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.
Beginning with version 4.0.0, the Node SDK returns a Promise for all methods when a callback is not specified.
The package location moved to ibm-watson. It remains available at watson-developer-cloud but is not updated there. Use ibm-watson to stay up to date.
The IBM Watson Unity SDK has the following requirements.
- The SDK requires Unity version 2018.2 or later to support 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 does not support the WebGL projects. Change your build settings to any platform except
WebGL.
For 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>7.3.0</version>
</dependency>
Gradle
compile 'com.ibm.watson:ibm-watson:7.3.0'
GitHub
The code examples on this tab use the client library that is provided for Node.js.
Installation
npm install ibm-watson
GitHub
The code examples on this tab use the client library that is provided for Python.
Installation
pip install --upgrade "ibm-watson>=3.2.0"
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/...
GitHub
The code examples on this tab use the client library that is provided for Swift.
Cocoapods
pod 'IBMWatsonLanguageTranslatorV3', '~> 2.2.0'
Carthage
github "watson-developer-cloud/swift-sdk" ~> 2.2.0
Swift Package Manager
.package(url: "https://github.com/watson-developer-cloud/swift-sdk", from: "2.2.0")
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 3.3.0
.NET CLI
dotnet add package IBM.Watson.LanguageTranslator.v3 -version 3.3.0
PackageReference
<PackageReference Include="IBM.Watson.LanguageTranslator.v3" Version="3.3.0" />
GitHub
The code examples on this tab use the client library that is provided for Unity.
Github
Authentication
IBM Cloud is migrating to token-based Identity and Access Management (IAM) authentication.
With some service instances, you authenticate to the API by using 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. Learn more about IAM.
If you pass in an API key, use
apikeyfor the username and the value of the API key as the password.If you pass in the value of the API key, the SDK manages the lifecycle of the tokens. If you pass a token, you maintain the token lifecycle. Learn more about IAM authentication with the SDK.
If you pass in the value of the API key, the SDK manages the lifecycle of the tokens. If you pass a token, you maintain the token lifecycle. Learn more about IAM authentication with the SDK.
If you pass in the value of the API key, the SDK manages the lifecycle of the tokens. If you pass a token, you maintain the token lifecycle. Learn more about IAM authentication with the SDK.
If you pass in the value of the API key, the SDK manages the lifecycle of the tokens. If you pass a token, you maintain the token lifecycle. Learn more about IAM authentication with the SDK.
If you pass in the value of the API key, the SDK manages the lifecycle of the tokens. If you pass a token, you maintain the token lifecycle. Learn more about IAM authentication with the SDK.
If you pass in the value of the API key, the SDK manages the lifecycle of the tokens. If you pass a token, you maintain the token lifecycle. Learn more about IAM authentication with the SDK.
If you pass in the value of the API key, the SDK manages the lifecycle of the tokens. If you pass a token, you maintain the token lifecycle. Learn more about IAM authentication with the SDK.
If you pass in the value of the API key, the SDK manages the lifecycle of the tokens. If you pass a token, you maintain the token lifecycle. Learn more about IAM authentication with the SDK.
In other instances, you authenticate by providing the username and password for the service instance. For more information, see Service credentials for Watson services.
To find out which authentication to use, view the service credentials by clicking the service instance in the Resource list.
The "username" and "password" in the service credentials are different from your IBM Cloud account IBMid and password.
IAM authentication. Replace {apikey} and {url} with your service credentials.
curl -u "apikey:{apikey}" -X {request_method} "{url}/{method}"
Basic authentication. Replace {username}, {password}, and {url} with your service credentials.
curl -u "{username}:{password}" -X {request_method} "{url}/{method}"
SDK managing the IAM token. Replace {apikey}, {version} and {url}.
IamOptions options = new IamOptions.Builder()
.apiKey("{apikey}")
.build();
LanguageTranslator languageTranslator = new LanguageTranslator("{version}", options);
languageTranslator.setEndPoint("{url}");
Basic authentication. Replace {username}, {password}, {version}, and {url}. Use either of the two constructors.
LanguageTranslator languageTranslator = new LanguageTranslator(
"{version}",
"{username}",
"{password}");
languageTranslator.setEndPoint("{url}");
or
LanguageTranslator languageTranslator = new LanguageTranslator("{version}");
languageTranslator.setUsernameAndPassword("{username}","{password}");
languageTranslator.setEndPoint("{url}");
SDK managing the IAM token. Replace {apikey}, {version} and {url}.
const LanguageTranslatorV3 = require('ibm-watson/language-translator/v3');
const languageTranslator = new LanguageTranslatorV3({
version: '{version}',
iam_apikey: '{apikey}',
url: '{url}'
});
Basic authentication. Replace {username}, {password}, {version}, and {url}.
const = require('ibm-watson/language-translator/v3');
const languageTranslator = new LanguageTranslatorV3({
version: '{version}',
username: '{username}',
password: '{password}',
url: '{url}'
});
SDK managing the IAM token. Replace {apikey}, {version} and {url}.
from ibm_watson import LanguageTranslatorV3
language_translator = LanguageTranslatorV3(
version='{version}',
iam_apikey='{apikey}',
url='{url}'
)
Basic authentication. Replace {username}, {password}, {version}, and {url}.
from ibm_watson import LanguageTranslatorV3
language_translator = LanguageTranslatorV3(
version='{version}',
username='{username}',
password='{password}',
url='{url}'
)
SDK managing the IAM token. Replace {apikey}, {version} and {url}.
require "ibm_watson"
language_translator = IBMWatson::LanguageTranslatorV3.new(
version: "{version}",
iam_apikey: "{apikey}",
url:"{url}"
)
Basic authentication. Replace {username}, {password}, {version}, and {url}.
require "ibm_watson"
language_translator = IBMWatson::LanguageTranslatorV3.new(
version: "{version}",
username: "{username}",
password: "{password}",
url:"{url}"
)
SDK managing the IAM token. Replace {apikey}, {version} and {url}.
import "github.com/watson-developer-cloud/go-sdk/languagetranslatorv3"
languageTranslator, languageTranslatorErr := languagetranslatorv3.NewLanguageTranslatorV3(&languagetranslatorv3.LanguageTranslatorV3Options{
Version: "{version}",
IAMApiKey: "{apikey}",
URL: "{url}",
})
Basic authentication. Replace {username}, {password}, {version}, and {url}.
import "github.com/watson-developer-cloud/go-sdk/languagetranslatorv3"
languageTranslator, languageTranslatorErr := languagetranslatorv3.NewLanguageTranslatorV3(&languagetranslatorv3.LanguageTranslatorV3Options{
Version: "{version}",
Username: "{username}",
Password: "{password}",
URL: "{url}",
})
SDK managing the IAM token. Replace {apikey}, {version} and {url}.
let languageTranslator = LanguageTranslator(version: "{version}", apiKey: "{apikey}")
languageTranslator.serviceURL = "{url}"
Basic authentication. Replace {username}, {password}, {version}, and {url}.
let languageTranslator = LanguageTranslator(version: "{version}", username: "{username}", password: "{password}")
languageTranslator.serviceURL = "{url}"
SDK managing the IAM token. Replace {apikey}, {version} and {url}.
IamConfig config = new IamConfig(
apikey: "{apikey}"
);
LanguageTranslator languageTranslator = new LanguageTranslator("{version}", config);
languageTranslator.SetEndpoint("{url}");
Basic authentication. Replace {username}, {password}, {version}, and {url}.
BasicAuthConfig config = new BasicauthConfig(
username: "{username}",
password: "{password}"
);
LanguageTranslator languageTranslator = new LanguageTranslator("{version}", config);
languageTranslator.SetEndpoint("{url}");
SDK managing the IAM token. Replace {apikey}, {version} and {url}.
