| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| datasets: |
| - ner |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: test4 |
| results: |
| - task: |
| name: Token Classification |
| type: token-classification |
| dataset: |
| name: ner |
| type: ner |
| config: default |
| split: train |
| args: default |
| metrics: |
| - name: Precision |
| type: precision |
| value: 0.594855305466238 |
| - name: Recall |
| type: recall |
| value: 0.6423611111111112 |
| - name: F1 |
| type: f1 |
| value: 0.6176961602671119 |
| - name: Accuracy |
| type: accuracy |
| value: 0.9579571605593911 |
| --- |
| |
| <!-- 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. --> |
|
|
| # test4 |
|
|
| This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the ner dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.3100 |
| - Precision: 0.5949 |
| - Recall: 0.6424 |
| - F1: 0.6177 |
| - Accuracy: 0.9580 |
|
|
| ## 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: 1e-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 | 418 | 0.2052 | 0.2415 | 0.2465 | 0.2440 | 0.9423 | |
| | 0.3341 | 2.0 | 836 | 0.1816 | 0.4286 | 0.4792 | 0.4525 | 0.9513 | |
| | 0.1296 | 3.0 | 1254 | 0.2039 | 0.4589 | 0.5035 | 0.4801 | 0.9526 | |
| | 0.0727 | 4.0 | 1672 | 0.2130 | 0.5237 | 0.5764 | 0.5488 | 0.9566 | |
| | 0.0553 | 5.0 | 2090 | 0.2290 | 0.5171 | 0.5764 | 0.5452 | 0.9551 | |
| | 0.0412 | 6.0 | 2508 | 0.2351 | 0.5390 | 0.5521 | 0.5455 | 0.9555 | |
| | 0.0412 | 7.0 | 2926 | 0.2431 | 0.5280 | 0.5903 | 0.5574 | 0.9542 | |
| | 0.0321 | 8.0 | 3344 | 0.2490 | 0.5825 | 0.625 | 0.6030 | 0.9570 | |
| | 0.0249 | 9.0 | 3762 | 0.2679 | 0.5764 | 0.5764 | 0.5764 | 0.9573 | |
| | 0.0192 | 10.0 | 4180 | 0.2574 | 0.5506 | 0.6042 | 0.5762 | 0.9558 | |
| | 0.0206 | 11.0 | 4598 | 0.2857 | 0.5498 | 0.5938 | 0.5710 | 0.9559 | |
| | 0.0147 | 12.0 | 5016 | 0.2638 | 0.5548 | 0.5972 | 0.5753 | 0.9550 | |
| | 0.0147 | 13.0 | 5434 | 0.2771 | 0.5677 | 0.5972 | 0.5821 | 0.9577 | |
| | 0.0129 | 14.0 | 5852 | 0.3016 | 0.5761 | 0.6181 | 0.5963 | 0.9549 | |
| | 0.0118 | 15.0 | 6270 | 0.3055 | 0.5587 | 0.6111 | 0.5837 | 0.9570 | |
| | 0.0099 | 16.0 | 6688 | 0.2937 | 0.5682 | 0.6076 | 0.5872 | 0.9564 | |
| | 0.0099 | 17.0 | 7106 | 0.3075 | 0.5313 | 0.6181 | 0.5714 | 0.9531 | |
| | 0.0085 | 18.0 | 7524 | 0.3079 | 0.6026 | 0.6424 | 0.6218 | 0.9580 | |
| | 0.0085 | 19.0 | 7942 | 0.3082 | 0.5833 | 0.6319 | 0.6067 | 0.9572 | |
| | 0.0074 | 20.0 | 8360 | 0.3100 | 0.5949 | 0.6424 | 0.6177 | 0.9580 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.23.1 |
| - Pytorch 1.12.1+cu113 |
| - Datasets 2.6.1 |
| - Tokenizers 0.13.1 |
|
|
