This is a Streamlit web application that performs zero-shot text classification using the Facebook BART model fine-tuned on the MNLI dataset. Zero-shot classification allows the model to classify text into classes that it has never seen during training.
!pip install requirements
!streamlit run app.py & npx localtunnel --port 8501
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Enter the sentence you want to classify in the provided text area.
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Enter the related words (candidate labels) in the sentence, separated by commas, in the provided text input.
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Click the "Classify" button to trigger the classification.
The application will then perform zero-shot classification on the input sentence using the specified candidate labels and display the classification results.
Zero-shot text classification is a technique that allows a model to classify text into classes it has never seen during training. In this application, we use the Facebook BART model fine-tuned on the MNLI dataset to perform zero-shot classification.
For any questions, suggestions, or issues related to this application, feel free to contact, Email : ceo@adople.com