metadata

language:
  - en
license: apache-2.0
base_model: bert-base-cased
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - matthews_correlation
model-index:
  - name: cola
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: GLUE COLA
          type: glue
          args: cola
        metrics:
          - name: Matthews Correlation
            type: matthews_correlation
            value: 0.5778184033685675

bert-base-cased-cola

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

Loss: 0.5006
Matthews Correlation: 0.5778

Model description

Please refer to this repository.

Intended uses

This model is for the artifact evaluation of the paper "SHAFT: Secure, Handy, Accurate, and Fast Transformer Inference."

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 64
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3.0

Framework versions

Transformers 4.42.0.dev0
Pytorch 2.0.1+cu118
Datasets 2.20.0
Tokenizers 0.19.1