Create README.md

README.md

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+---
+tags:
+- text-generation
+- gpt2
+- language-modeling
+- academic
+library_name: transformers
+license: mit
+datasets:
+- arxiv
+metrics:
+- perplexity
+---
+
+# AcademicAbstractGenerator: DistilGPT2 Fine-tuned for Scientific Text
+
+## 📑 Overview
+
+This model is a fine-tuned version of **DistilGPT2**, optimized for the task of generating short, high-quality, and structurally consistent academic abstract drafts. It has been trained exclusively on a corpus of abstracts from arXiv, focusing on fields like Computer Science and Physics.
+
+## 🤖 Model Architecture
+
+The model utilizes the **GPT-2** decoder-only transformer architecture, offering efficiency and speed due to the Distil model's reduced size.
+
+* **Base Model:** `distilgpt2` (a distilled, smaller version of GPT-2).
+* **Architecture:** Decoder-only transformer stack.
+* **Layers:** 6 transformer layers.
+* **Task:** Causal Language Modeling (Text Generation).
+* **Training Objective:** Minimizing the perplexity on academic text, enabling it to better capture formal structure, complex vocabulary, and typical flow of scientific summaries (Introduction -> Method -> Result -> Conclusion).
+
+## 🎯 Intended Use
+
+This model is intended for:
+1.  **Drafting:** Assisting researchers in generating initial abstract drafts for new papers.
+2.  **Ideation:** Exploring potential research directions by prompting the model with a topic sentence.
+3.  **Educational Purposes:** Learning about generative model capabilities in a specialized domain.
+
+## ⚠️ Limitations
+
+* **Factuality:** The model is a text generator, not a knowledge base. Generated content may contain plausible-sounding but **factually incorrect** claims or results. **Human review is mandatory.**
+* **Length:** Due to its base architecture and training data, it performs best on short sequences (under 256 tokens).
+* **Overfitting:** May occasionally repeat boilerplate phrases common in academic writing.
+
+## 💻 Example Code
+
+Use the `TextGenerationPipeline` for drafting abstracts:
+
+```python
+from transformers import pipeline, set_seed
+
+set_seed(42)
+
+# Load the model and tokenizer
+generator = pipeline('text-generation', model='[YOUR_HF_USERNAME]/AcademicAbstractGenerator')
+
+prompt = "We propose a novel attention mechanism for transformer models that significantly improves training efficiency."
+
+# Generate a 150-token abstract draft
+output = generator(
+    prompt,
+    max_length=150,
+    num_return_sequences=1,
+    temperature=0.7,
+    do_sample=True,
+    truncation=True
+)
+
+print(output[0]['generated_text'])