<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
bert-finetuned-ner-v4.010
This model is a fine-tuned version of bert-base-multilingual-cased on the caner dataset. It achieves the following results on the evaluation set:
- Loss: 0.3657
- Precision: 0.8622
- Recall: 0.8716
- F1: 0.8668
- Accuracy: 0.9393
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 |
---|---|---|---|---|---|---|---|
0.2718 | 1.0 | 3228 | 0.4023 | 0.8748 | 0.8019 | 0.8368 | 0.9265 |
0.2052 | 2.0 | 6456 | 0.3959 | 0.8243 | 0.8265 | 0.8254 | 0.9291 |
0.1584 | 3.0 | 9684 | 0.3657 | 0.8622 | 0.8716 | 0.8668 | 0.9393 |
Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2