---
library_name: transformers
license: mit
base_model: openai-community/gpt2-medium
tags:
- generated_from_trainer
model-index:
- name: gpt2-finetuned-headliner
  results: []
---

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# GPT2 Fine Tuned Headline Generator

- This model is trained on the [harvard/abcnews-dataset](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/SYBGZL) to generate news headlines
- This model is a fine-tuned version of [openai-community/gpt2-medium](https://huggingface.co/openai-community/gpt2-medium) on an unknown dataset.

## Model description

The model is fine-tuned for 2 epochs and 4k training samples from the abcnews dataset. This enables the model to generate news headline like text given a simple prompt

## Intended uses & limitations

This model is only for learning purposes only. The model easily hallucinates people names, locations and other artifacts & incidents.

## Training and evaluation data

The model leverages 2k test samples for evaluation

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 4
- num_epochs: 2

### Training results
The final output after 2 epochs is as follows:
TrainOutput(global_step=130, training_loss=5.044873604407678, metrics={'train_runtime': 140.587, 'train_samples_per_second': 59.166, 'train_steps_per_second': 0.925, 'total_flos': 248723096358912.0, 'train_loss': 5.044873604407678, 'epoch': 2.0})


### Framework versions

- Transformers 4.44.2
- Pytorch 2.3.1
- Datasets 2.21.0
- Tokenizers 0.19.1