Traning details push

README.md

@@ -62,6 +62,73 @@ The model may reflect biases present in the training data, such as jurisdictiona
 - Outputs should always be reviewed by a legal expert.
 - Avoid using for legal tasks where complete precision is mandatory.
 
+
+### Training Data
+- **Dataset:** Multi-LexSum
+- **Preprocessing:** Preprocessed for summarization tasks using tokenization.
+
+### Training Procedure
+
+#### Preprocessing
+- Tokenization and truncation were applied to the dataset.
+- Input sequences were capped at 1024 tokens.
+- Summaries were limited to:
+  - 150 tokens for short summaries.
+  - 300 tokens for long summaries.
+
+#### Training Hyperparameters
+- **Learning Rate:** 5e-5
+- **Batch Size:** 1 (gradient accumulation steps: 8)
+- **Epochs:** 3
+- **Optimizer:** AdamW
+- **Precision:** Mixed (fp16)
+
+#### Speeds, Sizes, Times
+- **Training Time:** ~4 hours
+- **Checkpoint Size:** ~892 MB
+- **Hardware:** NVIDIA Tesla V100
+
+## Evaluation
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+- Validation was performed on the `validation` split of the Multi-LexSum dataset, consisting of 4,818 examples.
+
+#### Metrics
+- **ROUGE-1:** 0.49
+- **ROUGE-2:** 0.35
+- **ROUGE-L:** 0.49
+
+### Results
+- The model produces reliable short and long summaries for legal documents, maintaining coherence and relevance.
+
+#### Summary
+- The fine-tuned T5 model demonstrated robust performance in summarizing legal documents, achieving competitive ROUGE scores.
+
+## Model Examination
+
+### Interpretability
+- The model generates human-readable summaries, making it highly interpretable for end-users in the legal domain.
+
+## Environmental Impact
+- **Carbon emissions** can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+  - **Hardware Type:** NVIDIA Tesla V100
+  - **Hours Used:** ~4 hours
+  - **Cloud Provider:** Google Colab
+  - **Compute Region:** US
+  - **Estimated Carbon Emissions:** Minimal due to short training time.
+
+## Technical Specifications
+
+### Model Architecture and Objective
+- The T5 architecture is designed for text-to-text tasks.
+- This fine-tuned model adapts T5 for legal text summarization, leveraging the flexibility of seq2seq learning.
+
+### Compute Infrastructure
+- **Hardware:** NVIDIA Tesla V100
+- **Software:** Hugging Face Transformers 4.46.3, PyTorch
+
 ## How to Get Started with the Model
 
 ```python