gokuls / mobilebert_sa_GLUE_Experiment_mrpc_256

huggingface.co
Total runs: 15
24-hour runs: 0
7-day runs: 1
30-day runs: 3
Model's Last Updated: January 25 2023
text-classification

Introduction of mobilebert_sa_GLUE_Experiment_mrpc_256

Model Details of mobilebert_sa_GLUE_Experiment_mrpc_256

mobilebert_sa_GLUE_Experiment_mrpc_256

This model is a fine-tuned version of google/mobilebert-uncased on the GLUE MRPC dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6111
  • Accuracy: 0.6912
  • F1: 0.7948
  • Combined Score: 0.7430
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: 128
  • eval_batch_size: 128
  • seed: 10
  • distributed_type: multi-GPU
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 50
Training results
Training Loss Epoch Step Validation Loss Accuracy F1 Combined Score
0.6431 1.0 29 0.6261 0.6838 0.8122 0.7480
0.6296 2.0 58 0.6235 0.6838 0.8122 0.7480
0.6306 3.0 87 0.6237 0.6838 0.8122 0.7480
0.6297 4.0 116 0.6238 0.6838 0.8122 0.7480
0.6276 5.0 145 0.6207 0.6838 0.8122 0.7480
0.6197 6.0 174 0.6213 0.6838 0.8122 0.7480
0.6065 7.0 203 0.6284 0.6912 0.8043 0.7478
0.5258 8.0 232 0.6111 0.6912 0.7948 0.7430
0.4596 9.0 261 0.6506 0.7034 0.8052 0.7543
0.3953 10.0 290 0.7271 0.7034 0.7932 0.7483
0.3426 11.0 319 0.9509 0.6740 0.7542 0.7141
0.2821 12.0 348 1.0021 0.6863 0.7808 0.7335
0.2177 13.0 377 1.0359 0.6691 0.7676 0.7184
Framework versions
  • Transformers 4.26.0
  • Pytorch 1.14.0a0+410ce96
  • Datasets 2.8.0
  • Tokenizers 0.13.2

Runs of gokuls mobilebert_sa_GLUE_Experiment_mrpc_256 on huggingface.co

15
Total runs
0
24-hour runs
2
3-day runs
1
7-day runs
3
30-day runs

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mobilebert_sa_GLUE_Experiment_mrpc_256 is an open source model from GitHub that offers a free installation service, and any user can find mobilebert_sa_GLUE_Experiment_mrpc_256 on GitHub to install. At the same time, huggingface.co provides the effect of mobilebert_sa_GLUE_Experiment_mrpc_256 install, users can directly use mobilebert_sa_GLUE_Experiment_mrpc_256 installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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