IamTokenOptions tokenOptions = new IamTokenOptions()
{
IamApiKey = "{apikey}"
};
Credentials credentials = new Credentials(tokenOptions);
while (!credentials.HasTokenData())
{
yield return null;
}
LanguageTranslatorService languageTranslator = new LanguageTranslatorService("{version}", credentials);
languageTranslator.Url = "{url}";
Basic authentication. Replace {username}, {password}, {version}, and {url}.
Credentials credentials = new Credentials("{username}", "{password}");
LanguageTranslatorService languageTranslator = new LanguageTranslatorService("{version}", credentials);
languageTranslator.Url = "{url}";
Service endpoint
The service endpoint is based on the location of the service instance. For example, when Language Translator is hosted in Frankfurt, the base URL is https://gateway-fra.watsonplatform.net/language-translator/api. The URL might also be different when you use IBM Cloud Dedicated.
To find out which URL to use, view the service credentials by clicking the service instance in the Resource list.
Use that URL in your requests to Language Translator.
Set the correct service URL by calling the setEndPoint() method of the service instance.
Set the correct service URL by calling the url parameter when you create the service instance.
Set the correct service URL by calling the url parameter when you create the service instance or by calling the set_url() method of the service instance.
Set the correct service URL by calling the url parameter when you create the service instance or by calling the url= method of the service instance.
Set the correct service URL by the URL parameter when you create the service instance or by calling the SetURL= 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 SetEndpoint() method of the service instance.
Set the correct service URL by setting the Url property of the service instance.
Service endpoints by location:
- Dallas:
https://gateway.watsonplatform.net/language-translator/api - Washington DC:
https://gateway-wdc.watsonplatform.net/language-translator/api - Frankfurt:
https://gateway-fra.watsonplatform.net/language-translator/api - Sydney:
https://gateway-syd.watsonplatform.net/language-translator/api - Tokyo:
https://gateway-tok.watsonplatform.net/language-translator/api - London:
https://gateway-lon.watsonplatform.net/language-translator/api
All locations might not support Language Translator. For details, see Service availability.
Dallas API endpoint example
curl -u "apikey:{apikey}" -X {request_method} "https://gateway.watsonplatform.net/language-translator/api"
Your service instance might not use this URL
Default URL
https://gateway.watsonplatform.net/language-translator/api
Example for the Washington DC location
LanguageTranslator languageTranslator = new LanguageTranslator("{version}");
languageTranslator.setEndPoint("https://gateway-wdc.watsonplatform.net/language-translator/api");
Default URL
https://gateway.watsonplatform.net/language-translator/api
Example for the Washington DC location
const LanguageTranslatorV3 = require('ibm-watson/language-translator/v3');
const languageTranslator = new LanguageTranslatorV3({
version: '{version}',
iam_apikey: '{apikey}',
url: 'https://gateway-wdc.watsonplatform.net/language-translator/api'
});
Default URL
https://gateway.watsonplatform.net/language-translator/api
Examples for the Washington DC location in the constructor and after instantiation
language_translator = LanguageTranslatorV3(
version='{version}',
iam_apikey='{apikey}',
url='https://gateway-wdc.watsonplatform.net/language-translator/api'
)
or
language_translator.set_url('https://gateway-wdc.watsonplatform.net/language-translator/api')
Default URL
https://gateway.watsonplatform.net/language-translator/api
Examples for the Washington DC location in the constructor and after instantiation
language_translator = IBMWatson::.new(
version: "{version}",
api_key: "{apikey}",
url: "https://gateway-wdc.watsonplatform.net/language-translator/api"
)
or
language_translator.url = "https://gateway-wdc.watsonplatform.net/language-translator/api"
Default URL
https://gateway.watsonplatform.net/language-translator/api
Examples for the Washington DC location in the constructor and after instantiation
languageTranslator, languageTranslatorErr := languagetranslatorv3.NewLanguageTranslatorV3(&languagetranslatorv3.LanguageTranslatorV3Options{
Version: "{version}",
IAMApiKey: "{apikey}",
URL: "https://gateway-wdc.watsonplatform.net/language-translator/api",
})
or
languageTranslator.SetURL("https://gateway-wdc.watsonplatform.net/language-translator/api")
Default URL
https://gateway.watsonplatform.net/language-translator/api
Example for the Washington DC location
let languageTranslator = LanguageTranslator(version: "{version}", apiKey: "{apikey}")
languageTranslator.serviceURL = "https://gateway-wdc.watsonplatform.net/language-translator/api"
Default URL
https://gateway.watsonplatform.net/language-translator/api
Example for the Washington DC location
IamConfig config = new IamConfig(
apikey: "{apikey}"
);
LanguageTranslator languageTranslator = new LanguageTranslator("{version}", config);
languageTranslator.SetEndpoint("https://gateway-wdc.watsonplatform.net/language-translator/api");
Default URL
https://gateway.watsonplatform.net/language-translator/api
Example for the Washington DC location
IamTokenOptions tokenOptions = new IamTokenOptions()
{
IamApiKey = "{apikey}"
};
Credentials credentials = new Credentials(tokenOptions);
while (!credentials.HasTokenData())
{
yield return null;
}
LanguageTranslatorService languageTranslator = new LanguageTranslatorService("{version}", credentials);
languageTranslator.Url = "https://gateway-wdc.watsonplatform.net/language-translator/api";
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 absolutely 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 disable_ssl_verification parameter to true when you create the service instance.
To disable SSL verification, call the disable_SSL_verification method on the service instance.
To disable SSL verification, call the configure_http_client method on the service instance and set the disable_ssl parameter to true.
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, call the DisableSslVerification method on the service instance.
Example that disables SSL verification
IAM authentication. Replace {apikey} and {url} with your service credentials.
curl -k -u "apikey:{apikey}" -X {request_method} "{url}/{method}"
Example that disables SSL verification
IamOptions options = new IamOptions.Builder()
.apiKey("{apikey}")
.build();
LanguageTranslator languageTranslator = new LanguageTranslator("{version}", options);
languageTranslator.setEndPoint("{url}");
HttpConfigOptions configOptions = new HttpConfigOptions.Builder()
.disableSslVerification(true)
.build();
languageTranslator.configureClient(configOptions);
Example that disables SSL verification
const LanguageTranslatorV3 = require('ibm-watson/language-translator/v3');
const languageTranslator = new LanguageTranslatorV3({
version: '{version}',
iam_apikey: '{apikey}',
url: '{url}',
disable_ssl_verification: true,
});
Example that disables SSL verification
from ibm_watson import LanguageTranslatorV3
language_translator = LanguageTranslatorV3(
version='{version}',
iam_apikey='{apikey}',
url='{url}'
)
language_translator.disable_SSL_verification()
Example that disables SSL verification
require "ibm_watson"
language_translator = IBMWatson::LanguageTranslatorV3.new(
version: "{version}",
iam_apikey: "{apikey}",
url:"{url}"
)
language_translator.configure_http_client(disable_ssl: true)
Example that disables SSL verification
import "github.com/watson-developer-cloud/go-sdk/languagetranslatorv3"
languageTranslator, languageTranslatorErr := languagetranslatorv3.NewLanguageTranslatorV3(&languagetranslatorv3.LanguageTranslatorV3Options{
Version: "{version}",
IAMApiKey: "{apikey}",
URL: "{url}",
})
languageTranslator.DisableSSLVerification()
Example that disables SSL verification
let languageTranslator = LanguageTranslator(version: "{version}", apiKey: "{apikey}")
languageTranslator.disableSSLVerification()
Example that disables SSL verification
IamConfig config = new IamConfig(
apikey: "{apikey}"
);
LanguageTranslator languageTranslator = new LanguageTranslator("{version}", config);
languageTranslator.SetEndpoint("{url}");
languageTranslator.DisableSslVerification(true);
Example that disables SSL verification
IamTokenOptions tokenOptions = new IamTokenOptions()
{
IamApiKey = "{apikey}"
};
Credentials credentials = new Credentials(tokenOptions);
while (!credentials.HasTokenData())
{
yield return null;
}
LanguageTranslatorService languageTranslator = new LanguageTranslatorService("{version}", credentials);
languageTranslator.Url = "{url}";
languageTranslator.DisableSslVerification = true;
Versioning
API requests require a version parameter that takes a date in the format version=YYYY-MM-DD. When we change the API in a backwards-incompatible way, we release a new version date.
