metadata

language:
  - en
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - matthews_correlation
model-index:
  - name: electra-base-discriminator-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: 0.6579677841732349
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

electra-base-discriminator-CoLA

This model is a fine-tuned version of google/electra-base-discriminator on the GLUE COLA dataset. It achieves the following results on the evaluation set:

Loss: 0.3542
Matthews Correlation: 0.6580

Model description

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

{
    "epoch": 8.0,
    "eval_loss": 0.3541961908340454,
    "eval_matthews_correlation": 0.6579677841732349,
    "eval_runtime": 1.9552,
    "eval_samples": 1043,
    "eval_samples_per_second": 533.451,
    "eval_steps_per_second": 33.756
}

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: 8e-05
train_batch_size: 128
eval_batch_size: 16
seed: 22165
distributed_type: multi-GPU
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.03
num_epochs: 8.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Matthews Correlation
0.4004	1.0	67	0.3569	0.6340
0.2843	2.0	134	0.3542	0.6580
0.1228	3.0	201	0.4201	0.6412
0.0989	4.0	268	0.4780	0.6757
0.0681	5.0	335	0.4900	0.6925
0.0506	6.0	402	0.5837	0.6785
0.0093	7.0	469	0.6298	0.6652
0.0244	8.0	536	0.6292	0.6750

Framework versions

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