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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_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: 0.7784071234232006 |
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- name: F1 |
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type: f1 |
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value: 0.6885884111369878 |
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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_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: 0.4700 |
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- Accuracy: 0.7784 |
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- F1: 0.6886 |
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- Combined Score: 0.7335 |
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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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| 0.5294 | 1.0 | 2843 | 0.5076 | 0.7512 | 0.6636 | 0.7074 | |
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| 0.4791 | 2.0 | 5686 | 0.4889 | 0.7613 | 0.6369 | 0.6991 | |
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| 0.4622 | 3.0 | 8529 | 0.4821 | 0.7657 | 0.6475 | 0.7066 | |
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| 0.4463 | 4.0 | 11372 | 0.4831 | 0.7694 | 0.6730 | 0.7212 | |
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| 0.4288 | 5.0 | 14215 | 0.4724 | 0.7752 | 0.6784 | 0.7268 | |
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| 0.4129 | 6.0 | 17058 | 0.4806 | 0.7749 | 0.6893 | 0.7321 | |
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| 0.3969 | 7.0 | 19901 | 0.4700 | 0.7784 | 0.6886 | 0.7335 | |
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| 0.3813 | 8.0 | 22744 | 0.4802 | 0.7790 | 0.6962 | 0.7376 | |
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| 0.3664 | 9.0 | 25587 | 0.4765 | 0.7805 | 0.6952 | 0.7378 | |
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| 0.352 | 10.0 | 28430 | 0.4965 | 0.7768 | 0.7086 | 0.7427 | |
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| 0.3381 | 11.0 | 31273 | 0.4895 | 0.7845 | 0.6960 | 0.7403 | |
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| 0.3258 | 12.0 | 34116 | 0.5092 | 0.7844 | 0.7043 | 0.7444 | |
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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 2.8.0 |
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- Tokenizers 0.13.2 |
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