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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: qnli |
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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 QNLI |
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type: glue |
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args: qnli |
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metric: |
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name: Accuracy |
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type: accuracy |
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value: 0.9137836353651839 |
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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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# qnli |
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This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE QNLI dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3608 |
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- Accuracy: 0.9138 |
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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: 1e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 8 |
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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: 4.0 |
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### Training results |
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### Framework versions |
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- Transformers 4.9.1 |
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- Pytorch 1.9.0+cu102 |
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- Datasets 1.10.2 |
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- Tokenizers 0.10.3 |
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