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---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: gpt2-text-classification-v2
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. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/date3k2/gpt2-text-classification/runs/52an6tu1)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/date3k2/gpt2-text-classification/runs/lu5o1szk)
# gpt2-text-classification-v2
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: 0.2002
- Accuracy: 0.9342
- F1: 0.9340
- Recall: 0.9314
- Precision: 0.9367
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Accuracy | F1 | Validation Loss | Precision | Recall |
|:-------------:|:------:|:----:|:--------:|:------:|:---------------:|:---------:|:------:|
| 0.327 | 0.9974 | 260 | 0.8973 | 0.8929 | 0.2559 | 0.9333 | 0.8558 |
| 0.241 | 1.9987 | 521 | 0.919 | 0.9180 | 0.2039 | 0.9296 | 0.9066 |
| 0.244 | 3.0 | 782 | 0.9154 | 0.9192 | 0.2156 | 0.8799 | 0.9621 |
| 0.1843 | 3.9974 | 1042 | 0.9299 | 0.9288 | 0.1888 | 0.9427 | 0.9154 |
| 0.1608 | 4.9987 | 1303 | 0.9301 | 0.9291 | 0.1855 | 0.9428 | 0.9158 |
| 0.124 | 6.0 | 1564 | 0.9322 | 0.9319 | 0.1826 | 0.9357 | 0.9282 |
| 0.112 | 6.9974 | 1820 | 0.2099 | 0.9315 | 0.9303 | 0.9138 | 0.9473 |
| 0.0903 | 7.9987 | 2081 | 0.2002 | 0.9342 | 0.9340 | 0.9314 | 0.9367 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|