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.
The code examples on this tab use the client library that is provided for Java.
Maven
Important: Java SDK v7.0.0 only requires additional configuration to set the target. For details, see https://github.com/watson-developer-cloud/java-sdk#installation.
<dependency>
<groupId>com.ibm.watson</groupId>
<artifactId>ibm-watson</artifactId>
<version>7.0.0</version>
</dependency>
Gradle
compile 'com.ibm.watson:ibm-watson:7.0.0'
GitHub
The code examples on this tab use the client library that is provided for Node.js.
Installation
npm install --save watson-developer-cloud
GitHub
The code examples on this tab use the client library that is provided for Python.
Installation
pip install --upgrade "ibm-watson>=3.0.3"
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', '~> 2.0.1'
Carthage
github "watson-developer-cloud/swift-sdk" ~> 2.0.1
Swift Package Manager
.package(url: "https://github.com/watson-developer-cloud/swift-sdk", from: "2.0.1")
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.
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 on the Dashboard.
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}" "{url}/{method}"
Basic authentication. Replace {username}, {password}, and {url} with your service credentials.
curl -u "{username}:{password}" "{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}, `, 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('watson-developer-cloud/language-translator/v3');
const languageTranslator = new LanguageTranslatorV3({
version: '{version}',
iam_apikey: '{apikey}',
url: '{url}'
});
Basic authentication. Replace {username}, {password}, `, and{url}`.
const = require('watson-developer-cloud/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}, `, 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}, `, 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}, `, 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}, `, and{url}`.
let languageTranslator = LanguageTranslator(version: "{version}", username: "{username}", password: "{password}")
languageTranslator.serviceURL = "{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 on the Dashboard.
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.
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
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('watson-developer-cloud/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"
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 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 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 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 a ApiException that contains the following fields.
| Field | Description |
|---|---|
| code | The HTTP response code 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 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 a user-friendly error message. Some of the WatsonError cases contain associated values that reveal more information about the error.
| Field | Description |
|---|---|
| errorDescription | A user-friendly message that describes the error. |
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,
function (err, response) {
// The error will be the first argument of the callback
if (err.code == 404) {
// Handle Not Found (404) error
} else if (err.code == 413) {
// Handle Request Too Large (413) error
} else {
console.log('Unexpected error: ', err.code);
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 error
if languageTranslatorErr != nil {
panic(languageTranslatorErr)
}
// Call a method
response, responseErr := languageTranslator.methodName(&methodOptions)
// Check for error
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)
}
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 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 the request.
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 the request.
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.
Example header parameter in a request
ReturnType returnValue = languageTranslator.methodName(parameters)
.addHeader("Custom-Header", "{header_value}")
.execute();
Example header parameter in a request
languageTranslator.methodName({
parameters,
headers: {
'Custom-Header': '{header_value}'
}
},
function (err, response) {
if (err) {
console.log('error:', err);
} else {
console.log(response);
}
}
);
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
}
Response details
The Language Translator service might return information to the application in response headers.
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, specify the headers attribute on the third parameter (response) that is passed to the callback function.
Example request to access response headers
languageTranslator.methodName({
parameters
},
function (err, result, response) {
if (err) {
console.log('error:', err);
} else {
console.log(response.headers);
}
}
);
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.
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())
All response data is available in the WatsonResponse<T> object returned in each method's completionHandler.
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
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 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 result 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 ")
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.
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('watson-developer-cloud/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"
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/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 a DetailedResponse object.- 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.
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)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.
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: '2019-04-02', 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) }
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
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.
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)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
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: '2019-04-02', 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) }
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
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.
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)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.
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: '2019-04-02', 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) }
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
Status Code
Success
Invalid request. See the response body for details.
{ "languages": [ { "confidence": 0.9143, "language": "en-US" }, { "confidence": 0.0396, "language": "hu-HU" }, { "confidence": 0.0093, "language": "ro-RO" }, { "confidence": 0.005, "language": "nl-NL" } ] }{ "languages": [ { "confidence": 0.9143, "language": "en-US" }, { "confidence": 0.0396, "language": "hu-HU" }, { "confidence": 0.0093, "language": "ro-RO" }, { "confidence": 0.005, "language": "nl-NL" } ] }
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.
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)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.
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: '2019-04-02', 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) }
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
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 maxium 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 maxium 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 maxium 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 maxium 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 maxium 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 maxium 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 maxium 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)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.
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: '2019-04-02', 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) }
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.
DELETE /v3/models/{model_id}(languageTranslator *LanguageTranslatorV3) DeleteModel(deleteModelOptions *DeleteModelOptions) (*core.DetailedResponse, error)ServiceCall<Void> 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)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.
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: '2019-04-02', 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") }
"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.
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)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.
