bert-large-uncased-v10-ES-ner
This model is a fine-tuned version of bert-large-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5047
- Precision: 0.6503
- Recall: 0.7107
- F1: 0.6792
- Accuracy: 0.9157
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: 10
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
0.4056 | 1.75 | 500 | 0.3016 | 0.6128 | 0.6736 | 0.6417 | 0.9118 |
0.1603 | 3.5 | 1000 | 0.3561 | 0.6350 | 0.6756 | 0.6547 | 0.9105 |
0.0727 | 5.24 | 1500 | 0.4252 | 0.6654 | 0.7149 | 0.6892 | 0.9163 |
0.0341 | 6.99 | 2000 | 0.4574 | 0.6542 | 0.6839 | 0.6687 | 0.9124 |
0.0168 | 8.74 | 2500 | 0.5047 | 0.6503 | 0.7107 | 0.6792 | 0.9157 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
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