| --- |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: bert-finetuned-ner |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # bert-finetuned-ner |
|
|
| This model was trained from scratch on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.6370 |
| - Precision: 0.5313 |
| - Recall: 0.4530 |
| - F1: 0.4891 |
| - Accuracy: 0.9290 |
|
|
| ## 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: 20 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | No log | 1.0 | 125 | 0.5387 | 0.2190 | 0.0552 | 0.0882 | 0.8991 | |
| | No log | 2.0 | 250 | 0.4241 | 0.3430 | 0.1750 | 0.2317 | 0.9117 | |
| | No log | 3.0 | 375 | 0.4721 | 0.3502 | 0.1786 | 0.2366 | 0.9088 | |
| | 0.1529 | 4.0 | 500 | 0.6204 | 0.4300 | 0.2320 | 0.3014 | 0.9134 | |
| | 0.1529 | 5.0 | 625 | 0.6479 | 0.4470 | 0.2486 | 0.3195 | 0.9104 | |
| | 0.1529 | 6.0 | 750 | 0.4640 | 0.4532 | 0.4015 | 0.4258 | 0.9220 | |
| | 0.1529 | 7.0 | 875 | 0.5170 | 0.4288 | 0.4217 | 0.4253 | 0.9224 | |
| | 0.0229 | 8.0 | 1000 | 0.5846 | 0.5524 | 0.4273 | 0.4818 | 0.9233 | |
| | 0.0229 | 9.0 | 1125 | 0.5569 | 0.4644 | 0.4328 | 0.4480 | 0.9234 | |
| | 0.0229 | 10.0 | 1250 | 0.5818 | 0.5502 | 0.4438 | 0.4913 | 0.9258 | |
| | 0.0229 | 11.0 | 1375 | 0.6183 | 0.5607 | 0.4254 | 0.4838 | 0.9231 | |
| | 0.0048 | 12.0 | 1500 | 0.6148 | 0.5385 | 0.4254 | 0.4753 | 0.9250 | |
| | 0.0048 | 13.0 | 1625 | 0.6271 | 0.4896 | 0.4328 | 0.4594 | 0.9255 | |
| | 0.0048 | 14.0 | 1750 | 0.6475 | 0.5668 | 0.4217 | 0.4836 | 0.9267 | |
| | 0.0048 | 15.0 | 1875 | 0.6428 | 0.5704 | 0.4328 | 0.4921 | 0.9282 | |
| | 0.0016 | 16.0 | 2000 | 0.6577 | 0.5487 | 0.4254 | 0.4793 | 0.9270 | |
| | 0.0016 | 17.0 | 2125 | 0.6688 | 0.5556 | 0.4144 | 0.4747 | 0.9262 | |
| | 0.0016 | 18.0 | 2250 | 0.6481 | 0.5434 | 0.4383 | 0.4852 | 0.9282 | |
| | 0.0016 | 19.0 | 2375 | 0.6432 | 0.5428 | 0.4438 | 0.4883 | 0.9289 | |
| | 0.0007 | 20.0 | 2500 | 0.6370 | 0.5313 | 0.4530 | 0.4891 | 0.9290 | |
|
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|
| ### Framework versions |
|
|
| - Transformers 4.23.1 |
| - Pytorch 1.8.0 |
| - Datasets 2.6.1 |
| - Tokenizers 0.13.1 |
|
|