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: '2019-04-02', 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) }
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" }
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}$
curl --user apikey:{apikey} "{url}/v3/documents?version=2018-05-01"
An array of all previously submitted documents.
Status Code
Success
Invalid request. See the response body for details.
{ "documents": [ { "document_id": "1bdb9528-bf73-45eb-87fa-c4f519af23a0", "filename": "en.docx", "model_id": "en-de", "source": "en", "target": "de", "status": "processing", "created": "2018-08-24T08:20:30.5Z" }, { "document_id": "1bdb9528-bf73-45eb-87fa-c4f519af23a0", "filename": "en.pdf", "model_id": "9ef1eda2-e4dc-460f-b51a-efeec95f239f", "base_model_id": "en-fr", "source": "en", "target": "fr", "status": "available", "created": "2018-08-24T08:20:30.5Z", "completed": "2018-08-24T08:20:35.5Z" } ] }
Translate document
Submit a document for translation. You can submit the document contents in the file parameter, or you can reference a previously submitted document by document ID.
POST /v3/documentsQuery 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}$
Form Parameters
The model to use for translation.
model_idor bothsourceandtargetare required.Language code that specifies the language of the source document.
Language code that specifies the target language for translation.
To use a previously submitted document as the source for a new translation, enter the
document_idof the document.
curl --user apikey:{apikey} --request POST --form "model_id=en-fr" --form "file=@en.pdf" "{url}/v3/documents?version=2018-05-01"
Document information, including translation status.
System generated ID identifying a document being translated using one specific translation model
filename from the submission (if it was missing in the multipart-form, 'noname.
' is used. The status of the translation job associated with a submitted document
Possible values: [
processing,available,failed]A globally unique string that identifies the underlying model that is used for translation
Translation source language code
Translation target language code
The time when the document was submitted.
Model ID of the base model that was used to customize the model. If the model is not a custom model, this will be absent or an empty string.
The time when the translation completed.
Status Code
Success. Use the returned
document_idwith the Get document status method to get the status of the translation.Invalid request. See the response body for details.
The specified document was not found.
{ "document_id": "1bdb9528-bf73-45eb-87fa-c4f519af23a0", "filename": "en.pdf", "model_id": "en-fr", "source": "en", "target": "fr", "status": "processing", "created": "2018-08-24T08:20:30.5Z" }
Custom Headers
Allowable values: [
*/*,application/json]
Path Parameters
The document ID of the document
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}$
curl --user apikey:{apikey} "{url}/v3/documents/1bdb9528-bf73-45eb-87fa-c4f519af23a0?version=2018-05-01"
Document information, including translation status.
System generated ID identifying a document being translated using one specific translation model
filename from the submission (if it was missing in the multipart-form, 'noname.
' is used. The status of the translation job associated with a submitted document
Possible values: [
processing,available,failed]A globally unique string that identifies the underlying model that is used for translation
Translation source language code
Translation target language code
The time when the document was submitted.
Model ID of the base model that was used to customize the model. If the model is not a custom model, this will be absent or an empty string.
The time when the translation completed.
Status Code
Success.
Invalid request. See the response body for details.
{ "document_id": "1bdb9528-bf73-45eb-87fa-c4f519af23a0", "filename": "en.pdf", "model_id": "en-fr", "source": "en", "target": "fr", "status": "available", "created": "2018-08-24T08:20:30.5Z", "completed": "2018-08-24T08:20:35.5Z" }
Path Parameters
Document ID of the document 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}$
curl --user apikey:{apikey} --request DELETE "{url}/v3/documents/1bdb9528-bf73-45eb-87fa-c4f519af23a0?version=2018-05-01"
Status Code
The document was deleted.
Invalid request. See the response body for details.
The specified document was not found.
No Sample Response
Get translated document
Gets the translated document associated with the given document ID
GET /v3/documents/{document_id}/translated_documentCustom Headers
Allowable values: [
application/powerpoint,application/mspowerpoint,application/x-rtf,application/json,application/xml,application/vnd.ms-excel,application/vnd.openxmlformats-officedocument.spreadsheetml.sheet,application/vnd.ms-powerpoint,application/vnd.openxmlformats-officedocument.presentationml.presentation,application/msword,application/vnd.openxmlformats-officedocument.wordprocessingml.document,application/vnd.oasis.opendocument.spreadsheet,application/vnd.oasis.opendocument.presentation,application/vnd.oasis.opendocument.text,application/pdf,application/rtf,text/html,text/json,text/plain,text/richtext,text/rtf,text/xml]
Path Parameters
The document ID of the document that was submitted for translation
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}$
curl --user apikey:{apikey} --output "fr.pdf" "{url}/v3/documents/1bdb9528-bf73-45eb-87fa-c4f519af23a0/translated_document?version=2018-05-01"
Status Code
Success
Invalid request. See the response body for details.
The document was not found, or the translated document is not available.