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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: bert-base-cased-finetuned-filtered-0609 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bert-base-cased-finetuned-filtered-0609 |
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2410 |
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- Accuracy: 0.9748 |
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- Precision: 0.9751 |
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- Recall: 0.9748 |
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- F1: 0.9749 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 1000 |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.2028 | 1.0 | 3180 | 0.2405 | 0.9535 | 0.9561 | 0.9535 | 0.9538 | |
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| 0.1632 | 2.0 | 6360 | 0.1686 | 0.9660 | 0.9664 | 0.9660 | 0.9661 | |
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| 0.1203 | 3.0 | 9540 | 0.1625 | 0.9648 | 0.9655 | 0.9648 | 0.9648 | |
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| 0.1233 | 4.0 | 12720 | 0.1510 | 0.9698 | 0.9702 | 0.9698 | 0.9699 | |
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| 0.0823 | 5.0 | 15900 | 0.1600 | 0.9730 | 0.9732 | 0.9730 | 0.9730 | |
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| 0.0453 | 6.0 | 19080 | 0.1953 | 0.9723 | 0.9724 | 0.9723 | 0.9723 | |
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| 0.031 | 7.0 | 22260 | 0.1754 | 0.9755 | 0.9755 | 0.9755 | 0.9755 | |
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| 0.0166 | 8.0 | 25440 | 0.2155 | 0.9739 | 0.9740 | 0.9739 | 0.9739 | |
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| 0.0036 | 9.0 | 28620 | 0.2519 | 0.9730 | 0.9733 | 0.9730 | 0.9730 | |
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| 0.0035 | 10.0 | 31800 | 0.2410 | 0.9748 | 0.9751 | 0.9748 | 0.9749 | |
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### Framework versions |
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- Transformers 4.19.2 |
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- Pytorch 1.9.1+cu111 |
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- Datasets 1.16.1 |
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- Tokenizers 0.12.1 |
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