README.md

metadata

language:
  - en
license: mit
tags:
  - generated_from_trainer
  - deberta-v3
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 SST2
          type: glue
          args: sst2
        metrics:
          - type: accuracy
            value: 0.9403669724770642
            name: Accuracy
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: glue
          type: glue
          config: sst2
          split: validation
        metrics:
          - type: accuracy
            value: 0.9403669724770642
            name: Accuracy
            verified: true
            verifyToken: >-
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          - type: precision
            value: 0.9375
            name: Precision
            verified: true
            verifyToken: >-
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          - type: recall
            value: 0.9459459459459459
            name: Recall
            verified: true
            verifyToken: >-
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          - type: auc
            value: 0.9804217184474193
            name: AUC
            verified: true
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          - type: f1
            value: 0.9417040358744394
            name: F1
            verified: true
            verifyToken: >-
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          - type: loss
            value: 0.21338027715682983
            name: loss
            verified: true
            verifyToken: >-
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DeBERTa v3 (small) fine-tuned on SST2

This model is a fine-tuned version of microsoft/deberta-v3-small on the GLUE SST2 dataset. It achieves the following results on the evaluation set:

• Loss: 0.2134
• Accuracy: 0.9404

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
0.176	1.0	4210	0.2134	0.9404
0.1254	2.0	8420	0.2362	0.9415
0.0957	3.0	12630	0.3187	0.9335
0.0673	4.0	16840	0.3039	0.9266
0.0457	5.0	21050	0.3521	0.9312

Framework versions

• Transformers 4.13.0.dev0
• Pytorch 1.10.0+cu111
• Datasets 1.15.1
• Tokenizers 0.10.3