---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: SentimentT2_GPT2
  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. -->

# SentimentT2_GPT2

This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0308
- Accuracy: 0.8644
- F1: 0.8685
- Auc Roc: 0.9297
- Log Loss: 1.0307

## 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: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Auc Roc | Log Loss |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:-------:|:--------:|
| 1.1785        | 0.15  | 500   | 0.7334          | 0.8346   | 0.8400 | 0.9144  | 0.7334   |
| 1.1409        | 0.31  | 1000  | 0.8797          | 0.8520   | 0.8649 | 0.9269  | 0.8796   |
| 1.0906        | 0.46  | 1500  | 0.7869          | 0.8744   | 0.8805 | 0.9394  | 0.7869   |
| 1.0163        | 0.62  | 2000  | 0.8381          | 0.8706   | 0.8771 | 0.9366  | 0.8381   |
| 1.0602        | 0.77  | 2500  | 0.9904          | 0.8458   | 0.8616 | 0.9253  | 0.9904   |
| 1.1456        | 0.93  | 3000  | 0.8833          | 0.8483   | 0.8452 | 0.9275  | 0.8832   |
| 0.9662        | 1.08  | 3500  | 0.9737          | 0.8507   | 0.8618 | 0.9354  | 0.9737   |
| 0.8496        | 1.24  | 4000  | 0.9361          | 0.8619   | 0.8680 | 0.9351  | 0.9361   |
| 0.8571        | 1.39  | 4500  | 0.8660          | 0.8619   | 0.8702 | 0.9346  | 0.8660   |
| 0.7506        | 1.55  | 5000  | 0.9359          | 0.8507   | 0.8558 | 0.9316  | 0.9359   |
| 0.8236        | 1.7   | 5500  | 1.1721          | 0.8184   | 0.8433 | 0.9229  | 1.1721   |
| 0.6897        | 1.85  | 6000  | 0.9876          | 0.8532   | 0.8547 | 0.9318  | 0.9876   |
| 0.6699        | 2.01  | 6500  | 0.8947          | 0.8570   | 0.8671 | 0.9323  | 0.8946   |
| 0.6137        | 2.16  | 7000  | 0.9318          | 0.8557   | 0.8661 | 0.9344  | 0.9318   |
| 0.4646        | 2.32  | 7500  | 0.9943          | 0.8595   | 0.8660 | 0.9312  | 0.9944   |
| 0.7042        | 2.47  | 8000  | 0.9150          | 0.8657   | 0.8714 | 0.9345  | 0.9150   |
| 0.4079        | 2.63  | 8500  | 1.0215          | 0.8657   | 0.8750 | 0.9312  | 1.0214   |
| 0.4646        | 2.78  | 9000  | 0.9809          | 0.8619   | 0.8714 | 0.9310  | 0.9809   |
| 0.4707        | 2.94  | 9500  | 1.0151          | 0.8644   | 0.8719 | 0.9279  | 1.0150   |
| 0.5005        | 3.09  | 10000 | 1.0748          | 0.8607   | 0.8651 | 0.9289  | 1.0747   |
| 0.3817        | 3.24  | 10500 | 0.8819          | 0.8781   | 0.8858 | 0.9299  | 0.8818   |
| 0.279         | 3.4   | 11000 | 1.0542          | 0.8607   | 0.8627 | 0.9302  | 1.0541   |
| 0.3527        | 3.55  | 11500 | 1.0148          | 0.8607   | 0.8637 | 0.9312  | 1.0147   |
| 0.3873        | 3.71  | 12000 | 1.0421          | 0.8619   | 0.8648 | 0.9294  | 1.0420   |
| 0.3552        | 3.86  | 12500 | 1.0308          | 0.8644   | 0.8685 | 0.9297  | 1.0307   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1