init

README.md

@@ -1,3 +1,115 @@
 ---
 license: mit
+language:
+- en
+pipeline_tag: sentence-similarity
+datasets:
+- darrow-ai/LegalLensNLI
+metrics:
+- f1
+base_model:
+- ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli
+library_name: transformers
 ---
+# roberta_cnn_legal
+
+## Overview
+This repository hosts the uOttawa model developed for Subtask B (Legal Natural Language Inference) in the LegalLens-2024 shared task. The task focuses on classifying relationships between legal texts, such as determining if a premise (e.g., a summary of a legal complaint) entails, contradicts, or is neutral with respect to a hypothesis (e.g., an online review).
+## Model Details
+- **Model Type**: Transformer-based model combined with a Convolutional Neural Network (CNN)
+
+- **Framework**: PyTorch, Transformers library
+
+- **Training Data**: LegalLensNLI dataset provided by the LegalLens-2024 organizers
+
+- **Architecture**: Integration of RoBERTa (ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli) with a custom CNN for keyword pattern detection
+
+- **Use Case**: Classifying relationships between legal documents for applications like legal case matching and automated reasoning
+
+## Model Architecture
+The model architecture consists of:
+
+- **RoBERTa model**: Responsible for capturing contextual information from the input text.
+
+- **CNN model**: Used for keyword detection, including an embedding layer and three convolutional layers with filter sizes (2, 3, 4).
+
+- **Fully connected layer**: Combines the outputs from RoBERTa and CNN for the final classification.
+
+## Installation
+To use this model, clone this repository and make sure to have the following installed:
+
+```bash
+pip install torch
+pip install transformers
+```
+## Quick Start
+Load the model and run inference using the Hugging Face Transformers library:
+
+```code
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Load the model and tokenizer
+model = AutoModelForSequenceClassification.from_pretrained("nimamegh/roberta_cnn_legal")
+tokenizer = AutoTokenizer.from_pretrained("nimamegh/roberta_cnn_legal")
+
+# Example inputs
+premise = "The cat is on the mat."
+hypothesis = "The animal is on the mat."
+inputs = tokenizer(premise, hypothesis, return_tensors='pt')
+
+# Get predictions
+outputs = model(**inputs)
+predictions = outputs.logits.argmax(dim=-1)
+
+# Print the prediction result
+print("Predicted class:", predictions.item())
+
+# Interpretation (optional)
+label_map = {0: "Entailment", 1: "Neutral", 2: "Contradiction"}
+print("Result:", label_map[predictions.item()])
+```
+
+## Training Configuration
+
+- Learning Rate: 2e-5
+
+- Batch Size: 4 (train and evaluation)
+
+- Number of Epochs: 20
+
+- Weight Decay: 0.01
+
+- Optimizer: AdamW
+
+- Trainer Class: Used for fine-tuning with early stopping and warmup steps
+
+## Evaluation Metrics
+The model was evaluated using an F1-score across multiple domains in the validation set:
+
+- Average F1-score: 88.6%
+
+## Result
+
+- Performance on Hidden Test Set: F1-score of 0.724, achieving 5th place in the LegalLens-2024 competition.
+
+- Comparison:
+
+  - Falcon 7B: 81.02% (average across domains)
+
+  - RoBERTa base: 71.02% (average)
+
+  - uOttawa Model: 88.6% (average on validation)
+
+## Citation
+
+```bibtex
+@misc{meghdadi2024uottawalegallens2024transformerbasedclassification,
+      title={uOttawa at LegalLens-2024: Transformer-based Classification Experiments}, 
+      author={Nima Meghdadi and Diana Inkpen},
+      year={2024},
+      eprint={2410.21139},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2410.21139}, 
+}
+```