metadata

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

gpt2-finetuned-eli5

This model is a fine-tuned version of 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 It might produce outdated or incorrect information.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:

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)