efficient-fine-tuning-demo
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: 0.0975
- Precision: 0.8875
- Recall: 0.9090
- F1: 0.8981
- Accuracy: 0.9735
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: 0.0001
- 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 |
---|---|---|---|---|---|---|---|
0.184 | 1.0 | 477 | 0.1145 | 0.8392 | 0.8766 | 0.8575 | 0.9643 |
0.0877 | 2.0 | 954 | 0.0985 | 0.8827 | 0.9038 | 0.8931 | 0.9728 |
0.0448 | 3.0 | 1431 | 0.0975 | 0.8875 | 0.9090 | 0.8981 | 0.9735 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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Base model
google-bert/bert-base-multilingual-cased