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bert-finetuned-target
This model is a fine-tuned version of bert-base-multilingual-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2793
- Precision: 0.6688
- Recall: 0.7
- F1: 0.6840
- Accuracy: 0.9170
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 | 218 | 0.2489 | 0.6034 | 0.7 | 0.6481 | 0.9106 |
| No log | 2.0 | 436 | 0.2453 | 0.6830 | 0.6967 | 0.6898 | 0.9192 |
| 0.2156 | 3.0 | 654 | 0.2793 | 0.6688 | 0.7 | 0.6840 | 0.9170 |
Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.12.1
