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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- super_glue |
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model-index: |
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- name: qna2_deberta_model |
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results: [] |
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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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# boolq_deberta_model |
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This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the super_glue - boolq dataset. |
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It achieves the following results on the evaluation set: |
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- eval_loss: 0.4066 |
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- eval_accuracy: 0.8468 |
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- eval_runtime: 111.0255 |
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- eval_samples_per_second: 29.453 |
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- eval_steps_per_second: 1.846 |
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- epoch: 2.0 |
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- step: 1180 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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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: 3 |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.0+cu116 |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.2 |
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