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sumups-batch4-model
This model is a fine-tuned version of dslim/bert-base-NER on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.2992
- Precision: 0.0073
- Recall: 0.0337
- F1: 0.0119
- Accuracy: 0.4875
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 20 | 1.5243 | 0.0035 | 0.0119 | 0.0054 | 0.4106 |
| No log | 2.0 | 40 | 1.3877 | 0.0039 | 0.0119 | 0.0058 | 0.4421 |
| No log | 3.0 | 60 | 1.3144 | 0.0073 | 0.0238 | 0.0112 | 0.4723 |
| No log | 4.0 | 80 | 1.2957 | 0.0079 | 0.0356 | 0.0129 | 0.4877 |
| No log | 5.0 | 100 | 1.2992 | 0.0073 | 0.0337 | 0.0119 | 0.4875 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
