models/README.md

Models Directory

This directory contains pre-trained and fine-tuned models for the Intent-Aware Context-Preserving Summarization project.

Directory Structure

models/
├── README.md                 # This file
├── download_models.py        # Script to download pre-trained models
├── model_configs.json        # Model configurations and metadata
├── checkpoints/              # Fine-tuned model checkpoints
└── tokenizers/               # Tokenizer files

Available Models

Pre-trained Models from Hugging Face

Model Name	Model ID	Size	Best For
T5-Small	google-t5/t5-small	~77MB	Quick testing, prototyping
T5-Base	google-t5/t5-base	~220MB	General use, production
T5-Large	google-t5/t5-large	~738MB	High-quality summaries
BART-Base	facebook/bart-base	~558MB	General summarization
BART-Large-CNN	facebook/bart-large-cnn	~1.6GB	News/article summarization
PEGASUS-arXiv	google/pegasus-arxiv	~568MB	Scientific papers
PEGASUS-PubMed	google/pegasus-pubmed	~562MB	Medical/biomedical documents
LED-Base	allenai/led-base-16384	~660MB	Long documents (4096 tokens)

Downloading Models

Automatic Download

python models/download_models.py

Manual Download

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Download T5-base
tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-base")
model = AutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-base")

# Save locally
tokenizer.save_pretrained("models/t5-base-tokenizer")
model.save_pretrained("models/t5-base-model")

Fine-tuned Models

Fine-tuned models will be stored in models/checkpoints/ after training:

• intent-classifier-v1/ - Intent detection model
• summarizer-technical-v1/ - Fine-tuned summarization model
• summarizer-intent-aware-v1/ - Intent-aware summarization model

Model Configuration

Edit model_configs.json to configure:

• Model selection
• Tokenization parameters
• Generation settings
• Evaluation metrics

Using Models

Load Pre-trained Model

from src.models import SummarizationModelLoader

loader = SummarizationModelLoader(model_name='t5-base')
model, tokenizer = loader.load_model()

Load Fine-tuned Checkpoint

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_path = "models/checkpoints/summarizer-technical-v1"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

Storage Requirements

• Small Models: ~500MB total
• Large Models: ~3GB total
• With Fine-tuning Data: ~5-10GB

GPU Memory Requirements

Model	GPU Memory
T5-Small	2GB
T5-Base	6GB
T5-Large	12GB+
BART-Base	6GB
BART-Large-CNN	12GB+
LED-Base	8GB

Best Practices

1. Start with small models for testing and development
2. Use cached models to avoid repeated downloads
3. Monitor GPU memory when loading large models
4. Save fine-tuned models with meaningful version numbers
5. Document model changes in model metadata

Troubleshooting

Out of Memory Error

import torch
torch.cuda.empty_cache()  # Clear GPU cache
# Or use CPU: device='cpu'

Model Download Issues

• Check internet connection
• Verify Hugging Face API is accessible
• Try downloading specific model versions
• Use cache_dir parameter to specify custom location

Tokenizer Mismatch

Ensure tokenizer version matches model version:

tokenizer = AutoTokenizer.from_pretrained(model_path)  # Load matching tokenizer

References

• Hugging Face Models: https://huggingface.co/models
• Transformers Documentation: https://huggingface.co/docs/transformers
• Model Cards: https://huggingface.co/docs/hub/models-cards

Contributing

If you fine-tune new models:

1. Save with meaningful names and versions
2. Document performance metrics
3. Include training parameters
4. Add model cards with descriptions
5. Update this README

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Last Updated: January 15, 2024