BERT_NER_Ep5_PAD_50-finetuned-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: 0.3893
- Precision: 0.6540
- Recall: 0.7348
- F1: 0.6920
- Accuracy: 0.9006
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: 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: 7
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 288 | 0.3705 | 0.5852 | 0.6215 | 0.6028 | 0.8793 |
| 0.4885 | 2.0 | 576 | 0.3351 | 0.5925 | 0.7317 | 0.6548 | 0.8865 |
| 0.4885 | 3.0 | 864 | 0.3196 | 0.6471 | 0.7138 | 0.6788 | 0.8994 |
| 0.2172 | 4.0 | 1152 | 0.3368 | 0.6454 | 0.7323 | 0.6861 | 0.8992 |
| 0.2172 | 5.0 | 1440 | 0.3491 | 0.6507 | 0.7312 | 0.6886 | 0.9008 |
| 0.1459 | 6.0 | 1728 | 0.3833 | 0.6715 | 0.7018 | 0.6863 | 0.9013 |
| 0.1045 | 7.0 | 2016 | 0.3893 | 0.6540 | 0.7348 | 0.6920 | 0.9006 |
Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
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