| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: bert_base_96 |
| 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. --> |
|
|
| # bert_base_96 |
|
|
| This model is a fine-tuned version of [gokuls/bert_base_48](https://huggingface.co/gokuls/bert_base_48) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 2.6333 |
| - Accuracy: 0.5281 |
|
|
| ## Model description |
|
|
| More information needed |
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|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
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|
| More information needed |
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|
| ## Training procedure |
|
|
| ### Training hyperparameters |
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|
| The following hyperparameters were used during training: |
| - learning_rate: 1e-05 |
| - train_batch_size: 48 |
| - eval_batch_size: 48 |
| - seed: 10 |
| - distributed_type: multi-GPU |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 10000 |
| - num_epochs: 5 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:-----:|:------:|:---------------:|:--------:| |
| | 5.6041 | 0.08 | 10000 | 5.5567 | 0.1751 | |
| | 5.4727 | 0.16 | 20000 | 5.3950 | 0.1953 | |
| | 5.3385 | 0.25 | 30000 | 5.2277 | 0.2151 | |
| | 5.2033 | 0.33 | 40000 | 5.0607 | 0.2335 | |
| | 4.7807 | 0.41 | 50000 | 4.5611 | 0.2910 | |
| | 4.1994 | 0.49 | 60000 | 4.0039 | 0.3520 | |
| | 3.8039 | 0.57 | 70000 | 3.6509 | 0.3906 | |
| | 3.5516 | 0.66 | 80000 | 3.3794 | 0.4263 | |
| | 3.3199 | 0.74 | 90000 | 3.1446 | 0.4607 | |
| | 3.1682 | 0.82 | 100000 | 3.0053 | 0.4795 | |
| | 3.0597 | 0.9 | 110000 | 2.9135 | 0.4919 | |
| | 2.9814 | 0.98 | 120000 | 2.8331 | 0.5018 | |
| | 2.907 | 1.07 | 130000 | 2.7724 | 0.5100 | |
| | 2.8532 | 1.15 | 140000 | 2.7200 | 0.5170 | |
| | 2.8044 | 1.23 | 150000 | 2.6759 | 0.5227 | |
| | 2.7694 | 1.31 | 160000 | 2.6333 | 0.5281 | |
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| ### Framework versions |
|
|
| - Transformers 4.30.1 |
| - Pytorch 1.14.0a0+410ce96 |
| - Datasets 2.12.0 |
| - Tokenizers 0.13.3 |
|
|
