Update README.md

README.md

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   - split: test
     path: data/test-*
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+
+# Reformatted Legal Text Summarization Dataset
+
+## Overview
+
+This dataset is a restructured version of a Legal Text Summarization dataset, specifically filtered to include only entries from the `IN-Abs` dataset. The dataset consists of pairs of legal judgements and their corresponding summaries. Each judgement-summary pair is formatted into an **Instruction-Prompt style format** suitable for training language models for tasks like Summarization and Legal Text Analysis.
+
+## Dataset Structure
+
+- **Column Name:** `Judgement-Summary Pairs`
+- **Format:** The dataset contains one column named "Judgement-Summary Pairs", which holds the formatted instruction-prompt pairs. Each entry is a structured pair of a legal judgement and its corresponding summary.
+
+### Example of an Entry:
+
+```plaintext
+<s>[INST] Summarize the following judgement: [Judgement Text] [/INST] [Summary Text] </s>
+```
+Where:
+[Judgement Text] is the original legal judgement text.
+[Summary Text] is the summary of the judgement.
+
+### Sample Entry:
+
+```plaintext
+<s>[INST] Summarize the following judgement: The court finds that the defendant was not responsible for the damages caused as there was insufficient evidence to support the claims made by the plaintiff. [/INST] The court ruled in favor of the defendant due to lack of evidence. </s>
+```
+
+## Data Preparation Process:
+The dataset was prepared using the following steps:
+- Filtering: The dataset was filtered to include only entries from the IN-Abs dataset.
+- Transformation: Each judgement and its corresponding summary were formatted into instruction-prompt pairs using the following template:
+  - ```plaintext
+    <s>[INST] Summarize the following judgement: {judgement_text} [/INST] {summary_text} </s>
+    ```
+- Column Removal: All columns except the newly created "Judgement-Summary Pairs" were removed to streamline the dataset for usage.
+
+## Usage
+The reformatted dataset is suitable for training language models on legal text summarization tasks. Here’s how you can use it:
+
+- **Load the Dataset**: Use a data loading library like datasets or pandas to read the dataset.
+- **Training a Model**: You can train a model using the instruction-prompt pairs provided in the dataset. Each pair serves as an input-output example for supervised learning, particularly in the fields of natural language processing (NLP) and legal AI.
+- **Fine-tuning Existing Models**: Fine-tune pre-trained models such as GPT, BERT, or LLaMA on this dataset to adapt them for legal text summarization tasks.
+
+### License
+The dataset is made available for educational and research purposes. Please ensure that you have the appropriate permissions and rights to use the original data before utilizing this reformatted version.
+
+### Acknowledgements
+The dataset preparation and transformation were inspired by the need to improve legal text summarization and the creation of structured datasets for NLP tasks.
+Special thanks to the original data providers and contributors to legal text summarization research
+
+### Contact
+For questions or further information, please contact **Ajay Mukund S** at *[email protected]*.