXchange (TMX) is an XML specification for the exchange of translation memories. - XLIFF (
.xliff
) - XML Localization Interchange File Format (XLIFF) is an XML specification for the exchange of translation memories. - CSV (
.csv
) - Comma-separated values (CSV) file with two columns for aligned sentences and phrases. The first row must have two language codes. The first column is for the source language code, and the second column is for the target language code. - TSV (
.tsv
or.tab
) - Tab-separated values (TSV) file with two columns for aligned sentences and phrases. The first row must have two language codes. The first column is for the source language code, and the second column is for the target language code. - JSON (
.json
) - Custom JSON format for specifying aligned sentences and phrases. - Microsoft Excel (
.xls
or.xlsx
) - Excel file with the first two columns for aligned sentences and phrases. The first row contains the language code.
You must encode all text data in UTF-8 format. For more information, see Supported document formats for training data.
Specifying file formats
You can indicate the format of a file by including the file extension with the file name. Use the file extensions shown in Supported file formats.
Alternatively, you can omit the file extension and specify one of the following content-type
specifications for the file:
- TMX -
application/x-tmx+xml
- XLIFF -
application/xliff+xml
- CSV -
text/csv
- TSV -
text/tab-separated-values
- JSON -
application/json
- Microsoft Excel -
application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
For example, with curl
, use the following content-type
specification to indicate the format of a CSV file named glossary:
--form "forced_glossary=@glossary;type=text/csv"
Uploads training files to customize a translation model. You can customize a model with a forced glossary or with a parallel corpus:
- Use a forced glossary to force certain terms and phrases to be translated in a specific way. You can upload only a single forced glossary file for a model. The size of a forced glossary file for a custom model is limited to 10 MB.
- Use a parallel corpus when you want your custom model to learn from general translation patterns in parallel sentences in your samples. What your model learns from a parallel corpus can improve translation results for input text that the model has not been trained on. You can upload multiple parallel corpora files with a request. To successfully train with parallel corpora, the corpora files must contain a cumulative total of at least 5000 parallel sentences. The cumulative size of all uploaded corpus files for a custom model is limited to 250 MB.
Depending on the type of customization and the size of the uploaded files, training time can range from minutes for a glossary to several hours for a large parallel corpus. To create a model that is customized with a parallel corpus and a forced glossary, customize the model with a parallel corpus first and then customize the resulting model with a forced glossary.
You can create a maximum of 10 custom models per language pair. For more information about customizing a translation model, including the formatting and character restrictions for data files, see Customizing your model.
Supported file formats
You can provide your training data for customization in the following document formats:
- TMX (
.tmx
) - Translation Memory eXchange (TMX) is an XML specification for the exchange of translation memories. - XLIFF (
.xliff
) - XML Localization Interchange File Format (XLIFF) is an XML specification for the exchange of translation memories. - CSV (
.csv
) - Comma-separated values (CSV) file with two columns for aligned sentences and phrases. The first row must have two language codes. The first column is for the source language code, and the second column is for the target language code. - TSV (
.tsv
or.tab
) - Tab-separated values (TSV) file with two columns for aligned sentences and phrases. The first row must have two language codes. The first column is for the source language code, and the second column is for the target language code. - JSON (
.json
) - Custom JSON format for specifying aligned sentences and phrases. - Microsoft Excel (
.xls
or.xlsx
) - Excel file with the first two columns for aligned sentences and phrases. The first row contains the language code.
You must encode all text data in UTF-8 format. For more information, see Supported document formats for training data.
Specifying file formats
You can indicate the format of a file by including the file extension with the file name. Use the file extensions shown in Supported file formats.
Alternatively, you can omit the file extension and specify one of the following content-type
specifications for the file:
- TMX -
application/x-tmx+xml
- XLIFF -
application/xliff+xml
- CSV -
text/csv
- TSV -
text/tab-separated-values
- JSON -
application/json
- Microsoft Excel -
application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
For example, with curl
, use the following content-type
specification to indicate the format of a CSV file named glossary:
--form "forced_glossary=@glossary;type=text/csv"
.
Uploads training files to customize a translation model. You can customize a model with a forced glossary or with a parallel corpus:
- Use a forced glossary to force certain terms and phrases to be translated in a specific way. You can upload only a single forced glossary file for a model. The size of a forced glossary file for a custom model is limited to 10 MB.
- Use a parallel corpus when you want your custom model to learn from general translation patterns in parallel sentences in your samples. What your model learns from a parallel corpus can improve translation results for input text that the model has not been trained on. You can upload multiple parallel corpora files with a request. To successfully train with parallel corpora, the corpora files must contain a cumulative total of at least 5000 parallel sentences. The cumulative size of all uploaded corpus files for a custom model is limited to 250 MB.
Depending on the type of customization and the size of the uploaded files, training time can range from minutes for a glossary to several hours for a large parallel corpus. To create a model that is customized with a parallel corpus and a forced glossary, customize the model with a parallel corpus first and then customize the resulting model with a forced glossary.
You can create a maximum of 10 custom models per language pair. For more information about customizing a translation model, including the formatting and character restrictions for data files, see Customizing your model.
Supported file formats
You can provide your training data for customization in the following document formats:
- TMX (
.tmx
) - Translation Memory eXchange (TMX) is an XML specification for the exchange of translation memories. - XLIFF (
.xliff
) - XML Localization Interchange File Format (XLIFF) is an XML specification for the exchange of translation memories. - CSV (
.csv
) - Comma-separated values (CSV) file with two columns for aligned sentences and phrases. The first row must have two language codes. The first column is for the source language code, and the second column is for the target language code. - TSV (
.tsv
or.tab
) - Tab-separated values (TSV) file with two columns for aligned sentences and phrases. The first row must have two language codes. The first column is for the source language code, and the second column is for the target language code. - JSON (
.json
) - Custom JSON format for specifying aligned sentences and phrases. - Microsoft Excel (
.xls
or.xlsx
) - Excel file with the first two columns for aligned sentences and phrases. The first row contains the language code.
You must encode all text data in UTF-8 format. For more information, see Supported document formats for training data.
Specifying file formats
You can indicate the format of a file by including the file extension with the file name. Use the file extensions shown in Supported file formats.
Alternatively, you can omit the file extension and specify one of the following content-type
specifications for the file:
- TMX -
application/x-tmx+xml
- XLIFF -
application/xliff+xml
- CSV -
text/csv
- TSV -
text/tab-separated-values
- JSON -
application/json
- Microsoft Excel -
application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
For example, with curl
, use the following content-type
specification to indicate the format of a CSV file named glossary:
--form "forced_glossary=@glossary;type=text/csv"
.
Uploads training files to customize a translation model. You can customize a model with a forced glossary or with a parallel corpus:
- Use a forced glossary to force certain terms and phrases to be translated in a specific way. You can upload only a single forced glossary file for a model. The size of a forced glossary file for a custom model is limited to 10 MB.
- Use a parallel corpus when you want your custom model to learn from general translation patterns in parallel sentences in your samples. What your model learns from a parallel corpus can improve translation results for input text that the model has not been trained on. You can upload multiple parallel corpora files with a request. To successfully train with parallel corpora, the corpora files must contain a cumulative total of at least 5000 parallel sentences. The cumulative size of all uploaded corpus files for a custom model is limited to 250 MB.
Depending on the type of customization and the size of the uploaded files, training time can range from minutes for a glossary to several hours for a large parallel corpus. To create a model that is customized with a parallel corpus and a forced glossary, customize the model with a parallel corpus first and then customize the resulting model with a forced glossary.
You can create a maximum of 10 custom models per language pair. For more information about customizing a translation model, including the formatting and character restrictions for data files, see Customizing your model.
Supported file formats
You can provide your training data for customization in the following document formats:
- TMX (
.tmx
) - Translation Memory eXchange (TMX) is an XML specification for the exchange of translation memories. - XLIFF (
.xliff
) - XML Localization Interchange File Format (XLIFF) is an XML specification for the exchange of translation memories. - CSV (
.csv
) - Comma-separated values (CSV) file with two columns for aligned sentences and phrases. The first row must have two language codes. The first column is for the source language code, and the second column is for the target language code. - TSV (
.tsv
or.tab
) - Tab-separated values (TSV) file with two columns for aligned sentences and phrases. The first row must have two language codes. The first column is for the source language code, and the second column is for the target language code. - JSON (
.json
) - Custom JSON format for specifying aligned sentences and phrases. - Microsoft Excel (
.xls
or.xlsx
) - Excel file with the first two columns for aligned sentences and phrases. The first row contains the language code.
You must encode all text data in UTF-8 format. For more information, see Supported document formats for training data.
Specifying file formats
You can indicate the format of a file by including the file extension with the file name. Use the file extensions shown in Supported file formats.
Alternatively, you can omit the file extension and specify one of the following content-type
specifications for the file:
- TMX -
application/x-tmx+xml
- XLIFF -
application/xliff+xml
- CSV -
text/csv
- TSV -
text/tab-separated-values
- JSON -
application/json
- Microsoft Excel -
application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
For example, with curl
, use the following content-type
specification to indicate the format of a CSV file named glossary:
--form "forced_glossary=@glossary;type=text/csv"
.
Uploads training files to customize a translation model. You can customize a model with a forced glossary or with a parallel corpus:
- Use a forced glossary to force certain terms and phrases to be translated in a specific way. You can upload only a single forced glossary file for a model. The size of a forced glossary file for a custom model is limited to 10 MB.
- Use a parallel corpus when you want your custom model to learn from general translation patterns in parallel sentences in your samples. What your model learns from a parallel corpus can improve translation results for input text that the model has not been trained on. You can upload multiple parallel corpora files with a request. To successfully train with parallel corpora, the corpora files must contain a cumulative total of at least 5000 parallel sentences. The cumulative size of all uploaded corpus files for a custom model is limited to 250 MB.
