peft safetensors generated_from_trainer dataset:emotion base_model:gpt2 license:mit region:us
---
license: mit
library_name: peft
tags:
- generated_from_trainer
datasets:
- emotion
base_model: gpt2
model-index:
- name: output
  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. -->

# output

This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8303

## 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: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 0.25  | 250  | 4.8185          |
| No log        | 0.5   | 500  | 4.3814          |
| No log        | 0.75  | 750  | 4.1230          |
| No log        | 1.0   | 1000 | 4.0088          |
| No log        | 1.25  | 1250 | 3.9536          |
| No log        | 1.5   | 1500 | 3.9208          |
| No log        | 1.75  | 1750 | 3.8946          |
| 4.644         | 2.0   | 2000 | 3.8799          |
| 4.644         | 2.25  | 2250 | 3.8651          |
| 4.644         | 2.5   | 2500 | 3.8552          |
| 4.644         | 2.75  | 2750 | 3.8464          |
| 4.644         | 3.0   | 3000 | 3.8399          |
| 4.644         | 3.25  | 3250 | 3.8364          |
| 4.644         | 3.5   | 3500 | 3.8333          |
| 4.644         | 3.75  | 3750 | 3.8311          |
| 4.0742        | 4.0   | 4000 | 3.8303          |


### Framework versions

- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.15.0
- Tokenizers 0.15.1