gokulsrinivasagan / distilbert_base_train_mnli

huggingface.co
Total runs: 4
24-hour runs: 0
7-day runs: 0
30-day runs: -5
Model's Last Updated: January 28 2025
text-classification

Introduction of distilbert_base_train_mnli

Model Details of distilbert_base_train_mnli

distilbert_base_train_mnli

This model is a fine-tuned version of gokulsrinivasagan/distilbert_base_train on the GLUE MNLI dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7366
  • Accuracy: 0.6847
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: 256
  • eval_batch_size: 256
  • seed: 10
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 50
Training results
Training Loss Epoch Step Validation Loss Accuracy
0.9464 1.0 1534 0.8279 0.6259
0.7887 2.0 3068 0.7683 0.6624
0.6959 3.0 4602 0.7424 0.6854
0.6184 4.0 6136 0.7529 0.6899
0.5414 5.0 7670 0.7874 0.6879
0.4648 6.0 9204 0.8281 0.6882
0.3943 7.0 10738 0.9039 0.6865
0.3321 8.0 12272 1.0392 0.6811
Framework versions
  • Transformers 4.46.3
  • Pytorch 2.2.1+cu118
  • Datasets 2.17.0
  • Tokenizers 0.20.3

Runs of gokulsrinivasagan distilbert_base_train_mnli on huggingface.co

4
Total runs
0
24-hour runs
0
3-day runs
0
7-day runs
-5
30-day runs

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https://huggingface.co/gokulsrinivasagan/distilbert_base_train_mnli

distilbert_base_train_mnli install

distilbert_base_train_mnli is an open source model from GitHub that offers a free installation service, and any user can find distilbert_base_train_mnli on GitHub to install. At the same time, huggingface.co provides the effect of distilbert_base_train_mnli install, users can directly use distilbert_base_train_mnli installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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https://huggingface.co/gokulsrinivasagan/distilbert_base_train_mnli

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