Update README.md

README.md

@@ -1,12 +1,153 @@
 ---
+language: en
+license: apache-2.0
+pipeline_tag: summarization
 base_model: nsi319/legal-pegasus
 tags:
-- pegasus
+- summarization
 - legal
+- pegasus
 - billsum
-- summarization
+- long-document
 - abstractive-summarization
-- legal-nlp
 - finetuned
-- nsi-legal-pegasus
----
+- legal-nlp
+- domain-adaptation
+datasets:
+- FiscalNote/billsum
+metrics:
+- bertscore
+---
+
+# 📘 Legal Pegasus – BillSum Fine-Tuned
+
+**Fine-tuned version of NSI’s Legal Pegasus for abstractive summarization of legal and legislative documents.**
+
+This model fine-tunes **nsi319/legal-pegasus**, a legally-pretrained Pegasus model, on the **BillSum dataset** and additional cleaned summaries to generate concise, context-aware, and structured legal summaries.  
+It improves coherence, domain terminology handling, and section-wise reasoning in long-form legal and policy text.
+
+---
+
+# 🧠 Base Model
+
+This model builds on:
+
+👉 **[nsi319/legal-pegasus](https://huggingface.co/nsi319/legal-pegasus)**  
+Pretrained on large-scale legal corpora including:
+
+- Statutes  
+- Case law  
+- Legislative documents  
+- Regulatory material  
+
+This provides strong legal-domain grounding before fine-tuning.
+
+---
+
+# 📚 Fine-Tuning Dataset
+
+- **BillSum** (US Congressional + California bills)
+- Additional cleaned legal-style summaries  
+- Documents range from **2k to 12k+ tokens**
+
+---
+
+# ⚙️ Training Configuration
+
+| Setting | Value |
+|--------|--------|
+| Base model | nsi319/legal-pegasus |
+| Epochs | 8 |
+| Learning rate | 2e-5 |
+| Optimizer | AdamW |
+| Weight decay | 0.01 |
+| Batch size | 1 |
+| Gradient accumulation | 4 |
+| Max input length | 1024 tokens |
+| Max summary length | 256 tokens |
+| FP16 | Yes |
+| Warmup | 500 steps |
+| Scheduler | Linear |
+
+Training was performed on **Kaggle T4 GPU (16GB)**.
+
+---
+
+# 🧪 Evaluation Metrics
+
+## **ROUGE Scores (Test Set)**  
+| Metric | F1 |
+|--------|------|
+| ROUGE-1 | ~0.55 |
+| ROUGE-2 | ~0.35 |
+| ROUGE-L | ~0.41 |
+
+## **BERTScore (Semantic Similarity)**  
+| Metric | Score |
+|--------|--------|
+| Precision | 0.884 |
+| Recall | 0.894 |
+| F1 | 0.886 |
+
+**BERTScore** is emphasized since legal summarization requires semantic preservation rather than lexical overlap.
+
+---
+
+# 🏗️ Long-Document Summarization Strategy
+
+Pegasus supports ~1024 tokens, so long legal documents (3k–30k tokens) were handled using:
+
+- Sentence/paragraph splitting  
+- Token-based chunking  
+- Sliding-window segmentation  
+- Chunk-wise summarization  
+- Second-pass “summary-of-summaries” rewriting  
+
+This enables effective summarization far beyond the backbone context limit.
+
+---
+
+# 📌 Intended Use
+
+This model is intended for:
+
+- Legal document summarization  
+- Bill/policy analysis  
+- Legislative NLP pipelines  
+- AI assistants for law students  
+- Preprocessing for downstream legal reasoning tasks  
+
+---
+
+# ⚠️ Limitations
+
+- English only  
+- Long documents require external chunking  
+- May simplify dense legal definitions  
+- Not suitable for legal citations or case-law cross referencing  
+- Not intended for production-grade legal decisions  
+
+---
+
+# 🔧 Usage Example
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+model_name = "Anurag33Gaikwad/legal-pegasus-billsum-summarization"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+text = """Your long legal or legislative text here…"""
+
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=1024)
+
+summary_ids = model.generate(
+    inputs["input_ids"],
+    num_beams=5,
+    max_length=256,
+    early_stopping=True
+)
+
+print(tokenizer.decode(summary_ids[0], skip_special_tok_