metadata

license: apache-2.0
tags:
  - classification
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
model-index:
  - name: sentence-acceptability
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: glue
          type: glue
          config: cola
          split: validation
          args: cola
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8216682646212847

sentence-acceptability

This model is a fine-tuned version of bert-base-uncased on the glue dataset. It achieves the following results on the evaluation set:

Loss: 0.8257
Accuracy: 0.8217

Model description

This model classifies English sentences according to two different labels: 1 if the sentence is grammatically acceptable and 0 if the sentence is grammatically unacceptable.

Training and evaluation data

The model was trained on the "cola" split of the glue dataset, using the 8551 instances of its "train" split. For the evaluation, the 1043 sentences of the "evaluation" split were used.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.4868	1.0	1069	0.6279	0.7862
0.3037	2.0	2138	0.6184	0.8140
0.177	3.0	3207	0.8257	0.8217

Framework versions

Transformers 4.26.1
Pytorch 1.13.1+cu116
Datasets 2.9.0
Tokenizers 0.13.2