Depending on the type of customization and the size of the uploaded files, training time can range from minutes for a glossary to several hours for a large parallel corpus. To create a model that is customized with a parallel corpus and a forced glossary, customize the model with a parallel corpus first and then customize the resulting model with a forced glossary.
You can create a maximum of 10 custom models per language pair. For more information about customizing a translation model, including the formatting and character restrictions for data files, see Customizing your model.
Supported file formats
You can provide your training data for customization in the following document formats:
- TMX (
.tmx
) - Translation Memory eXchange (TMX) is an XML specification for the exchange of translation memories. - XLIFF (
.xliff
) - XML Localization Interchange File Format (XLIFF) is an XML specification for the exchange of translation memories. - CSV (
.csv
) - Comma-separated values (CSV) file with two columns for aligned sentences and phrases. The first row must have two language codes. The first column is for the source language code, and the second column is for the target language code. - TSV (
.tsv
or.tab
) - Tab-separated values (TSV) file with two columns for aligned sentences and phrases. The first row must have two language codes. The first column is for the source language code, and the second column is for the target language code. - JSON (
.json
) - Custom JSON format for specifying aligned sentences and phrases. - Microsoft Excel (
.xls
or.xlsx
) - Excel file with the first two columns for aligned sentences and phrases. The first row contains the language code.
You must encode all text data in UTF-8 format. For more information, see Supported document formats for training data.
Specifying file formats
You can indicate the format of a file by including the file extension with the file name. Use the file extensions shown in Supported file formats.
Alternatively, you can omit the file extension and specify one of the following content-type
specifications for the file:
- TMX -
application/x-tmx+xml
- XLIFF -
application/xliff+xml
- CSV -
text/csv
- TSV -
text/tab-separated-values
- JSON -
application/json
- Microsoft Excel -
application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
For example, with curl
, use the following content-type
specification to indicate the format of a CSV file named glossary:
--form "forced_glossary=@glossary;type=text/csv"
.
Uploads training files to customize a translation model. You can customize a model with a forced glossary or with a parallel corpus:
- Use a forced glossary to force certain terms and phrases to be translated in a specific way. You can upload only a single forced glossary file for a model. The size of a forced glossary file for a custom model is limited to 10 MB.
- Use a parallel corpus when you want your custom model to learn from general translation patterns in parallel sentences in your samples. What your model learns from a parallel corpus can improve translation results for input text that the model has not been trained on. You can upload multiple parallel corpora files with a request. To successfully train with parallel corpora, the corpora files must contain a cumulative total of at least 5000 parallel sentences. The cumulative size of all uploaded corpus files for a custom model is limited to 250 MB.
Depending on the type of customization and the size of the uploaded files, training time can range from minutes for a glossary to several hours for a large parallel corpus. To create a model that is customized with a parallel corpus and a forced glossary, customize the model with a parallel corpus first and then customize the resulting model with a forced glossary.
You can create a maximum of 10 custom models per language pair. For more information about customizing a translation model, including the formatting and character restrictions for data files, see Customizing your model.
Supported file formats
You can provide your training data for customization in the following document formats:
- TMX (
.tmx
) - Translation Memory eXchange (TMX) is an XML specification for the exchange of translation memories. - XLIFF (
.xliff
) - XML Localization Interchange File Format (XLIFF) is an XML specification for the exchange of translation memories. - CSV (
.csv
) - Comma-separated values (CSV) file with two columns for aligned sentences and phrases. The first row must have two language codes. The first column is for the source language code, and the second column is for the target language code. - TSV (
.tsv
or.tab
) - Tab-separated values (TSV) file with two columns for aligned sentences and phrases. The first row must have two language codes. The first column is for the source language code, and the second column is for the target language code. - JSON (
.json
) - Custom JSON format for specifying aligned sentences and phrases. - Microsoft Excel (
.xls
or.xlsx
) - Excel file with the first two columns for aligned sentences and phrases. The first row contains the language code.
You must encode all text data in UTF-8 format. For more information, see Supported document formats for training data.
Specifying file formats
You can indicate the format of a file by including the file extension with the file name. Use the file extensions shown in Supported file formats.
Alternatively, you can omit the file extension and specify one of the following content-type
specifications for the file:
- TMX -
application/x-tmx+xml
- XLIFF -
application/xliff+xml
- CSV -
text/csv
- TSV -
text/tab-separated-values
- JSON -
application/json
- Microsoft Excel -
application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
For example, with curl
, use the following content-type
specification to indicate the format of a CSV file named glossary:
--form "forced_glossary=@glossary;type=text/csv"
.
Uploads training files to customize a translation model. You can customize a model with a forced glossary or with a parallel corpus:
- Use a forced glossary to force certain terms and phrases to be translated in a specific way. You can upload only a single forced glossary file for a model. The size of a forced glossary file for a custom model is limited to 10 MB.
- Use a parallel corpus when you want your custom model to learn from general translation patterns in parallel sentences in your samples. What your model learns from a parallel corpus can improve translation results for input text that the model has not been trained on. You can upload multiple parallel corpora files with a request. To successfully train with parallel corpora, the corpora files must contain a cumulative total of at least 5000 parallel sentences. The cumulative size of all uploaded corpus files for a custom model is limited to 250 MB.
Depending on the type of customization and the size of the uploaded files, training time can range from minutes for a glossary to several hours for a large parallel corpus. To create a model that is customized with a parallel corpus and a forced glossary, customize the model with a parallel corpus first and then customize the resulting model with a forced glossary.
You can create a maximum of 10 custom models per language pair. For more information about customizing a translation model, including the formatting and character restrictions for data files, see Customizing your model.
Supported file formats
You can provide your training data for customization in the following document formats:
- TMX (
.tmx
) - Translation Memory eXchange (TMX) is an XML specification for the exchange of translation memories. - XLIFF (
.xliff
) - XML Localization Interchange File Format (XLIFF) is an XML specification for the exchange of translation memories. - CSV (
.csv
) - Comma-separated values (CSV) file with two columns for aligned sentences and phrases. The first row must have two language codes. The first column is for the source language code, and the second column is for the target language code. - TSV (
.tsv
or.tab
) - Tab-separated values (TSV) file with two columns for aligned sentences and phrases. The first row must have two language codes. The first column is for the source language code, and the second column is for the target language code. - JSON (
.json
) - Custom JSON format for specifying aligned sentences and phrases. - Microsoft Excel (
.xls
or.xlsx
) - Excel file with the first two columns for aligned sentences and phrases. The first row contains the language code.
You must encode all text data in UTF-8 format. For more information, see Supported document formats for training data.
Specifying file formats
You can indicate the format of a file by including the file extension with the file name. Use the file extensions shown in Supported file formats.
Alternatively, you can omit the file extension and specify one of the following content-type
specifications for the file:
- TMX -
application/x-tmx+xml
- XLIFF -
application/xliff+xml
- CSV -
text/csv
- TSV -
text/tab-separated-values
- JSON -
application/json
- Microsoft Excel -
application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
For example, with curl
, use the following content-type
specification to indicate the format of a CSV file named glossary:
--form "forced_glossary=@glossary;type=text/csv"
.
Uploads training files to customize a translation model. You can customize a model with a forced glossary or with a parallel corpus:
- Use a forced glossary to force certain terms and phrases to be translated in a specific way. You can upload only a single forced glossary file for a model. The size of a forced glossary file for a custom model is limited to 10 MB.
- Use a parallel corpus when you want your custom model to learn from general translation patterns in parallel sentences in your samples. What your model learns from a parallel corpus can improve translation results for input text that the model has not been trained on. You can upload multiple parallel corpora files with a request. To successfully train with parallel corpora, the corpora files must contain a cumulative total of at least 5000 parallel sentences. The cumulative size of all uploaded corpus files for a custom model is limited to 250 MB.
Depending on the type of customization and the size of the uploaded files, training time can range from minutes for a glossary to several hours for a large parallel corpus. To create a model that is customized with a parallel corpus and a forced glossary, customize the model with a parallel corpus first and then customize the resulting model with a forced glossary.
You can create a maximum of 10 custom models per language pair. For more information about customizing a translation model, including the formatting and character restrictions for data files, see Customizing your model.
Supported file formats
You can provide your training data for customization in the following document formats:
- TMX (
.tmx
) - Translation Memory eXchange (TMX) is an XML specification for the exchange of translation memories. - XLIFF (
.xliff
) - XML Localization Interchange File Format (XLIFF) is an XML specification for the exchange of translation memories. - CSV (
.csv
) - Comma-separated values (CSV) file with two columns for aligned sentences and phrases. The first row must have two language codes. The first column is for the source language code, and the second column is for the target language code. - TSV (
.tsv
or.tab
) - Tab-separated values (TSV) file with two columns for aligned sentences and phrases. The first row must have two language codes. The first column is for the source language code, and the second column is for the target language code. - JSON (
.json
) - Custom JSON format for specifying aligned sentences and phrases. - Microsoft Excel (
.xls
or.xlsx
) - Excel file with the first two columns for aligned sentences and phrases. The first row contains the language code.
You must encode all text data in UTF-8 format. For more information, see Supported document formats for training data.
Specifying file formats
You can indicate the format of a file by including the file extension with the file name. Use the file extensions shown in Supported file formats.
Alternatively, you can omit the file extension and specify one of the following content-type
specifications for the file:
- TMX -
application/x-tmx+xml
- XLIFF -
application/xliff+xml
- CSV -
text/csv
- TSV -
text/tab-separated-values
- JSON -
application/json
- Microsoft Excel -
application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
For example, with curl
, use the following content-type
specification to indicate the format of a CSV file named glossary:
--form "forced_glossary=@glossary;type=text/csv"
.
