---
license: apache-2.0
base_model: distilbert/distilgpt2
tags:
- generated_from_trainer
datasets:
- eli5_category
model-index:
- name: gpt2-funetuned-eli5
  results: []
language:
- en
metrics:
- perplexity
library_name: transformers
pipeline_tag: text-generation
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->


# gpt2-finetuned-eli5

This model is a fine-tuned version of [distilbert/distilgpt2](https://huggingface.co/distilbert/distilgpt2), fine-tuned on the `eli5_category` dataset. It has been trained to generate human-like responses to questions, specifically tailored to the Explain Like I'm 5 (ELI5) community. This model aims to provide clear and concise answers suitable for a general audience.

## Model Description

The `gpt2-finetuned-eli5` model is based on the DistilGPT-2 architecture, which is a smaller, faster, and more efficient version of GPT-2. It retains most of GPT-2's capabilities while being more computationally efficient. The model is particularly adept at generating text that resembles human-written responses, making it suitable for tasks involving natural language understanding and generation.

### Key Features:
- **Architecture**: DistilGPT-2, a distilled version of GPT-2.
- **Purpose**: Generating clear and concise explanations suitable for general audiences, particularly in response to questions typical of the ELI5 community.
- **Model Size**: Smaller and more efficient than the original GPT-2, with reduced computational requirements.

## Intended Uses & Limitations

### Intended Uses:
- **Question Answering**: Provide simplified and easy-to-understand answers to a wide range of questions.
- **Text Generation**: Generate coherent and contextually relevant text based on a given prompt.
- **Educational Tools**: Assist in educational content creation by generating simple explanations of complex topics.
- **Chatbots**: Improve the conversational abilities of chatbots by providing human-like responses.

### Limitations:
- **Simplification Risks**: While the model excels at providing simplified explanations, it might oversimplify or miss nuances, especially with complex topics.
- **Dataset Bias**: The model's behavior reflects the data it was trained on. It might exhibit biases present in the training data, leading to inappropriate or biased responses.
- **Factually Inaccurate Responses**: The model does not have real-time access to factual databases, and its knowledge is based on the data it was trained on. It might produce outdated or incorrect information.
- **Limited Knowledge Cut-off**: The model's training data only includes information up to a certain date, and it does not know about events or developments beyond that time.

## Training and Evaluation Data

### Training Data:
- **Dataset**: The model was fine-tuned on the `eli5_category` dataset, which consists of questions and answers from the Explain Like I'm 5 (ELI5) community. This dataset contains a variety of topics where users seek simple and clear explanations.

### Evaluation Data:
- The evaluation data consisted of a subset of the ELI5 dataset that was held out during training. The model's performance was assessed based on its ability to generate coherent and contextually appropriate responses.

## Training Procedure

### Training Hyperparameters:
- **Learning Rate**: 2e-05
- **Train Batch Size**: 8
- **Eval Batch Size**: 8
- **Seed**: 42
- **Optimizer**: Adam with betas=(0.9, 0.999) and epsilon=1e-08
- **Learning Rate Scheduler Type**: Linear
- **Number of Epochs**: 3.0

### Training Results:

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.8522        | 1.0   | 1289 | 3.8307          |
| 3.8093        | 2.0   | 2578 | 3.8280          |
| 3.7661        | 3.0   | 3867 | 3.8269          |

- The model achieved a final validation loss of 3.8269, indicating a consistent improvement in training performance.

### Framework Versions:
- **Transformers**: 4.42.4
- **PyTorch**: 2.3.1+cu121
- **Datasets**: 2.21.0
- **Tokenizers**: 0.19.1

## Ethical Considerations

- **Bias and Fairness**: The model's responses might reflect biases present in the training data. Users should be aware of potential biases and verify the information generated.
- **Privacy**: The model was trained on publicly available data. However, care should be taken to avoid using the model for generating content that may violate privacy norms.

## Example Usage

To generate text using the `gpt2-finetuned-eli5` model, you can use the following code:

```python
from transformers import pipeline

# Load the text generation pipeline
generator = pipeline("text-generation", model="ashaduzzaman/gpt2-funetuned-eli5")

# Provide a prompt
prompt = "Somatic hypermutation allows the immune system to"

# Generate text
generator(prompt)
```