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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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model-index: |
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- name: finetuned_bert-base-uncased |
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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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# finetuned_bert-base-uncased |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.8732 |
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- Accuracy: 0.4263 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
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| 1.7365 | 1.0 | 502 | 1.5167 | 0.4288 | |
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| 1.3495 | 2.0 | 1004 | 1.4797 | 0.4592 | |
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| 1.1131 | 3.0 | 1506 | 1.5093 | 0.4527 | |
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| 0.9213 | 4.0 | 2008 | 1.6501 | 0.4522 | |
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| 0.7787 | 5.0 | 2510 | 1.7494 | 0.4407 | |
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| 0.6594 | 6.0 | 3012 | 1.8600 | 0.4417 | |
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| 0.5807 | 7.0 | 3514 | 1.9974 | 0.4412 | |
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| 0.5142 | 8.0 | 4016 | 2.0887 | 0.4273 | |
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| 0.4716 | 9.0 | 4518 | 2.1556 | 0.4273 | |
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| 0.4364 | 10.0 | 5020 | 2.2847 | 0.4348 | |
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| 0.3934 | 11.0 | 5522 | 2.3842 | 0.4298 | |
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| 0.3774 | 12.0 | 6024 | 2.4663 | 0.4228 | |
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| 0.3498 | 13.0 | 6526 | 2.5637 | 0.4253 | |
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| 0.337 | 14.0 | 7028 | 2.6162 | 0.4273 | |
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| 0.3191 | 15.0 | 7530 | 2.6466 | 0.4268 | |
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| 0.3081 | 16.0 | 8032 | 2.6214 | 0.4288 | |
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| 0.2889 | 17.0 | 8534 | 2.8064 | 0.4258 | |
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| 0.2831 | 18.0 | 9036 | 2.8042 | 0.4228 | |
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| 0.2733 | 19.0 | 9538 | 2.8510 | 0.4288 | |
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| 0.2648 | 20.0 | 10040 | 2.8732 | 0.4263 | |
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### Framework versions |
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- Transformers 4.26.0 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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