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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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- deberta-v3 |
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datasets: |
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- glue |
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metrics: |
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- accuracy |
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- f1 |
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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 MRPC |
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type: glue |
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args: mrpc |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8921568627450981 |
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- name: F1 |
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type: f1 |
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value: 0.9233449477351917 |
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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: mrpc |
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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: 0.8921568627450981 |
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verified: true |
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- name: Precision |
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type: precision |
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value: 0.8983050847457628 |
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verified: true |
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- name: Recall |
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type: recall |
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value: 0.9498207885304659 |
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verified: true |
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- name: AUC |
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type: auc |
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value: 0.9516129032258065 |
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verified: true |
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- name: F1 |
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type: f1 |
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value: 0.9233449477351917 |
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verified: true |
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- name: loss |
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type: loss |
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value: 0.2787226438522339 |
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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 MRPC |
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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 MRPC dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2787 |
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- Accuracy: 0.8922 |
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- F1: 0.9233 |
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- Combined Score: 0.9078 |
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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: 10.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| |
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| No log | 1.0 | 230 | 0.2787 | 0.8922 | 0.9233 | 0.9078 | |
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| No log | 2.0 | 460 | 0.3651 | 0.875 | 0.9137 | 0.8944 | |
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| No log | 3.0 | 690 | 0.5238 | 0.8799 | 0.9179 | 0.8989 | |
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| No log | 4.0 | 920 | 0.4712 | 0.8946 | 0.9222 | 0.9084 | |
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| 0.2147 | 5.0 | 1150 | 0.5704 | 0.8946 | 0.9262 | 0.9104 | |
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| 0.2147 | 6.0 | 1380 | 0.5697 | 0.8995 | 0.9284 | 0.9140 | |
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| 0.2147 | 7.0 | 1610 | 0.6651 | 0.8922 | 0.9214 | 0.9068 | |
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| 0.2147 | 8.0 | 1840 | 0.6726 | 0.8946 | 0.9239 | 0.9093 | |
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| 0.0183 | 9.0 | 2070 | 0.7250 | 0.8848 | 0.9177 | 0.9012 | |
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| 0.0183 | 10.0 | 2300 | 0.7093 | 0.8922 | 0.9223 | 0.9072 | |
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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 1.15.1 |
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- Tokenizers 0.10.3 |
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