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--- |
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title: Zero Short Text Classification |
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emoji: 🐠 |
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colorFrom: red |
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colorTo: blue |
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sdk: gradio |
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sdk_version: 5.34.1 |
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app_file: app.py |
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pinned: false |
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license: mit |
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short_description: Zero-shot classification means no training data is needed. |
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--- |
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# 🔍 Zero-Shot Text Classification with BART and XLM-RoBERTa |
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This Hugging Face Space is inspired by the article: |
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🔗 [Zero-Shot Text Classification with BART and XLM-RoBERTa – C# Corner](https://www.c-sharpcorner.com/article/zero-shot-text-classification-with-bart-and-xlm-roberta/) |
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## 💡 What this app does: |
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- Takes any raw text input. |
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- Accepts user-defined labels (comma-separated). |
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- Uses Hugging Face's `pipeline("zero-shot-classification")` to predict the most relevant label(s) using: |
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- **facebook/bart-large-mnli** or |
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- **joeddav/xlm-roberta-large-xnli** |
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## 📦 Models Supported |
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- `facebook/bart-large-mnli` (English only) |
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- `joeddav/xlm-roberta-large-xnli` (Multilingual) |
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## ✅ Use Cases |
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- Categorizing feedback, support tickets, news headlines, etc. |
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- Works without any custom training — zero-shot! |
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## 🛠 How it Works |
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The model is prompted with your text and list of labels. It computes the probability of each label being appropriate, and returns scores. |
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--- |
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Read the full article here: |
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👉 [https://www.c-sharpcorner.com/article/zero-shot-text-classification-with-bart-and-xlm-roberta/](https://www.c-sharpcorner.com/article/zero-shot-text-classification-with-bart-and-xlm-roberta/) |
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