readme: ---
  language: es
  tags:
  - zero-shot-classification
  - nli
  - pytorch
  datasets:
  - xnli
  license: mit
  pipeline_tag: zero-shot-classification
  widget:
  - text: "El autor se perfila, a los 50 años de su muerte, como uno de los grandes de su siglo"
    candidate_labels: "cultura, sociedad, economia, salud, deportes"
  ---
  
  # bert-base-spanish-wwm-cased-xnli
  
  **UPDATE, 15.10.2021: Check out our new zero-shot classifiers, much more lightweight and even outperforming this one: [zero-shot SELECTRA small](https://huggingface.co/Recognai/zeroshot_selectra_small) and [zero-shot SELECTRA medium](https://huggingface.co/Recognai/zeroshot_selectra_medium).**
  
  ## Model description
  
  This model is a fine-tuned version of the [spanish BERT model](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) with the Spanish portion of the XNLI dataset. You can have a look at the [training script](https://huggingface.co/Recognai/bert-base-spanish-wwm-cased-xnli/blob/main/zeroshot_training_script.py) for details of the training.
  
  ### How to use
  
  You can use this model with Hugging Face's [zero-shot-classification pipeline](https://discuss.huggingface.co/t/new-pipeline-for-zero-shot-text-classification/681):
  ```python
  from transformers import pipeline
  classifier = pipeline("zero-shot-classification", 
                         model="Recognai/bert-base-spanish-wwm-cased-xnli")
  
  classifier(
      "El autor se perfila, a los 50 años de su muerte, como uno de los grandes de su siglo",
      candidate_labels=["cultura", "sociedad", "economia", "salud", "deportes"],
      hypothesis_template="Este ejemplo es {}."
  )
  """output
  {'sequence': 'El autor se perfila, a los 50 años de su muerte, como uno de los grandes de su siglo',
   'labels': ['cultura', 'sociedad', 'economia', 'salud', 'deportes'],
   'scores': [0.38897448778152466,
    0.22997373342514038,
    0.1658431738615036,
    0.1205764189362526,
    0.09463217109441757]}
  """
  ```
  ## Eval results
  
  
  Accuracy for the test set:
  
  |                             | XNLI-es |
  |-----------------------------|---------|
  |bert-base-spanish-wwm-cased-xnli | 79.9% |