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dslim/bert-large-NER

A fine-tuned BERT model that achieves state-of-the-art performance on the CoNLL-2003 Named Entity Recognition task. The model was trained on the English version of the standard CoNLL-2003 dataset and distinguishes between four types of entities: location, organization, person, and miscellaneous.

A fine-tuned BERT model that achieves state-of-the-art performance on the CoNLL-2003 Named Entity Recognition task. The model was trained on the English version of the standard CoNLL-2003 dataset and distinguishes between four types of entities: location, organization, person, and miscellaneous.

Public
$0.0005 / sec

HTTP/cURL API

You can use cURL or any other http client to run inferences:

curl -X POST \
    -d '{"input": "My name is John Doe and I live in San Francisco."}'  \
    -H "Authorization: bearer $DEEPINFRA_TOKEN"  \
    -H 'Content-Type: application/json'  \
    'https://api.deepinfra.com/v1/inference/dslim/bert-large-NER'

which will give you back something similar to:

{
  "results": [
    {
      "entity_group": "B-PER",
      "score": 0.9997528195381165,
      "word": "John",
      "start": 11,
      "end": 15
    },
    {
      "entity_group": "I-PER",
      "score": 0.9995835423469543,
      "word": "Doe",
      "start": 16,
      "end": 19
    },
    {
      "entity_group": "B-LOC",
      "score": 0.9997485280036926,
      "word": "San",
      "start": 34,
      "end": 37
    },
    {
      "entity_group": "I-LOC",
      "score": 0.9994599223136902,
      "word": "Francisco",
      "start": 38,
      "end": 47
    }
  ],
  "request_id": null,
  "inference_status": {
    "status": "unknown",
    "runtime_ms": 0,
    "cost": 0.0,
    "tokens_generated": 0,
    "tokens_input": 0
  }
}

Input fields

inputstring

text input


webhookfile

The webhook to call when inference is done, by default you will get the output in the response of your inference request

Input Schema

Output Schema