readme: ---
  language: es
  thumbnail: https://i.imgur.com/DUlT077.jpg
  widget:
  - text: "España es un país muy <mask> en la UE"
  ---
  
  # RuPERTa: the Spanish RoBERTa 🎃<img src="https://abs-0.twimg.com/emoji/v2/svg/1f1ea-1f1f8.svg" alt="spain flag" width="25"/>
  
  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).
  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.
  The architecture is the same as `roberta-base`:
  
  `roberta.base:` **RoBERTa** using the **BERT-base architecture 125M** params
  
  ## Benchmarks 🧾 
  WIP (I continue working on it) 🚧
  
  | Task/Dataset     |    F1 | Precision | Recall |                                                                        Fine-tuned model |                                                                                                                                                                                                                                                                                               Reproduce it |
  | -------- | ----: | --------: | -----: | --------------------------------------------------------------------------------------: | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
  | POS      | 97.39 |     97.47 |  97.32 | [RuPERTa-base-finetuned-pos](https://huggingface.co/mrm8488/RuPERTa-base-finetuned-pos) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mrm8488/shared_colab_notebooks/blob/master/RuPERTa_base_finetuned_POS.ipynb)
  | NER      | 77.55 |     75.53 |  79.68 | [RuPERTa-base-finetuned-ner](https://huggingface.co/mrm8488/RuPERTa-base-finetuned-ner) |
  | SQUAD-es v1 |  to-do |       |    |[RuPERTa-base-finetuned-squadv1](https://huggingface.co/mrm8488/RuPERTa-base-finetuned-squadv1)
  | SQUAD-es v2 |  to-do |       |  |[RuPERTa-base-finetuned-squadv2](https://huggingface.co/mrm8488/RuPERTa-base-finetuned-squadv2)
  
  ## Model in action 🔨
  
  ### Usage for POS and NER 🏷
  
  ```python
  import torch
  from transformers import AutoModelForTokenClassification, AutoTokenizer
  
  id2label = {
      "0": "B-LOC",
      "1": "B-MISC",
      "2": "B-ORG",
      "3": "B-PER",
      "4": "I-LOC",
      "5": "I-MISC",
      "6": "I-ORG",
      "7": "I-PER",
      "8": "O"
  }
  
  tokenizer = AutoTokenizer.from_pretrained('mrm8488/RuPERTa-base-finetuned-ner')
  model = AutoModelForTokenClassification.from_pretrained('mrm8488/RuPERTa-base-finetuned-ner')
  
  text ="Julien, CEO de HF, nació en Francia."
  
  input_ids = torch.tensor(tokenizer.encode(text)).unsqueeze(0)
  
  outputs = model(input_ids)
  last_hidden_states = outputs[0]
  
  for m in last_hidden_states:
    for index, n in enumerate(m):
      if(index > 0 and index <= len(text.split(" "))):
        print(text.split(" ")[index-1] + ": " + id2label[str(torch.argmax(n).item())])
  
  # Output:
  '''
  Julien,: I-PER
  CEO: O
  de: O
  HF,: B-ORG
  nació: I-PER
  en: I-PER
  Francia.: I-LOC
  '''
  ```
  
  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)
  
  ### Fast usage for LM with `pipelines` 🧪
  
  ```python
  from transformers import AutoModelWithLMHead, AutoTokenizer
  model = AutoModelWithLMHead.from_pretrained('mrm8488/RuPERTa-base')
  tokenizer = AutoTokenizer.from_pretrained("mrm8488/RuPERTa-base", do_lower_case=True)
  
  from transformers import pipeline
  
  pipeline_fill_mask = pipeline("fill-mask", model=model, tokenizer=tokenizer)
  
  pipeline_fill_mask("España es un país muy <mask> en la UE")
  ```
  
  ```json
  [
    {
      "score": 0.1814306527376175,
      "sequence": "<s> españa es un país muy importante en la ue</s>",
      "token": 1560
    },
    {
      "score": 0.024842597544193268,
      "sequence": "<s> españa es un país muy fuerte en la ue</s>",
      "token": 2854
    },
    {
      "score": 0.02473250962793827,
      "sequence": "<s> españa es un país muy pequeño en la ue</s>",
      "token": 2948
    },
    {
      "score": 0.023991240188479424,
      "sequence": "<s> españa es un país muy antiguo en la ue</s>",
      "token": 5240
    },
    {
      "score": 0.0215945765376091,
      "sequence": "<s> españa es un país muy popular en la ue</s>",
      "token": 5782
    }
  ]
  ```
  
  ## Acknowledgments
  
  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.
  
  > Created by [Manuel Romero