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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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- f1 |
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model-index: |
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- name: mobilebert_sa_GLUE_Experiment_logit_kd_qqp_128 |
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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 QQP |
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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: 0.7871877318822657 |
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- name: F1 |
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type: f1 |
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value: 0.7061676115019466 |
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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_qqp_128 |
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This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE QQP dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6884 |
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- Accuracy: 0.7872 |
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- F1: 0.7062 |
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- Combined Score: 0.7467 |
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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 | F1 | Combined Score | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| |
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| 0.9518 | 1.0 | 2843 | 0.8352 | 0.7536 | 0.6530 | 0.7033 | |
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| 0.8249 | 2.0 | 5686 | 0.7766 | 0.7607 | 0.6219 | 0.6913 | |
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| 0.7847 | 3.0 | 8529 | 0.7625 | 0.7648 | 0.6402 | 0.7025 | |
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| 0.7498 | 4.0 | 11372 | 0.7551 | 0.7638 | 0.6197 | 0.6917 | |
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| 0.7137 | 5.0 | 14215 | 0.7387 | 0.7691 | 0.6545 | 0.7118 | |
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| 0.6762 | 6.0 | 17058 | 0.7165 | 0.7753 | 0.6720 | 0.7237 | |
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| 0.6373 | 7.0 | 19901 | 0.7042 | 0.7783 | 0.6765 | 0.7274 | |
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| 0.6045 | 8.0 | 22744 | 0.7075 | 0.7799 | 0.6902 | 0.7350 | |
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| 0.5729 | 9.0 | 25587 | 0.7233 | 0.7792 | 0.6639 | 0.7215 | |
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| 0.545 | 10.0 | 28430 | 0.7088 | 0.7805 | 0.7180 | 0.7493 | |
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| 0.5183 | 11.0 | 31273 | 0.6884 | 0.7872 | 0.7062 | 0.7467 | |
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| 0.4948 | 12.0 | 34116 | 0.7064 | 0.7869 | 0.7076 | 0.7472 | |
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| 0.4724 | 13.0 | 36959 | 0.7053 | 0.7884 | 0.7120 | 0.7502 | |
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| 0.4514 | 14.0 | 39802 | 0.7314 | 0.7903 | 0.7024 | 0.7464 | |
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| 0.4321 | 15.0 | 42645 | 0.7112 | 0.7891 | 0.7228 | 0.7560 | |
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| 0.4152 | 16.0 | 45488 | 0.7410 | 0.7909 | 0.7211 | 0.7560 | |
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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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