metadata
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- jigsaw_toxicity_pred
metrics:
- f1
- accuracy
model-index:
- name: final_model_toxicity_classification
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: jigsaw_toxicity_pred
type: jigsaw_toxicity_pred
config: default
split: test
args: default
metrics:
- name: F1
type: f1
value: 0.6775510204081633
- name: Accuracy
type: accuracy
value: 0.921
final_model_toxicity_classification
This model is a fine-tuned version of distilbert-base-uncased on the jigsaw_toxicity_pred dataset. It achieves the following results on the evaluation set:
- Loss: 0.2702
- F1: 0.6776
- Accuracy: 0.921
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: 4.910748967246961e-05
- train_batch_size: 32
- eval_batch_size: 8
- 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: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
|---|---|---|---|---|---|
| 0.0959 | 1.0 | 4987 | 0.1663 | 0.7248 | 0.94 |
| 0.0527 | 2.0 | 9974 | 0.2702 | 0.6776 | 0.921 |
Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1