Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,83 @@
+---
+language: en
+license: apache-2.0
+base_model: harsharajkumar273/T5-Base-Summarization
+tags:
+  - text-generation
+  - story-generation
+  - research-paper
+  - seq2seq
+  - t5
+  - lora
+  - peft
+datasets:
+  - custom
+metrics:
+  - bertscore
+  - sbert
+---
+
+# T5-Base-Story-Generation
+
+A fine-tuned model for transforming research paper summaries into engaging short stories. This is the second stage of a two-step **Research Paper Simplifier** pipeline, built on top of [harsharajkumar273/T5-Base-Summarization](https://huggingface.co/harsharajkumar273/T5-Base-Summarization).
+
+## Model Description
+
+This model takes a summary of a research paper and generates an immersive, narrative-style short story. Fine-tuned using LoRA (PEFT).
+
+## Pipeline
+
+```
+Research Paper ──► [T5-Base-Summarization] ──► Summary ──► [T5-Base-Story-Generation] ──► Story
+```
+
+## Training Details
+
+| Parameter | Value |
+|-----------|-------|
+| Base model | harsharajkumar273/T5-Base-Summarization |
+| Task | Story Generation |
+| Max input length | 512 tokens |
+| Max target length | 256 tokens |
+| Learning rate | 1e-4 |
+| Batch size | 4 |
+| Gradient accumulation steps | 4 |
+| Warmup steps | 500 |
+| Weight decay | 0.01 |
+| Fine-tuning method | LoRA (r=16, alpha=32, targets: q, v) |
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Stage 1: Summarize the paper
+sum_tokenizer = AutoTokenizer.from_pretrained("harsharajkumar273/T5-Base-Summarization")
+sum_model = AutoModelForSeq2SeqLM.from_pretrained("harsharajkumar273/T5-Base-Summarization")
+
+paper_text = "Your research paper text here..."
+word_count = len(paper_text.split())
+sum_prompt = f"Summarize this part of the research paper to less than {word_count // 10} words:\n{paper_text}"
+sum_inputs = sum_tokenizer(sum_prompt, return_tensors="pt", max_length=1024, truncation=True)
+sum_outputs = sum_model.generate(**sum_inputs, max_length=128, num_beams=4)
+summary = sum_tokenizer.decode(sum_outputs[0], skip_special_tokens=True)
+
+# Stage 2: Generate a story from the summary
+story_tokenizer = AutoTokenizer.from_pretrained("harsharajkumar273/T5-Base-Story-Generation")
+story_model = AutoModelForSeq2SeqLM.from_pretrained("harsharajkumar273/T5-Base-Story-Generation")
+
+story_inputs = story_tokenizer(summary, return_tensors="pt", max_length=512, truncation=True)
+story_outputs = story_model.generate(**story_inputs, max_length=256, num_beams=4)
+story = story_tokenizer.decode(story_outputs[0], skip_special_tokens=True)
+print(story)
+```
+
+## Evaluation Metrics
+
+Evaluated using BERTScore and SBERTScore on a held-out 10% test split.
+
+## Related Models
+
+- [harsharajkumar273/T5-Base-Summarization](https://huggingface.co/harsharajkumar273/T5-Base-Summarization) — previous stage
+- [harsharajkumar273/Bart-Base-Story-Generation](https://huggingface.co/harsharajkumar273/Bart-Base-Story-Generation)
+- [harsharajkumar273/ProphetNet-Large-Story-Generation](https://huggingface.co/harsharajkumar273/ProphetNet-Large-Story-Generation)