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
The Language Translator service 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 illegal 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 that contains 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 that contains 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 that contains 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 that contains 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 Language Translator 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 Language Translator 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 Language Translator 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(&languageTranslatorv3.LanguageTranslatorV3Options{})
// Check for errors
if languageTranslatorErr != nil {
panic(languageTranslatorErr)
}
// Call a method
response, 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 Watson languageTranslator 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.
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 -u "apikey:{apikey}" -X {request_method} --header "Request-Header: {header_value}" "{url}/{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
response, _ := languagetranslatorv3.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
IamConfig iamConfig = new IamConfig(
apikey: "{apikey}"
);
LanguageTranslator languageTranslator = new LanguageTranslator("{version}", iamConfig);
languageTranslator.SetEndpoint("{url}");
languageTranslator.WithHeader("Custom-Header", "header_value");
Example header parameter in a request
IamTokenOptions tokenOptions = new IamTokenOptions()
{
IamApiKey = "{apikey}"
};
Credentials credentials = new Credentials(tokenOptions);
while (!credentials.HasTokenData())
{
yield return null;
}
LanguageTranslatorService languageTranslator = new LanguageTranslatorService("{version}", credentials);
languageTranslator.Url = "{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 additional debugging information, include the --verbose (-v) option with the request.
Example request to access response headers
curl -u "apikey:{apikey}" -X {request_method} --include "{url}/{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();
To access information in the response headers, add the return_response parameter set to true and specify the headers attribute on the response object that is returned by the 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
const parameters = {
{parameters}
};
parameters.return_response = true;
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"
response, _ := languagetranslatorv3.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, all Watson services log requests and their results. Logging is done only to improve the services for future users. The logged data is not shared or made public.
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 languageTranslator = new LanguageTranslatorV3({
version: '{version}',
iam_apikey: '{apikey}',
url: '{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")
languagetranslatorv3.Service.SetDefaultHeaders(headers)
Example request
languageTranslator.defaultHeaders["X-Watson-Learning-Opt-Out"] = "true"
Example request
IamConfig config = new IamConfig(
apikey: "{apikey}"
);
LanguageTranslator languageTranslator = new LanguageTranslator("{version}", config);
languageTranslator.SetEndpoint("{url}");
languageTranslator.WithHeader("X-Watson-Learning-Opt-Out", "true");
Example request
IamTokenOptions tokenOptions = new IamTokenOptions()
{
IamApiKey = "{apikey}"
};
Credentials credentials = new Credentials(tokenOptions,);
while (!credentials.HasTokenData())
{
yield return null;
}
LanguageTranslatorService languageTranslator = new LanguageTranslatorService("{version}", credentials);
languageTranslator.Url = "{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
executemethod of theServiceCallinterface. You can call theexecutemethod directly from an instance of the service. - To call a method asynchronously, use the
enqueuemethod of theServiceCallinterface to receive a callback when the response arrives. The ServiceCallback interface of the method's argument providesonResponseandonFailuremethods 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
.awaitchainable method of theConcurrent::Asyncmodule.Calling a method directly (without
.await) returns aDetailedResponseobject.- To call a method asynchronously, use the
.asyncchainable method of theConcurrent::Asyncmodule.
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)
Methods
Translate
Translates the input text from the source language to the target language.
Translates the input text from the source language to the target language.
Translates the input text from the source language to the target language.
Translates the input text from the source language to the target language.
Translates the input text from the source language to the target language.
Translates the input text from the source language to the target language.
Translates the input text from the source language to the target language.
Translates the input text from the source language to the target language.
Translates the input text from the source language to the target language.
POST /v3/translate(languageTranslator *LanguageTranslatorV3) Translate(translateOptions *TranslateOptions) (*core.DetailedResponse, error)ServiceCall<TranslationResult> translate(TranslateOptions translateOptions)translate(params, [callback()])translate(self, text, model_id=None, source=None, target=None, **kwargs)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)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 API version you want to use. Specify dates in YYYY-MM-DD format. The current version is
2018-05-01.Constraints: Value must match regular expression
^\d{4}-\d{2}-\d{2}$
The translate request containing the text, and either a model ID or source and target language pair.
Input text in UTF-8 encoding. Multiple entries will result in multiple translations in the response.
A globally unique string that identifies the underlying model that is used for translation
Translation source language code
Translation target language code
The Translate options.
Input text in UTF-8 encoding. Multiple entries will result in multiple translations in the response.
A globally unique string that identifies the underlying model that is used for translation.
Translation source language code.
Translation target language code.
The translate options.
Input text in UTF-8 encoding. Multiple entries will result in multiple translations in the response.
A globally unique string that identifies the underlying model that is used for translation.
Translation source language code.
Translation target language code.
parameters
Input text in UTF-8 encoding. Multiple entries will result in multiple translations in the response.
A globally unique string that identifies the underlying model that is used for translation.
Translation source language code.
Translation target language code.
parameters
Input text in UTF-8 encoding. Multiple entries will result in multiple translations in the response.
A globally unique string that identifies the underlying model that is used for translation.
Translation source language code.
Translation target language code.
parameters
Input text in UTF-8 encoding. Multiple entries will result in multiple translations in the response.
A globally unique string that identifies the underlying model that is used for translation.
Translation source language code.
Translation target language code.
parameters
Input text in UTF-8 encoding. Multiple entries will result in multiple translations in the response.
A globally unique string that identifies the underlying model that is used for translation.
Translation source language code.
Translation target language code.
parameters
Input text in UTF-8 encoding. Multiple entries will result in multiple translations in the response.
A globally unique string that identifies the underlying model that is used for translation.
Translation source language code.
Translation target language code.
parameters
Input text in UTF-8 encoding. Multiple entries will result in multiple translations in the response.
A globally unique string that identifies the underlying model that is used for translation.
Translation source language code.
Translation target language code.
