UPLOAD_INSTRUCTIONS.md

Instructions to Upload to Hugging Face

This repository is ready to be pushed to Hugging Face Model Hub!

Quick Setup (5 minutes)

Step 1: Create Hugging Face Repository

1. Go to https://huggingface.co/new
2. Fill in:
   • Model name: nfqa-multilingual-classifier
   • License: Apache 2.0 (recommended) or your choice
   • Visibility: Public (or Private if you prefer)
3. Click "Create model"
4. Important: Copy your repository URL from the page

Step 2: Get Your Access Token

1. Go to https://huggingface.co/settings/tokens
2. Click "New token"
3. Name: model-upload
4. Type: Write (important!)
5. Click "Generate token"
6. Copy the token (you won't see it again)

Step 3: Connect This Repository

Replace YOUR_USERNAME with your actual Hugging Face username:

cd /Users/alisalman/thesis/nfqa-multilingual-classifier

# Add Hugging Face as remote
git remote add origin https://huggingface.co/YOUR_USERNAME/nfqa-multilingual-classifier

# Configure git to use your HF credentials
git config credential.helper store

# Push to Hugging Face (you'll be prompted for username and token)
git push -u origin master

When prompted:

• Username: Your Hugging Face username
• Password: Paste your access token (not your password!)

Step 4: Verify Upload

1. Go to https://huggingface.co/YOUR_USERNAME/nfqa-multilingual-classifier
2. You should see:
   • ✅ All model files (11 files)
   • ✅ README with full documentation
   • ✅ Training visualizations (confusion matrix, training curves)
   • ✅ Model card with usage examples
3. Test the Inference API widget with a question

---

Alternative: Use Hugging Face CLI

If you prefer using the CLI:

# Install if not already installed
pip install --upgrade huggingface_hub

# Login
huggingface-cli login
# Paste your token when prompted

# Create repository
huggingface-cli repo create nfqa-multilingual-classifier --type model

# Upload
cd /Users/alisalman/thesis/nfqa-multilingual-classifier
huggingface-cli upload nfqa-multilingual-classifier . --repo-type model

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What's Included

This repository contains:

✅ Model Files (1.1 GB total):

• model.safetensors - Model weights
• config.json - Model configuration
• tokenizer.json - Tokenizer
• tokenizer_config.json - Tokenizer settings
• sentencepiece.bpe.model - Vocabulary
• special_tokens_map.json - Special tokens

✅ Documentation:

• README.md - Comprehensive model card
• classification_report.txt - Per-category performance
• test_results.json - Detailed evaluation metrics

✅ Visualizations:

• confusion_matrix.png - Test set confusion matrix
• training_curves.png - Training/validation curves

✅ Git Configuration:

• .gitattributes - LFS tracking for large files
• .gitignore - Ignore patterns

---

Before You Push

Update README Placeholders

Edit README.md and replace:

• [Your Name/Organization] → Your actual name
• [Specify your license] → Your license choice
• your-username/nfqa-multilingual-classifier → Your actual repo URL
• [Your email] → Your contact email
• [Your repository] → Your GitHub repo (if any)

You can edit directly on Hugging Face after uploading, or do it now:

nano README.md
# or use your preferred editor

---

Troubleshooting

Error: "Repository not found"

• Make sure you created the repository on huggingface.co first
• Check that the username in the URL matches your HF username

Error: "Authentication failed"

• Make sure you're using your token as password, not your account password
• Verify the token has Write permissions
• Try git credential reject to clear cached credentials

Error: "Large file not properly tracked"

• LFS is already configured in this repo
• Just push normally, git-lfs will handle large files automatically

Upload is very slow

• The model is ~1.1 GB, this is normal
• It may take 5-15 minutes depending on your internet speed
• Git LFS uploads large files efficiently

---

After Upload

1. Test the model:

   from transformers import pipeline
   
   classifier = pipeline("text-classification",
                        model="YOUR_USERNAME/nfqa-multilingual-classifier")
   result = classifier("What is the capital of France?")
   print(result)
   

2. Add widget examples in the README YAML front matter (optional)

3. Share your model on social media, papers, etc.

4. Monitor usage at https://huggingface.co/YOUR_USERNAME/nfqa-multilingual-classifier/tree/main

---

Quick Reference

# View repository status
cd /Users/alisalman/thesis/nfqa-multilingual-classifier
git status

# View commit history
git log --oneline

# Check remote URL
git remote -v

# Push updates (after making changes)
git add .
git commit -m "Update model card"
git push

---

Need help?

• Hugging Face Docs: https://huggingface.co/docs/hub
• Git LFS Guide: https://git-lfs.github.com/

Ready to push? Follow Step 3 above!