---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: gpt2-p10k-cossine
  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. -->

# gpt2-p10k-cossine

This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0234

## 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: 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: cosine
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| No log        | 0.2   | 200   | 0.0386          |
| No log        | 0.4   | 400   | 0.0523          |
| 0.1425        | 0.6   | 600   | 1.0542          |
| 0.1425        | 0.8   | 800   | 1.0459          |
| 0.9177        | 1.0   | 1000  | 0.3677          |
| 0.9177        | 1.2   | 1200  | 0.0296          |
| 0.9177        | 1.4   | 1400  | 0.0284          |
| 0.0421        | 1.6   | 1600  | 0.0275          |
| 0.0421        | 1.8   | 1800  | 0.0274          |
| 0.029         | 2.0   | 2000  | 0.0264          |
| 0.029         | 2.2   | 2200  | 0.0258          |
| 0.029         | 2.4   | 2400  | 0.0256          |
| 0.0276        | 2.6   | 2600  | 0.0254          |
| 0.0276        | 2.8   | 2800  | 0.0252          |
| 0.0265        | 3.0   | 3000  | 0.0251          |
| 0.0265        | 3.2   | 3200  | 0.0247          |
| 0.0265        | 3.4   | 3400  | 0.0247          |
| 0.0256        | 3.6   | 3600  | 0.0246          |
| 0.0256        | 3.8   | 3800  | 0.0252          |
| 0.0262        | 4.0   | 4000  | 0.0249          |
| 0.0262        | 4.2   | 4200  | 0.0244          |
| 0.0262        | 4.4   | 4400  | 0.0242          |
| 0.0255        | 4.6   | 4600  | 0.0242          |
| 0.0255        | 4.8   | 4800  | 0.0248          |
| 0.0251        | 5.0   | 5000  | 0.0240          |
| 0.0251        | 5.2   | 5200  | 0.0242          |
| 0.0251        | 5.4   | 5400  | 0.0239          |
| 0.0254        | 5.6   | 5600  | 0.0239          |
| 0.0254        | 5.8   | 5800  | 0.0237          |
| 0.0244        | 6.0   | 6000  | 0.0237          |
| 0.0244        | 6.2   | 6200  | 0.0239          |
| 0.0244        | 6.4   | 6400  | 0.0237          |
| 0.0244        | 6.6   | 6600  | 0.0237          |
| 0.0244        | 6.8   | 6800  | 0.0238          |
| 0.0246        | 7.0   | 7000  | 0.0236          |
| 0.0246        | 7.2   | 7200  | 0.0235          |
| 0.0246        | 7.4   | 7400  | 0.0235          |
| 0.0242        | 7.6   | 7600  | 0.0235          |
| 0.0242        | 7.8   | 7800  | 0.0235          |
| 0.0244        | 8.0   | 8000  | 0.0236          |
| 0.0244        | 8.2   | 8200  | 0.0234          |
| 0.0244        | 8.4   | 8400  | 0.0235          |
| 0.0246        | 8.6   | 8600  | 0.0234          |
| 0.0246        | 8.8   | 8800  | 0.0234          |
| 0.0237        | 9.0   | 9000  | 0.0234          |
| 0.0237        | 9.2   | 9200  | 0.0234          |
| 0.0237        | 9.4   | 9400  | 0.0234          |
| 0.0241        | 9.6   | 9600  | 0.0234          |
| 0.0241        | 9.8   | 9800  | 0.0234          |
| 0.024         | 10.0  | 10000 | 0.0234          |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1