curl --user apikey:{apikey} --request POST --header "Content-Type: application/json" --data "{\"text\":[\"Hello\"],\"model_id\":\"en-es\"}" "{url}/v3/translate?version=2018-05-01"
IamOptions options = new IamOptions.Builder() .apiKey("{iam_api_key}"}) .build(); LanguageTranslator languageTranslator = new LanguageTranslator( "2018-05-01", options); languageTranslator.setEndPoint("{url}"); TranslateOptions translateOptions = new TranslateOptions.Builder() .addText("Hello") .modelId("en-es") .build(); TranslationResult result = languageTranslator.translate(translateOptions) .execute().getResult(); System.out.println(result);
const LanguageTranslatorV3 = require('watson-developer-cloud/language-translator/v3'); const languageTranslator = new LanguageTranslatorV3({ version: '2018-05-01', iam_apikey: '{apikey}', url: '{url}', }); const translateParams = { text: 'Hello', model_id: '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 language_translator = LanguageTranslatorV3( version='2018-05-01', iam_apikey='{iam_api_key}', url='{url}') translation = language_translator.translate( text='Hello', model_id='en-es').get_result() print(json.dumps(translation, indent=2, ensure_ascii=False))
require "json" require "ibm_watson/language_translator_v3" include IBMWatson language_translator = LanguageTranslatorV3.new( version: "2018-05-01", iam_apikey: "{iam_apikey}" ) translation = language_translator.translate( text: "Hello", model_id: "en-es" ) puts JSON.pretty_generate(translation.result)
package main import ( "encoding/json" "fmt" "github.com/IBM/go-sdk-core/core" "github.com/watson-developer-cloud/go-sdk/languagetranslatorv3" ) func main() { languageTranslator, languageTranslatorErr := languagetranslatorv3. NewLanguageTranslatorV3(&languagetranslatorv3.LanguageTranslatorV3Options{ URL: "{url}", Version: "2018-05-01", IAMApiKey: "{apikey}", }) if languageTranslatorErr != nil { panic(languageTranslatorErr) } response, responseErr := languageTranslator.Translate( &languagetranslatorv3.TranslateOptions{ Text: []string{"Hello"}, ModelID: core.StringPtr("en-es"), }, ) if responseErr != nil { panic(responseErr) } result := languageTranslator.GetTranslateResult(response) b, _ := json.MarshalIndent(result, "", " ") fmt.Println(string(b)) }
let languageTranslator = LanguageTranslator(version: "2018-05-01", apiKey: "{apikey}") languageTranslator.serviceURL = "{url}" languageTranslator.translate(text: ["Hello"], modelID: "en-es") { response, error in guard let translation = response?.result else { print(error?.localizedDescription ?? "unknown error") return } print(translation) }
IamConfig config = new IamConfig( apikey: "{apikey}" ); LanguageTranslatorService service = new LanguageTranslatorService("2018-05-01", config); service.SetEndpoint(url); var result = service.Translate( text: new List<string>() { "Hello" }, modelId: "en-es" ); Console.WriteLine(result.Response);
IamTokenOptions tokenOptions = new IamTokenOptions() { IamApiKey = "{apikey}" }; Credentials credentials = new Credentials(tokenOptions, "{url}"); while(!credentials.HasTokenData()) { yield return null; } LanguageTranslatorService service = new LanguageTranslatorService("2018-05-01", credentials); 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" }, modelId: "en-es" ); while (translateResponse == null) yield return null;
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.
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.
Translation output in UTF-8.
Translations
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.
Translation output in UTF-8.
translations
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.
Translation output in UTF-8.
translations
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.
Translation output in UTF-8.
translations
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.
Translation output in UTF-8.
translations
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.
Translation output in UTF-8.
translations
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.
Translation output in UTF-8.
Translations
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.
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
{ "translations": [ { "translation": "Hola" } ], "word_count": 1, "character_count": 5 }{ "translations": [ { "translation": "Hola" } ], "word_count": 1, "character_count": 5 }
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) (*core.DetailedResponse, error)ServiceCall<IdentifiableLanguages> listIdentifiableLanguages()listIdentifiableLanguages(params, [callback()])list_identifiable_languages(self, **kwargs)list_identifiable_languagesfunc listIdentifiableLanguages(
headers: [String: String]? = nil,
completionHandler: @escaping (WatsonResponse<IdentifiableLanguages>?, WatsonError?) -> Void)ListIdentifiableLanguages()ListIdentifiableLanguages(Callback<IdentifiableLanguages> callback)Query Parameters
Release date of the API version you want to use. Specify dates in YYYY-MM-DD format. The current version is
2018-05-01.Constraints: Value must match regular expression
^\d{4}-\d{2}-\d{2}$
No Request Parameters
No Request Parameters
No Request Parameters
No Request Parameters
No Request Parameters
No Request Parameters
No Request Parameters
No Request Parameters
curl --user apikey:{apikey} "{url}/v3/identifiable_languages?version=2018-05-01"
IamOptions options = new IamOptions.Builder() .apiKey("{iam_api_key}"}) .build(); LanguageTranslator languageTranslator = new LanguageTranslator( "2018-05-01", options); languageTranslator.setEndPoint("{url}"); IdentifiableLanguages languages = languageTranslator.listIdentifiableLanguages() .execute().getResult(); System.out.println(languages);
const LanguageTranslatorV3 = require('watson-developer-cloud/language-translator/v3'); const languageTranslator = new LanguageTranslatorV3({ version: '2018-05-01', iam_apikey: '{apikey}', url: '{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 language_translator = LanguageTranslatorV3( version='2018-05-01', iam_apikey='{iam_api_key}', url='{url}') languages = language_translator.list_identifiable_languages().get_result() print(json.dumps(languages, indent=2))
require "json" require "ibm_watson/language_translator_v3" include IBMWatson language_translator = LanguageTranslatorV3.new( version: "2018-05-01", iam_apikey: "{iam_apikey}" ) languages = language_translator.list_identifiable_languages puts JSON.pretty_generate(languages.result)
package main import ( "encoding/json" "fmt" "github.com/watson-developer-cloud/go-sdk/languagetranslatorv3" ) func main() { languageTranslator, languageTranslatorErr := languagetranslatorv3. NewLanguageTranslatorV3(&languagetranslatorv3.LanguageTranslatorV3Options{ URL: "{url}", Version: "2018-05-01", IAMApiKey: "{apikey}", }) if languageTranslatorErr != nil { panic(languageTranslatorErr) } response, responseErr := languageTranslator.ListIdentifiableLanguages( &languagetranslatorv3.ListIdentifiableLanguagesOptions{}, ) if responseErr != nil { panic(responseErr) } result := languageTranslator.GetListIdentifiableLanguagesResult(response) b, _ := json.MarshalIndent(result, "", " ") fmt.Println(string(b)) }
let languageTranslator = LanguageTranslator(version: "2018-05-01", apiKey: "{apikey}") languageTranslator.serviceURL = "{url}" languageTranslator.listIdentifiableLanguages() { response, error in guard let languagesList = response?.result else { print(error?.localizedDescription ?? "unknown error") return } print(languagesList) }
IamConfig config = new IamConfig( apikey: "{apikey}" ); LanguageTranslatorService service = new LanguageTranslatorService("2018-05-01", config); var result = service.ListIdentifiableLanguages(); Console.WriteLine(result.Response);
IamTokenOptions tokenOptions = new IamTokenOptions() { IamApiKey = "{apikey}" }; Credentials credentials = new Credentials(tokenOptions, "{url}"); while(!credentials.HasTokenData()) { yield return null; } LanguageTranslatorService service = new LanguageTranslatorService("2018-05-01", credentials); 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;
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) (*core.DetailedResponse, error)ServiceCall<IdentifiedLanguages> identify(IdentifyOptions identifyOptions)identify(params, [callback()])identify(self, text, **kwargs)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)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 API version you want to use. Specify dates in YYYY-MM-DD format. The current version is
2018-05-01.Constraints: Value must match regular expression
^\d{4}-\d{2}-\d{2}$
Input text in UTF-8 format.
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 --user apikey:{apikey} --request POST --header "Content-Type: text/plain" --data "Language translator translates text from one language to another" "{url}/v3/identify?version=2018-05-01"
IamOptions options = new IamOptions.Builder() .apiKey("{iam_api_key}"}) .build(); LanguageTranslator languageTranslator = new LanguageTranslator( "2018-05-01", options); languageTranslator.setEndPoint("{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('watson-developer-cloud/language-translator/v3'); const languageTranslator = new LanguageTranslatorV3({ version: '2018-05-01', iam_apikey: '{apikey}', url: '{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 language_translator = LanguageTranslatorV3( version='2018-05-01', iam_apikey='{iam_api_key}', 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/language_translator_v3" include IBMWatson language_translator = LanguageTranslatorV3.new( version: "2018-05-01", iam_apikey: "{iam_apikey}" ) language = language_translator.identify( text: "Language translator translates text from one language to another" ) puts JSON.pretty_generate(language.result)
package main import ( "encoding/json" "fmt" "github.com/IBM/go-sdk-core/core" "github.com/watson-developer-cloud/go-sdk/languagetranslatorv3" ) func main() { languageTranslator, languageTranslatorErr := languagetranslatorv3. NewLanguageTranslatorV3(&languagetranslatorv3.LanguageTranslatorV3Options{ URL: "{url}", Version: "2018-05-01", IAMApiKey: "{apikey}", }) if languageTranslatorErr != nil { panic(languageTranslatorErr) } response, responseErr := languageTranslator.Identify( &languagetranslatorv3.IdentifyOptions{ Text: core.StringPtr("Language translator translates text from one language to another"), }, ) if responseErr != nil { panic(responseErr) } result := languageTranslator.GetIdentifyResult(response) b, _ := json.MarshalIndent(result, "", " ") fmt.Println(string(b)) }
let languageTranslator = LanguageTranslator(version: "2018-05-01", apiKey: "{apikey}") 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) }
IamConfig config = new IamConfig( apikey: "{apikey}" ); LanguageTranslatorService service = new LanguageTranslatorService("2018-05-01", config); var result = service.Identify( text: "Language translator translates text from one language to another" ); Console.WriteLine(result.Response);
IamTokenOptions tokenOptions = new IamTokenOptions() { IamApiKey = "{apikey}" }; Credentials credentials = new Credentials(tokenOptions, "{url}"); while(!credentials.HasTokenData()) { yield return null; } LanguageTranslatorService service = new LanguageTranslatorService("2018-05-01", credentials); 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;
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.
