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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: google-bert/bert-large-uncased |
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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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- f1 |
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model-index: |
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- name: bert-large-csb |
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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-large-csb |
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This model is a fine-tuned version of [google-bert/bert-large-uncased](https://huggingface.co/google-bert/bert-large-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3276 |
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- Accuracy: 0.8637 |
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- F1: 0.8635 |
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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: 1e-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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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
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| 0.4806 | 1.0 | 228 | 0.3276 | 0.8637 | 0.8635 | |
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| 0.3325 | 2.0 | 456 | 0.3070 | 0.8527 | 0.8530 | |
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| 0.2308 | 3.0 | 684 | 0.3310 | 0.8593 | 0.8585 | |
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| 0.1562 | 4.0 | 912 | 0.5863 | 0.8571 | 0.8547 | |
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| 0.1152 | 5.0 | 1140 | 0.7901 | 0.8462 | 0.8448 | |
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| 0.0424 | 6.0 | 1368 | 1.0230 | 0.8374 | 0.8342 | |
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| 0.018 | 7.0 | 1596 | 0.9910 | 0.8505 | 0.8499 | |
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| 0.0293 | 8.0 | 1824 | 1.1121 | 0.8484 | 0.8471 | |
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| 0.0075 | 9.0 | 2052 | 1.2002 | 0.8462 | 0.8446 | |
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| 0.0067 | 10.0 | 2280 | 1.1791 | 0.8440 | 0.8425 | |
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### Framework versions |
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- Transformers 4.57.3 |
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- Pytorch 2.2.1 |
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- Datasets 4.4.1 |
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- Tokenizers 0.22.1 |
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