bert-finetuned-ner
This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2990
- Precision: 0.5319
- Recall: 0.5722
- F1: 0.5513
- Accuracy: 0.9084
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 | 118 | 0.3671 | 0.3949 | 0.4439 | 0.4180 | 0.8833 |
| No log | 2.0 | 236 | 0.3071 | 0.5181 | 0.5481 | 0.5327 | 0.9033 |
| No log | 3.0 | 354 | 0.2990 | 0.5319 | 0.5722 | 0.5513 | 0.9084 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
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Model tree for rubinho/bert-finetuned-ner
Base model
google-bert/bert-base-cased