Translation
Translation is the task of converting text from one language to another.
For more details about the translation task, check out its dedicated page! You will find examples and related materials.
Recommended models
- google-t5/t5-base: A general-purpose Transformer that can be used to translate from English to German, French, or Romanian.
This is only a subset of the supported models. Find the model that suits you best here.
Using the API
Python
JavaScript
cURL
import requests
API_URL = "https://api-inference.huggingface.co/models/google-t5/t5-base"
headers = {"Authorization": "Bearer hf_***"}
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return response.json()
output = query({
"inputs": "Меня зовут Вольфганг и я живу в Берлине",
})To use the Python client, see huggingface_hub’s package reference.
API specification
Request
| Payload | ||
|---|---|---|
| inputs* | string | The text to translate. |
| parameters | object | Additional inference parameters for Translation |
| src_lang | string | The source language of the text. Required for models that can translate from multiple languages. |
| tgt_lang | string | Target language to translate to. Required for models that can translate to multiple languages. |
| clean_up_tokenization_spaces | boolean | Whether to clean up the potential extra spaces in the text output. |
| truncation | enum | Possible values: do_not_truncate, longest_first, only_first, only_second. |
| generate_parameters | object | Additional parametrization of the text generation algorithm. |
Some options can be configured by passing headers to the Inference API. Here are the available headers:
| Headers | ||
|---|---|---|
| authorization | string | Authentication header in the form 'Bearer: hf_****' when hf_**** is a personal user access token with Inference API permission. You can generate one from your settings page. |
| x-use-cache | boolean, default to true | There is a cache layer on the inference API to speed up requests we have already seen. Most models can use those results as they are deterministic (meaning the outputs will be the same anyway). However, if you use a nondeterministic model, you can set this parameter to prevent the caching mechanism from being used, resulting in a real new query. Read more about caching here. |
| x-wait-for-model | boolean, default to false | If the model is not ready, wait for it instead of receiving 503. It limits the number of requests required to get your inference done. It is advised to only set this flag to true after receiving a 503 error, as it will limit hanging in your application to known places. Read more about model availability here. |
For more information about Inference API headers, check out the parameters guide.
Response
| Body | ||
|---|---|---|
| translation_text | string | The translated text. |