---
library_name: transformers
license: mit
base_model: 100daggers/reuters-gpt2-text-gen
tags:
- generated_from_trainer
model-index:
- name: reuters-gpt2-text-gen
  results: []
---

<!-- 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. -->

# reuters-gpt2-text-gen

This model is a fine-tuned version of [100daggers/reuters-gpt2-text-gen](https://huggingface.co./100daggers/reuters-gpt2-text-gen) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.9508

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 4.2626        | 0.9987 | 286  | 4.4727          |
| 3.7809        | 1.9974 | 572  | 4.2128          |
| 3.5797        | 2.9996 | 859  | 4.0473          |
| 3.2899        | 3.9983 | 1145 | 3.9684          |
| 3.2056        | 4.9935 | 1430 | 3.9508          |


### Framework versions

- Transformers 4.44.1
- Pytorch 2.0.1
- Datasets 2.21.0
- Tokenizers 0.19.1