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Products_NER2
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3482
- Precision: 0.9155
- Recall: 0.9208
- F1: 0.9182
- Accuracy: 0.9400
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: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.2267 | 1.0 | 2470 | 0.1614 | 0.8379 | 0.8791 | 0.8580 | 0.9212 |
| 0.1363 | 2.0 | 4940 | 0.1230 | 0.8602 | 0.8968 | 0.8781 | 0.9332 |
| 0.1047 | 3.0 | 7410 | 0.1183 | 0.8808 | 0.9063 | 0.8934 | 0.9360 |
| 0.0931 | 4.0 | 9880 | 0.1139 | 0.8909 | 0.9119 | 0.9013 | 0.9387 |
| 0.085 | 5.0 | 12350 | 0.1153 | 0.8889 | 0.9110 | 0.8998 | 0.9390 |
| 0.0835 | 6.0 | 14820 | 0.1257 | 0.9043 | 0.9165 | 0.9104 | 0.9398 |
| 0.0728 | 7.0 | 17290 | 0.1218 | 0.8987 | 0.9149 | 0.9067 | 0.9393 |
| 0.069 | 8.0 | 19760 | 0.1457 | 0.9040 | 0.9154 | 0.9097 | 0.9389 |
| 0.0616 | 9.0 | 22230 | 0.1606 | 0.9090 | 0.9166 | 0.9128 | 0.9386 |
| 0.0559 | 10.0 | 24700 | 0.1726 | 0.9122 | 0.9189 | 0.9156 | 0.9397 |
| 0.0504 | 11.0 | 27170 | 0.1998 | 0.9131 | 0.9192 | 0.9161 | 0.9396 |
| 0.043 | 12.0 | 29640 | 0.2015 | 0.9126 | 0.9194 | 0.9160 | 0.9402 |
| 0.0389 | 13.0 | 32110 | 0.2388 | 0.9129 | 0.9195 | 0.9162 | 0.9394 |
| 0.035 | 14.0 | 34580 | 0.2569 | 0.9135 | 0.9202 | 0.9169 | 0.9397 |
| 0.0311 | 15.0 | 37050 | 0.2718 | 0.9156 | 0.9207 | 0.9181 | 0.9400 |
| 0.028 | 16.0 | 39520 | 0.2886 | 0.9158 | 0.9208 | 0.9183 | 0.9403 |
| 0.0246 | 17.0 | 41990 | 0.3054 | 0.9145 | 0.9201 | 0.9173 | 0.9392 |
| 0.0212 | 18.0 | 44460 | 0.3252 | 0.9155 | 0.9206 | 0.9180 | 0.9398 |
| 0.0192 | 19.0 | 46930 | 0.3333 | 0.9157 | 0.9210 | 0.9183 | 0.9402 |
| 0.017 | 20.0 | 49400 | 0.3482 | 0.9155 | 0.9208 | 0.9182 | 0.9400 |
Framework versions
- Transformers 4.33.2
- Pytorch 1.13.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
