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prot_bert-fine-tuned-toxicity_3
Niki548/prot_bert-fine-tuned-toxicity_3
prot_bert-fine-tuned-toxicity_3 is a text classification model from Niki548. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Jun 21, 2024
- Updated
- Jun 21, 2024
- SHA
- 06e19466
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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. -->
prot_bert-fine-tuned-toxicity_3
This model is a fine-tuned version of Rostlab/prot_bert on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0450
- Accuracy: 0.7419
- Precision: 0.7530
- Recall: 0.7419
- F1: 0.7340
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: 15
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.5735 | 1.0 | 16 | 0.5968 | 0.8065 | 0.8093 | 0.8065 | 0.8044 |
| 0.4855 | 2.0 | 32 | 0.4939 | 0.8387 | 0.8391 | 0.8387 | 0.8380 |
| 0.3499 | 3.0 | 48 | 0.5234 | 0.8065 | 0.8093 | 0.8065 | 0.8044 |
| 0.3094 | 4.0 | 64 | 0.4639 | 0.8387 | 0.8391 | 0.8387 | 0.8380 |
| 0.235 | 5.0 | 80 | 0.5654 | 0.8387 | 0.8391 | 0.8387 | 0.8380 |
| 0.1608 | 6.0 | 96 | 0.6409 | 0.8387 | 0.8391 | 0.8387 | 0.8380 |
| 0.1147 | 7.0 | 112 | 0.6271 | 0.8387 | 0.8391 | 0.8387 | 0.8380 |
| 0.171 | 8.0 | 128 | 0.7704 | 0.8065 | 0.8093 | 0.8065 | 0.8044 |
| 0.1762 | 9.0 | 144 | 0.7978 | 0.8065 | 0.8093 | 0.8065 | 0.8044 |
| 0.1438 | 10.0 | 160 | 0.9561 | 0.7419 | 0.7530 | 0.7419 | 0.7340 |
| 0.0953 | 11.0 | 176 | 1.0074 | 0.7419 | 0.7530 | 0.7419 | 0.7340 |
| 0.0394 | 12.0 | 192 | 1.0037 | 0.7419 | 0.7530 | 0.7419 | 0.7340 |
| 0.0896 | 13.0 | 208 | 1.0128 | 0.7419 | 0.7530 | 0.7419 | 0.7340 |
| 0.0849 | 14.0 | 224 | 1.0404 | 0.7419 | 0.7530 | 0.7419 | 0.7340 |
| 0.0207 | 15.0 | 240 | 1.0450 | 0.7419 | 0.7530 | 0.7419 | 0.7340 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- Created
- Jun 21, 2024
- Updated
- Jun 21, 2024