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README.md
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---
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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_add_GLUE_Experiment_logit_kd_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
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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: 0.7878440366972477
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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_add_GLUE_Experiment_logit_kd_sst2
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This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the glue dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.9595
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- Accuracy: 0.7878
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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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| 1.5405 | 1.0 | 527 | 1.4225 | 0.5539 |
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| 1.3567 | 2.0 | 1054 | 1.4707 | 0.5482 |
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| 1.2859 | 3.0 | 1581 | 1.4661 | 0.5677 |
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| 1.2563 | 4.0 | 2108 | 1.4136 | 0.5665 |
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| 1.2414 | 5.0 | 2635 | 1.4239 | 0.5940 |
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| 1.2288 | 6.0 | 3162 | 1.4443 | 0.5745 |
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| 0.7679 | 7.0 | 3689 | 0.7870 | 0.7878 |
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| 0.4135 | 8.0 | 4216 | 0.7778 | 0.8016 |
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| 0.3376 | 9.0 | 4743 | 0.8673 | 0.7993 |
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| 0.2972 | 10.0 | 5270 | 0.8790 | 0.7901 |
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| 0.2734 | 11.0 | 5797 | 0.9525 | 0.7913 |
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| 0.2569 | 12.0 | 6324 | 0.9557 | 0.7936 |
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| 0.2431 | 13.0 | 6851 | 0.9595 | 0.7878 |
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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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