bert-finetuned-ner
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: 0.3981
- Precision: 0.7405
- Recall: 0.7955
- F1: 0.7670
- 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: 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 |
---|---|---|---|---|---|---|---|
0.7704 | 1.0 | 680 | 0.4612 | 0.6723 | 0.7181 | 0.6944 | 0.8563 |
0.415 | 2.0 | 1360 | 0.4036 | 0.7267 | 0.7769 | 0.7510 | 0.8791 |
0.2574 | 3.0 | 2040 | 0.3981 | 0.7405 | 0.7955 | 0.7670 | 0.8878 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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