Uploads training files to customize a translation model. You can customize a model with a forced glossary or with a parallel corpus:
- Use a forced glossary to force certain terms and phrases to be translated in a specific way. You can upload only a single forced glossary file for a model. The size of a forced glossary file for a custom model is limited to 10 MB.
- Use a parallel corpus when you want your custom model to learn from general translation patterns in parallel sentences in your samples. What your model learns from a parallel corpus can improve translation results for input text that the model has not been trained on. You can upload multiple parallel corpora files with a request. To successfully train with parallel corpora, the corpora files must contain a cumulative total of at least 5000 parallel sentences. The cumulative size of all uploaded corpus files for a custom model is limited to 250 MB.
Depending on the type of customization and the size of the uploaded files, training time can range from minutes for a glossary to several hours for a large parallel corpus. To create a model that is customized with a parallel corpus and a forced glossary, customize the model with a parallel corpus first and then customize the resulting model with a forced glossary.
You can create a maximum of 10 custom models per language pair. For more information about customizing a translation model, including the formatting and character restrictions for data files, see Customizing your model.
Supported file formats
You can provide your training data for customization in the following document formats:
- TMX (
.tmx
) - Translation Memory eXchange (TMX) is an XML specification for the exchange of translation memories. - XLIFF (
.xliff
) - XML Localization Interchange File Format (XLIFF) is an XML specification for the exchange of translation memories. - CSV (
.csv
) - Comma-separated values (CSV) file with two columns for aligned sentences and phrases. The first row must have two language codes. The first column is for the source language code, and the second column is for the target language code. - TSV (
.tsv
or.tab
) - Tab-separated values (TSV) file with two columns for aligned sentences and phrases. The first row must have two language codes. The first column is for the source language code, and the second column is for the target language code. - JSON (
.json
) - Custom JSON format for specifying aligned sentences and phrases. - Microsoft Excel (
.xls
or.xlsx
) - Excel file with the first two columns for aligned sentences and phrases. The first row contains the language code.
You must encode all text data in UTF-8 format. For more information, see Supported document formats for training data.
Specifying file formats
You can indicate the format of a file by including the file extension with the file name. Use the file extensions shown in Supported file formats.
Alternatively, you can omit the file extension and specify one of the following content-type
specifications for the file:
- TMX -
application/x-tmx+xml
- XLIFF -
application/xliff+xml
- CSV -
text/csv
- TSV -
text/tab-separated-values
- JSON -
application/json
- Microsoft Excel -
application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
For example, with curl
, use the following content-type
specification to indicate the format of a CSV file named glossary:
--form "forced_glossary=@glossary;type=text/csv"
.
POST /v3/models
(languageTranslator *LanguageTranslatorV3) CreateModel(createModelOptions *CreateModelOptions) (result *TranslationModel, response *core.DetailedResponse, err error)
(languageTranslator *LanguageTranslatorV3) CreateModelWithContext(ctx context.Context, createModelOptions *CreateModelOptions) (result *TranslationModel, response *core.DetailedResponse, err error)
ServiceCall<TranslationModel> createModel(CreateModelOptions createModelOptions)
createModel(params)
create_model(self,
base_model_id: str,
*,
forced_glossary: BinaryIO = None,
forced_glossary_content_type: str = None,
parallel_corpus: BinaryIO = None,
parallel_corpus_content_type: str = None,
name: str = None,
**kwargs
) -> DetailedResponse
create_model(base_model_id:, forced_glossary: nil, forced_glossary_content_type: nil, parallel_corpus: nil, parallel_corpus_content_type: nil, name: nil)
func createModel(
baseModelID: String,
forcedGlossary: Data? = nil,
forcedGlossaryContentType: String? = nil,
parallelCorpus: Data? = nil,
parallelCorpusContentType: String? = nil,
name: String? = nil,
headers: [String: String]? = nil,
completionHandler: @escaping (WatsonResponse<TranslationModel>?, WatsonError?) -> Void)
CreateModel(string baseModelId, System.IO.MemoryStream forcedGlossary = null, string forcedGlossaryContentType = null, System.IO.MemoryStream parallelCorpus = null, string parallelCorpusContentType = null, string name = null)
CreateModel(Callback<TranslationModel> callback, string baseModelId, System.IO.MemoryStream forcedGlossary = null, string forcedGlossaryContentType = null, System.IO.MemoryStream parallelCorpus = null, string parallelCorpusContentType = null, string name = null)
Request
Instantiate the CreateModelOptions
struct and set the fields to provide parameter values for the CreateModel
method.
Use the CreateModelOptions.Builder
to create a CreateModelOptions
object that contains the parameter values for the createModel
method.
Query Parameters
Release date of the version of the API you want to use. Specify dates in YYYY-MM-DD format. The current version is
2018-05-01
.The ID of the translation model to use as the base for customization. To see available models and IDs, use the
List models
method. Most models that are provided with the service are customizable. In addition, all models that you create with parallel corpora customization can be further customized with a forced glossary.An optional model name that you can use to identify the model. Valid characters are letters, numbers, dashes, underscores, spaces, and apostrophes. The maximum length of the name is 32 characters.
Form Parameters
A file with forced glossary terms for the source and target languages. The customizations in the file completely overwrite the domain translation data, including high frequency or high confidence phrase translations.
You can upload only one glossary file for a custom model, and the glossary can have a maximum size of 10 MB. A forced glossary must contain single words or short phrases. For more information, see Supported file formats in the method description.
With
curl
, use--form forced_glossary=@{filename}
.A file with parallel sentences for the source and target languages. You can upload multiple parallel corpus files in one request by repeating the parameter. All uploaded parallel corpus files combined must contain at least 5000 parallel sentences to train successfully. You can provide a maximum of 500,000 parallel sentences across all corpora.
A single entry in a corpus file can contain a maximum of 80 words. All corpora files for a custom model can have a cumulative maximum size of 250 MB. For more information, see Supported file formats in the method description.
With
curl
, use--form parallel_corpus=@{filename}
.
WithContext method only
A context.Context instance that you can use to specify a timeout for the operation or to cancel an in-flight request.
The CreateModel options.
The ID of the translation model to use as the base for customization. To see available models and IDs, use the
List models
method. Most models that are provided with the service are customizable. In addition, all models that you create with parallel corpora customization can be further customized with a forced glossary.A file with forced glossary terms for the source and target languages. The customizations in the file completely overwrite the domain translation data, including high frequency or high confidence phrase translations.
You can upload only one glossary file for a custom model, and the glossary can have a maximum size of 10 MB. A forced glossary must contain single words or short phrases. For more information, see Supported file formats in the method description.
With
curl
, use--form forced_glossary=@{filename}
..The content type of forcedGlossary.
Allowable values: [
application/x-tmx+xml
,application/xliff+xml
,text/csv
,text/tab-separated-values
,application/json
,application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
]A file with parallel sentences for the source and target languages. You can upload multiple parallel corpus files in one request by repeating the parameter. All uploaded parallel corpus files combined must contain at least 5000 parallel sentences to train successfully. You can provide a maximum of 500,000 parallel sentences across all corpora.
A single entry in a corpus file can contain a maximum of 80 words. All corpora files for a custom model can have a cumulative maximum size of 250 MB. For more information, see Supported file formats in the method description.
With
curl
, use--form parallel_corpus=@{filename}
..The content type of parallelCorpus.
Allowable values: [
application/x-tmx+xml
,application/xliff+xml
,text/csv
,text/tab-separated-values
,application/json
,application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
]An optional model name that you can use to identify the model. Valid characters are letters, numbers, dashes, underscores, spaces, and apostrophes. The maximum length of the name is 32 characters.
The createModel options.
The ID of the translation model to use as the base for customization. To see available models and IDs, use the
List models
method. Most models that are provided with the service are customizable. In addition, all models that you create with parallel corpora customization can be further customized with a forced glossary.A file with forced glossary terms for the source and target languages. The customizations in the file completely overwrite the domain translation data, including high frequency or high confidence phrase translations.
You can upload only one glossary file for a custom model, and the glossary can have a maximum size of 10 MB. A forced glossary must contain single words or short phrases. For more information, see Supported file formats in the method description.
With
curl
, use--form forced_glossary=@{filename}
..The content type of forcedGlossary. Values for this parameter can be obtained from the HttpMediaType class.
Allowable values: [
application/x-tmx+xml
,application/xliff+xml
,text/csv
,text/tab-separated-values
,application/json
,application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
]A file with parallel sentences for the source and target languages. You can upload multiple parallel corpus files in one request by repeating the parameter. All uploaded parallel corpus files combined must contain at least 5000 parallel sentences to train successfully. You can provide a maximum of 500,000 parallel sentences across all corpora.
A single entry in a corpus file can contain a maximum of 80 words. All corpora files for a custom model can have a cumulative maximum size of 250 MB. For more information, see Supported file formats in the method description.
With
curl
, use--form parallel_corpus=@{filename}
..The content type of parallelCorpus. Values for this parameter can be obtained from the HttpMediaType class.
Allowable values: [
application/x-tmx+xml
,application/xliff+xml
,text/csv
,text/tab-separated-values
,application/json
,application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
]An optional model name that you can use to identify the model. Valid characters are letters, numbers, dashes, underscores, spaces, and apostrophes. The maximum length of the name is 32 characters.
parameters
The ID of the translation model to use as the base for customization. To see available models and IDs, use the
List models
method. Most models that are provided with the service are customizable. In addition, all models that you create with parallel corpora customization can be further customized with a forced glossary.A file with forced glossary terms for the source and target languages. The customizations in the file completely overwrite the domain translation data, including high frequency or high confidence phrase translations.
