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bert-finetuned-targetexpressionaug
This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.8066
- Precision: 0.5904
- Recall: 0.6194
- F1: 0.6046
- Accuracy: 0.7562
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: 3
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
No log | 1.0 | 424 | 0.7548 | 0.5213 | 0.6004 | 0.5581 | 0.7359 |
0.8331 | 2.0 | 848 | 0.7577 | 0.5580 | 0.6116 | 0.5836 | 0.7518 |
0.4452 | 3.0 | 1272 | 0.8066 | 0.5904 | 0.6194 | 0.6046 | 0.7562 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2