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--- |
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language: |
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- en |
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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: hBERTv2_new_no_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: 0.5306859205776173 |
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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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# hBERTv2_new_no_pretrain_rte |
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This model is a fine-tuned version of [](https://huggingface.co/) on the GLUE RTE dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6981 |
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- Accuracy: 0.5307 |
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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: 4e-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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| 0.7697 | 1.0 | 20 | 0.7526 | 0.5271 | |
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| 0.7285 | 2.0 | 40 | 0.7208 | 0.5271 | |
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| 0.7201 | 3.0 | 60 | 0.7112 | 0.5343 | |
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| 0.7043 | 4.0 | 80 | 0.6981 | 0.5307 | |
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| 0.6569 | 5.0 | 100 | 0.7251 | 0.5235 | |
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| 0.5762 | 6.0 | 120 | 0.8571 | 0.4765 | |
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| 0.4336 | 7.0 | 140 | 0.9540 | 0.4765 | |
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| 0.3299 | 8.0 | 160 | 1.2464 | 0.4838 | |
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| 0.2561 | 9.0 | 180 | 1.4299 | 0.5018 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 1.14.0a0+410ce96 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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