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fine-tunning-roberta-bne-hate-offensive
esmarquez17/fine-tunning-roberta-bne-hate-offensive
fine-tunning-roberta-bne-hate-offensive is a text classification model from esmarquez17. 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
- roberta
- Access
- Public
- Created
- Nov 24, 2023
- Updated
- Nov 28, 2023
- SHA
- 6deb64d8
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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. -->
fine-tunning-roberta-bne-hate-offensive
This model is a fine-tuned version of BSC-LT/roberta-base-bne on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6211
- Hate/offensive F1: 0.8958
- Hate/offensive Precision: 0.9421
- Hate/offensive Recall: 0.8539
- Normal F1: 0.9519
- Normal Precision: 0.9308
- Normal Recall: 0.9740
- Micro F1: 0.9342
- Acc: 0.9342
- Macro F1: 0.9239
- Macro Precision: 0.9364
- Macro Recall: 0.9139
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Hate/offensive F1 | Hate/offensive Precision | Hate/offensive Recall | Normal F1 | Normal Precision | Normal Recall | Micro F1 | Acc | Macro F1 | Macro Precision | Macro Recall |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.2865 | 1.0 | 1064 | 0.3398 | 0.8953 | 0.9324 | 0.8610 | 0.9510 | 0.9336 | 0.9691 | 0.9332 | 0.9332 | 0.9231 | 0.9330 | 0.9150 |
| 0.1527 | 2.0 | 2128 | 0.3417 | 0.8942 | 0.9204 | 0.8695 | 0.9497 | 0.9370 | 0.9627 | 0.9318 | 0.9318 | 0.9220 | 0.9287 | 0.9161 |
| 0.047 | 3.0 | 3192 | 0.5626 | 0.8914 | 0.9668 | 0.8270 | 0.9518 | 0.9199 | 0.9859 | 0.9332 | 0.9332 | 0.9216 | 0.9434 | 0.9064 |
| 0.0145 | 4.0 | 4256 | 0.5874 | 0.8986 | 0.9396 | 0.8610 | 0.9528 | 0.9338 | 0.9726 | 0.9356 | 0.9356 | 0.9257 | 0.9367 | 0.9168 |
| 0.0029 | 5.0 | 5320 | 0.6211 | 0.8958 | 0.9421 | 0.8539 | 0.9519 | 0.9308 | 0.9740 | 0.9342 | 0.9342 | 0.9239 | 0.9364 | 0.9139 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- apache-2.0
- Created
- Nov 24, 2023
- Updated
- Nov 28, 2023