| --- |
| license: mit |
| base_model: prajjwal1/bert-tiny |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| model-index: |
| - name: MM05 |
| 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. --> |
|
|
| # MM05 |
|
|
| 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: 0.3184 |
| - Accuracy: 0.99 |
| - F1: 0.9950 |
|
|
| ## 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: 3e-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: 7 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
| | No log | 0.0 | 50 | 0.6884 | 0.58 | 0.4258 | |
| | No log | 0.01 | 100 | 0.6988 | 0.42 | 0.2485 | |
| | No log | 0.01 | 150 | 0.6952 | 0.42 | 0.2485 | |
| | No log | 0.02 | 200 | 0.6886 | 0.58 | 0.4258 | |
| | No log | 0.02 | 250 | 0.6889 | 0.59 | 0.4481 | |
| | No log | 0.02 | 300 | 0.6920 | 0.59 | 0.5916 | |
| | No log | 0.03 | 350 | 0.6917 | 0.57 | 0.5535 | |
| | No log | 0.03 | 400 | 0.6947 | 0.45 | 0.3250 | |
| | No log | 0.04 | 450 | 0.6541 | 0.69 | 0.6866 | |
| | 0.6877 | 0.04 | 500 | 0.6117 | 0.7 | 0.6829 | |
| | 0.6877 | 0.04 | 550 | 0.5938 | 0.71 | 0.7030 | |
| | 0.6877 | 0.05 | 600 | 0.5851 | 0.74 | 0.7390 | |
| | 0.6877 | 0.05 | 650 | 0.5721 | 0.77 | 0.7645 | |
| | 0.6877 | 0.06 | 700 | 0.5612 | 0.77 | 0.7704 | |
| | 0.6877 | 0.06 | 750 | 0.5368 | 0.76 | 0.7612 | |
| | 0.6877 | 0.06 | 800 | 0.5013 | 0.77 | 0.7696 | |
| | 0.6877 | 0.07 | 850 | 0.4831 | 0.78 | 0.7792 | |
| | 0.6877 | 0.07 | 900 | 0.4831 | 0.78 | 0.7792 | |
| | 0.6877 | 0.08 | 950 | 0.4573 | 0.8 | 0.7886 | |
| | 0.5813 | 0.08 | 1000 | 0.4576 | 0.79 | 0.7792 | |
| | 0.5813 | 0.08 | 1050 | 0.4483 | 0.81 | 0.7956 | |
| | 0.5813 | 0.09 | 1100 | 0.4377 | 0.8 | 0.7886 | |
| | 0.5813 | 0.09 | 1150 | 0.4297 | 0.81 | 0.7956 | |
| | 0.5813 | 0.1 | 1200 | 0.4287 | 0.81 | 0.7956 | |
| | 0.5813 | 0.1 | 1250 | 0.4301 | 0.81 | 0.7956 | |
| | 0.5813 | 0.1 | 1300 | 0.4286 | 0.81 | 0.7956 | |
| | 0.5813 | 0.11 | 1350 | 0.4193 | 0.81 | 0.7956 | |
| | 0.5813 | 0.11 | 1400 | 0.4088 | 0.81 | 0.7956 | |
| | 0.5813 | 0.12 | 1450 | 0.4107 | 0.81 | 0.7956 | |
| | 0.4699 | 0.12 | 1500 | 0.4016 | 0.81 | 0.7956 | |
| | 0.4699 | 0.12 | 1550 | 0.4056 | 0.81 | 0.7956 | |
| | 0.4699 | 0.13 | 1600 | 0.4095 | 0.81 | 0.7956 | |
| | 0.4699 | 0.13 | 1650 | 0.3973 | 0.81 | 0.7956 | |
| | 0.4699 | 0.14 | 1700 | 0.3907 | 0.81 | 0.7956 | |
| | 0.4699 | 0.14 | 1750 | 0.3907 | 0.81 | 0.7956 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.35.2 |
| - Pytorch 2.1.0+cu121 |
| - Datasets 2.16.0 |
| - Tokenizers 0.15.0 |
|
|