You can upload only one glossary file for a custom model, and the glossary can have a maximum size of 10 MB. A forced glossary must contain single words or short phrases. For more information, see Supported file formats in the method description.
With
curl
, use--form forced_glossary=@{filename}
..The content type of forcedGlossary.
Allowable values: [
application/x-tmx+xml
,application/xliff+xml
,text/csv
,text/tab-separated-values
,application/json
,application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
]A file with parallel sentences for the source and target languages. You can upload multiple parallel corpus files in one request by repeating the parameter. All uploaded parallel corpus files combined must contain at least 5000 parallel sentences to train successfully. You can provide a maximum of 500,000 parallel sentences across all corpora.
A single entry in a corpus file can contain a maximum of 80 words. All corpora files for a custom model can have a cumulative maximum size of 250 MB. For more information, see Supported file formats in the method description.
With
curl
, use--form parallel_corpus=@{filename}
..The content type of parallelCorpus.
Allowable values: [
application/x-tmx+xml
,application/xliff+xml
,text/csv
,text/tab-separated-values
,application/json
,application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
]An optional model name that you can use to identify the model. Valid characters are letters, numbers, dashes, underscores, spaces, and apostrophes. The maximum length of the name is 32 characters.
parameters
The ID of the translation model to use as the base for customization. To see available models and IDs, use the
List models
method. Most models that are provided with the service are customizable. In addition, all models that you create with parallel corpora customization can be further customized with a forced glossary.A file with forced glossary terms for the source and target languages. The customizations in the file completely overwrite the domain translation data, including high frequency or high confidence phrase translations.
You can upload only one glossary file for a custom model, and the glossary can have a maximum size of 10 MB. A forced glossary must contain single words or short phrases. For more information, see Supported file formats in the method description.
With
curl
, use--form forced_glossary=@{filename}
..The content type of forced_glossary.
Allowable values: [
application/x-tmx+xml
,application/xliff+xml
,text/csv
,text/tab-separated-values
,application/json
,application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
]A file with parallel sentences for the source and target languages. You can upload multiple parallel corpus files in one request by repeating the parameter. All uploaded parallel corpus files combined must contain at least 5000 parallel sentences to train successfully. You can provide a maximum of 500,000 parallel sentences across all corpora.
A single entry in a corpus file can contain a maximum of 80 words. All corpora files for a custom model can have a cumulative maximum size of 250 MB. For more information, see Supported file formats in the method description.
With
curl
, use--form parallel_corpus=@{filename}
..The content type of parallel_corpus.
Allowable values: [
application/x-tmx+xml
,application/xliff+xml
,text/csv
,text/tab-separated-values
,application/json
,application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
]An optional model name that you can use to identify the model. Valid characters are letters, numbers, dashes, underscores, spaces, and apostrophes. The maximum length of the name is 32 characters.
parameters
The ID of the translation model to use as the base for customization. To see available models and IDs, use the
List models
method. Most models that are provided with the service are customizable. In addition, all models that you create with parallel corpora customization can be further customized with a forced glossary.A file with forced glossary terms for the source and target languages. The customizations in the file completely overwrite the domain translation data, including high frequency or high confidence phrase translations.
You can upload only one glossary file for a custom model, and the glossary can have a maximum size of 10 MB. A forced glossary must contain single words or short phrases. For more information, see Supported file formats in the method description.
With
curl
, use--form forced_glossary=@{filename}
..The content type of forced_glossary.
Allowable values: [
application/x-tmx+xml
,application/xliff+xml
,text/csv
,text/tab-separated-values
,application/json
,application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
]A file with parallel sentences for the source and target languages. You can upload multiple parallel corpus files in one request by repeating the parameter. All uploaded parallel corpus files combined must contain at least 5000 parallel sentences to train successfully. You can provide a maximum of 500,000 parallel sentences across all corpora.
A single entry in a corpus file can contain a maximum of 80 words. All corpora files for a custom model can have a cumulative maximum size of 250 MB. For more information, see Supported file formats in the method description.
With
curl
, use--form parallel_corpus=@{filename}
..The content type of parallel_corpus.
Allowable values: [
application/x-tmx+xml
,application/xliff+xml
,text/csv
,text/tab-separated-values
,application/json
,application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
]An optional model name that you can use to identify the model. Valid characters are letters, numbers, dashes, underscores, spaces, and apostrophes. The maximum length of the name is 32 characters.
parameters
The ID of the translation model to use as the base for customization. To see available models and IDs, use the
List models
method. Most models that are provided with the service are customizable. In addition, all models that you create with parallel corpora customization can be further customized with a forced glossary.A file with forced glossary terms for the source and target languages. The customizations in the file completely overwrite the domain translation data, including high frequency or high confidence phrase translations.
You can upload only one glossary file for a custom model, and the glossary can have a maximum size of 10 MB. A forced glossary must contain single words or short phrases. For more information, see Supported file formats in the method description.
With
curl
, use--form forced_glossary=@{filename}
..The content type of forcedGlossary.
Allowable values: [
application/x-tmx+xml
,application/xliff+xml
,text/csv
,text/tab-separated-values
,application/json
,application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
]A file with parallel sentences for the source and target languages. You can upload multiple parallel corpus files in one request by repeating the parameter. All uploaded parallel corpus files combined must contain at least 5000 parallel sentences to train successfully. You can provide a maximum of 500,000 parallel sentences across all corpora.
A single entry in a corpus file can contain a maximum of 80 words. All corpora files for a custom model can have a cumulative maximum size of 250 MB. For more information, see Supported file formats in the method description.
With
curl
, use--form parallel_corpus=@{filename}
..The content type of parallelCorpus.
Allowable values: [
application/x-tmx+xml
,application/xliff+xml
,text/csv
,text/tab-separated-values
,application/json
,application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
]An optional model name that you can use to identify the model. Valid characters are letters, numbers, dashes, underscores, spaces, and apostrophes. The maximum length of the name is 32 characters.
parameters
The ID of the translation model to use as the base for customization. To see available models and IDs, use the
List models
method. Most models that are provided with the service are customizable. In addition, all models that you create with parallel corpora customization can be further customized with a forced glossary.A file with forced glossary terms for the source and target languages. The customizations in the file completely overwrite the domain translation data, including high frequency or high confidence phrase translations.
You can upload only one glossary file for a custom model, and the glossary can have a maximum size of 10 MB. A forced glossary must contain single words or short phrases. For more information, see Supported file formats in the method description.
With
curl
, use--form forced_glossary=@{filename}
..The content type of forcedGlossary.
Allowable values: [
application/x-tmx+xml
,application/xliff+xml
,text/csv
,text/tab-separated-values
,application/json
,application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
]A file with parallel sentences for the source and target languages. You can upload multiple parallel corpus files in one request by repeating the parameter. All uploaded parallel corpus files combined must contain at least 5000 parallel sentences to train successfully. You can provide a maximum of 500,000 parallel sentences across all corpora.
A single entry in a corpus file can contain a maximum of 80 words. All corpora files for a custom model can have a cumulative maximum size of 250 MB. For more information, see Supported file formats in the method description.
With
curl
, use--form parallel_corpus=@{filename}
..The content type of parallelCorpus.
Allowable values: [
application/x-tmx+xml
,application/xliff+xml
,text/csv
,text/tab-separated-values
,application/json
,application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
]An optional model name that you can use to identify the model. Valid characters are letters, numbers, dashes, underscores, spaces, and apostrophes. The maximum length of the name is 32 characters.
parameters
The ID of the translation model to use as the base for customization. To see available models and IDs, use the
List models
method. Most models that are provided with the service are customizable. In addition, all models that you create with parallel corpora customization can be further customized with a forced glossary.A file with forced glossary terms for the source and target languages. The customizations in the file completely overwrite the domain translation data, including high frequency or high confidence phrase translations.
You can upload only one glossary file for a custom model, and the glossary can have a maximum size of 10 MB. A forced glossary must contain single words or short phrases. For more information, see Supported file formats in the method description.
With
curl
, use--form forced_glossary=@{filename}
..The content type of forcedGlossary.
Allowable values: [
application/x-tmx+xml
,application/xliff+xml
,text/csv
,text/tab-separated-values
,application/json
,application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
]A file with parallel sentences for the source and target languages. You can upload multiple parallel corpus files in one request by repeating the parameter. All uploaded parallel corpus files combined must contain at least 5000 parallel sentences to train successfully. You can provide a maximum of 500,000 parallel sentences across all corpora.
A single entry in a corpus file can contain a maximum of 80 words. All corpora files for a custom model can have a cumulative maximum size of 250 MB. For more information, see Supported file formats in the method description.
With
curl
, use--form parallel_corpus=@{filename}
..The content type of parallelCorpus.
