metadata
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
gpt2-hatexplain
This model is a fine-tuned version of 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