bert-ner-custom-v2
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1316
- Precision: 0.8231
- Recall: 0.8357
- F1: 0.8294
- Accuracy: 0.9613
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 |
|---|---|---|---|---|---|---|---|
| 0.1442 | 1.0 | 4796 | 0.1375 | 0.8052 | 0.8311 | 0.8179 | 0.9573 |
| 0.1046 | 2.0 | 9592 | 0.1273 | 0.8260 | 0.8315 | 0.8287 | 0.9606 |
| 0.0834 | 3.0 | 14388 | 0.1316 | 0.8231 | 0.8357 | 0.8294 | 0.9613 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
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Model tree for nicoheiniger/bert-ner-custom-v2
Base model
google-bert/bert-base-uncased