| | --- |
| | license: gpl-3.0 |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - precision |
| | - recall |
| | - f1 |
| | - accuracy |
| | model-index: |
| | - name: Cbert_base_ws-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. --> |
| |
|
| | # Cbert_base_ws-finetuned-ner |
| |
|
| | This model is a fine-tuned version of [ckiplab/bert-base-chinese-ws](https://huggingface.co/ckiplab/bert-base-chinese-ws) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.0582 |
| | - Precision: 0.9602 |
| | - Recall: 0.9633 |
| | - F1: 0.9617 |
| | - Accuracy: 0.9827 |
| |
|
| | ## 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: 18 |
| | - eval_batch_size: 18 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 10 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | | 0.0482 | 0.64 | 1000 | 0.0509 | 0.9601 | 0.9582 | 0.9592 | 0.9817 | |
| | | 0.0364 | 1.28 | 2000 | 0.0521 | 0.9590 | 0.9615 | 0.9602 | 0.9820 | |
| | | 0.0341 | 1.92 | 3000 | 0.0548 | 0.9546 | 0.9625 | 0.9585 | 0.9812 | |
| | | 0.0264 | 2.56 | 4000 | 0.0550 | 0.9593 | 0.9623 | 0.9608 | 0.9822 | |
| | | 0.0227 | 3.19 | 5000 | 0.0582 | 0.9602 | 0.9633 | 0.9617 | 0.9827 | |
| | | 0.021 | 3.83 | 6000 | 0.0595 | 0.9581 | 0.9624 | 0.9603 | 0.9820 | |
| | | 0.0162 | 4.47 | 7000 | 0.0686 | 0.9574 | 0.9626 | 0.9600 | 0.9819 | |
| | | 0.0159 | 5.11 | 8000 | 0.0719 | 0.9596 | 0.9614 | 0.9605 | 0.9822 | |
| | | 0.0144 | 5.75 | 9000 | 0.0732 | 0.9590 | 0.9620 | 0.9605 | 0.9822 | |
| | | 0.0109 | 6.39 | 10000 | 0.0782 | 0.9599 | 0.9626 | 0.9612 | 0.9824 | |
| | | 0.0122 | 7.03 | 11000 | 0.0803 | 0.9605 | 0.9620 | 0.9612 | 0.9825 | |
| | | 0.0097 | 7.67 | 12000 | 0.0860 | 0.9591 | 0.9620 | 0.9605 | 0.9822 | |
| | | 0.0087 | 8.31 | 13000 | 0.0877 | 0.9591 | 0.9616 | 0.9603 | 0.9821 | |
| | | 0.0087 | 8.95 | 14000 | 0.0902 | 0.9585 | 0.9630 | 0.9607 | 0.9823 | |
| | | 0.0078 | 9.58 | 15000 | 0.0929 | 0.9589 | 0.9621 | 0.9605 | 0.9821 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.13.0 |
| | - Pytorch 1.8.0+cu111 |
| | - Datasets 2.4.0 |
| | - Tokenizers 0.10.3 |
| |
|
