--- 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% |