readme: ---
  language: es
  thumbnail: https://imgur.com/uxAvBfh
  tags:
  - QA
  - SQuAD
  
  ---
  
  # Electricidad small + Spanish SQuAD v1 ⚡❓
  
  [Electricidad-small-discriminator](https://huggingface.co/mrm8488/electricidad-small-discriminator) fine-tuned on [Spanish SQUAD v1.1 dataset](https://github.com/ccasimiro88/TranslateAlignRetrieve/tree/master/SQuAD-es-v1.1) for **Q&A** downstream task.
  
  ## Details of the downstream task (Q&A) - Dataset 📚
  
  [SQuAD-es-v1.1](https://github.com/ccasimiro88/TranslateAlignRetrieve/tree/master/SQuAD-es-v1.1)
  
  | Dataset split | # Samples |
  | ------------- | --------- |
  | Train         | 130 K     |
  | Test          | 11 K      |
  
  ## Model training 🏋️‍
  
  The model was trained on a Tesla P100 GPU and 25GB of RAM with the following command:
  
  ```bash
  python /content/transformers/examples/question-answering/run_squad.py \
    --model_type electra \
    --model_name_or_path 'mrm8488/electricidad-small-discriminator' \
    --do_eval \
    --do_train \
    --do_lower_case \
    --train_file '/content/dataset/train-v1.1-es.json' \
    --predict_file '/content/dataset/dev-v1.1-es.json' \
    --per_gpu_train_batch_size 16 \
    --learning_rate 3e-5 \
    --num_train_epochs 10 \
    --max_seq_length 384 \
    --doc_stride 128 \
    --output_dir '/content/electricidad-small-finetuned-squadv1-es' \
    --overwrite_output_dir \
    --save_steps 1000
  ```
  
  ## Test set Results 🧾
  
  | Metric | # Value   |
  | ------ | --------- |
  | **EM** | **46.82** |
  | **F1** | **64.79** |
  
  ```json
  {
  'exact': 46.82119205298013,
  'f1': 64.79435260021918,
  'total': 10570,
  'HasAns_exact': 46.82119205298013,
  HasAns_f1': 64.79435260021918,
  'HasAns_total': 10570,
  'best_exact': 46.82119205298013,
  'best_exact_thresh': 0.0,
  'best_f1': 64.79435260021918,
  'best_f1_thresh': 0.0
  }
  ```
  
  ### Model in action 🚀
  
  Fast usage with **pipelines**:
  
  ```python
  from transformers import pipeline
  
  qa_pipeline = pipeline(
      "question-answering",
      model="mrm8488/electricidad-small-finetuned-squadv1-es",
      tokenizer="mrm8488/electricidad-small-finetuned-squadv1-es"
  )
  
  context = "Manuel ha creado una versión del modelo Electra small en español que alcanza una puntuación F1 de 65 en el dataset SQUAD-es y sólo pesa 50 MB"
  
  q1 = "Cuál es su marcador F1?"
  q2 = "¿Cuál es el tamaño del modelo?"
  q3 = "¿Quién lo ha creado?"
  q4 = "¿Que es lo que ha hecho Manuel?"
  
  
  questions = [q1, q2, q3, q4]
  
  for question in questions:
    result = qa_pipeline({
      'context': context,
      'question': question})
    print(result)
  
  # Output:
  {'score': 0.14836778166355025, 'start': 98, 'end': 100, 'answer': '65'}
  {'score': 0.32219420810758237, 'start': 136, 'end': 140, 'answer': '50 MB'}
  {'score': 0.9672326951118713, 'start': 0, 'end': 6, 'answer': 'Manuel'}
  {'score': 0.23552458113848118, 'start': 10, 'end': 53, 'answer': 'creado una versión del modelo Electra small'}
  ```
  
  > Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
  
  > Made with <span style="color: #e25555;">&hearts;</span> in Spain
  