README.md

metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - super_glue
metrics:
  - accuracy
base_model: microsoft/deberta-v3-base
model-index:
  - name: yes_no_qna_deberta_model
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: super_glue
          type: super_glue
          config: boolq
          split: train
          args: boolq
        metrics:
          - type: accuracy
            value: 0.8507645259938837
            name: Accuracy

yes_no_qna_deberta_model

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

• Loss: 0.5570
• Accuracy: 0.8508

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: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.583	1.0	590	0.4086	0.8251
0.348	2.0	1180	0.4170	0.8465
0.2183	3.0	1770	0.5570	0.8508

Framework versions

• Transformers 4.25.1
• Pytorch 1.13.0+cu116
• Datasets 2.8.0
• Tokenizers 0.13.2