Constraints: 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.
Constraints: 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.
Constraints: 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.
Constraints: 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.
Constraints: 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.
Constraints: 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.
Constraints: 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.
Constraints: 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) (*core.DetailedResponse, error)ServiceCall<TranslationModels> listModels(ListModelsOptions listModelsOptions)listModels(params, [callback()])list_models(self, source=None, target=None, default_models=None, **kwargs)list_models(source: nil, target: nil, default_models: nil)func listModels(
source: String? = nil,
target: String? = nil,
defaultModels: Bool? = nil,
headers: [String: String]? = nil,
completionHandler: @escaping (WatsonResponse<TranslationModels>?, WatsonError?) -> Void)ListModels(string source = null, string target = null, bool? defaultModels = null)ListModels(Callback<TranslationModels> callback, string source = null, string target = null, bool? defaultModels = null)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 API version you want to use. Specify dates in YYYY-MM-DD format. The current version is
2018-05-01.Constraints: Value must match regular expression
^\d{4}-\d{2}-\d{2}$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 will return all models (default and non-default) for each language pair. To return only default models, set this to
true. To return only non-default models, set this tofalse. There is exactly one default model per language pair, the IBM provided base model.
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 will return all models (default and non-default) for each language pair. To return only default models, set this to
true. To return only non-default models, set this tofalse. There is exactly one default model per language pair, the IBM provided base model.
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 will return all models (default and non-default) for each language pair. To return only default models, set this to
true. To return only non-default models, set this tofalse. There is exactly one default model per language pair, the IBM provided base model.
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 will return all models (default and non-default) for each language pair. To return only default models, set this to
true. To return only non-default models, set this tofalse. There is exactly one default model per language pair, the IBM provided base model.
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 will return all models (default and non-default) for each language pair. To return only default models, set this to
true. To return only non-default models, set this tofalse. There is exactly one default model per language pair, the IBM provided base model.
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 will return all models (default and non-default) for each language pair. To return only default models, set this to
true. To return only non-default models, set this tofalse. There is exactly one default model per language pair, the IBM provided base model.
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 will return all models (default and non-default) for each language pair. To return only default models, set this to
true. To return only non-default models, set this tofalse. There is exactly one default model per language pair, the IBM provided base model.
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 will return all models (default and non-default) for each language pair. To return only default models, set this to
true. To return only non-default models, set this tofalse. There is exactly one default model per language pair, the IBM provided base model.
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 will return all models (default and non-default) for each language pair. To return only default models, set this to
true. To return only non-default models, set this tofalse. There is exactly one default model per language pair, the IBM provided base model.
curl --user apikey:{apikey} "{url}/v3/models?version=2018-05-01"
IamOptions options = new IamOptions.Builder() .apiKey("{iam_api_key}"}) .build(); LanguageTranslator languageTranslator = new LanguageTranslator( "2018-05-01", options); languageTranslator.setEndPoint("{url}"); TranslationModels models = languageTranslator.listModels() .execute().getResult(); System.out.println(models);
const LanguageTranslatorV3 = require('watson-developer-cloud/language-translator/v3'); const languageTranslator = new LanguageTranslatorV3({ version: '2018-05-01', iam_apikey: '{apikey}', url: '{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 language_translator = LanguageTranslatorV3( version='2018-05-01', iam_apikey='{iam_api_key}', url='{url}') models = language_translator.list_models().get_result() print(json.dumps(models, indent=2))
require "json" require "ibm_watson/language_translator_v3" include IBMWatson language_translator = LanguageTranslatorV3.new( version: "2018-05-01", iam_apikey: "{iam_apikey}" ) models = language_translator.list_models puts JSON.pretty_generate(models.result)
package main import ( "encoding/json" "fmt" "github.com/watson-developer-cloud/go-sdk/languagetranslatorv3" ) func main() { languageTranslator, languageTranslatorErr := languagetranslatorv3. NewLanguageTranslatorV3(&languagetranslatorv3.LanguageTranslatorV3Options{ URL: "{url}", Version: "2018-05-01", IAMApiKey: "{apikey}", }) if languageTranslatorErr != nil { panic(languageTranslatorErr) } response, responseErr := languageTranslator.ListModels( &languagetranslatorv3.ListModelsOptions{}, ) if responseErr != nil { panic(responseErr) } result := languageTranslator.GetListModelsResult(response) b, _ := json.MarshalIndent(result, "", " ") fmt.Println(string(b)) }
let languageTranslator = LanguageTranslator(version: "2018-05-01", apiKey: "{apikey}") languageTranslator.serviceURL = "{url}" languageTranslator.listModels() { response, error in guard let models = response?.result else { print(error?.localizedDescription ?? "unknown error") return } print(models) }
IamConfig config = new IamConfig( apikey: "{apikey}" ); LanguageTranslatorService service = new LanguageTranslatorService("2018-05-01", config); var result = service.ListModels(); Console.WriteLine(result.Response);
IamTokenOptions tokenOptions = new IamTokenOptions() { IamApiKey = "{apikey}" }; Credentials credentials = new Credentials(tokenOptions, "{url}"); while(!credentials.HasTokenData()) { yield return null; } LanguageTranslatorService service = new LanguageTranslatorService("2018-05-01", credentials); 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;
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 Translation Memory eXchange (TMX) files to customize a translation model.
You can either customize a model with a forced glossary or with a corpus that contains parallel sentences. To create a model that is customized with a parallel corpus and a forced glossary, proceed in two steps: customize with a parallel corpus first and then customize the resulting model with a glossary. Depending on the type of customization and the size of the uploaded corpora, training can range from minutes for a glossary to several hours for a large parallel corpus. You can upload a single forced glossary file and this file must be less than 10 MB. You can upload multiple parallel corpora tmx files. The cumulative file size of all uploaded files is limited to 250 MB. To successfully train with a parallel corpus you must have at least 5,000 parallel sentences in your corpus.
You can have a maximum of 10 custom models per language pair.
Uploads Translation Memory eXchange (TMX) files to customize a translation model.
You can either customize a model with a forced glossary or with a corpus that contains parallel sentences. To create a model that is customized with a parallel corpus and a forced glossary, proceed in two steps: customize with a parallel corpus first and then customize the resulting model with a glossary. Depending on the type of customization and the size of the uploaded corpora, training can range from minutes for a glossary to several hours for a large parallel corpus. You can upload a single forced glossary file and this file must be less than 10 MB. You can upload multiple parallel corpora tmx files. The cumulative file size of all uploaded files is limited to 250 MB. To successfully train with a parallel corpus you must have at least 5,000 parallel sentences in your corpus.
You can have a maximum of 10 custom models per language pair.
Uploads Translation Memory eXchange (TMX) files to customize a translation model.
