Create README.md
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Julien Chaumond
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---
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language: spanish
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thumbnail: https://i.imgur.com/jgBdimh.png
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---
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# Spanish BERT (BETO) + NER
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This model is a fine-tuned on [NER-C](https://www.kaggle.com/nltkdata/conll-corpora) of the Spanish BERT cased [(BETO)](https://github.com/dccuchile/beto) for **NER** downstream task.
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## Details of the downstream task (NER) - Dataset
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- [Dataset: CONLL Corpora ES](https://www.kaggle.com/nltkdata/conll-corpora)
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I preprocessed the dataset and splitted it as train / dev (80/20)
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| Dataset | # Examples |
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| ---------------------- | ----- |
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| Train | 8.7 K |
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| Dev | 2.2 K |
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- [Fine-tune on NER script](https://github.com/huggingface/transformers/blob/master/examples/run_ner.py)
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```bash
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!export NER_DIR='/content/ner_dataset'
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!python /content/transformers/examples/run_ner.py \
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--model_type bert \
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--model_name_or_path dccuchile/bert-base-spanish-wwm-cased \
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--do_train \
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--do_eval \
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--data_dir '/content/ner_dataset' \
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--num_train_epochs 15.0 \
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--max_seq_length 384 \
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--output_dir /content/model_output \
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--save_steps 5000 \
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```
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## Comparison:
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| Model | # score |
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| :--------------------------------------------------------------------------------------------------------------: | :-------: |
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| bert-base-spanish-wwm-cased (BETO) | 88.43 |
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| [bert-spanish-cased-finetuned-ner (this one)](https://huggingface.co/mrm8488/bert-spanish-cased-finetuned-ner) | **89.65** |
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| Best Multilingual BERT | 87.38 |
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```
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***** All metrics on Eval results *****
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f1 = 0.8965040489828165
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loss = 0.11504213575173258
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precision = 0.893679858239811
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recall = 0.8993461462254805
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```
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> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
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> Made with <span style="color: #e25555;">♥</span> in Spain
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