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End of training
41e5f6f
metadata
license: apache-2.0
base_model: t5-base
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
model-index:
  - name: t5-base_cola_dense_epochs-5
    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: 0.822627037392138

t5-base_cola_dense_epochs-5

This model is a fine-tuned version of t5-base on the glue dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5026
  • Accuracy: 0.8226

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: 5e-05
  • train_batch_size: 32
  • eval_batch_size: 64
  • seed: 0
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 20
  • num_epochs: 5

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.5712 0.19 50 0.5805 0.6913
0.4693 0.37 100 0.6260 0.7661
0.4731 0.56 150 0.5279 0.8054
0.3707 0.75 200 0.5165 0.8025
0.4729 0.93 250 0.5145 0.8102
0.3929 1.12 300 0.4773 0.8188
0.3369 1.31 350 0.5014 0.8198
0.3757 1.49 400 0.5183 0.8188
0.4206 1.68 450 0.5743 0.8198
0.4196 1.87 500 0.5026 0.8226
0.3098 2.05 550 0.5289 0.8236
0.2852 2.24 600 0.5562 0.8265
0.2936 2.43 650 0.5312 0.8303
0.2072 2.61 700 0.4904 0.8313
0.2809 2.8 750 0.5394 0.8341
0.2685 2.99 800 0.5905 0.8332
0.2215 3.17 850 0.5835 0.8341
0.3543 3.36 900 0.5556 0.8332
0.239 3.54 950 0.5419 0.8351
0.257 3.73 1000 0.5587 0.8351
0.2958 3.92 1050 0.5982 0.8341
0.2785 4.1 1100 0.5978 0.8360
0.1975 4.29 1150 0.6067 0.8341
0.2222 4.48 1200 0.5947 0.8380

Framework versions

  • Transformers 4.34.1
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.6
  • Tokenizers 0.14.1