You can either customize a model with a forced glossary or with a corpus that contains parallel sentences. To create a model that is customized with a parallel corpus and a forced glossary, proceed in two steps: customize with a parallel corpus first and then customize the resulting model with a glossary. Depending on the type of customization and the size of the uploaded corpora, training can range from minutes for a glossary to several hours for a large parallel corpus. You can upload a single forced glossary file and this file must be less than 10 MB. You can upload multiple parallel corpora tmx files. The cumulative file size of all uploaded files is limited to 250 MB. To successfully train with a parallel corpus you must have at least 5,000 parallel sentences in your corpus.
You can have a maximum of 10 custom models per language pair.
Uploads Translation Memory eXchange (TMX) files to customize a translation model.
You can either customize a model with a forced glossary or with a corpus that contains parallel sentences. To create a model that is customized with a parallel corpus and a forced glossary, proceed in two steps: customize with a parallel corpus first and then customize the resulting model with a glossary. Depending on the type of customization and the size of the uploaded corpora, training can range from minutes for a glossary to several hours for a large parallel corpus. You can upload a single forced glossary file and this file must be less than 10 MB. You can upload multiple parallel corpora tmx files. The cumulative file size of all uploaded files is limited to 250 MB. To successfully train with a parallel corpus you must have at least 5,000 parallel sentences in your corpus.
You can have a maximum of 10 custom models per language pair.
Uploads Translation Memory eXchange (TMX) files to customize a translation model.
You can either customize a model with a forced glossary or with a corpus that contains parallel sentences. To create a model that is customized with a parallel corpus and a forced glossary, proceed in two steps: customize with a parallel corpus first and then customize the resulting model with a glossary. Depending on the type of customization and the size of the uploaded corpora, training can range from minutes for a glossary to several hours for a large parallel corpus. You can upload a single forced glossary file and this file must be less than 10 MB. You can upload multiple parallel corpora tmx files. The cumulative file size of all uploaded files is limited to 250 MB. To successfully train with a parallel corpus you must have at least 5,000 parallel sentences in your corpus.
You can have a maximum of 10 custom models per language pair.
Uploads Translation Memory eXchange (TMX) files to customize a translation model.
You can either customize a model with a forced glossary or with a corpus that contains parallel sentences. To create a model that is customized with a parallel corpus and a forced glossary, proceed in two steps: customize with a parallel corpus first and then customize the resulting model with a glossary. Depending on the type of customization and the size of the uploaded corpora, training can range from minutes for a glossary to several hours for a large parallel corpus. You can upload a single forced glossary file and this file must be less than 10 MB. You can upload multiple parallel corpora tmx files. The cumulative file size of all uploaded files is limited to 250 MB. To successfully train with a parallel corpus you must have at least 5,000 parallel sentences in your corpus.
You can have a maximum of 10 custom models per language pair.
Uploads Translation Memory eXchange (TMX) files to customize a translation model.
You can either customize a model with a forced glossary or with a corpus that contains parallel sentences. To create a model that is customized with a parallel corpus and a forced glossary, proceed in two steps: customize with a parallel corpus first and then customize the resulting model with a glossary. Depending on the type of customization and the size of the uploaded corpora, training can range from minutes for a glossary to several hours for a large parallel corpus. You can upload a single forced glossary file and this file must be less than 10 MB. You can upload multiple parallel corpora tmx files. The cumulative file size of all uploaded files is limited to 250 MB. To successfully train with a parallel corpus you must have at least 5,000 parallel sentences in your corpus.
You can have a maximum of 10 custom models per language pair.
Uploads Translation Memory eXchange (TMX) files to customize a translation model.
You can either customize a model with a forced glossary or with a corpus that contains parallel sentences. To create a model that is customized with a parallel corpus and a forced glossary, proceed in two steps: customize with a parallel corpus first and then customize the resulting model with a glossary. Depending on the type of customization and the size of the uploaded corpora, training can range from minutes for a glossary to several hours for a large parallel corpus. You can upload a single forced glossary file and this file must be less than 10 MB. You can upload multiple parallel corpora tmx files. The cumulative file size of all uploaded files is limited to 250 MB. To successfully train with a parallel corpus you must have at least 5,000 parallel sentences in your corpus.
You can have a maximum of 10 custom models per language pair.
Uploads Translation Memory eXchange (TMX) files to customize a translation model.
You can either customize a model with a forced glossary or with a corpus that contains parallel sentences. To create a model that is customized with a parallel corpus and a forced glossary, proceed in two steps: customize with a parallel corpus first and then customize the resulting model with a glossary. Depending on the type of customization and the size of the uploaded corpora, training can range from minutes for a glossary to several hours for a large parallel corpus. You can upload a single forced glossary file and this file must be less than 10 MB. You can upload multiple parallel corpora tmx files. The cumulative file size of all uploaded files is limited to 250 MB. To successfully train with a parallel corpus you must have at least 5,000 parallel sentences in your corpus.
You can have a maximum of 10 custom models per language pair.
POST /v3/models(languageTranslator *LanguageTranslatorV3) CreateModel(createModelOptions *CreateModelOptions) (*core.DetailedResponse, error)ServiceCall<TranslationModel> createModel(CreateModelOptions createModelOptions)createModel(params, [callback()])create_model(self, base_model_id, forced_glossary=None, parallel_corpus=None, name=None, **kwargs)create_model(base_model_id:, forced_glossary: nil, parallel_corpus: nil, name: nil)func createModel(
baseModelID: String,
forcedGlossary: Data? = nil,
parallelCorpus: Data? = nil,
name: String? = nil,
headers: [String: String]? = nil,
completionHandler: @escaping (WatsonResponse<TranslationModel>?, WatsonError?) -> Void)CreateModel(string baseModelId, System.IO.MemoryStream forcedGlossary = null, System.IO.MemoryStream parallelCorpus = null, string name = null)CreateModel(Callback<TranslationModel> callback, string baseModelId, System.IO.MemoryStream forcedGlossary = null, System.IO.MemoryStream parallelCorpus = null, string name = null)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 API version you want to use. Specify dates in YYYY-MM-DD format. The current version is
2018-05-01.Constraints: Value must match regular expression
^\d{4}-\d{2}-\d{2}$The model ID of the model to use as the base for customization. To see available models, use the
List modelsmethod. Usually all IBM provided models are customizable. In addition, all your models that have been created via parallel corpus 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 is 32 characters.
Form Parameters
A TMX file with your customizations. The customizations in the file completely overwrite the domain translaton data, including high frequency or high confidence phrase translations. You can upload only one glossary with a file size less than 10 MB per call. A forced glossary should contain single words or short phrases.
A TMX file with parallel sentences for source and target language. You can upload multiple parallel_corpus files in one request. All uploaded parallel_corpus files combined, your parallel corpus must contain at least 5,000 parallel sentences to train successfully.
The CreateModel options.
The model ID of the model to use as the base for customization. To see available models, use the
List modelsmethod. Usually all IBM provided models are customizable. In addition, all your models that have been created via parallel corpus customization, can be further customized with a forced glossary.A TMX file with your customizations. The customizations in the file completely overwrite the domain translaton data, including high frequency or high confidence phrase translations. You can upload only one glossary with a file size less than 10 MB per call. A forced glossary should contain single words or short phrases.
A TMX file with parallel sentences for source and target language. You can upload multiple parallel_corpus files in one request. All uploaded parallel_corpus files combined, your parallel corpus must contain at least 5,000 parallel sentences to train successfully.
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 is 32 characters.
The createModel options.
The model ID of the model to use as the base for customization. To see available models, use the
List modelsmethod. Usually all IBM provided models are customizable. In addition, all your models that have been created via parallel corpus customization, can be further customized with a forced glossary.A TMX file with your customizations. The customizations in the file completely overwrite the domain translaton data, including high frequency or high confidence phrase translations. You can upload only one glossary with a file size less than 10 MB per call. A forced glossary should contain single words or short phrases.
