--- language: es license: cc-by-4.0 datasets: Babelscape/wikineural --- # 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 |