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--- |
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license: mit |
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base_model: prajjwal1/bert-tiny |
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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: MM03-PC |
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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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# MM03-PC |
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This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5909 |
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- Accuracy: 0.71 |
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- F1: 0.8304 |
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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: 3e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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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- num_epochs: 5 |
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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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| No log | 0.0 | 50 | 0.6916 | 0.53 | 0.3672 | |
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| No log | 0.01 | 100 | 0.6924 | 0.53 | 0.3672 | |
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| No log | 0.01 | 150 | 0.6922 | 0.53 | 0.3672 | |
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| No log | 0.01 | 200 | 0.6927 | 0.56 | 0.5593 | |
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| No log | 0.02 | 250 | 0.6903 | 0.53 | 0.3672 | |
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| No log | 0.02 | 300 | 0.6884 | 0.53 | 0.3672 | |
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| No log | 0.03 | 350 | 0.6875 | 0.53 | 0.3842 | |
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| No log | 0.03 | 400 | 0.6865 | 0.59 | 0.5100 | |
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| No log | 0.03 | 450 | 0.6835 | 0.59 | 0.5193 | |
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| 0.6925 | 0.04 | 500 | 0.6792 | 0.58 | 0.5732 | |
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| 0.6925 | 0.04 | 550 | 0.6717 | 0.74 | 0.7324 | |
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| 0.6925 | 0.04 | 600 | 0.6558 | 0.73 | 0.7248 | |
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| 0.6925 | 0.05 | 650 | 0.6456 | 0.65 | 0.6286 | |
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| 0.6925 | 0.05 | 700 | 0.6371 | 0.74 | 0.7342 | |
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| 0.6925 | 0.06 | 750 | 0.6353 | 0.64 | 0.6403 | |
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| 0.6925 | 0.06 | 800 | 0.6331 | 0.72 | 0.7096 | |
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| 0.6925 | 0.06 | 850 | 0.6298 | 0.73 | 0.7248 | |
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| 0.6925 | 0.07 | 900 | 0.6341 | 0.69 | 0.6743 | |
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| 0.6925 | 0.07 | 950 | 0.6302 | 0.61 | 0.6102 | |
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| 0.6691 | 0.07 | 1000 | 0.6161 | 0.63 | 0.6297 | |
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| 0.6691 | 0.08 | 1050 | 0.6035 | 0.75 | 0.7486 | |
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| 0.6691 | 0.08 | 1100 | 0.6015 | 0.74 | 0.7370 | |
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| 0.6691 | 0.08 | 1150 | 0.5958 | 0.73 | 0.7298 | |
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| 0.6691 | 0.09 | 1200 | 0.5895 | 0.73 | 0.7263 | |
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| 0.6691 | 0.09 | 1250 | 0.5921 | 0.73 | 0.7263 | |
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| 0.6691 | 0.1 | 1300 | 0.5935 | 0.73 | 0.7285 | |
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| 0.6691 | 0.1 | 1350 | 0.5853 | 0.73 | 0.7275 | |
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| 0.6691 | 0.1 | 1400 | 0.5952 | 0.74 | 0.7381 | |
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| 0.6691 | 0.11 | 1450 | 0.5811 | 0.76 | 0.7582 | |
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| 0.6482 | 0.11 | 1500 | 0.5849 | 0.7 | 0.6933 | |
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| 0.6482 | 0.11 | 1550 | 0.5827 | 0.71 | 0.7044 | |
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| 0.6482 | 0.12 | 1600 | 0.5741 | 0.71 | 0.7026 | |
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| 0.6482 | 0.12 | 1650 | 0.5782 | 0.73 | 0.7275 | |
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| 0.6482 | 0.12 | 1700 | 0.5704 | 0.74 | 0.7370 | |
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| 0.6482 | 0.13 | 1750 | 0.5704 | 0.74 | 0.7396 | |
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| 0.6482 | 0.13 | 1800 | 0.5592 | 0.72 | 0.7154 | |
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| 0.6482 | 0.14 | 1850 | 0.5661 | 0.72 | 0.7137 | |
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| 0.6482 | 0.14 | 1900 | 0.5762 | 0.71 | 0.7044 | |
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| 0.6482 | 0.14 | 1950 | 0.5702 | 0.71 | 0.7044 | |
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| 0.6226 | 0.15 | 2000 | 0.5677 | 0.73 | 0.7285 | |
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| 0.6226 | 0.15 | 2050 | 0.5649 | 0.73 | 0.7285 | |
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| 0.6226 | 0.15 | 2100 | 0.5583 | 0.74 | 0.7370 | |
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| 0.6226 | 0.16 | 2150 | 0.5712 | 0.7 | 0.6951 | |
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| 0.6226 | 0.16 | 2200 | 0.5661 | 0.7 | 0.6951 | |
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| 0.6226 | 0.17 | 2250 | 0.5452 | 0.76 | 0.7573 | |
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| 0.6226 | 0.17 | 2300 | 0.5448 | 0.75 | 0.7493 | |
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| 0.6226 | 0.17 | 2350 | 0.5424 | 0.75 | 0.7493 | |
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| 0.6226 | 0.18 | 2400 | 0.5444 | 0.75 | 0.7477 | |
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| 0.6226 | 0.18 | 2450 | 0.5400 | 0.75 | 0.7477 | |
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| 0.6058 | 0.18 | 2500 | 0.5393 | 0.75 | 0.7493 | |
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| 0.6058 | 0.19 | 2550 | 0.5495 | 0.75 | 0.7486 | |
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| 0.6058 | 0.19 | 2600 | 0.5309 | 0.76 | 0.7590 | |
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| 0.6058 | 0.19 | 2650 | 0.5242 | 0.73 | 0.7298 | |
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| 0.6058 | 0.2 | 2700 | 0.5239 | 0.73 | 0.7298 | |
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| 0.6058 | 0.2 | 2750 | 0.5201 | 0.71 | 0.7098 | |
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| 0.6058 | 0.21 | 2800 | 0.5087 | 0.73 | 0.7285 | |
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| 0.6058 | 0.21 | 2850 | 0.5041 | 0.75 | 0.7486 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.0 |
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- Tokenizers 0.15.0 |
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