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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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base_model: bert-base-uncased |
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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: 1.1947 |
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- Accuracy: 0.6793 |
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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: 32 |
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- eval_batch_size: 32 |
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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_ratio: 0.1 |
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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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| 1.2761 | 1.0 | 102 | 1.3225 | 0.3375 | |
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| 0.9847 | 2.0 | 204 | 1.0792 | 0.5509 | |
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| 0.6882 | 3.0 | 306 | 0.9260 | 0.6382 | |
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| 0.5099 | 4.0 | 408 | 0.9072 | 0.6634 | |
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| 0.4614 | 5.0 | 510 | 0.9115 | 0.6867 | |
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| 0.3406 | 6.0 | 612 | 1.0022 | 0.6751 | |
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| 0.189 | 7.0 | 714 | 1.0881 | 0.6751 | |
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| 0.2179 | 8.0 | 816 | 1.1520 | 0.6712 | |
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| 0.2085 | 9.0 | 918 | 1.2567 | 0.6896 | |
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| 0.1914 | 10.0 | 1020 | 1.2074 | 0.6828 | |
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| 0.1271 | 11.0 | 1122 | 1.3389 | 0.6887 | |
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| 0.1236 | 12.0 | 1224 | 1.3539 | 0.6790 | |
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| 0.0946 | 13.0 | 1326 | 1.4042 | 0.6838 | |
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| 0.0968 | 14.0 | 1428 | 1.4079 | 0.6877 | |
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| 0.1095 | 15.0 | 1530 | 1.4884 | 0.6799 | |
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| 0.1102 | 16.0 | 1632 | 1.5244 | 0.6790 | |
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| 0.1159 | 17.0 | 1734 | 1.5238 | 0.6799 | |
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| 0.1448 | 18.0 | 1836 | 1.5568 | 0.6780 | |
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| 0.1105 | 19.0 | 1938 | 1.5629 | 0.6780 | |
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| 0.092 | 20.0 | 2040 | 1.5588 | 0.6809 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.2 |
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