readme: ---
  language: es
  license: cc-by-4.0
  ---
  # NER-fine-tuned-BETO: model fine-tuned from BETO for NER task.
  ---
  Language: es
  Datasets:
  - conll2002
  - Babelscape/wikineural
  
  ## Introduction
  [NER-fine-tuned-BETO] is a NER model that was fine-tuned from BETO on the 2002 Conll and the WikiNEuRal spanish datasets.
  Model was trained on the Conll 2002 train dataset (~8320 sentences) and a bootstrapped dataset of WikiNEuRal, where we re-evaluate the dataset and only keep the sentences where all the labels matched the predictions made.
  Model was evaluated on the test dataset of Conll2002.
  
  ## Training data
  Training data was classified as follow:
  |Abbreviation|  Description  |
  |:----------:|:-------------:|
  |     O      | Outside of NE |
  |    PER     | Person’s name |
  |    ORG     | Organization  |
  |    LOC     |  Location     |
  |   MISC     | Miscellaneous |
  
  Alongside the IOB formatting, this is:
    - B-LABEL if the word is at the beggining of the entity.
    - I-LABEL if the word is part of the entity name, but not the first word.
    
  ## How to use NER-fine-tuned-BETO with HuggingFace
  Load the model and its tokenizer :
  
  ```python
  from transformers import AutoTokenizer, AutoModelForTokenClassification
  
  tokenizer = AutoTokenizer.from_pretrained("NazaGara/NER-fine-tuned-BETO", use_auth_token=True)
  model = AutoModelForTokenClassification.from_pretrained("NazaGara/NER-fine-tuned-BETO", use_auth_token=True)
  
  nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
  nlp('Ignacio se fue de viaje por Buenos aires')
  
  [{'entity_group': 'PER',
    'score': 0.9997764,
    'word': 'Ignacio',
    'start': 0,
    'end': 7},
   {'entity_group': 'LOC',
    'score': 0.9997932,
    'word': 'Buenos aires',
    'start': 28,
    'end': 40}]
  
  ```
  ## Model Performance
  Overall
  | precision | recall | f1-score |
  |:---------:|:------:|:--------:|
  | 0.9833    | 0.8950 | 0.8998   |
  
  By classes
  | class  | precision | recall | f1-score |
  |:------:|:---------:|:------:|:--------:|
  | O      | 0.9958    | 0.9965 | 0.990    |
  | B-PER  | 0.9572    | 0.9741 | 0.9654   |
  | I-PER  | 0.9487    | 0.9921 | 0.9699   |
  | B-ORG  | 0.8823    | 0.9264 | 0.9038   |
  | I-ORG  | 0.9253    | 0.9264 | 0.9117   |
  | B-LOC  | 0.8967    | 0.8736 | 0.8850   |
  | I-LOC  | 0.8870    | 0.8215 | 0.8530   |
  | B-MISC | 0.7541    | 0.7964 | 0.7747   |
  | I-MISC | 0.9026    | 0.7827 | 0.8384   |
  