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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_logit_kd_pretrain_rte |
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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 RTE |
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type: glue |
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config: rte |
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split: validation |
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args: rte |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.5451263537906137 |
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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_pretrain_rte |
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This model is a fine-tuned version of [gokuls/mobilebert_sa_pre-training-complete](https://huggingface.co/gokuls/mobilebert_sa_pre-training-complete) on the GLUE RTE dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3884 |
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- Accuracy: 0.5451 |
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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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| 0.4107 | 1.0 | 20 | 0.3951 | 0.5126 | |
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| 0.3757 | 2.0 | 40 | 0.3914 | 0.4982 | |
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| 0.347 | 3.0 | 60 | 0.3884 | 0.5451 | |
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| 0.3072 | 4.0 | 80 | 0.4022 | 0.5126 | |
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| 0.2762 | 5.0 | 100 | 0.4116 | 0.5271 | |
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| 0.2457 | 6.0 | 120 | 0.4073 | 0.5271 | |
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| 0.2215 | 7.0 | 140 | 0.4115 | 0.5487 | |
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| 0.2059 | 8.0 | 160 | 0.4231 | 0.5343 | |
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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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