Allowable values: [
application/x-tmx+xml
,application/xliff+xml
,text/csv
,text/tab-separated-values
,application/json
,application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
]An optional model name that you can use to identify the model. Valid characters are letters, numbers, dashes, underscores, spaces, and apostrophes. The maximum length of the name is 32 characters.
curl -X POST --user apikey:{apikey} --form forced_glossary=@glossary.tmx "{url}/v3/models?version=2018-05-01&base_model_id=en-es&name=custom-en-es"
IamAuthenticator authenticator = new IamAuthenticator( apikey: "{apikey}" ); LanguageTranslatorService languageTranslator = new LanguageTranslatorService("2018-05-01", authenticator); languageTranslator.SetServiceUrl("{url}"); DetailedResponse<TranslationModel> result; using (FileStream fs = File.OpenRead("glossary.tmx")) { using (MemoryStream ms = new MemoryStream()) { fs.CopyTo(ms); result = service.CreateModel( baseModelId: "en-es", forcedGlossary: ms, name: "custom-en-es" ); } } Console.WriteLine(result.Response);
package main import ( "encoding/json" "fmt" "github.com/IBM/go-sdk-core/v5/core" "github.com/watson-developer-cloud/go-sdk/v2/languagetranslatorv3" ) func main() { authenticator := &core.IamAuthenticator{ ApiKey: "{apikey}", } options := &languagetranslatorv3.LanguageTranslatorV3Options{ Version: "2018-05-01", Authenticator: authenticator, } languageTranslator, languageTranslatorErr := languagetranslatorv3. NewLanguageTranslatorV3(options) if languageTranslatorErr != nil { panic(languageTranslatorErr) } languageTranslator.SetServiceURL("{url}") glossary, glossaryErr := os.Open("glossary.tmx") if glossaryErr != nil { panic(glossaryErr) } defer glossary.Close() result, _, responseErr := languageTranslator.CreateModel( &languagetranslatorv3.CreateModelOptions{ BaseModelID: core.StringPtr("en-es"), Name: core.StringPtr("custom-en-es"), ForcedGlossary: glossary, }, ) if responseErr != nil { panic(responseErr) } b, _ := json.MarshalIndent(result, "", " ") fmt.Println(string(b)) }
IamAuthenticator authenticator = new IamAuthenticator("{apikey}"); LanguageTranslator languageTranslator = new LanguageTranslator("2018-05-01", authenticator); languageTranslator.setServiceUrl("{url}"); File glossary = new File("glossary.tmx"); CreateModelOptions createModelOptions = new CreateModelOptions.Builder() .baseModelId("en-es") .name("custom-en-es") .forcedGlossary(glossary) .build(); TranslationModel model = languageTranslator.createModel(createModelOptions) .execute().getResult(); System.out.println(model);
const fs = require('fs'); const LanguageTranslatorV3 = require('ibm-watson/language-translator/v3'); const { IamAuthenticator } = require('ibm-watson/auth'); const languageTranslator = new LanguageTranslatorV3({ version: '2018-05-01', authenticator: new IamAuthenticator({ apikey: '{apikey}', }), serviceUrl: '{url}', }); const createModelParams = { name: 'custom-en-es', baseModelId: 'en-es', forcedGlossary: fs.createReadStream('./glossary.tmx'), }; languageTranslator.createModel(createModelParams) .then(translationModel => { console.log(JSON.stringify(translationModel, null, 2)); }) .catch(err => { console.log('error:', err); });
import json from ibm_watson import LanguageTranslatorV3 from ibm_cloud_sdk_core.authenticators import IAMAuthenticator authenticator = IAMAuthenticator('{apikey}') language_translator = LanguageTranslatorV3( version='2018-05-01', authenticator=authenticator ) language_translator.set_service_url('{url}') with open('glossary.tmx', 'rb') as glossary: response = language_translator.create_model( base_model_id = 'en-es', forced_glossary = glossary, name = 'custom-en-es' ).get_result() print(json.dumps(response, indent=2))
require "json" require "ibm_watson/authenticators" require "ibm_watson/language_translator_v3" include IBMWatson authenticator = Authenticators::IamAuthenticator.new( apikey: "{apikey}" ) language_translator = LanguageTranslatorV3.new( version: "2018-05-01", authenticator: authenticator ) language_translator.service_url = "{url}" File.open("glossary.tmx") do |glossary| response = language_translator.create_model( base_model_id: "en-es", name: "custom-en-es", forced_glossary: glossary ) puts JSON.pretty_generate(response.result) end
let authenticator = WatsonIAMAuthenticator(apiKey: "{apikey}") let languageTranslator = LanguageTranslator(version: "2018-05-01", authenticator: authenticator) languageTranslator.serviceURL = "{url}" let glossaryURL = Bundle.main.url(forResource: "glossary", withExtension: "tmx") let glossary = try! Data(contentsOf: glossaryURL!) languageTranslator.createModel( baseModelID: "en-es", forcedGlossary: glossary, name: "custom-en-es") { response, error in guard let model = response?.result else { print(error?.localizedDescription ?? "unknown error") return } print(model) }
var authenticator = new IamAuthenticator( apikey: "{apikey}" ); while (!authenticator.CanAuthenticate()) yield return null; var languageTranslator = new LanguageTranslatorService("2018-05-01", authenticator); languageTranslator.SetServiceUrl("{url}"); TranslationModel createModelResponse = null; using (FileStream fs = File.OpenRead("glossary.tmx")) { using (MemoryStream ms = new MemoryStream()) { fs.CopyTo(ms); service.CreateModel( callback: (DetailedResponse<TranslationModel> response, IBMError error) => { Log.Debug("LanguageTranslatorServiceV3", "CreateModel result: {0}", response.Response); createModelResponse = response.Result; customModelId = createModelResponse.ModelId; }, baseModelId: "en-es", forcedGlossary: ms, name: "custom-en-es" ); while (createModelResponse == null) yield return null; } }
Response
Response payload for models.
A globally unique string that identifies the underlying model that is used for translation
Optional name that can be specified when the model is created.
Translation source language code
Translation target language code
Model ID of the base model that was used to customize the model. If the model is not a custom model, this will be an empty string.
The domain of the translation model.
Whether this model can be used as a base for customization. Customized models are not further customizable, and some base models are not customizable.
Whether or not the model is a default model. A default model is the model for a given language pair that will be used when that language pair is specified in the source and target parameters.
Either an empty string, indicating the model is not a custom model, or the ID of the service instance that created the model.
Availability of a model.
Possible values: [
uploading
,uploaded
,dispatching
,queued
,training
,trained
,publishing
,available
,deleted
,error
]
Response payload for models.
A globally unique string that identifies the underlying model that is used for translation.
Optional name that can be specified when the model is created.
Translation source language code.
Translation target language code.
Model ID of the base model that was used to customize the model. If the model is not a custom model, this will be an empty string.
The domain of the translation model.
Whether this model can be used as a base for customization. Customized models are not further customizable, and some base models are not customizable.
Whether or not the model is a default model. A default model is the model for a given language pair that will be used when that language pair is specified in the source and target parameters.
Either an empty string, indicating the model is not a custom model, or the ID of the service instance that created the model.
Availability of a model.
Possible values: [
uploading
,uploaded
,dispatching
,queued
,training
,trained
,publishing
,available
,deleted
,error
]
Response payload for models.
A globally unique string that identifies the underlying model that is used for translation.
Optional name that can be specified when the model is created.
Translation source language code.
Translation target language code.
Model ID of the base model that was used to customize the model. If the model is not a custom model, this will be an empty string.
The domain of the translation model.
Whether this model can be used as a base for customization. Customized models are not further customizable, and some base models are not customizable.
Whether or not the model is a default model. A default model is the model for a given language pair that will be used when that language pair is specified in the source and target parameters.
Either an empty string, indicating the model is not a custom model, or the ID of the service instance that created the model.
Availability of a model.
Possible values: [
uploading
,uploaded
,dispatching
,queued
,training
,trained
,publishing
,available
,deleted
,error
]
Response payload for models.
A globally unique string that identifies the underlying model that is used for translation.
Optional name that can be specified when the model is created.
Translation source language code.
Translation target language code.
Model ID of the base model that was used to customize the model. If the model is not a custom model, this will be an empty string.
The domain of the translation model.
Whether this model can be used as a base for customization. Customized models are not further customizable, and some base models are not customizable.
Whether or not the model is a default model. A default model is the model for a given language pair that will be used when that language pair is specified in the source and target parameters.
Either an empty string, indicating the model is not a custom model, or the ID of the service instance that created the model.
Availability of a model.
Possible values: [
uploading
,uploaded
,dispatching
,queued
,training
,trained
,publishing
,available
,deleted
,error
]
Response payload for models.
A globally unique string that identifies the underlying model that is used for translation.
Optional name that can be specified when the model is created.
Translation source language code.
Translation target language code.
Model ID of the base model that was used to customize the model. If the model is not a custom model, this will be an empty string.
The domain of the translation model.
Whether this model can be used as a base for customization. Customized models are not further customizable, and some base models are not customizable.
Whether or not the model is a default model. A default model is the model for a given language pair that will be used when that language pair is specified in the source and target parameters.
Either an empty string, indicating the model is not a custom model, or the ID of the service instance that created the model.
Availability of a model.
Possible values: [
uploading
,uploaded
,dispatching
,queued
,training
,trained
,publishing
,available
,deleted
,error
]
Response payload for models.
A globally unique string that identifies the underlying model that is used for translation.
Optional name that can be specified when the model is created.
Translation source language code.
Translation target language code.
Model ID of the base model that was used to customize the model. If the model is not a custom model, this will be an empty string.
The domain of the translation model.
Whether this model can be used as a base for customization. Customized models are not further customizable, and some base models are not customizable.
Whether or not the model is a default model. A default model is the model for a given language pair that will be used when that language pair is specified in the source and target parameters.
Either an empty string, indicating the model is not a custom model, or the ID of the service instance that created the model.
Availability of a model.
Possible values: [
uploading
,uploaded
,dispatching
,queued
,training
,trained
,publishing
,available
,deleted
,error
]
Response payload for models.
A globally unique string that identifies the underlying model that is used for translation.
Optional name that can be specified when the model is created.
Translation source language code.
Translation target language code.
Model ID of the base model that was used to customize the model. If the model is not a custom model, this will be an empty string.
The domain of the translation model.
Whether this model can be used as a base for customization. Customized models are not further customizable, and some base models are not customizable.
Whether or not the model is a default model. A default model is the model for a given language pair that will be used when that language pair is specified in the source and target parameters.
Either an empty string, indicating the model is not a custom model, or the ID of the service instance that created the model.
