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language: es |
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thumbnail: |
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--- |
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# RuPERTa-base (Spanish RoBERTa) + NER 馃巸馃彿 |
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This model is a fine-tuned on [NER-C](https://www.kaggle.com/nltkdata/conll-corpora) version of [RuPERTa-base](https://huggingface.co/mrm8488/RuPERTa-base) 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) 馃摎 |
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| Dataset | # Examples | |
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| ---------------------- | ----- | |
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| Train | 329 K | |
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| Dev | 40 K | |
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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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- Labels covered: |
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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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## Metrics on evaluation set 馃Ь |
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| Metric | # score | |
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| :------------------------------------------------------------------------------------: | :-------: | |
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| F1 | **77.55** |
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| Precision | **75.53** | |
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| Recall | **79.68** | |
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## Model in action 馃敤 |
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Example of usage: |
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```python |
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import torch |
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from transformers import AutoModelForTokenClassification, AutoTokenizer |
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id2label = { |
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"0": "B-LOC", |
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"1": "B-MISC", |
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"2": "B-ORG", |
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"3": "B-PER", |
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"4": "I-LOC", |
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"5": "I-MISC", |
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"6": "I-ORG", |
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"7": "I-PER", |
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"8": "O" |
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} |
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text ="Julien, CEO de HF, naci贸 en Francia." |
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input_ids = torch.tensor(tokenizer.encode(text)).unsqueeze(0) |
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outputs = model(input_ids) |
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last_hidden_states = outputs[0] |
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for m in last_hidden_states: |
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for index, n in enumerate(m): |
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if(index > 0 and index <= len(text.split(" "))): |
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print(text.split(" ")[index-1] + ": " + id2label[str(torch.argmax(n).item())]) |
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''' |
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Output: |
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-------- |
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Julien,: I-PER |
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CEO: O |
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de: O |
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HF,: B-ORG |
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naci贸: I-PER |
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en: I-PER |
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Francia.: I-LOC |
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''' |
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``` |
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Yeah! Not too bad 馃帀 |
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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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