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--- |
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base_model: gokuls/model_v1_complete_training_wt_init_48_mini_freeze_new |
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tags: |
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- generated_from_trainer |
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datasets: |
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- massive |
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metrics: |
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- accuracy |
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model-index: |
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- name: hbertv1-massive-logit_KD-mini |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: massive |
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type: massive |
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config: en-US |
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split: validation |
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args: en-US |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8598130841121495 |
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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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# hbertv1-massive-logit_KD-mini |
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This model is a fine-tuned version of [gokuls/model_v1_complete_training_wt_init_48_mini_freeze_new](https://huggingface.co/gokuls/model_v1_complete_training_wt_init_48_mini_freeze_new) on the massive dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4640 |
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- Accuracy: 0.8598 |
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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: 5e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 33 |
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- distributed_type: multi-GPU |
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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: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 3.5547 | 1.0 | 180 | 2.3028 | 0.4481 | |
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| 1.9374 | 2.0 | 360 | 1.2686 | 0.6513 | |
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| 1.2845 | 3.0 | 540 | 0.9328 | 0.7324 | |
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| 0.9981 | 4.0 | 720 | 0.7684 | 0.7836 | |
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| 0.8273 | 5.0 | 900 | 0.6834 | 0.7998 | |
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| 0.7068 | 6.0 | 1080 | 0.6369 | 0.8062 | |
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| 0.6043 | 7.0 | 1260 | 0.5804 | 0.8205 | |
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| 0.535 | 8.0 | 1440 | 0.5475 | 0.8396 | |
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| 0.4763 | 9.0 | 1620 | 0.5247 | 0.8396 | |
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| 0.4245 | 10.0 | 1800 | 0.5122 | 0.8470 | |
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| 0.3794 | 11.0 | 1980 | 0.5038 | 0.8460 | |
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| 0.3424 | 12.0 | 2160 | 0.5057 | 0.8465 | |
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| 0.3194 | 13.0 | 2340 | 0.4977 | 0.8485 | |
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| 0.2897 | 14.0 | 2520 | 0.4973 | 0.8534 | |
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| 0.2688 | 15.0 | 2700 | 0.4714 | 0.8574 | |
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| 0.255 | 16.0 | 2880 | 0.4763 | 0.8480 | |
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| 0.2401 | 17.0 | 3060 | 0.4856 | 0.8510 | |
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| 0.2286 | 18.0 | 3240 | 0.4713 | 0.8578 | |
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| 0.2138 | 19.0 | 3420 | 0.4753 | 0.8500 | |
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| 0.2022 | 20.0 | 3600 | 0.4641 | 0.8544 | |
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| 0.1937 | 21.0 | 3780 | 0.4640 | 0.8598 | |
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| 0.1802 | 22.0 | 3960 | 0.4788 | 0.8505 | |
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| 0.1719 | 23.0 | 4140 | 0.4520 | 0.8593 | |
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| 0.17 | 24.0 | 4320 | 0.4703 | 0.8564 | |
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| 0.159 | 25.0 | 4500 | 0.4620 | 0.8554 | |
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| 0.1566 | 26.0 | 4680 | 0.4825 | 0.8549 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 1.14.0a0+410ce96 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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