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bert-base-multilingual-cased-finetuned-ner
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: 0.0351
- Precision: 0.6111
- Recall: 0.6692
- F1: 0.6388
- Accuracy: 0.9898
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: 4
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
0.0843 | 1.0 | 591 | 0.0414 | 0.6125 | 0.3726 | 0.4634 | 0.9882 |
0.046 | 2.0 | 1182 | 0.0370 | 0.5144 | 0.6122 | 0.5590 | 0.9874 |
0.0369 | 3.0 | 1773 | 0.0331 | 0.6364 | 0.6122 | 0.6240 | 0.9898 |
0.0267 | 4.0 | 2364 | 0.0351 | 0.6111 | 0.6692 | 0.6388 | 0.9898 |
Framework versions
- Transformers 4.30.1
- Pytorch 1.11.0+cu102
- Datasets 2.1.0
- Tokenizers 0.13.3