---
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'])