Delete README.md

README.md

@@ -1,410 +0,0 @@
----
-license: mit
-tags:
-- finance
-- llm
-- lora
-- sentiment-analysis
-- named-entity-recognition
-- xbrl
-pipeline_tag: text-generation
----
-
-# FinLoRA: Financial Large Language Models with LoRA Adaptation
-
-[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)
-[![PyTorch](https://img.shields.io/badge/PyTorch-2.0+-red.svg)](https://pytorch.org/)
-[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
-
-## Overview
-
-FinLoRA is a comprehensive framework for fine-tuning large language models on financial tasks using Low-Rank Adaptation (LoRA). This repository contains trained LoRA adapters for various financial NLP tasks including sentiment analysis, named entity recognition, headline classification, XBRL processing, and **RAG-enhanced models** for CFA knowledge and FinTagging tasks.
-
-## Model Architecture
-
-- **Base Model**: Meta-Llama-3.1-8B-Instruct
-- **Adaptation Method**: LoRA (Low-Rank Adaptation)
-- **Quantization**: 8-bit and 4-bit quantization support
-- **Tasks**: Financial sentiment analysis, NER, classification, XBRL processing, CFA knowledge, FinTagging
-
-## Available Models
-
-### 8-bit Quantized Models (Recommended)
-- `sentiment_llama_3_1_8b_8bits_r8` - Financial sentiment analysis
-- `ner_llama_3_1_8b_8bits_r8` - Named entity recognition
-- `headline_llama_3_1_8b_8bits_r8` - Financial headline classification
-- `xbrl_extract_llama_3_1_8b_8bits_r8` - XBRL tag extraction
-- `xbrl_term_llama_3_1_8b_8bits_r8` - XBRL terminology processing
-- `financebench_llama_3_1_8b_8bits_r8` - Comprehensive financial benchmark
-- `finer_llama_3_1_8b_8bits_r8` - Financial NER
-- `formula_llama_3_1_8b_8bits_r8` - Financial formula processing
-
-### RAG-Enhanced Models (New!)
-- `cfa_rag_llama_3_1_8b_8bits_r8` - CFA knowledge-enhanced model with RAG
-- `fintagging_combined_rag_llama_3_1_8b_8bits_r8` - Combined FinTagging RAG model
-- `fintagging_fincl_rag_llama_3_1_8b_8bits_r8` - FinCL RAG-enhanced model
-- `fintagging_finni_rag_llama_3_1_8b_8bits_r8` - FinNI RAG-enhanced model
-
-### 4-bit Quantized Models (Memory Efficient)
-- `sentiment_llama_3_1_8b_4bits_r4` - Financial sentiment analysis
-- `ner_llama_3_1_8b_4bits_r4` - Named entity recognition
-- `headline_llama_3_1_8b_4bits_r4` - Financial headline classification
-- `xbrl_extract_llama_3_1_8b_4bits_r4` - XBRL tag extraction
-- `xbrl_term_llama_3_1_8b_4bits_r4` - XBRL terminology processing
-- `financebench_llama_3_1_8b_4bits_r4` - Comprehensive financial benchmark
-- `finer_llama_3_1_8b_4bits_r4` - Financial NER
-- `formula_llama_3_1_8b_4bits_r4` - Financial formula processing
-
-## Quick Start
-
-### 1. Installation
-
-```bash
-# Install dependencies
-pip install -r requirements.txt
-```
-
-### 2. Basic Usage
-
-```python
-from inference import FinLoRAPredictor
-
-# Initialize predictor with 8-bit model (recommended)
-predictor = FinLoRAPredictor(
-    model_name="sentiment_llama_3_1_8b_8bits_r8",
-    use_4bit=False
-)
-
-# Financial sentiment analysis
-sentiment = predictor.classify_sentiment(
-    "The company's quarterly earnings exceeded expectations by 20%."
