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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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- f1
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model-index:
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- name: distilbert_add_GLUE_Experiment_logit_kd_qqp
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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: qqp
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split: validation
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args: qqp
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.6451150136037596
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- name: F1
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type: f1
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value: 0.07444200748290544
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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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# distilbert_add_GLUE_Experiment_logit_kd_qqp
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6730
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- Accuracy: 0.6451
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- F1: 0.0744
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- Combined Score: 0.3598
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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: 256
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- eval_batch_size: 256
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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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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
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| 0.7968 | 1.0 | 1422 | 0.7159 | 0.6323 | 0.0030 | 0.3176 |
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| 0.6542 | 2.0 | 2844 | 0.6925 | 0.6338 | 0.0115 | 0.3226 |
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| 0.5893 | 3.0 | 4266 | 0.6695 | 0.6348 | 0.0172 | 0.3260 |
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| 0.5538 | 4.0 | 5688 | 0.7068 | 0.6386 | 0.0393 | 0.3390 |
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| 0.5323 | 5.0 | 7110 | 0.6670 | 0.6500 | 0.1014 | 0.3757 |
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| 0.5181 | 6.0 | 8532 | 0.6738 | 0.6420 | 0.0573 | 0.3497 |
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| 0.5082 | 7.0 | 9954 | 0.6623 | 0.6425 | 0.0601 | 0.3513 |
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| 0.5012 | 8.0 | 11376 | 0.6995 | 0.6412 | 0.0536 | 0.3474 |
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| 0.4957 | 9.0 | 12798 | 0.6836 | 0.6472 | 0.0858 | 0.3665 |
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| 0.4911 | 10.0 | 14220 | 0.6778 | 0.6484 | 0.0922 | 0.3703 |
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| 0.4874 | 11.0 | 15642 | 0.7183 | 0.6415 | 0.0550 | 0.3483 |
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| 0.484 | 12.0 | 17064 | 0.6730 | 0.6451 | 0.0744 | 0.3598 |
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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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