hfl/chinese-bert-wwm-ext
Chinese pre-trained BERT with Whole Word Masking, which can be used for various NLP tasks such as question answering, sentiment analysis, named entity recognition, etc. This work is based on the original BERT model but with additional whole word masking techniques to improve its performance on out-of-vocabulary words.
Chinese pre-trained BERT with Whole Word Masking, which can be used for various NLP tasks such as question answering, sentiment analysis, named entity recognition, etc. This work is based on the original BERT model but with additional whole word masking techniques to improve its performance on out-of-vocabulary words.
HTTP/cURL API
You can use cURL or any other http client to run inferences:
curl -X POST \
-d '{"input": "Where is my [MASK]?"}' \
-H "Authorization: bearer $DEEPINFRA_TOKEN" \
-H 'Content-Type: application/json' \
'https://api.deepinfra.com/v1/inference/hfl/chinese-bert-wwm-ext'
which will give you back something similar to:
{
"results": [
{
"sequence": "where is my father?",
"score": 0.08898820728063583,
"token": 2269,
"token_str": "father"
},
{
"sequence": "where is my mother?",
"score": 0.07864926755428314,
"token": 2388,
"token_str": "mother"
}
],
"request_id": null,
"inference_status": {
"status": "unknown",
"runtime_ms": 0,
"cost": 0.0,
"tokens_generated": 0,
"tokens_input": 0
}
}
Input fields
webhookfile
The webhook to call when inference is done, by default you will get the output in the response of your inference request
