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Tagged_Uni_250v9_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of bert-base-cased on the tagged_uni250v9_wikigold_split dataset. It achieves the following results on the evaluation set:
- Loss: 0.2786
- Precision: 0.5877
- Recall: 0.5263
- F1: 0.5553
- Accuracy: 0.9093
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 | 88 | 0.3533 | 0.3574 | 0.2156 | 0.2690 | 0.8658 |
| No log | 2.0 | 176 | 0.2946 | 0.5370 | 0.4529 | 0.4914 | 0.8999 |
| No log | 3.0 | 264 | 0.2786 | 0.5877 | 0.5263 | 0.5553 | 0.9093 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
