README.md

metadata

language:
  - en
license: mit
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - matthews_correlation
model-index:
  - name: deberta-v3-xsmall-CoLA
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: GLUE COLA
          type: glue
          config: cola
          split: validation
          args: cola
        metrics:
          - name: Matthews Correlation
            type: matthews_correlation
            value: 0.5894856058137782
widget:
  - text: The cat sat on the mat.
    example_title: Correct grammatical sentence
  - text: Me and my friend going to the store.
    example_title: Incorrect subject-verb agreement
  - text: I ain't got no money.
    example_title: Incorrect verb conjugation and double negative
  - text: She don't like pizza no more.
    example_title: Incorrect verb conjugation and double negative
  - text: They is arriving tomorrow.
    example_title: Incorrect verb conjugation

deberta-v3-xsmall-CoLA

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

• Loss: 0.4237
• Matthews Correlation: 0.5895

Model description

Trying to find a decent optimum between accuracy/quality and inference speed.

{
    "epoch": 3.0,
    "eval_loss": 0.423,
    "eval_matthews_correlation": 0.589,
    "eval_runtime": 5.0422,
    "eval_samples": 1043,
    "eval_samples_per_second": 206.853,
    "eval_steps_per_second": 51.763
}

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: 6e-05
• train_batch_size: 32
• eval_batch_size: 4
• seed: 16105
• distributed_type: multi-GPU
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.03
• num_epochs: 3.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Matthews Correlation
0.3945	1.0	67	0.4323	0.5778
0.3214	2.0	134	0.4237	0.5895
0.3059	3.0	201	0.4636	0.5795

Framework versions

• Transformers 4.27.0.dev0
• Pytorch 1.13.1+cu117
• Datasets 2.8.0
• Tokenizers 0.13.1