Zeroshot Selectra Small

🚀 Zero-shot SELECTRA: A zero-shot classifier based on SELECTRA

Zero-shot SELECTRA is a SELECTRA model fine-tuned on the Spanish portion of the XNLI dataset. It can be used with Hugging Face's Zero-shot pipeline to perform zero-shot classifications.

Compared to our previous zero-shot classifier based on BETO, zero-shot SELECTRA is much more lightweight. As shown in the Metrics section, the small version (with 5 times fewer parameters) performs slightly worse, while the medium version (with 3 times fewer parameters) outperforms the BETO-based zero-shot classifier.

🚀 Quick Start

✨ Features

• Based on the SELECTRA model fine-tuned on the Spanish part of the XNLI dataset.
• Can be used with Hugging Face's Zero-shot pipeline for zero-shot classifications.
• More lightweight compared to the previous BETO-based zero-shot classifier.

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

from transformers import pipeline
classifier = pipeline("zero-shot-classification", 
                       model="Recognai/zeroshot_selectra_medium")

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': ['sociedad', 'cultura', 'salud', 'economia', 'deportes'],
 'scores': [0.3711881935596466,
  0.25650349259376526,
  0.17355826497077942,
  0.1641489565372467,
  0.03460107371211052]}
"""

⚠️ Important Note

The hypothesis_template parameter is important and should be in Spanish. In the widget on the right, this parameter is set to its default value: "This example is {}.", so different results are expected.

📚 Documentation

Metrics

• XNLI: The stated accuracy refers to the test portion of the XNLI dataset, after finetuning the model on the training portion.
• MLSUM: For this accuracy, we take the test set of the MLSUM dataset and classify the summaries of 5 selected labels. For details, check out our evaluation notebook

Training

Check out our training notebook for all the details.

Authors

• David Fidalgo (GitHub)
• Daniel Vila (GitHub)
• Francisco Aranda (GitHub)
• Javier Lopez (GitHub)

📄 License

This project is licensed under the apache-2.0 license.