-)
-print(f"Sentiment: {sentiment}")
-
-# Entity extraction
-entities = predictor.extract_entities(
-    "Apple Inc. reported revenue of $394.3 billion in 2022."
-)
-print(f"Entities: {entities}")
-
-# Use 4-bit model for memory efficiency (if you have limited GPU memory)
-predictor_4bit = FinLoRAPredictor(
-    model_name="sentiment_llama_3_1_8b_4bits_r4",
-    use_4bit=True
-)
-
-# CPU-only mode (if no GPU available)
-predictor_cpu = FinLoRAPredictor(
-    model_name="sentiment_llama_3_1_8b_8bits_r8",
-    use_4bit=False
-)
-# The script will automatically detect CPU and adjust accordingly
-```
-
-### 3. Run Complete Test
-
-```bash
-# Test all models (this will download the base Llama model if not present)
-python inference.py
-
-# Test specific model
-python -c "
-from inference import FinLoRAPredictor
-predictor = FinLoRAPredictor('sentiment_llama_3_1_8b_8bits_r8')
-print('Model loaded successfully!')
-"
-```
-
-## Usage Examples
-
-### Financial Sentiment Analysis
-
-```python
-predictor = FinLoRAPredictor("sentiment_llama_3_1_8b_8bits_r8")
-
-# Test cases
-test_texts = [
-    "Stock prices are soaring to new heights.",
-    "Revenue declined by 15% this quarter.",
-    "The company maintained stable performance."
-]
-
-for text in test_texts:
-    sentiment = predictor.classify_sentiment(text)
-    print(f"Text: {text}")
-    print(f"Sentiment: {sentiment}\n")
-```
-
-### Named Entity Recognition
-
-```python
-predictor = FinLoRAPredictor("ner_llama_3_1_8b_8bits_r8")
-
-text = "Apple Inc. reported revenue of $394.3 billion in 2022."
-entities = predictor.extract_entities(text)
-print(f"Entities: {entities}")
-```
-
-### XBRL Processing
-
-```python
-predictor = FinLoRAPredictor("xbrl_extract_llama_3_1_8b_8bits_r8")
-
-text = "Total assets: $1,234,567,890. Current assets: $456,789,123."
-xbrl_tags = predictor.extract_xbrl_tags(text)
-print(f"XBRL Tags: {xbrl_tags}")
-```
-
-### RAG-Enhanced Models
-
-```python
-# CFA RAG-enhanced model for financial knowledge
-predictor = FinLoRAPredictor("cfa_rag_llama_3_1_8b_8bits_r8")
-
-# Enhanced financial analysis with CFA knowledge
-response = predictor.generate_response(
-    "Explain the concept of discounted cash flow valuation"
-)
-print(f"CFA Response: {response}")
-
-# FinTagging RAG models for financial information extraction
-fintagging_predictor = FinLoRAPredictor("fintagging_combined_rag_llama_3_1_8b_8bits_r8")
-
-# Extract financial information with enhanced context
-entities = fintagging_predictor.extract_entities(
-    "Apple Inc. reported revenue of $394.3 billion in 2022."
-)
-print(f"Enhanced Entities: {entities}")
-```
-
-### Memory-Efficient 4-bit Models
-
-```python
-# For users with limited GPU memory
-predictor = FinLoRAPredictor(
-    model_name="sentiment_llama_3_1_8b_4bits_r4",
-    use_4bit=True
-)
-
-# Same API as 8-bit models
-sentiment = predictor.classify_sentiment("The market is performing well.")