A TMX file with parallel sentences for source and target language. You can upload multiple parallel_corpus files in one request. All uploaded parallel_corpus files combined, your parallel corpus must contain at least 5,000 parallel sentences to train successfully.
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 is 32 characters.
parameters
The model ID of the model to use as the base for customization. To see available models, use the
List modelsmethod. Usually all IBM provided models are customizable. In addition, all your models that have been created via parallel corpus customization, can be further customized with a forced glossary.A TMX file with your customizations. The customizations in the file completely overwrite the domain translaton data, including high frequency or high confidence phrase translations. You can upload only one glossary with a file size less than 10 MB per call. A forced glossary should contain single words or short phrases.
A TMX file with parallel sentences for source and target language. You can upload multiple parallel_corpus files in one request. All uploaded parallel_corpus files combined, your parallel corpus must contain at least 5,000 parallel sentences to train successfully.
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 is 32 characters.
parameters
The model ID of the model to use as the base for customization. To see available models, use the
List modelsmethod. Usually all IBM provided models are customizable. In addition, all your models that have been created via parallel corpus customization, can be further customized with a forced glossary.A TMX file with your customizations. The customizations in the file completely overwrite the domain translaton data, including high frequency or high confidence phrase translations. You can upload only one glossary with a file size less than 10 MB per call. A forced glossary should contain single words or short phrases.
A TMX file with parallel sentences for source and target language. You can upload multiple parallel_corpus files in one request. All uploaded parallel_corpus files combined, your parallel corpus must contain at least 5,000 parallel sentences to train successfully.
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 is 32 characters.
parameters
The model ID of the model to use as the base for customization. To see available models, use the
List modelsmethod. Usually all IBM provided models are customizable. In addition, all your models that have been created via parallel corpus customization, can be further customized with a forced glossary.A TMX file with your customizations. The customizations in the file completely overwrite the domain translaton data, including high frequency or high confidence phrase translations. You can upload only one glossary with a file size less than 10 MB per call. A forced glossary should contain single words or short phrases.
A TMX file with parallel sentences for source and target language. You can upload multiple parallel_corpus files in one request. All uploaded parallel_corpus files combined, your parallel corpus must contain at least 5,000 parallel sentences to train successfully.
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 is 32 characters.
parameters
The model ID of the model to use as the base for customization. To see available models, use the
List modelsmethod. Usually all IBM provided models are customizable. In addition, all your models that have been created via parallel corpus customization, can be further customized with a forced glossary.A TMX file with your customizations. The customizations in the file completely overwrite the domain translaton data, including high frequency or high confidence phrase translations. You can upload only one glossary with a file size less than 10 MB per call. A forced glossary should contain single words or short phrases.
A TMX file with parallel sentences for source and target language. You can upload multiple parallel_corpus files in one request. All uploaded parallel_corpus files combined, your parallel corpus must contain at least 5,000 parallel sentences to train successfully.
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 is 32 characters.
parameters
The model ID of the model to use as the base for customization. To see available models, use the
List modelsmethod. Usually all IBM provided models are customizable. In addition, all your models that have been created via parallel corpus customization, can be further customized with a forced glossary.A TMX file with your customizations. The customizations in the file completely overwrite the domain translaton data, including high frequency or high confidence phrase translations. You can upload only one glossary with a file size less than 10 MB per call. A forced glossary should contain single words or short phrases.
A TMX file with parallel sentences for source and target language. You can upload multiple parallel_corpus files in one request. All uploaded parallel_corpus files combined, your parallel corpus must contain at least 5,000 parallel sentences to train successfully.
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 is 32 characters.
parameters
The model ID of the model to use as the base for customization. To see available models, use the
List modelsmethod. Usually all IBM provided models are customizable. In addition, all your models that have been created via parallel corpus customization, can be further customized with a forced glossary.A TMX file with your customizations. The customizations in the file completely overwrite the domain translaton data, including high frequency or high confidence phrase translations. You can upload only one glossary with a file size less than 10 MB per call. A forced glossary should contain single words or short phrases.
A TMX file with parallel sentences for source and target language. You can upload multiple parallel_corpus files in one request. All uploaded parallel_corpus files combined, your parallel corpus must contain at least 5,000 parallel sentences to train successfully.
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 is 32 characters.
curl --user apikey:{apikey} --request POST --form forced_glossary=@glossary.tmx "{url}/v3/models?version=2018-05-01&base_model_id=en-es&name=custom-en-es"
IamOptions options = new IamOptions.Builder() .apiKey("{iam_api_key}"}) .build(); LanguageTranslator languageTranslator = new LanguageTranslator( "2018-05-01", options); languageTranslator.setEndPoint("{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('watson-developer-cloud/language-translator/v3'); const languageTranslator = new LanguageTranslatorV3({ version: '2018-05-01', iam_apikey: '{apikey}', url: '{url}', }); const createModelParams = { name: 'custom-en-es', base_model_id: 'en-es', forced_glossary: 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 language_translator = LanguageTranslatorV3( version='2018-05-01', iam_apikey='{iam_api_key}', 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/language_translator_v3" include IBMWatson language_translator = LanguageTranslatorV3.new( version: "2018-05-01", iam_apikey: "{iam_apikey}" ) 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
package main import ( "encoding/json" "fmt" "github.com/IBM/go-sdk-core/core" "github.com/watson-developer-cloud/go-sdk/languagetranslatorv3" ) func main() { languageTranslator, languageTranslatorErr := languagetranslatorv3. NewLanguageTranslatorV3(&languagetranslatorv3.LanguageTranslatorV3Options{ URL: "{url}", Version: "2018-05-01", IAMApiKey: "{apikey}", }) if languageTranslatorErr != nil { panic(languageTranslatorErr) } glossary, glossaryErr := os.Open("glossary.tmx") if glossaryErr != nil { panic(glossaryErr) } defer glossary.Close() response, responseErr := languageTranslator.CreateModel( &languagetranslatorv3.CreateModelOptions{ BaseModelID: core.StringPtr("en-es"), Name: core.StringPtr("custom-en-es"), ForcedGlossary: glossary, }, ) if responseErr != nil { panic(responseErr) } result := languageTranslator.GetCreateModelResult(response) b, _ := json.MarshalIndent(result, "", " ") fmt.Println(string(b)) }
let languageTranslator = LanguageTranslator(version: "2018-05-01", apiKey: "{apikey}") 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) }
IamConfig config = new IamConfig( apikey: "{apikey}" ); LanguageTranslatorService service = new LanguageTranslatorService("2018-05-01", config); 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);
IamTokenOptions tokenOptions = new IamTokenOptions() { IamApiKey = "{apikey}" }; Credentials credentials = new Credentials(tokenOptions, "{url}"); while(!credentials.HasTokenData()) { yield return null; } LanguageTranslatorService service = new LanguageTranslatorService("2018-05-01", credentials); 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 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've hit the maximum limit of customizations for this user
