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Migrate model card from transformers-repo

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Read announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755
Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/mrm8488/bert-spanish-cased-finetuned-ner/README.md

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+ ---
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+ language: es
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+ thumbnail: https://i.imgur.com/jgBdimh.png
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+ ---
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+
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+ # Spanish BERT (BETO) + NER
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+
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+ This model is a fine-tuned on [NER-C](https://www.kaggle.com/nltkdata/conll-corpora) version of the Spanish BERT cased [(BETO)](https://github.com/dccuchile/beto) for **NER** downstream task.
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+
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+ ## Details of the downstream task (NER) - Dataset
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+
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+ - [Dataset: CONLL Corpora ES](https://www.kaggle.com/nltkdata/conll-corpora)
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+
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+ I preprocessed the dataset and split it as train / dev (80/20)
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+
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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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+
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+
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+ - [Fine-tune on NER script provided by Huggingface](https://github.com/huggingface/transformers/blob/master/examples/token-classification/run_ner_old.py)
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+
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+ - Labels covered:
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+
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+ ```
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+ B-LOC
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+ B-MISC
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+ B-ORG
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+ B-PER
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+ I-LOC
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+ I-MISC
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+ I-ORG
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+ I-PER
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+ O
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+ ```
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+
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+ ## Metrics on evaluation set:
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+
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+ | Metric | # score |
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+ | :------------------------------------------------------------------------------------: | :-------: |
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+ | F1 | **90.17**
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+ | Precision | **89.86** |
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+ | Recall | **90.47** |
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+
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+ ## Comparison:
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+
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+ | Model | # F1 score |Size(MB)|
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+ | :--------------------------------------------------------------------------------------------------------------: | :-------: |:------|
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+ | bert-base-spanish-wwm-cased (BETO) | 88.43 | 421
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+ | [bert-spanish-cased-finetuned-ner (this one)](https://huggingface.co/mrm8488/bert-spanish-cased-finetuned-ner) | **90.17** | 420 |
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+ | Best Multilingual BERT | 87.38 | 681 |
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+ |[TinyBERT-spanish-uncased-finetuned-ner](https://huggingface.co/mrm8488/TinyBERT-spanish-uncased-finetuned-ner) | 70.00 | **55** |
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+
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+ ## Model in action
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+
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+ Fast usage with **pipelines**:
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+
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+ ```python
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+ from transformers import pipeline
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+
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+ nlp_ner = pipeline(
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+ "ner",
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+ model="mrm8488/bert-spanish-cased-finetuned-ner",
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+ tokenizer=(
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+ 'mrm8488/bert-spanish-cased-finetuned-ner',
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+ {"use_fast": False}
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+ ))
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+
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+ text = 'Mis amigos están pensando viajar a Londres este verano'
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+
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+ nlp_ner(text)
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+
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+ #Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
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+ ```
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+
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+ > Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
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+
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+ > Made with <span style="color: #e25555;">&hearts;</span> in Spain