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bert-finetuned-ner-30percent
This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4543
- Precision: 0.6879
- Recall: 0.7613
- F1: 0.7227
- Accuracy: 0.8878
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: 2022
- 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 | 23 | 0.5153 | 0.6498 | 0.7523 | 0.6973 | 0.8612 |
No log | 2.0 | 46 | 0.4693 | 0.6675 | 0.7568 | 0.7094 | 0.8786 |
No log | 3.0 | 69 | 0.4543 | 0.6879 | 0.7613 | 0.7227 | 0.8878 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2