distilbert-base-uncased-finetuned-tags
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: 0.3638
- Precision: 0.3692
- Recall: 0.4066
- F1: 0.3870
- Accuracy: 0.8897
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
No log | 1.0 | 477 | 0.3551 | 0.3149 | 0.3479 | 0.3306 | 0.8862 |
0.3016 | 2.0 | 954 | 0.3543 | 0.3746 | 0.3903 | 0.3823 | 0.8889 |
0.2158 | 3.0 | 1431 | 0.3638 | 0.3692 | 0.4066 | 0.3870 | 0.8897 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2+cpu
- Datasets 2.16.1
- Tokenizers 0.15.2
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