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mrm8488/bert-spanish-cased-finetuned-ner

This paper presents a fine-tuned Spanish BERT model (BETO) for the Named Entity Recognition (NER) task. The model was trained on the CONLL Corpora ES dataset and achieved an F1 score of 90.17%. The authors also compared their model with other state-of-the-art models, including a multilingual BERT and a TinyBERT model, and demonstrated its effectiveness in identifying entities in Spanish text.

This paper presents a fine-tuned Spanish BERT model (BETO) for the Named Entity Recognition (NER) task. The model was trained on the CONLL Corpora ES dataset and achieved an F1 score of 90.17%. The authors also compared their model with other state-of-the-art models, including a multilingual BERT and a TinyBERT model, and demonstrated its effectiveness in identifying entities in Spanish text.

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/mrm8488/bert-spanish-cased-finetuned-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