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README.md

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+---
+language: en
+license: apache-2.0
+base_model: microsoft/prophetnet-large-uncased
+tags:
+  - summarization
+  - research-paper
+  - seq2seq
+  - prophetnet
+  - lora
+  - peft
+datasets:
+  - custom
+metrics:
+  - rouge
+  - bertscore
+---
+
+# ProphetNet-Large-Summarization
+
+A fine-tuned version of [microsoft/prophetnet-large-uncased](https://huggingface.co/microsoft/prophetnet-large-uncased) 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. Fine-tuned using LoRA (PEFT) with 4-bit quantization for efficient training.
+
+## Pipeline
+
+```
+Research Paper ──► [ProphetNet-Large-Summarization] ──► Summary ──► [ProphetNet-Large-Story-Generation] ──► Story
+```
+
+## Training Details
+
+| Parameter | Value |
+|-----------|-------|
+| Base model | microsoft/prophetnet-large-uncased |
+| Task | Summarization |
+| Max input length | 2048 tokens |
+| Max target length | 256 tokens |
+| Learning rate | 3e-5 |
+| Batch size | 2 |
+| Gradient accumulation steps | 4 |
+| Warmup steps | 1500 |
+| Weight decay | 0.01 |
+| Fine-tuning method | LoRA (r=16, alpha=64, targets: query_proj, value_proj) |
+| Quantization | 4-bit NF4 (bitsandbytes) |
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("harsharajkumar273/ProphetNet-Large-Summarization")
+model = AutoModelForSeq2SeqLM.from_pretrained("harsharajkumar273/ProphetNet-Large-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=2048, truncation=True)
+outputs = model.generate(**inputs, max_length=256, num_beams=4)
+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/Bart-Base-Summarization](https://huggingface.co/harsharajkumar273/Bart-Base-Summarization)
+- [harsharajkumar273/T5-Base-Summarization](https://huggingface.co/harsharajkumar273/T5-Base-Summarization)
+- [harsharajkumar273/ProphetNet-Large-Story-Generation](https://huggingface.co/harsharajkumar273/ProphetNet-Large-Story-Generation) — next stage