---
library_name: transformers
license: mit
base_model: gpt2
tags:
- generated_from_trainer
datasets:
- hatexplain
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: gpt2-hatexplain
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: hatexplain
      type: hatexplain
      config: plain_text
      split: validation
      args: plain_text
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6917879417879418
    - name: Precision
      type: precision
      value: 0.6837783251259969
    - name: Recall
      type: recall
      value: 0.6917879417879418
    - name: F1
      type: f1
      value: 0.6822435740647693
---

<!-- 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-hatexplain

This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the hatexplain dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7507
- Accuracy: 0.6918
- Precision: 0.6838
- Recall: 0.6918
- F1: 0.6822

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.7396        | 1.0   | 962  | 0.7567          | 0.6790   | 0.6713    | 0.6790 | 0.6641 |
| 0.6697        | 2.0   | 1924 | 0.7486          | 0.6842   | 0.6769    | 0.6842 | 0.6783 |
| 0.7573        | 3.0   | 2886 | 0.7685          | 0.6748   | 0.6658    | 0.6748 | 0.6656 |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.5.1+cu118
- Datasets 3.1.0
- Tokenizers 0.21.0