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README.md

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+---
+language: en
+license: apache-2.0
+base_model: facebook/bart-base
+tags:
+  - summarization
+  - research-paper
+  - seq2seq
+  - bart
+datasets:
+  - custom
+metrics:
+  - rouge
+  - bertscore
+---
+
+# Bart-Base-Summarization
+
+A fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) for summarizing research papers into concise summaries. This is the first stage of a two-step **Research Paper Simplifier** pipeline.
+
+## Model Description
+
+This model takes a section of a research paper as input and generates a plain-language summary approximately 1/10th the length of the original text. It was fine-tuned end-to-end (no LoRA) on a custom dataset of research papers.
+
+## Pipeline
+
+```
+Research Paper ──► [Bart-Base-Summarization] ──► Summary ──► [Bart-Base-Story-Generation] ──► Story
+```
+
+## Training Details
+
+| Parameter | Value |
+|-----------|-------|
+| Base model | facebook/bart-base |
+| Task | Summarization |
+| Max input length | 1024 tokens |
+| Max target length | 128 tokens |
+| Learning rate | 5e-5 |
+| Batch size | 8 |
+| Warmup steps | 1000 |
+| Weight decay | 0.01 |
+| Fine-tuning method | Full fine-tuning |
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("harsharajkumar273/Bart-Base-Summarization")
+model = AutoModelForSeq2SeqLM.from_pretrained("harsharajkumar273/Bart-Base-Summarization")
+
+text = "Your research paper section here..."
+word_count = len(text.split())
+prompt = f"Summarize this part of the research paper to less than {word_count // 10} words:\n{text}"
+
+inputs = tokenizer(prompt, return_tensors="pt", max_length=1024, truncation=True)
+outputs = model.generate(**inputs, max_length=128, num_beams=4, length_penalty=1.0)
+summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(summary)
+```
+
+## Evaluation Metrics
+
+Evaluated using ROUGE and BERTScore on a held-out 10% test split.
+
+## Related Models
+
+- [harsharajkumar273/T5-Base-Summarization](https://huggingface.co/harsharajkumar273/T5-Base-Summarization)
+- [harsharajkumar273/ProphetNet-Large-Summarization](https://huggingface.co/harsharajkumar273/ProphetNet-Large-Summarization)
+- [harsharajkumar273/Bart-Base-Story-Generation](https://huggingface.co/harsharajkumar273/Bart-Base-Story-Generation) — next stage