-```
-
-## Evaluation
-
-### For Competition Organizers
-
-This section provides guidance for evaluating the submitted models:
-
-#### 1. Quick Model Test
-```bash
-# Test if all models can be loaded successfully
-python test_submission.py
-```
-
-#### 2. Comprehensive Evaluation
-```bash
-# Run full evaluation on all models and datasets
-python comprehensive_evaluation.py
-
-# Check results
-cat comprehensive_evaluation_results.json
-```
-
-#### 3. Incremental Evaluation
-```bash
-# Run evaluation on missing tasks
-python incremental_evaluation.py
-
-# Check results
-cat incremental_evaluation_results.json
-```
-
-#### 4. Evaluation Results
-The evaluation results are provided in:
-- `comprehensive_evaluation_results.json` - Complete evaluation results
-- `incremental_evaluation_results.json` - Missing task evaluation results
-
-#### 5. Model Performance Summary
-All models have been evaluated on multiple financial datasets. See the Performance Results section below for detailed metrics.
-
-### For Researchers
-
-Run comprehensive evaluation on financial datasets:
-
-```bash
-# Run full evaluation
-python comprehensive_evaluation.py
-
-# Run incremental evaluation
-python incremental_evaluation.py
-
-# Run robust evaluation
-python robust_incremental.py
-```
-
-## Performance Results
-
-The models have been evaluated on multiple financial datasets:
-
-| Task | Dataset | F1 Score | Accuracy |
-|------|---------|----------|----------|
-| Sentiment Analysis | Financial Phrasebank | 0.333 | 0.500 |
-| NER | Financial NER | 0.889 | 0.800 |
-| Classification | Headline Classification | 0.697 | 0.700 |
-| XBRL Processing | XBRL Tag Extraction | - | 0.200 |
-| Sentiment Analysis | FIQA SA | 0.727 | 0.700 |
-
-## Project Structure
-
-```
-finlora_hf_submission/
-├── models/                          # 8-bit LoRA model adapters (13 models)
-│   ├── sentiment_llama_3_1_8b_8bits_r8/
-│   ├── ner_llama_3_1_8b_8bits_r8/
-│   ├── headline_llama_3_1_8b_8bits_r8/
-│   ├── xbrl_extract_llama_3_1_8b_8bits_r8/
-│   ├── xbrl_term_llama_3_1_8b_8bits_r8/
-│   ├── financebench_llama_3_1_8b_8bits_r8/
-│   ├── finer_llama_3_1_8b_8bits_r8/
-│   ├── formula_llama_3_1_8b_8bits_r8/
-│   ├── cfa_rag_llama_3_1_8b_8bits_r8/          # NEW: CFA RAG model
-│   ├── fintagging_combined_rag_llama_3_1_8b_8bits_r8/  # NEW: Combined RAG
-│   ├── fintagging_fincl_rag_llama_3_1_8b_8bits_r8/     # NEW: FinCL RAG
-│   ├── fintagging_finni_rag_llama_3_1_8b_8bits_r8/     # NEW: FinNI RAG
-│   └── xbrl_train.jsonl-meta-llama-Llama-3.1-8B-Instruct-8bits_r8/
-├── models_4bit/                     # 4-bit LoRA model adapters (8 models)
-│   ├── sentiment_llama_3_1_8b_4bits_r4/
-│   ├── ner_llama_3_1_8b_4bits_r4/
-│   ├── headline_llama_3_1_8b_4bits_r4/
-│   ├── xbrl_extract_llama_3_1_8b_4bits_r4/
-│   ├── xbrl_term_llama_3_1_8b_4bits_r4/
-│   ├── financebench_llama_3_1_8b_4bits_r4/
-│   ├── finer_llama_3_1_8b_4bits_r4/
-│   └── formula_llama_3_1_8b_4bits_r4/
-├── testdata/                        # Evaluation datasets
-│   ├── FinCL-eval-subset.csv
-│   └── FinNI-eval-subset.csv
-├── rag_system/                      # RAG system components
-├── inference.py                     # Main inference script
-├── comprehensive_evaluation.py      # Full evaluation script
-├── incremental_evaluation.py        # Incremental evaluation
-├── robust_incremental.py           # Robust evaluation
-├── missing_tests.py                # Missing test detection
-├── requirements.txt                 # Python dependencies
-└── README.md                       # This file
-```
-
-## Environment Requirements
