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generic_ner_model
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0999
- Precision: 0.8727
- Recall: 0.8953
- F1: 0.8838
- Accuracy: 0.9740
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
Used the Ontonotes 5 data for fine-tuning. https://huggingface.co/datasets/tner/ontonotes5#label-id
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.1083 | 1.0 | 1958 | 0.1007 | 0.8684 | 0.8836 | 0.8759 | 0.9723 |
| 0.0679 | 2.0 | 3916 | 0.0977 | 0.8672 | 0.8960 | 0.8813 | 0.9738 |
| 0.0475 | 3.0 | 5874 | 0.0999 | 0.8727 | 0.8953 | 0.8838 | 0.9740 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
