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--- |
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base_model: gokuls/model_v1_complete_training_wt_init_48_tiny_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-tiny |
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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: 0.8465322183964584 |
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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-tiny |
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This model is a fine-tuned version of [gokuls/model_v1_complete_training_wt_init_48_tiny_freeze_new](https://huggingface.co/gokuls/model_v1_complete_training_wt_init_48_tiny_freeze_new) on the massive dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5468 |
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- Accuracy: 0.8465 |
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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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| 4.0471 | 1.0 | 180 | 3.2580 | 0.2258 | |
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| 2.9727 | 2.0 | 360 | 2.3478 | 0.3778 | |
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| 2.3183 | 3.0 | 540 | 1.8643 | 0.5081 | |
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| 1.9162 | 4.0 | 720 | 1.5331 | 0.6375 | |
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| 1.6284 | 5.0 | 900 | 1.3079 | 0.6931 | |
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| 1.4163 | 6.0 | 1080 | 1.1495 | 0.7241 | |
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| 1.263 | 7.0 | 1260 | 1.0287 | 0.7437 | |
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| 1.1491 | 8.0 | 1440 | 0.9566 | 0.7575 | |
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| 1.0652 | 9.0 | 1620 | 0.8881 | 0.7644 | |
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| 0.9661 | 10.0 | 1800 | 0.8426 | 0.7801 | |
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| 0.9077 | 11.0 | 1980 | 0.7980 | 0.7796 | |
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| 0.8466 | 12.0 | 2160 | 0.7675 | 0.7919 | |
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| 0.7996 | 13.0 | 2340 | 0.7422 | 0.7934 | |
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| 0.7605 | 14.0 | 2520 | 0.7323 | 0.7954 | |
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| 0.7156 | 15.0 | 2700 | 0.6864 | 0.8067 | |
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| 0.6867 | 16.0 | 2880 | 0.6730 | 0.8131 | |
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| 0.6493 | 17.0 | 3060 | 0.6548 | 0.8160 | |
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| 0.6245 | 18.0 | 3240 | 0.6495 | 0.8136 | |
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| 0.6038 | 19.0 | 3420 | 0.6282 | 0.8224 | |
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| 0.57 | 20.0 | 3600 | 0.6123 | 0.8224 | |
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| 0.556 | 21.0 | 3780 | 0.6020 | 0.8308 | |
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| 0.5334 | 22.0 | 3960 | 0.5943 | 0.8298 | |
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| 0.5101 | 23.0 | 4140 | 0.5778 | 0.8323 | |
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| 0.4948 | 24.0 | 4320 | 0.5740 | 0.8337 | |
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| 0.4824 | 25.0 | 4500 | 0.5772 | 0.8337 | |
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| 0.4728 | 26.0 | 4680 | 0.5712 | 0.8342 | |
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| 0.4596 | 27.0 | 4860 | 0.5691 | 0.8337 | |
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| 0.4436 | 28.0 | 5040 | 0.5670 | 0.8396 | |
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| 0.4367 | 29.0 | 5220 | 0.5542 | 0.8367 | |
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| 0.4249 | 30.0 | 5400 | 0.5512 | 0.8406 | |
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| 0.4117 | 31.0 | 5580 | 0.5450 | 0.8387 | |
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| 0.4051 | 32.0 | 5760 | 0.5468 | 0.8465 | |
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| 0.4 | 33.0 | 5940 | 0.5464 | 0.8401 | |
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| 0.3939 | 34.0 | 6120 | 0.5451 | 0.8446 | |
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| 0.3801 | 35.0 | 6300 | 0.5387 | 0.8441 | |
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| 0.3708 | 36.0 | 6480 | 0.5353 | 0.8421 | |
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| 0.3686 | 37.0 | 6660 | 0.5320 | 0.8455 | |
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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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