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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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model-index: |
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- name: mobilebert_sa_GLUE_Experiment_mnli_256 |
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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 MNLI |
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type: glue |
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config: mnli |
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split: validation_matched |
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args: mnli |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.6030309194467046 |
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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_mnli_256 |
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This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MNLI dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8790 |
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- Accuracy: 0.6030 |
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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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| 1.0008 | 1.0 | 3068 | 0.9490 | 0.5405 | |
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| 0.9205 | 2.0 | 6136 | 0.9166 | 0.5675 | |
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| 0.8928 | 3.0 | 9204 | 0.9022 | 0.5786 | |
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| 0.872 | 4.0 | 12272 | 0.8843 | 0.5967 | |
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| 0.8531 | 5.0 | 15340 | 0.8807 | 0.5959 | |
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| 0.8359 | 6.0 | 18408 | 0.8763 | 0.5999 | |
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| 0.8197 | 7.0 | 21476 | 0.8815 | 0.6009 | |
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| 0.8028 | 8.0 | 24544 | 0.9012 | 0.5934 | |
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| 0.786 | 9.0 | 27612 | 0.8633 | 0.6191 | |
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| 0.769 | 10.0 | 30680 | 0.8734 | 0.6098 | |
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| 0.752 | 11.0 | 33748 | 0.8682 | 0.6220 | |
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| 0.736 | 12.0 | 36816 | 0.8741 | 0.6175 | |
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| 0.7204 | 13.0 | 39884 | 0.8994 | 0.6048 | |
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| 0.7038 | 14.0 | 42952 | 0.8940 | 0.6079 | |
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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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