-
-### Minimum Requirements (CPU Mode)
-- Python 3.8+
-- PyTorch 2.0+
-- 8GB RAM
-- No GPU required
-
-### Recommended Requirements (GPU Mode)
-- Python 3.9+
-- PyTorch 2.1+
-- CUDA 11.8+ (for NVIDIA GPUs)
-- 16GB+ GPU memory
-- 32GB+ RAM
-
-### Installation Instructions
-
-```bash
-# 1. Clone or download this repository
-# 2. Install dependencies
-pip install -r requirements.txt
-
-# 3. For GPU support (optional but recommended)
-pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
-
-# 4. Verify installation
-python -c "import torch; print(f'PyTorch version: {torch.__version__}'); print(f'CUDA available: {torch.cuda.is_available()}')"
-```
-
-### Troubleshooting
-
-**If you encounter memory issues:**
-- Use 4-bit models instead of 8-bit models
-- Reduce batch size in inference
-- Use CPU mode if GPU memory is insufficient
-
-**If models fail to load:**
-- Ensure all model files are present in the correct directories
-- Check that the base model (Llama-3.1-8B-Instruct) can be downloaded from HuggingFace
-- Verify internet connection for model downloads
-
-**Important Notes for Competition Organizers:**
-- The base model (Llama-3.1-8B-Instruct) will be automatically downloaded from HuggingFace on first use (~15GB)
-- All LoRA adapters are included in this submission and do not require additional downloads
-- Models work in both CPU and GPU modes, with automatic device detection
-- RAG-enhanced models require the same base model as regular models
-
-## Model Details
-
-### Training Configuration
-- **LoRA Rank**: 8
-- **LoRA Alpha**: 16
-- **Learning Rate**: 1e-4
-- **Batch Size**: 4
-- **Epochs**: 3-5
-- **Quantization**: 8-bit (BitsAndBytes) / 4-bit (NF4)
-
-### Training Data
-- Financial Phrasebank
-- FinGPT datasets (NER, Headline, XBRL)
-- BloombergGPT financial datasets
-- Custom financial text datasets
-
-## Citation
-
-If you use this work in your research, please cite:
-
-```bibtex
-@article{finlora2024,
-  title={FinLoRA: Financial Large Language Models with LoRA Adaptation},
-  author={Your Name},
-  journal={Financial AI Conference},
-  year={2024}
-}
-```
-
-## License
-
-This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
-
-## Contributing
-
-Contributions are welcome! Please feel free to submit a Pull Request.
-
-## Contact
-
-For questions and support, please open an issue or contact [your-email@example.com](mailto:your-email@example.com).
-
-## Submission Summary
-
-### What's Included
-- **21 Total Models**: 13 8-bit models (9 original + 4 RAG-enhanced) + 8 4-bit models
-- **Complete Evaluation Results**: Comprehensive and incremental evaluation results
-- **RAG-Enhanced Models**: CFA and FinTagging models with enhanced knowledge
-- **Cross-Platform Support**: Works on CPU, GPU, and various memory configurations
-- **Ready-to-Use**: All dependencies specified, automatic device detection
-
-### Quick Start for Competition Organizers
-1. Install dependencies: `pip install -r requirements.txt`
-2. Test submission: `python test_submission.py`
-3. Run evaluation: `python comprehensive_evaluation.py`
-4. Check results: `cat comprehensive_evaluation_results.json`
-
-### Model Categories
-- **Financial NLP**: Sentiment, NER, Classification, XBRL processing
-- **RAG-Enhanced**: CFA knowledge and FinTagging with retrieval augmentation
-- **Memory Options**: Both 8-bit and 4-bit quantized versions available
-
-## Acknowledgments
-
-- Meta for the Llama-3.1-8B-Instruct base model
-- Hugging Face for the transformers and PEFT libraries
-- The financial NLP community for datasets and benchmarks