---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: all-base-rerun-new-loop2
  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. -->

# all-base-rerun-new-loop2

This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 4.0969

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 6
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.3479        | 0.29  | 500   | 5.3389          |
| 5.0203        | 0.58  | 1000  | 4.9188          |
| 4.6949        | 0.87  | 1500  | 4.6855          |
| 4.4434        | 1.16  | 2000  | 4.5414          |
| 4.2872        | 1.46  | 2500  | 4.4217          |
| 4.1743        | 1.75  | 3000  | 4.3230          |
| 4.0791        | 2.04  | 3500  | 4.2448          |
| 3.8856        | 2.33  | 4000  | 4.2016          |
| 3.8509        | 2.62  | 4500  | 4.1489          |
| 3.8144        | 2.91  | 5000  | 4.0998          |
| 3.6394        | 3.2   | 5500  | 4.0935          |
| 3.5747        | 3.49  | 6000  | 4.0638          |
| 3.5592        | 3.78  | 6500  | 4.0296          |
| 3.4711        | 4.07  | 7000  | 4.0278          |
| 3.3061        | 4.37  | 7500  | 4.0241          |
| 3.2984        | 4.66  | 8000  | 4.0105          |
| 3.2917        | 4.95  | 8500  | 3.9989          |
| 3.1462        | 5.24  | 9000  | 4.0090          |
| 3.1241        | 5.53  | 9500  | 4.0085          |
| 3.1176        | 5.82  | 10000 | 4.0075          |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3