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--- |
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language: |
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- en |
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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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- glue |
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metrics: |
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- accuracy |
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model-index: |
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- name: deberta-v3-small |
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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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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9150649826102873 |
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- task: |
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type: natural-language-inference |
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name: Natural Language Inference |
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dataset: |
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name: glue |
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type: glue |
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config: qnli |
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split: validation |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.914881933003844 |
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verified: true |
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- name: Precision |
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type: precision |
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value: 0.9195906432748538 |
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verified: true |
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- name: Recall |
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type: recall |
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value: 0.9112640347700108 |
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verified: true |
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- name: AUC |
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type: auc |
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value: 0.9718281171793548 |
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verified: true |
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- name: F1 |
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type: f1 |
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value: 0.9154084045843187 |
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verified: true |
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- name: loss |
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type: loss |
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value: 0.21421395242214203 |
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verified: true |
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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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# DeBERTa-v3-small fine-tuned on QNLI |
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This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the GLUE QNLI dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2143 |
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- Accuracy: 0.9151 |
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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: 3e-05 |
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- train_batch_size: 16 |
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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: 5.0 |
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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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| 0.2823 | 1.0 | 6547 | 0.2143 | 0.9151 | |
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| 0.1996 | 2.0 | 13094 | 0.2760 | 0.9103 | |
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| 0.1327 | 3.0 | 19641 | 0.3293 | 0.9169 | |
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| 0.0811 | 4.0 | 26188 | 0.4278 | 0.9193 | |
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| 0.05 | 5.0 | 32735 | 0.5110 | 0.9176 | |
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### Framework versions |
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- Transformers 4.13.0.dev0 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 1.16.1 |
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- Tokenizers 0.10.3 |
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