Spaces:
Sleeping
Sleeping
File size: 2,333 Bytes
67c64b3 d6b9484 67c64b3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 |
---
title: Zero Shot Classifier
emoji: 🏃
colorFrom: purple
colorTo: yellow
sdk: gradio
sdk_version: 5.35.0
app_file: app.py
pinned: false
license: apache-2.0
short_description: Zero-shot text label predictor.
---
# 🧠 Zero-Shot Text Classifier (Hugging Face Version)
A smart and lightweight web app built with **Gradio** and **Transformers** that classifies your input text into the most likely label — using **Zero-Shot Learning**.
## 🧠 About the Model
- **Pipeline**: `zero-shot-classification`
- **Model**: `facebook/bart-large-mnli`
- **Framework**: Hugging Face Transformers
- **Task**: Predict a relevant label even if the model wasn't trained on it
## 💡 Features
- Accepts custom comma-separated labels
- Returns top predictions with confidence scores
- Works in real-time — hosted via Hugging Face Spaces
## ⚙️ Instructions for Users
This app uses **zero-shot classification** to find the most relevant label based on your input and label list.
👉 **How to use:**
1. Enter a sentence or paragraph
2. Enter comma-separated labels like: `Technology, Sports, Food`
3. The app will return top labels with confidence scores
⚠️ **Note:**
- Avoid overlapping or vague labels. It may reduce prediction accuracy.
- For example, a sentence about economy and healthcare might score both **Finance** and **Health**.
- The answer may reflect multiple topics if the sentence spans more than one area — this is expected behavior in such cases.
✅ **Example 1:**
- **Text:** `Roger Federer won another grand slam title, cementing his legacy in tennis.`
- **Labels:** `['Politics', 'Fashion', 'Sports']`
- **Prediction:** `Sports — 99.2%`
✅ **Example 2:**
- **Text:** `The chef used fresh ingredients and spices to prepare a delicious Indian curry.`
- **Labels:** `['Food', 'Health', 'Travel']`
- **Prediction:** `Food — 88.9%`
✅ **Example 3:**
- **Text:** `Climate change is leading to rising sea levels and more frequent extreme weather events.`
- **Labels:** `['Environment', 'Fashion', 'Technology']`
- **Prediction:** `Environment — 88.5%`
---
## 🚀 How to Run Locally
Install the required packages:
```bash
pip install -r requirements.txt
```
Then run the app:
```bash
python app.py
```
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|