metadata

language:
  - en
license: mit
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
base_model: microsoft/deberta-v3-small
model-index:
  - name: deberta-v3-small
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: GLUE QNLI
          type: glue
          args: qnli
        metrics:
          - type: accuracy
            value: 0.9150649826102873
            name: Accuracy
      - task:
          type: natural-language-inference
          name: Natural Language Inference
        dataset:
          name: glue
          type: glue
          config: qnli
          split: validation
        metrics:
          - type: accuracy
            value: 0.914881933003844
            name: Accuracy
            verified: true
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          - type: precision
            value: 0.9195906432748538
            name: Precision
            verified: true
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          - type: recall
            value: 0.9112640347700108
            name: Recall
            verified: true
            verifyToken: >-
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          - type: auc
            value: 0.9718281171793548
            name: AUC
            verified: true
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          - type: f1
            value: 0.9154084045843187
            name: F1
            verified: true
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          - type: loss
            value: 0.21421395242214203
            name: loss
            verified: true
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DeBERTa-v3-small fine-tuned on QNLI

This model is a fine-tuned version of microsoft/deberta-v3-small on the GLUE QNLI dataset. It achieves the following results on the evaluation set:

Loss: 0.2143
Accuracy: 0.9151

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 3e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.2823	1.0	6547	0.2143	0.9151
0.1996	2.0	13094	0.2760	0.9103
0.1327	3.0	19641	0.3293	0.9169
0.0811	4.0	26188	0.4278	0.9193
0.05	5.0	32735	0.5110	0.9176

Framework versions

Transformers 4.13.0.dev0
Pytorch 1.10.0+cu111
Datasets 1.16.1
Tokenizers 0.10.3