Availability of a model.
Possible values: [
uploading
,uploaded
,dispatching
,queued
,training
,trained
,publishing
,available
,deleted
,error
]
Response payload for models.
A globally unique string that identifies the underlying model that is used for translation.
Optional name that can be specified when the model is created.
Translation source language code.
Translation target language code.
Model ID of the base model that was used to customize the model. If the model is not a custom model, this will be an empty string.
The domain of the translation model.
Whether this model can be used as a base for customization. Customized models are not further customizable, and some base models are not customizable.
Whether or not the model is a default model. A default model is the model for a given language pair that will be used when that language pair is specified in the source and target parameters.
Either an empty string, indicating the model is not a custom model, or the ID of the service instance that created the model.
Availability of a model.
Possible values: [
uploading
,uploaded
,dispatching
,queued
,training
,trained
,publishing
,available
,deleted
,error
]
Response payload for models.
A globally unique string that identifies the underlying model that is used for translation.
Optional name that can be specified when the model is created.
Translation source language code.
Translation target language code.
Model ID of the base model that was used to customize the model. If the model is not a custom model, this will be an empty string.
The domain of the translation model.
Whether this model can be used as a base for customization. Customized models are not further customizable, and some base models are not customizable.
Whether or not the model is a default model. A default model is the model for a given language pair that will be used when that language pair is specified in the source and target parameters.
Either an empty string, indicating the model is not a custom model, or the ID of the service instance that created the model.
Availability of a model.
Possible values: [
uploading
,uploaded
,dispatching
,queued
,training
,trained
,publishing
,available
,deleted
,error
]
Status Code
Success: Training has successfully started
Bad request: The
base_model_id
is not specified or you have reached the maximum limit of customizations for this service instance.Forbidden: You may have reached the limit of 10 custom models per language pair or the service instance's plan id does not allow for customization. Currently, only service instances on the advanced or premium plans are allowed to customize
Not found: The base_model_id value is invalid
File size exceeded: Payload body exceeded the limit
{ "model_id": "9f8d9c6f-2123-462f-9793-f17fdcb77cd6", "source": "en", "target": "es", "base_model_id": "en-es", "domain": "general", "customizable": false, "default_model": false, "owner": "212eabae-32bb-4e7d-8873-b28e2364fd11", "status": "dispatching", "name": "custom-en-es" }
{ "model_id": "9f8d9c6f-2123-462f-9793-f17fdcb77cd6", "source": "en", "target": "es", "base_model_id": "en-es", "domain": "general", "customizable": false, "default_model": false, "owner": "212eabae-32bb-4e7d-8873-b28e2364fd11", "status": "dispatching", "name": "custom-en-es" }
Delete model
Deletes a custom translation model.
Deletes a custom translation model.
Deletes a custom translation model.
Deletes a custom translation model.
Deletes a custom translation model.
Deletes a custom translation model.
Deletes a custom translation model.
Deletes a custom translation model.
Deletes a custom translation model.
DELETE /v3/models/{model_id}
(languageTranslator *LanguageTranslatorV3) DeleteModel(deleteModelOptions *DeleteModelOptions) (result *DeleteModelResult, response *core.DetailedResponse, err error)
(languageTranslator *LanguageTranslatorV3) DeleteModelWithContext(ctx context.Context, deleteModelOptions *DeleteModelOptions) (result *DeleteModelResult, response *core.DetailedResponse, err error)
ServiceCall<DeleteModelResult> deleteModel(DeleteModelOptions deleteModelOptions)
deleteModel(params)
delete_model(self,
model_id: str,
**kwargs
) -> DetailedResponse
delete_model(model_id:)
func deleteModel(
modelID: String,
headers: [String: String]? = nil,
completionHandler: @escaping (WatsonResponse<DeleteModelResult>?, WatsonError?) -> Void)
DeleteModel(string modelId)
DeleteModel(Callback<DeleteModelResult> callback, string modelId)
Request
Instantiate the DeleteModelOptions
struct and set the fields to provide parameter values for the DeleteModel
method.
Use the DeleteModelOptions.Builder
to create a DeleteModelOptions
object that contains the parameter values for the deleteModel
method.
Path Parameters
Model ID of the model to delete.
Query Parameters
Release date of the version of the API you want to use. Specify dates in YYYY-MM-DD format. The current version is
2018-05-01
.
WithContext method only
A context.Context instance that you can use to specify a timeout for the operation or to cancel an in-flight request.
The DeleteModel options.
Model ID of the model to delete.
The deleteModel options.
Model ID of the model to delete.
parameters
Model ID of the model to delete.
parameters
Model ID of the model to delete.
parameters
Model ID of the model to delete.
parameters
Model ID of the model to delete.
parameters
Model ID of the model to delete.
parameters
Model ID of the model to delete.
curl -X DELETE --user apikey:{apikey} "{url}/v3/models/9f8d9c6f-2123-462f-9793-f17fdcb77cd6?version=2018-05-01"
IamAuthenticator authenticator = new IamAuthenticator( apikey: "{apikey}" ); LanguageTranslatorService languageTranslator = new LanguageTranslatorService("2018-05-01", authenticator); languageTranslator.SetServiceUrl("{url}"); var result = service.DeleteModel( modelId: "9f8d9c6f-2123-462f-9793-f17fdcb77cd6" ); Console.WriteLine(result.Response);
package main import ( "github.com/IBM/go-sdk-core/v5/core" "github.com/watson-developer-cloud/go-sdk/v2/languagetranslatorv3" ) func main() { authenticator := &core.IamAuthenticator{ ApiKey: "{apikey}", } options := &languagetranslatorv3.LanguageTranslatorV3Options{ Version: "2018-05-01", Authenticator: authenticator, } languageTranslator, languageTranslatorErr := languagetranslatorv3. NewLanguageTranslatorV3(options) if languageTranslatorErr != nil { panic(languageTranslatorErr) } languageTranslator.SetServiceURL("{url}") _, responseErr := languageTranslator.DeleteModel( &languagetranslatorv3.DeleteModelOptions{ ModelID: core.StringPtr("9f8d9c6f-2123-462f-9793-f17fdcb77cd6"), }, ) if responseErr != nil { panic(responseErr) } }
IamAuthenticator authenticator = new IamAuthenticator("{apikey}"); LanguageTranslator languageTranslator = new LanguageTranslator("2018-05-01", authenticator); languageTranslator.setServiceUrl("{url}"); DeleteModelOptions deleteModelOptions = new DeleteModelOptions.Builder() .modelId("9f8d9c6f-2123-462f-9793-f17fdcb77cd6") .build(); languageTranslator.deleteModel(deleteModelOptions).execute();
const LanguageTranslatorV3 = require('ibm-watson/language-translator/v3'); const { IamAuthenticator } = require('ibm-watson/auth'); const languageTranslator = new LanguageTranslatorV3({ version: '2018-05-01', authenticator: new IamAuthenticator({ apikey: '{apikey}', }), serviceUrl: '{url}', }); const deleteModelParams = { modelId: '{model_id}', }; languageTranslator.deleteModel(deleteModelParams) .then(deleteModelResult => { console.log(JSON.stringify(deleteModelResult, null, 2)); }) .catch(err => { console.log('error:', err); });
import json from ibm_watson import LanguageTranslatorV3 from ibm_cloud_sdk_core.authenticators import IAMAuthenticator authenticator = IAMAuthenticator('{apikey}') language_translator = LanguageTranslatorV3( version='2018-05-01', authenticator=authenticator ) language_translator.set_service_url('{url}') model = language_translator.delete_model('9f8d9c6f-2123-462f-9793-f17fdcb77cd6').get_result() print(json.dumps(model, indent=2))
require "json" require "ibm_watson/authenticators" require "ibm_watson/language_translator_v3" include IBMWatson authenticator = Authenticators::IamAuthenticator.new( apikey: "{apikey}" ) language_translator = LanguageTranslatorV3.new( version: "2018-05-01", authenticator: authenticator ) language_translator.service_url = "{url}" model = language_translator.delete_model( model_id: "9f8d9c6f-2123-462f-9793-f17fdcb77cd6" ) puts JSON.pretty_generate(model.result)
let authenticator = WatsonIAMAuthenticator(apiKey: "{apikey}") let languageTranslator = LanguageTranslator(version: "2018-05-01", authenticator: authenticator) languageTranslator.serviceURL = "{url}" languageTranslator.deleteModel(modelID: "{modelID}") { _, error in if let error = error { print(error.localizedDescription) return } print("model deleted") }
var authenticator = new IamAuthenticator( apikey: "{apikey}" ); while (!authenticator.CanAuthenticate()) yield return null; var languageTranslator = new LanguageTranslatorService("2018-05-01", authenticator); languageTranslator.SetServiceUrl("{url}"); DeleteModelResult deleteModelResponse = null; service.DeleteModel( callback: (DetailedResponse<DeleteModelResult> response, IBMError error) => { Log.Debug("LanguageTranslatorServiceV3", "DeleteModel result: {0}", response.Response); deleteModelResponse = response.Result; }, modelId: "9f8d9c6f-2123-462f-9793-f17fdcb77cd6" ); while (deleteModelResponse == null) yield return null;
Response
"OK" indicates that the model was successfully deleted.
"OK" indicates that the model was successfully deleted.
"OK" indicates that the model was successfully deleted.
"OK" indicates that the model was successfully deleted.
"OK" indicates that the model was successfully deleted.
"OK" indicates that the model was successfully deleted.
"OK" indicates that the model was successfully deleted.
"OK" indicates that the model was successfully deleted.
"OK" indicates that the model was successfully deleted.
Status Code
Success: Model deleted
Invalid request. See the response body for details.