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) (*core.DetailedResponse, error)ServiceCall<DeleteModelResult> deleteModel(DeleteModelOptions deleteModelOptions)deleteModel(params, [callback()])delete_model(self, model_id, **kwargs)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)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 API version you want to use. Specify dates in YYYY-MM-DD format. The current version is
2018-05-01.Constraints: Value must match regular expression
^\d{4}-\d{2}-\d{2}$
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 --user apikey:{apikey} --request DELETE "{url}/v3/models/9f8d9c6f-2123-462f-9793-f17fdcb77cd6?version=2018-05-01"
IamOptions options = new IamOptions.Builder() .apiKey("{iam_api_key}"}) .build(); LanguageTranslator languageTranslator = new LanguageTranslator( "2018-05-01", options); languageTranslator.setEndPoint("{url}"); DeleteModelOptions deleteModelOptions = new DeleteModelOptions.Builder() .modelId("9f8d9c6f-2123-462f-9793-f17fdcb77cd6") .build(); languageTranslator.deleteModel(deleteModelOptions).execute();
const LanguageTranslatorV3 = require('watson-developer-cloud/language-translator/v3'); const languageTranslator = new LanguageTranslatorV3({ version: '2018-05-01', iam_apikey: '{apikey}', url: '{url}', }); const deleteModelParams = { model_id: '{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 language_translator = LanguageTranslatorV3( version='2018-05-01', iam_apikey='{iam_api_key}', 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/language_translator_v3" include IBMWatson language_translator = LanguageTranslatorV3.new( version: "2018-05-01", iam_apikey: "{iam_apikey}" ) model = language_translator.delete_model( model_id: "9f8d9c6f-2123-462f-9793-f17fdcb77cd6" ) puts JSON.pretty_generate(model.result)
package main import ( "github.com/IBM/go-sdk-core/core" "github.com/watson-developer-cloud/go-sdk/languagetranslatorv3" ) func main() { languageTranslator, languageTranslatorErr := languagetranslatorv3. NewLanguageTranslatorV3(&languagetranslatorv3.LanguageTranslatorV3Options{ URL: "{url}", Version: "2018-05-01", IAMApiKey: "{apikey}", }) if languageTranslatorErr != nil { panic(languageTranslatorErr) } _, responseErr := languageTranslator.DeleteModel( &languagetranslatorv3.DeleteModelOptions{ ModelID: core.StringPtr("9f8d9c6f-2123-462f-9793-f17fdcb77cd6"), }, ) if responseErr != nil { panic(responseErr) } }
let languageTranslator = LanguageTranslator(version: "2018-05-01", apiKey: "{apikey}") languageTranslator.serviceURL = "{url}" languageTranslator.deleteModel(modelID: modelID) { _, error in if let error = error { print(error.localizedDescription) return } print("model deleted") }
IamConfig config = new IamConfig( apikey: "{apikey}" ); LanguageTranslatorService service = new LanguageTranslatorService("2018-05-01", config); var result = service.DeleteModel( modelId: "9f8d9c6f-2123-462f-9793-f17fdcb77cd6" ); Console.WriteLine(result.Response);
IamTokenOptions tokenOptions = new IamTokenOptions() { IamApiKey = "{apikey}" }; Credentials credentials = new Credentials(tokenOptions, "{url}"); while(!credentials.HasTokenData()) { yield return null; } LanguageTranslatorService service = new LanguageTranslatorService("2018-05-01", credentials); 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;
"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 will have 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 will have 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 will have 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 will have 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 will have 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 will have 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 will have 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 will have 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 will have a status of available.
GET /v3/models/{model_id}(languageTranslator *LanguageTranslatorV3) GetModel(getModelOptions *GetModelOptions) (*core.DetailedResponse, error)ServiceCall<TranslationModel> getModel(GetModelOptions getModelOptions)getModel(params, [callback()])get_model(self, model_id, **kwargs)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)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 API version you want to use. Specify dates in YYYY-MM-DD format. The current version is
2018-05-01.Constraints: Value must match regular expression
^\d{4}-\d{2}-\d{2}$
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 --user apikey:{apikey} "{url}/v3/models/9f8d9c6f-2123-462f-9793-f17fdcb77cd6?version=2018-05-01"
IamOptions options = new IamOptions.Builder() .apiKey("{iam_api_key}"}) .build(); LanguageTranslator languageTranslator = new LanguageTranslator( "2018-05-01", options); languageTranslator.setEndPoint("{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('watson-developer-cloud/language-translator/v3'); const languageTranslator = new LanguageTranslatorV3({ version: '2018-05-01', iam_apikey: '{apikey}', url: '{url}', }); const getModelParams = { model_id: '{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 language_translator = LanguageTranslatorV3( version='2018-05-01', iam_apikey='{iam_api_key}', 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/language_translator_v3" include IBMWatson language_translator = LanguageTranslatorV3.new( version: "2018-05-01", iam_apikey: "{iam_apikey}" ) model_details = language_translator.get_model( model_id: "9f8d9c6f-2123-462f-9793-f17fdcb77cd6" ) puts JSON.pretty_generate(model_details.result)
package main import ( "encoding/json" "fmt" "github.com/IBM/go-sdk-core/core" "github.com/watson-developer-cloud/go-sdk/languagetranslatorv3" ) func main() { languageTranslator, languageTranslatorErr := languagetranslatorv3. NewLanguageTranslatorV3(&languagetranslatorv3.LanguageTranslatorV3Options{ URL: "{url}", Version: "2018-05-01", IAMApiKey: "{apikey}", }) if languageTranslatorErr != nil { panic(languageTranslatorErr) } response, responseErr := languageTranslator.GetModel( &languagetranslatorv3.GetModelOptions{ ModelID: core.StringPtr("9f8d9c6f-2123-462f-9793-f17fdcb77cd6"), }, ) if responseErr != nil { panic(responseErr) } result := languageTranslator.GetGetModelResult(response) b, _ := json.MarshalIndent(result, "", " ") fmt.Println(string(b)) }
let languageTranslator = LanguageTranslator(version: "2018-05-01", apiKey: "{apikey}") languageTranslator.serviceURL = "{url}" languageTranslator.getModel(modelID: modelID) { response, error in guard let model = response?.result else { print(error?.localizedDescription ?? "unknown error") return } print(model) }
IamConfig config = new IamConfig( apikey: "{apikey}" ); LanguageTranslatorService service = new LanguageTranslatorService("2018-05-01", config); var result = service.GetModel( modelId: "9f8d9c6f-2123-462f-9793-f17fdcb77cd6" ); Console.WriteLine(result.Response);
IamTokenOptions tokenOptions = new IamTokenOptions() { IamApiKey = "{apikey}" }; Credentials credentials = new Credentials(tokenOptions, "{url}"); while(!credentials.HasTokenData()) { yield return null; } LanguageTranslatorService service = new LanguageTranslatorService("2018-05-01", credentials); 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 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) (*core.DetailedResponse, error)ServiceCall<DocumentList> listDocuments()listDocuments(params, [callback()])list_documents(self, **kwargs)list_documentsfunc listDocuments(
headers: [String: String]? = nil,
completionHandler: @escaping (WatsonResponse<DocumentList>?, WatsonError?) -> Void)ListDocuments()ListDocuments(Callback<DocumentList> callback)Query Parameters
Release date of the API version you want to use. Specify dates in YYYY-MM-DD format. The current version is
2018-05-01.Constraints: Value must match regular expression
^\d{4}-\d{2}-\d{2}$
No Request Parameters
No Request Parameters
No Request Parameters
No Request Parameters
No Request Parameters
No Request Parameters
No Request Parameters
No Request Parameters
curl --user apikey:{apikey} "{url}/v3/documents?version=2018-05-01"
const LanguageTranslatorV3 = require('watson-developer-cloud/language-translator/v3'); const languageTranslator = new LanguageTranslatorV3({ version: '2018-05-01', iam_apikey: '{apikey}', url: '{url}', }); languageTranslator.listDocuments() .then(result => { console.log(JSON.stringify(result, null, 2)); }) .catch(err => { console.log('error:', err); });
IamConfig config = new IamConfig( apikey: "{apikey}" ); LanguageTranslatorService service = new LanguageTranslatorService("2018-05-01", config); var result = service.ListDocuments(); Console.WriteLine(result.Response);
IamTokenOptions tokenOptions = new IamTokenOptions() { IamApiKey = "{apikey}" }; Credentials credentials = new Credentials(tokenOptions, "{url}"); while(!credentials.HasTokenData()) { yield return null; } LanguageTranslatorService service = new LanguageTranslatorService("2018-05-01", credentials); DocumentList documents = null; service.ListDocuments( callback: (DetailedResponse<DocumentList> response, IBMError error) => { Log.Debug("LanguageTranslatorServiceV3", "List Documents result: {0}", response.Response); documents = response.Result; } ); while (documents == null) yield return null;
An array of all previously submitted documents.
An array of all previously submitted documents.