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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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- matthews_correlation |
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model-index: |
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- name: mobilebert_add_GLUE_Experiment_cola |
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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 COLA |
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type: glue |
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config: cola |
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split: validation |
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args: cola |
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metrics: |
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- name: Matthews Correlation |
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type: matthews_correlation |
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value: 0.0 |
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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_cola |
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This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE COLA dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6127 |
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- Matthews Correlation: 0.0 |
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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 | Matthews Correlation | |
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|:-------------:|:-----:|:----:|:---------------:|:--------------------:| |
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| 0.6126 | 1.0 | 67 | 0.6183 | 0.0 | |
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| 0.6078 | 2.0 | 134 | 0.6179 | 0.0 | |
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| 0.6072 | 3.0 | 201 | 0.6183 | 0.0 | |
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| 0.6062 | 4.0 | 268 | 0.6164 | 0.0 | |
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| 0.601 | 5.0 | 335 | 0.6127 | 0.0 | |
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| 0.5928 | 6.0 | 402 | 0.6148 | 0.0 | |
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| 0.588 | 7.0 | 469 | 0.6224 | 0.0 | |
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| 0.582 | 8.0 | 536 | 0.6174 | 0.0029 | |
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| 0.5807 | 9.0 | 603 | 0.6301 | 0.0029 | |
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| 0.5743 | 10.0 | 670 | 0.6156 | 0.0438 | |
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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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