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distilbert-toxic-comment
dipeshpandit/distilbert-toxic-comment
distilbert-toxic-comment is a text classification model from dipeshpandit. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- distilbert
- Access
- Public
- Created
- Dec 1, 2025
- Updated
- Dec 1, 2025
- SHA
- 2d4b567f
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Base models
Model card
From the Hugging Face model README
<!-- 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. -->
checkpoints-toxic
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0510
- Macro F1: 0.6969
- Micro F1: 0.7858
- Precision: 0.6672
- Recall: 0.7298
- Hamming Loss: 0.0163
- Roc Auc Macro: 0.9885
- Roc Auc Micro: 0.9900
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: 3.2719182895564304e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Macro F1 | Micro F1 | Precision | Recall | Hamming Loss | Roc Auc Macro | Roc Auc Micro |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.0402 | 1.0 | 2244 | 0.0387 | 0.6473 | 0.7850 | 0.6806 | 0.6427 | 0.0160 | 0.9901 | 0.9920 |
| 0.0336 | 2.0 | 4488 | 0.0385 | 0.6798 | 0.7912 | 0.6727 | 0.6974 | 0.0161 | 0.9898 | 0.9924 |
| 0.0259 | 3.0 | 6732 | 0.0414 | 0.6896 | 0.7872 | 0.6520 | 0.7360 | 0.0165 | 0.9900 | 0.9918 |
| 0.0199 | 4.0 | 8976 | 0.0463 | 0.6761 | 0.7852 | 0.6525 | 0.7041 | 0.0165 | 0.9887 | 0.9905 |
| 0.0151 | 5.0 | 11220 | 0.0510 | 0.6969 | 0.7858 | 0.6672 | 0.7298 | 0.0163 | 0.9885 | 0.9900 |
Model Evaluation
Per Label Threshold
| Label ID | Label Name | Optimal Threshold | F1 Score (Validation) |
|---|---|---|---|
| 0 | toxic | 0.69 | 0.8312 |
| 1 | severe_toxic | 0.44 | 0.5564 |
| 2 | obscene | 0.60 | 0.8383 |
| 3 | threat | 0.47 | 0.6275 |
| 4 | insult | 0.32 | 0.7636 |
| 5 | identity_hate | 0.55 | 0.6179 |
Model Performance on Test Set
| Metric | Value |
|---|---|
| F1 Score (Micro) | 0.6668 |
| F1 Score (Macro) | 0.6083 |
| Precision (Micro) | 0.5642 |
| Recall (Micro) | 0.8152 |
| Precision (Macro) | 0.5264 |
| Recall (Macro) | 0.7475 |
Framework versions
- Transformers 4.56.0
- Pytorch 2.8.0+cu129
- Datasets 4.4.1
- Tokenizers 0.22.0
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Dec 1, 2025
- Updated
- Dec 1, 2025