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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
  - f1
model-index:
  - name: albert-base-v2-finetuned-mrpc
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: glue
          type: glue
          config: mrpc
          split: validation
          args: mrpc
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8627450980392157
          - name: F1
            type: f1
            value: 0.900709219858156

albert-base-v2-finetuned-mrpc

This model is a fine-tuned version of albert-base-v2 on the glue dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5610
  • Accuracy: 0.8627
  • F1: 0.9007

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: 2e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 95
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 5

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
No log 1.0 115 0.3358 0.8676 0.9004
No log 2.0 230 0.3140 0.8676 0.9029
No log 3.0 345 0.3763 0.8897 0.9201
No log 4.0 460 0.4980 0.8725 0.9085
0.2512 5.0 575 0.5610 0.8627 0.9007

Framework versions

  • Transformers 4.28.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.13.3