93 lines
2.1 KiB
Markdown
93 lines
2.1 KiB
Markdown
---
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language: spanish
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thumbnail:
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---
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# RuPERTa-base (Spanish RoBERTa) + NER 🎃🏷
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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) 📚
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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.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 🧾
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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 🔨
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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, nació 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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nació: 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 🎉
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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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