The specified model was not found.
{ "status": "OK" }
{ "status": "OK" }
Get model details
Gets information about a translation model, including training status for custom models. Use this API call to poll the status of your customization request. A successfully completed training has a status of available
.
Gets information about a translation model, including training status for custom models. Use this API call to poll the status of your customization request. A successfully completed training has a status of available
.
Gets information about a translation model, including training status for custom models. Use this API call to poll the status of your customization request. A successfully completed training has a status of available
.
Gets information about a translation model, including training status for custom models. Use this API call to poll the status of your customization request. A successfully completed training has a status of available
.
Gets information about a translation model, including training status for custom models. Use this API call to poll the status of your customization request. A successfully completed training has a status of available
.
Gets information about a translation model, including training status for custom models. Use this API call to poll the status of your customization request. A successfully completed training has a status of available
.
Gets information about a translation model, including training status for custom models. Use this API call to poll the status of your customization request. A successfully completed training has a status of available
.
Gets information about a translation model, including training status for custom models. Use this API call to poll the status of your customization request. A successfully completed training has a status of available
.
Gets information about a translation model, including training status for custom models. Use this API call to poll the status of your customization request. A successfully completed training has a status of available
.
GET /v3/models/{model_id}
(languageTranslator *LanguageTranslatorV3) GetModel(getModelOptions *GetModelOptions) (result *TranslationModel, response *core.DetailedResponse, err error)
(languageTranslator *LanguageTranslatorV3) GetModelWithContext(ctx context.Context, getModelOptions *GetModelOptions) (result *TranslationModel, response *core.DetailedResponse, err error)
ServiceCall<TranslationModel> getModel(GetModelOptions getModelOptions)
getModel(params)
get_model(self,
model_id: str,
**kwargs
) -> DetailedResponse
get_model(model_id:)
func getModel(
modelID: String,
headers: [String: String]? = nil,
completionHandler: @escaping (WatsonResponse<TranslationModel>?, WatsonError?) -> Void)
GetModel(string modelId)
GetModel(Callback<TranslationModel> callback, string modelId)
Request
Instantiate the GetModelOptions
struct and set the fields to provide parameter values for the GetModel
method.
Use the GetModelOptions.Builder
to create a GetModelOptions
object that contains the parameter values for the getModel
method.
Path Parameters
Model ID of the model to get
Query Parameters
Release date of the version of the API you want to use. Specify dates in YYYY-MM-DD format. The current version is
2018-05-01
.
WithContext method only
A context.Context instance that you can use to specify a timeout for the operation or to cancel an in-flight request.
The GetModel options.
Model ID of the model to get.
The getModel options.
Model ID of the model to get.
parameters
Model ID of the model to get.
parameters
Model ID of the model to get.
parameters
Model ID of the model to get.
parameters
Model ID of the model to get.
parameters
Model ID of the model to get.
parameters
Model ID of the model to get.
curl -X GET --user apikey:{apikey} "{url}/v3/models/9f8d9c6f-2123-462f-9793-f17fdcb77cd6?version=2018-05-01"
IamAuthenticator authenticator = new IamAuthenticator( apikey: "{apikey}" ); LanguageTranslatorService languageTranslator = new LanguageTranslatorService("2018-05-01", authenticator); languageTranslator.SetServiceUrl("{url}"); var result = service.GetModel( modelId: "9f8d9c6f-2123-462f-9793-f17fdcb77cd6" ); Console.WriteLine(result.Response);
package main import ( "encoding/json" "fmt" "github.com/IBM/go-sdk-core/v5/core" "github.com/watson-developer-cloud/go-sdk/v2/languagetranslatorv3" ) func main() { authenticator := &core.IamAuthenticator{ ApiKey: "{apikey}", } options := &languagetranslatorv3.LanguageTranslatorV3Options{ Version: "2018-05-01", Authenticator: authenticator, } languageTranslator, languageTranslatorErr := languagetranslatorv3. NewLanguageTranslatorV3(options) if languageTranslatorErr != nil { panic(languageTranslatorErr) } languageTranslator.SetServiceURL("{url}") result, _, responseErr := languageTranslator.GetModel( &languagetranslatorv3.GetModelOptions{ ModelID: core.StringPtr("9f8d9c6f-2123-462f-9793-f17fdcb77cd6"), }, ) if responseErr != nil { panic(responseErr) } b, _ := json.MarshalIndent(result, "", " ") fmt.Println(string(b)) }
IamAuthenticator authenticator = new IamAuthenticator("{apikey}"); LanguageTranslator languageTranslator = new LanguageTranslator("2018-05-01", authenticator); languageTranslator.setServiceUrl("{url}"); GetModelOptions getModelOptions = new GetModelOptions.Builder() .modelId("9f8d9c6f-2123-462f-9793-f17fdcb77cd6") .build(); TranslationModel model = languageTranslator.getModel(getModelOptions) .execute().getResult(); System.out.println(model);
const LanguageTranslatorV3 = require('ibm-watson/language-translator/v3'); const { IamAuthenticator } = require('ibm-watson/auth'); const languageTranslator = new LanguageTranslatorV3({ version: '2018-05-01', authenticator: new IamAuthenticator({ apikey: '{apikey}', }), serviceUrl: '{url}', }); const getModelParams = { modelId: '{model_id}', }; languageTranslator.getModel(getModelParams) .then(translationModel => { console.log(JSON.stringify(translationModel, null, 2)); }) .catch(err => { console.log('error:', err); });
import json from ibm_watson import LanguageTranslatorV3 from ibm_cloud_sdk_core.authenticators import IAMAuthenticator authenticator = IAMAuthenticator('{apikey}') language_translator = LanguageTranslatorV3( version='2018-05-01', authenticator=authenticator ) language_translator.set_service_url('{url}') model_details = language_translator.get_model('9f8d9c6f-2123-462f-9793-f17fdcb77cd6').get_result() print(json.dumps(model_details, indent=2))
require "json" require "ibm_watson/authenticators" require "ibm_watson/language_translator_v3" include IBMWatson authenticator = Authenticators::IamAuthenticator.new( apikey: "{apikey}" ) language_translator = LanguageTranslatorV3.new( version: "2018-05-01", authenticator: authenticator ) language_translator.service_url = "{url}" model_details = language_translator.get_model( model_id: "9f8d9c6f-2123-462f-9793-f17fdcb77cd6" ) puts JSON.pretty_generate(model_details.result)
let authenticator = WatsonIAMAuthenticator(apiKey: "{apikey}") let languageTranslator = LanguageTranslator(version: "2018-05-01", authenticator: authenticator) languageTranslator.serviceURL = "{url}" languageTranslator.getModel(modelID: "{modelID}") { response, error in guard let model = response?.result else { print(error?.localizedDescription ?? "unknown error") return } print(model) }
var authenticator = new IamAuthenticator( apikey: "{apikey}" ); while (!authenticator.CanAuthenticate()) yield return null; var languageTranslator = new LanguageTranslatorService("2018-05-01", authenticator); languageTranslator.SetServiceUrl("{url}"); TranslationModel getModelResponse = null; service.GetModel( callback: (DetailedResponse<TranslationModel> response, IBMError error) => { Log.Debug("LanguageTranslatorServiceV3", "GetModel result: {0}", response.Response); getModelResponse = response.Result; }, modelId: "9f8d9c6f-2123-462f-9793-f17fdcb77cd6" ); while (getModelResponse == null) yield return null;
Response
Response payload for models.
A globally unique string that identifies the underlying model that is used for translation
Optional name that can be specified when the model is created.
Translation source language code
Translation target language code
Model ID of the base model that was used to customize the model. If the model is not a custom model, this will be an empty string.
The domain of the translation model.
Whether this model can be used as a base for customization. Customized models are not further customizable, and some base models are not customizable.
Whether or not the model is a default model. A default model is the model for a given language pair that will be used when that language pair is specified in the source and target parameters.
Either an empty string, indicating the model is not a custom model, or the ID of the service instance that created the model.
Availability of a model.
Possible values: [
uploading
,uploaded
,dispatching
,queued
,training
,trained
,publishing
,available
,deleted
,error
]
Response payload for models.
A globally unique string that identifies the underlying model that is used for translation.
Optional name that can be specified when the model is created.
Translation source language code.
Translation target language code.
Model ID of the base model that was used to customize the model. If the model is not a custom model, this will be an empty string.
The domain of the translation model.
Whether this model can be used as a base for customization. Customized models are not further customizable, and some base models are not customizable.
Whether or not the model is a default model. A default model is the model for a given language pair that will be used when that language pair is specified in the source and target parameters.
Either an empty string, indicating the model is not a custom model, or the ID of the service instance that created the model.
Availability of a model.
Possible values: [
uploading
,uploaded
,dispatching
,queued
,training
,trained
,publishing
,available
,deleted
,error
]
Response payload for models.
A globally unique string that identifies the underlying model that is used for translation.
Optional name that can be specified when the model is created.
Translation source language code.
Translation target language code.
Model ID of the base model that was used to customize the model. If the model is not a custom model, this will be an empty string.
The domain of the translation model.
Whether this model can be used as a base for customization. Customized models are not further customizable, and some base models are not customizable.
Whether or not the model is a default model. A default model is the model for a given language pair that will be used when that language pair is specified in the source and target parameters.
Either an empty string, indicating the model is not a custom model, or the ID of the service instance that created the model.
Availability of a model.
Possible values: [
uploading
,uploaded
,dispatching
,queued
,training
,trained
,publishing
,available
,deleted
,error
]
Response payload for models.
A globally unique string that identifies the underlying model that is used for translation.
Optional name that can be specified when the model is created.
Translation source language code.
Translation target language code.
Model ID of the base model that was used to customize the model. If the model is not a custom model, this will be an empty string.
