| --- |
| license: apache-2.0 |
| base_model: distilbert-base-uncased |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: token_classification_test |
| 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. --> |
|
|
| # token_classification_test |
|
|
| This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.2859 |
| - Precision: 0.9187 |
| - Recall: 0.9095 |
| - F1: 0.9140 |
| - Accuracy: 0.9308 |
|
|
| ## 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: 64 |
| - eval_batch_size: 64 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 15 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | No log | 1.0 | 47 | 1.2700 | 0.6758 | 0.5896 | 0.6298 | 0.7121 | |
| | No log | 2.0 | 94 | 0.6468 | 0.8315 | 0.7864 | 0.8083 | 0.8461 | |
| | No log | 3.0 | 141 | 0.4607 | 0.8709 | 0.8422 | 0.8563 | 0.8845 | |
| | No log | 4.0 | 188 | 0.3841 | 0.8924 | 0.8686 | 0.8804 | 0.9047 | |
| | No log | 5.0 | 235 | 0.3380 | 0.9060 | 0.8905 | 0.8982 | 0.9180 | |
| | No log | 6.0 | 282 | 0.3164 | 0.9096 | 0.8934 | 0.9014 | 0.9213 | |
| | No log | 7.0 | 329 | 0.3072 | 0.9090 | 0.9001 | 0.9045 | 0.9227 | |
| | No log | 8.0 | 376 | 0.2997 | 0.9156 | 0.9009 | 0.9082 | 0.9258 | |
| | No log | 9.0 | 423 | 0.2940 | 0.9141 | 0.9058 | 0.9099 | 0.9269 | |
| | No log | 10.0 | 470 | 0.2904 | 0.9199 | 0.9076 | 0.9137 | 0.9312 | |
| | 0.5334 | 11.0 | 517 | 0.2894 | 0.9210 | 0.9093 | 0.9151 | 0.9314 | |
| | 0.5334 | 12.0 | 564 | 0.2884 | 0.9173 | 0.9081 | 0.9127 | 0.9295 | |
| | 0.5334 | 13.0 | 611 | 0.2862 | 0.9184 | 0.9089 | 0.9136 | 0.9305 | |
| | 0.5334 | 14.0 | 658 | 0.2859 | 0.9196 | 0.9103 | 0.9149 | 0.9310 | |
| | 0.5334 | 15.0 | 705 | 0.2859 | 0.9187 | 0.9095 | 0.9140 | 0.9308 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 4.31.0 |
| - Pytorch 2.0.1+cu118 |
| - Datasets 2.14.3 |
| - Tokenizers 0.13.3 |
|
|
