| --- |
| license: mit |
| base_model: prajjwal1/bert-tiny |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| model-index: |
| - name: TestForColab |
| 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. --> |
|
|
| # TestForColab |
|
|
| This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.6515 |
| - Accuracy: 0.56 |
| - F1: 0.5579 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 2e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 10 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
| | No log | 0.0 | 50 | 0.6897 | 0.54 | 0.3787 | |
| | No log | 0.01 | 100 | 0.6899 | 0.6 | 0.5926 | |
| | No log | 0.01 | 150 | 0.6952 | 0.46 | 0.2899 | |
| | No log | 0.01 | 200 | 0.6874 | 0.63 | 0.6194 | |
| | No log | 0.02 | 250 | 0.6849 | 0.64 | 0.6092 | |
| | No log | 0.02 | 300 | 0.6929 | 0.46 | 0.2899 | |
| | No log | 0.02 | 350 | 0.6830 | 0.6 | 0.5390 | |
| | No log | 0.03 | 400 | 0.6821 | 0.54 | 0.3787 | |
| | No log | 0.03 | 450 | 0.6812 | 0.63 | 0.6095 | |
| | 0.6924 | 0.03 | 500 | 0.6806 | 0.62 | 0.6077 | |
| | 0.6924 | 0.04 | 550 | 0.6770 | 0.62 | 0.5969 | |
| | 0.6924 | 0.04 | 600 | 0.6805 | 0.58 | 0.5746 | |
| | 0.6924 | 0.04 | 650 | 0.6800 | 0.59 | 0.5857 | |
| | 0.6924 | 0.05 | 700 | 0.6732 | 0.63 | 0.6008 | |
| | 0.6924 | 0.05 | 750 | 0.6820 | 0.56 | 0.5387 | |
| | 0.6924 | 0.05 | 800 | 0.6652 | 0.64 | 0.6253 | |
| | 0.6924 | 0.06 | 850 | 0.6634 | 0.59 | 0.5896 | |
| | 0.6924 | 0.06 | 900 | 0.6604 | 0.61 | 0.6103 | |
| | 0.6924 | 0.06 | 950 | 0.6733 | 0.62 | 0.5936 | |
| | 0.6842 | 0.07 | 1000 | 0.6590 | 0.65 | 0.6176 | |
| | 0.6842 | 0.07 | 1050 | 0.6549 | 0.6 | 0.6005 | |
| | 0.6842 | 0.07 | 1100 | 0.6521 | 0.63 | 0.6242 | |
| | 0.6842 | 0.08 | 1150 | 0.6524 | 0.61 | 0.6015 | |
| | 0.6842 | 0.08 | 1200 | 0.6587 | 0.57 | 0.5634 | |
| | 0.6842 | 0.09 | 1250 | 0.6515 | 0.56 | 0.5579 | |
|
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|
| ### Framework versions |
|
|
| - Transformers 4.35.2 |
| - Pytorch 2.1.0+cu118 |
| - Datasets 2.15.0 |
| - Tokenizers 0.15.0 |
|
|
