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--- |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- glue |
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metrics: |
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- accuracy |
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model-index: |
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- name: mobilebert_sa_GLUE_Experiment_logit_kd_pretrain_sst2 |
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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: GLUE SST2 |
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type: glue |
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config: sst2 |
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split: validation |
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args: sst2 |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.926605504587156 |
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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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# mobilebert_sa_GLUE_Experiment_logit_kd_pretrain_sst2 |
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This model is a fine-tuned version of [gokuls/mobilebert_sa_pre-training-complete](https://huggingface.co/gokuls/mobilebert_sa_pre-training-complete) on the GLUE SST2 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2364 |
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- Accuracy: 0.9266 |
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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: 128 |
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- eval_batch_size: 128 |
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- seed: 10 |
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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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| 0.4176 | 1.0 | 527 | 0.2978 | 0.9197 | |
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| 0.1807 | 2.0 | 1054 | 0.2951 | 0.9174 | |
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| 0.1163 | 3.0 | 1581 | 0.2749 | 0.9186 | |
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| 0.0862 | 4.0 | 2108 | 0.2988 | 0.9083 | |
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| 0.0695 | 5.0 | 2635 | 0.2760 | 0.9174 | |
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| 0.0598 | 6.0 | 3162 | 0.2695 | 0.9151 | |
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| 0.0525 | 7.0 | 3689 | 0.2723 | 0.9255 | |
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| 0.0464 | 8.0 | 4216 | 0.2430 | 0.9243 | |
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| 0.0422 | 9.0 | 4743 | 0.2814 | 0.9243 | |
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| 0.0395 | 10.0 | 5270 | 0.2464 | 0.9163 | |
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| 0.0357 | 11.0 | 5797 | 0.2390 | 0.9197 | |
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| 0.0341 | 12.0 | 6324 | 0.2713 | 0.9197 | |
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| 0.0328 | 13.0 | 6851 | 0.2685 | 0.9220 | |
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| 0.0315 | 14.0 | 7378 | 0.2585 | 0.9186 | |
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| 0.0296 | 15.0 | 7905 | 0.2367 | 0.9220 | |
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| 0.0283 | 16.0 | 8432 | 0.2560 | 0.9186 | |
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| 0.0277 | 17.0 | 8959 | 0.2635 | 0.9174 | |
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| 0.0269 | 18.0 | 9486 | 0.2364 | 0.9266 | |
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| 0.026 | 19.0 | 10013 | 0.2749 | 0.9209 | |
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| 0.0252 | 20.0 | 10540 | 0.2507 | 0.9174 | |
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| 0.0248 | 21.0 | 11067 | 0.2769 | 0.9163 | |
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| 0.0248 | 22.0 | 11594 | 0.2543 | 0.9220 | |
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| 0.024 | 23.0 | 12121 | 0.2677 | 0.9209 | |
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### Framework versions |
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- Transformers 4.26.0 |
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- Pytorch 1.14.0a0+410ce96 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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