bert-en-ner
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.5661
- Precision: 0.1135
- Recall: 0.1068
- F1: 0.1101
- Accuracy: 0.6401
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 |
---|---|---|---|---|---|---|---|
No log | 1.0 | 25 | 2.1783 | 0.0 | 0.0 | 0.0 | 0.5424 |
No log | 2.0 | 50 | 1.7064 | 0.0382 | 0.0221 | 0.0280 | 0.5927 |
No log | 3.0 | 75 | 1.5661 | 0.1135 | 0.1068 | 0.1101 | 0.6401 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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