metadata

language:
  - en
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - nyu-mll/glue
metrics:
  - accuracy
  - f1
model-index:
  - name: mrpc
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: GLUE MRPC
          type: glue
          args: mrpc
        metrics:
          - type: accuracy
            value: 0.8823529411764706
            name: Accuracy
          - type: f1
            value: 0.9180887372013652
            name: F1

mrpc

This model is a fine-tuned version of bert-large-uncased-whole-word-masking on the GLUE MRPC dataset. It achieves the following results on the evaluation set:

Loss: 0.3680
Accuracy: 0.8824
F1: 0.9181
Combined Score: 0.9002

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: 3e-05
train_batch_size: 32
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
lr_scheduler_type: linear
num_epochs: 3.0

Training results

Framework versions

Transformers 4.18.0
Pytorch 1.10.0a0+gitfe03f8c
Datasets 2.1.0
Tokenizers 0.12.1