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---
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
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