Jean-Baptiste/roberta-large-ner-english cover image

Jean-Baptiste/roberta-large-ner-english

We present a fine-tuned RoBERTa model for English named entity recognition, achieving high performance on both formal and informal datasets. Our approach uses a simplified version of the CONLL2003 dataset and removes unnecessary prefixes for improved efficiency. The resulting model shows superiority over other models, especially on entities that do not begin with uppercase letters, and can be used for various applications such as email signature detection.

We present a fine-tuned RoBERTa model for English named entity recognition, achieving high performance on both formal and informal datasets. Our approach uses a simplified version of the CONLL2003 dataset and removes unnecessary prefixes for improved efficiency. The resulting model shows superiority over other models, especially on entities that do not begin with uppercase letters, and can be used for various applications such as email signature detection.

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/Jean-Baptiste/roberta-large-ner-english'

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