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Create model card for RuPERTa-base (#6016)
* Update README.md * Update model_cards/mrm8488/RuPERTa-base/README.md Co-authored-by: Julien Chaumond <chaumond@gmail.com> Co-authored-by: Julien Chaumond <chaumond@gmail.com>
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Manuel Romero
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---
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language: es
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thumbnail: https://i.imgur.com/DUlT077.jpg
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widget:
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- text: "España es un país muy <mask> en la UE"
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---
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# RuPERTa: the Spanish RoBERTa 🎃<img src="https://abs-0.twimg.com/emoji/v2/svg/1f1ea-1f1f8.svg" alt="spain flag" width="25"/>
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RuPERTa-base (uncased) is a [RoBERTa model](https://github.com/pytorch/fairseq/tree/master/examples/roberta) trained on a *uncased* verison of [big Spanish corpus](https://github.com/josecannete/spanish-corpora).
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RoBERTa iterates on BERT's pretraining procedure, including training the model longer, with bigger batches over more data; removing the next sentence prediction objective; training on longer sequences; and dynamically changing the masking pattern applied to the training data.
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The architecture is the same as `roberta-base`:
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`roberta.base:` **RoBERTa** using the **BERT-base architecture 125M** params
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## Benchmarks 🧾
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WIP (I continue working on it) 🚧
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| Task | F1 | Precision | Recall | Fine-tuned model | Reproduce it |
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| -------- | ----: | --------: | -----: | --------------------------------------------------------------------------------------: | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
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| POS | 97.39 | 97.47 | 97.32 | [RuPERTa-base-finetuned-pos](https://huggingface.co/mrm8488/RuPERTa-base-finetuned-pos) | [](https://colab.research.google.com/github/mrm8488/shared_colab_notebooks/blob/master/RuPERTa_base_finetuned_POS.ipynb)
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| NER | 77.55 | 75.53 | 79.68 | [RuPERTa-base-finetuned-ner](https://huggingface.co/mrm8488/RuPERTa-base-finetuned-ner) |
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| SQUAD-es v1 | to-do | | |
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| SQUAD-es v2 | to-do | | |
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## Model in action 🔨
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### Usage for POS and NER 🏷
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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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tokenizer = AutoTokenizer.from_pretrained('mrm8488/RuPERTa-base-finetuned-ner')
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model = AutoModelForTokenClassification.from_pretrained('mrm8488/RuPERTa-base-finetuned-ner')
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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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# 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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For **POS** just change the `id2label` dictionary and the model path to [mrm8488/RuPERTa-base-finetuned-pos](https://huggingface.co/mrm8488/RuPERTa-base-finetuned-pos)
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### Fast usage for LM with `pipelines` 🧪
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```python
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from transformers import AutoModelWithLMHead, AutoTokenizer
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model = AutoModelWithLMHead.from_pretrained('mrm8488/RuPERTa-base')
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tokenizer = AutoTokenizer.from_pretrained("mrm8488/RuPERTa-base", do_lower_case=True)
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from transformers import pipeline
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pipeline_fill_mask = pipeline("fill-mask", model=model, tokenizer=tokenizer)
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pipeline_fill_mask("España es un país muy <mask> en la UE")
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```
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```json
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[
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{
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"score": 0.1814306527376175,
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"sequence": "<s> españa es un país muy importante en la ue</s>",
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"token": 1560
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},
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{
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"score": 0.024842597544193268,
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"sequence": "<s> españa es un país muy fuerte en la ue</s>",
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"token": 2854
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},
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{
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"score": 0.02473250962793827,
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"sequence": "<s> españa es un país muy pequeño en la ue</s>",
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"token": 2948
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},
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{
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"score": 0.023991240188479424,
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"sequence": "<s> españa es un país muy antiguo en la ue</s>",
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"token": 5240
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},
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{
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"score": 0.0215945765376091,
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"sequence": "<s> españa es un país muy popular en la ue</s>",
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"token": 5782
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}
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]
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```
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## Acknowledgments
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I thank [🤗/transformers team](https://github.com/huggingface/transformers) for answering my doubts and Google for helping me with the [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc) program.
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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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