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multibertfinetuned2906
This model is a fine-tuned version of bert-base-multilingual-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3736
- Precision: 0.7185
- Recall: 0.7180
- F1: 0.7182
- Accuracy: 0.9254
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: 5e-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: 5
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
No log | 1.0 | 145 | 0.3877 | 0.7219 | 0.6767 | 0.6986 | 0.9218 |
No log | 2.0 | 290 | 0.4076 | 0.72 | 0.6892 | 0.7043 | 0.9137 |
No log | 3.0 | 435 | 0.3736 | 0.7185 | 0.7180 | 0.7182 | 0.9254 |
0.0637 | 4.0 | 580 | 0.4199 | 0.7240 | 0.7128 | 0.7184 | 0.9216 |
0.0637 | 5.0 | 725 | 0.4591 | 0.7206 | 0.7121 | 0.7163 | 0.9209 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3