Remove upload instructions and example usage scripts for zero-shot classification model

UPLOAD_INSTRUCTIONS.md

@@ -1,112 +0,0 @@
-# How to Upload Your Model to Hugging Face
-
-Follow these steps to upload your zero-shot classification model to Hugging Face and make it available for use through the transformers library.
-
-## Prerequisites
-
-1. Install required packages:
-
-```bash
-pip install huggingface_hub transformers
-```
-
-2. Create a Hugging Face account at https://huggingface.co/
-
-3. Get your access token:
-   - Go to https://huggingface.co/settings/tokens
-   - Create a new token with "Write" permissions
-   - Copy the token (keep it secure!)
-
-## Upload Steps
-
-### Method 1: Using the Web Interface (Recommended for beginners)
-
-1. Go to https://huggingface.co/new
-2. Choose "Model" and give it a name (e.g., `zero-shot-classification`)
-3. Set visibility (Public/Private)
-4. Click "Create model repository"
-5. Upload files using the web interface:
-   - Drag and drop all files from your model directory
-   - Or use git (see Method 2)
-
-### Method 2: Using Git/Command Line
-
-1. Login to Hugging Face CLI:
-
-```bash
-huggingface-cli login
-# Enter your token when prompted
-```
-
-2. Clone your repository:
-
-```bash
-git clone https://huggingface.co/YOUR_USERNAME/zero-shot-classification
-cd zero-shot-classification
-```
-
-3. Copy your model files:
-
-```bash
-# Copy all files from your model directory to the cloned repository
-cp /path/to/your/model/* .
-```
-
-4. Upload to Hugging Face:
-
-```bash
-git add .
-git commit -m "Upload XLM-RoBERTa zero-shot classification model"
-git push
-```
-
-### Method 3: Using Python API
-
-```python
-from huggingface_hub import HfApi, create_repo
-
-# Initialize API
-api = HfApi()
-
-# Create repository (optional if not exists)
-repo_id = "YOUR_USERNAME/zero-shot-classification"
-create_repo(repo_id, repo_type="model", private=False)
-
-# Upload files
-api.upload_folder(
-    folder_path="/path/to/your/model/directory",
-    repo_id=repo_id,
-    repo_type="model"
-)
-```
-
-## Important Notes
-
-1. **Replace placeholders**: Before uploading, make sure to replace `YOUR_USERNAME` in the README.md and example files with your actual Hugging Face username.
-
-2. **Model card**: The README.md serves as your model card. Make sure it's complete and accurate.
-
-3. **File size**: Large files (>10MB) are automatically handled by Git LFS, which is already configured in .gitattributes.
-
-4. **Testing**: After upload, test your model:
-
-```python
-from transformers import pipeline
-classifier = pipeline("zero-shot-classification", model="YOUR_USERNAME/zero-shot-classification")
-```
-
-## Making Your Model Discoverable
-
-1. Add relevant tags in your README.md frontmatter
-2. Add a good description
-3. Include example usage
-4. Consider adding a model card with performance metrics
-
-## Troubleshooting
-
-- **Authentication errors**: Make sure your token has write permissions
-- **Large file errors**: Ensure Git LFS is properly configured
-- **Model loading errors**: Check that all required files are present (config.json, model files, tokenizer files)
-
-After successful upload, your model will be available at:
-`https://huggingface.co/YOUR_USERNAME/zero-shot-classification`

example_usage.py

@@ -1,95 +0,0 @@
-#!/usr/bin/env python3
-"""
-Example script demonstrating how to use the XLM-RoBERTa Zero-Shot Classification model.
-This script shows various use cases including multilingual classification.
-"""
-
-import torch
-from transformers import pipeline
-
-
-def main():
-    print("Loading XLM-RoBERTa Zero-Shot Classification model...")
-
-    # Initialize the zero-shot classification pipeline
-    # Replace 'YOUR_USERNAME/zero-shot-classification' with your actual model path
-    classifier = pipeline(
-        "zero-shot-classification",
-        model="YOUR_USERNAME/zero-shot-classification",
-        device=0 if torch.cuda.is_available() else -1,  # Use GPU if available
-    )
-
-    print("Model loaded successfully!\n")
-
-    # Example 1: English sentiment analysis
-    print("Example 1: English Sentiment Analysis")
-    text_en = "I love this new smartphone, it's absolutely amazing!"
-    labels_en = ["positive", "negative", "neutral"]
-
-    result = classifier(text_en, labels_en)
-    print(f"Text: {text_en}")
-    print(f"Predicted label: {result['labels'][0]} (score: {result['scores'][0]:.4f})")
-    print()
-
-    # Example 2: Multilingual topic classification
-    print("Example 2: Multilingual Topic Classification")
-    texts = [
-        (
-            "English",
-            "The government announced new economic policies today.",
-            ["politics", "sports", "technology", "entertainment"],
-        ),
-        (
-            "Spanish",
-            "El nuevo iPhone tiene características increíbles.",
-            ["tecnología", "deportes", "política", "entretenimiento"],
-        ),
-        (
-            "French",
-            "Le match de football était très excitant hier soir.",
-            ["sport", "politique", "technologie", "divertissement"],
-        ),
-        (
-            "German",
-            "Die neue KI-Technologie wird die Zukunft verändern.",
-            ["Technologie", "Sport", "Politik", "Unterhaltung"],
-        ),
-    ]
-
-    for language, text, labels in texts:
-        result = classifier(text, labels)
-        print(f"{language}: {text}")
-        print(f"Predicted: {result['labels'][0]} (score: {result['scores'][0]:.4f})")
-        print()
-
-    # Example 3: Multi-label classification
-    print("Example 3: Multi-label Classification")
-    text_multi = "This movie has great action scenes and amazing special effects, but the story is quite boring."
-    labels_multi = ["action", "drama", "comedy", "boring", "exciting", "visual effects"]
-
-    result = classifier(text_multi, labels_multi, multi_label=True)
-    print(f"Text: {text_multi}")
-    print("All predictions:")
-    for label, score in zip(result["labels"], result["scores"]):
-        print(f"  {label}: {score:.4f}")
-    print()
-
-    # Example 4: Custom hypothesis template
-    print("Example 4: Custom Hypothesis Template (Spanish)")
-    text_es = "Esta película es realmente fantástica y emocionante."
-    labels_es = ["positivo", "negativo", "neutro"]
-    hypothesis_template = "Este texto es {}."
-
-    result = classifier(text_es, labels_es, hypothesis_template=hypothesis_template)
-    print(f"Text: {text_es}")
-    print(f"Predicted: {result['labels'][0]} (score: {result['scores'][0]:.4f})")
-    print(f"Using custom template: '{hypothesis_template}'")
-    print()
-
-    print(
-        "Demo completed! You can now use this model for your own zero-shot classification tasks."
-    )
-
-
-if __name__ == "__main__":
-    main()