The domain of the translation model.
Whether this model can be used as a base for customization. Customized models are not further customizable, and some base models are not customizable.
Whether or not the model is a default model. A default model is the model for a given language pair that will be used when that language pair is specified in the source and target parameters.
Either an empty string, indicating the model is not a custom model, or the ID of the service instance that created the model.
Availability of a model.
Possible values: [
uploading
,uploaded
,dispatching
,queued
,training
,trained
,publishing
,available
,deleted
,error
]
Response payload for models.
A globally unique string that identifies the underlying model that is used for translation.
Optional name that can be specified when the model is created.
Translation source language code.
Translation target language code.
Model ID of the base model that was used to customize the model. If the model is not a custom model, this will be an empty string.
The domain of the translation model.
Whether this model can be used as a base for customization. Customized models are not further customizable, and some base models are not customizable.
Whether or not the model is a default model. A default model is the model for a given language pair that will be used when that language pair is specified in the source and target parameters.
Either an empty string, indicating the model is not a custom model, or the ID of the service instance that created the model.
Availability of a model.
Possible values: [
uploading
,uploaded
,dispatching
,queued
,training
,trained
,publishing
,available
,deleted
,error
]
Response payload for models.
A globally unique string that identifies the underlying model that is used for translation.
Optional name that can be specified when the model is created.
Translation source language code.
Translation target language code.
Model ID of the base model that was used to customize the model. If the model is not a custom model, this will be an empty string.
The domain of the translation model.
Whether this model can be used as a base for customization. Customized models are not further customizable, and some base models are not customizable.
Whether or not the model is a default model. A default model is the model for a given language pair that will be used when that language pair is specified in the source and target parameters.
Either an empty string, indicating the model is not a custom model, or the ID of the service instance that created the model.
Availability of a model.
Possible values: [
uploading
,uploaded
,dispatching
,queued
,training
,trained
,publishing
,available
,deleted
,error
]
Response payload for models.
A globally unique string that identifies the underlying model that is used for translation.
Optional name that can be specified when the model is created.
Translation source language code.
Translation target language code.
Model ID of the base model that was used to customize the model. If the model is not a custom model, this will be an empty string.
The domain of the translation model.
Whether this model can be used as a base for customization. Customized models are not further customizable, and some base models are not customizable.
Whether or not the model is a default model. A default model is the model for a given language pair that will be used when that language pair is specified in the source and target parameters.
Either an empty string, indicating the model is not a custom model, or the ID of the service instance that created the model.
Availability of a model.
Possible values: [
uploading
,uploaded
,dispatching
,queued
,training
,trained
,publishing
,available
,deleted
,error
]
Response payload for models.
A globally unique string that identifies the underlying model that is used for translation.
Optional name that can be specified when the model is created.
Translation source language code.
Translation target language code.
Model ID of the base model that was used to customize the model. If the model is not a custom model, this will be an empty string.
The domain of the translation model.
Whether this model can be used as a base for customization. Customized models are not further customizable, and some base models are not customizable.
Whether or not the model is a default model. A default model is the model for a given language pair that will be used when that language pair is specified in the source and target parameters.
Either an empty string, indicating the model is not a custom model, or the ID of the service instance that created the model.
Availability of a model.
Possible values: [
uploading
,uploaded
,dispatching
,queued
,training
,trained
,publishing
,available
,deleted
,error
]
Response payload for models.
A globally unique string that identifies the underlying model that is used for translation.
Optional name that can be specified when the model is created.
Translation source language code.
Translation target language code.
Model ID of the base model that was used to customize the model. If the model is not a custom model, this will be an empty string.
The domain of the translation model.
Whether this model can be used as a base for customization. Customized models are not further customizable, and some base models are not customizable.
Whether or not the model is a default model. A default model is the model for a given language pair that will be used when that language pair is specified in the source and target parameters.
Either an empty string, indicating the model is not a custom model, or the ID of the service instance that created the model.
Availability of a model.
Possible values: [
uploading
,uploaded
,dispatching
,queued
,training
,trained
,publishing
,available
,deleted
,error
]
Status Code
Status : OK
Invalid request. See the response body for details.
The specified model was not found.
{ "model_id": "9f8d9c6f-2123-462f-9793-f17fdcb77cd6", "source": "en", "target": "es", "base_model_id": "en-es", "domain": "general", "customizable": false, "default_model": false, "owner": "212eabae-32bb-4e7d-8873-b28e2364fd11", "status": "available", "name": "custom-en-es" }
{ "model_id": "9f8d9c6f-2123-462f-9793-f17fdcb77cd6", "source": "en", "target": "es", "base_model_id": "en-es", "domain": "general", "customizable": false, "default_model": false, "owner": "212eabae-32bb-4e7d-8873-b28e2364fd11", "status": "available", "name": "custom-en-es" }
List documents
Lists documents that have been submitted for translation
Lists documents that have been submitted for translation.
Lists documents that have been submitted for translation.
Lists documents that have been submitted for translation.
Lists documents that have been submitted for translation.
Lists documents that have been submitted for translation.
Lists documents that have been submitted for translation.
Lists documents that have been submitted for translation.
Lists documents that have been submitted for translation.
GET /v3/documents
(languageTranslator *LanguageTranslatorV3) ListDocuments(listDocumentsOptions *ListDocumentsOptions) (result *DocumentList, response *core.DetailedResponse, err error)
(languageTranslator *LanguageTranslatorV3) ListDocumentsWithContext(ctx context.Context, listDocumentsOptions *ListDocumentsOptions) (result *DocumentList, response *core.DetailedResponse, err error)
ServiceCall<DocumentList> listDocuments(ListDocumentsOptions listDocumentsOptions)
listDocuments(params)
list_documents(self,
**kwargs
) -> DetailedResponse
list_documents
func listDocuments(
headers: [String: String]? = nil,
completionHandler: @escaping (WatsonResponse<DocumentList>?, WatsonError?) -> Void)
ListDocuments()
ListDocuments(Callback<DocumentList> callback)
Request
Instantiate the ListDocumentsOptions
struct and set the fields to provide parameter values for the ListDocuments
method.
Use the ListDocumentsOptions.Builder
to create a ListDocumentsOptions
object that contains the parameter values for the listDocuments
method.
Query Parameters
Release date of the version of the API you want to use. Specify dates in YYYY-MM-DD format. The current version is
2018-05-01
.
WithContext method only
A context.Context instance that you can use to specify a timeout for the operation or to cancel an in-flight request.
parameters
parameters
parameters
parameters
parameters
parameters
curl -X GET --user apikey:{apikey} "{url}/v3/documents?version=2018-05-01"
IamAuthenticator authenticator = new IamAuthenticator( apikey: "{apikey}" ); LanguageTranslatorService languageTranslator = new LanguageTranslatorService("2018-05-01", authenticator); languageTranslator.SetServiceUrl("{url}"); var result = service.ListDocuments(); Console.WriteLine(result.Response);
package main import ( "encoding/json" "fmt" "github.com/IBM/go-sdk-core/v5/core" "github.com/watson-developer-cloud/go-sdk/v2/languagetranslatorv3" ) func main() { authenticator := &core.IamAuthenticator{ ApiKey: "{apikey}", } options := &languagetranslatorv3.LanguageTranslatorV3Options{ Version: "2018-05-01", Authenticator: authenticator, } languageTranslator, languageTranslatorErr := languagetranslatorv3. NewLanguageTranslatorV3(options) if languageTranslatorErr != nil { panic(languageTranslatorErr) } languageTranslator.SetServiceURL("{url}") result, _, responseErr := languageTranslator.ListDocuments( &languagetranslatorv3.ListDocumentsOptions{}, ) if responseErr != nil { panic(responseErr) } b, _ := json.MarshalIndent(result, "", " ") fmt.Println(string(b)) }
IamAuthenticator authenticator = new IamAuthenticator("{apikey}"); LanguageTranslator languageTranslator = new LanguageTranslator("2018-05-01", authenticator); languageTranslator.setServiceUrl("{url}"); DocumentList response = languageTranslator.listDocuments().execute().getResult(); System.out.println(response);
const LanguageTranslatorV3 = require('ibm-watson/language-translator/v3'); const { IamAuthenticator } = require('ibm-watson/auth'); const languageTranslator = new LanguageTranslatorV3({ version: '2018-05-01', authenticator: new IamAuthenticator({ apikey: '{apikey}', }), serviceUrl: '{url}', }); languageTranslator.listDocuments() .then(result => { console.log(JSON.stringify(result, null, 2)); }) .catch(err => { console.log('error:', err); });
import json from ibm_watson import LanguageTranslatorV3 from ibm_cloud_sdk_core.authenticators import IAMAuthenticator authenticator = IAMAuthenticator('{apikey}') language_translator = LanguageTranslatorV3( version='2018-05-01', authenticator=authenticator ) language_translator.set_service_url('{url}') result = language_translator.list_documents().get_result() print(json.dumps(result, indent=2))
require "json" require "ibm_watson/authenticators" require "ibm_watson/language_translator_v3" include IBMWatson authenticator = Authenticators::IamAuthenticator.new( apikey: "{apikey}" ) language_translator = LanguageTranslatorV3.new( version: "2018-05-01", authenticator: authenticator ) language_translator.service_url = "{url}" document_list = language_translator.list_documents puts JSON.pretty_generate(document_list.result)
let authenticator = WatsonIAMAuthenticator(apiKey: "{apikey}") let languageTranslator = LanguageTranslator(version: "2018-05-01", authenticator: authenticator) languageTranslator.serviceURL = "{url}" languageTranslator.listDocuments() { response, error in guard let documents = response?.result?.documents else { print(error?.localizedDescription ??