README.md

metadata

language:
  - en
license: mit
tags:
  - generated_from_trainer
  - deberta-v3
datasets:
  - glue
metrics:
  - accuracy
model-index:
  - name: deberta-v3-small
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: GLUE SST2
          type: glue
          args: sst2
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9403669724770642
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: glue
          type: glue
          config: sst2
          split: validation
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9403669724770642
            verified: true
          - name: Precision
            type: precision
            value: 0.9375
            verified: true
          - name: Recall
            type: recall
            value: 0.9459459459459459
            verified: true
          - name: AUC
            type: auc
            value: 0.9804217184474193
            verified: true
          - name: F1
            type: f1
            value: 0.9417040358744394
            verified: true
          - name: loss
            type: loss
            value: 0.21338027715682983
            verified: true

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