philschmid / MiniLMv2-L6-H768-sst2

huggingface.co
Total runs: 4
24-hour runs: 0
7-day runs: 2
30-day runs: 1
Model's Last Updated: April 08 2022
text-classification

Introduction of MiniLMv2-L6-H768-sst2

Model Details of MiniLMv2-L6-H768-sst2

MiniLMv2-L6-H768-sst2

This model is a fine-tuned version of nreimers/MiniLMv2-L6-H768-distilled-from-RoBERTa-Large on the glue dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2013
  • Accuracy: 0.9427
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: 32
  • seed: 42
  • distributed_type: sagemaker_data_parallel
  • num_devices: 8
  • total_train_batch_size: 256
  • total_eval_batch_size: 256
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 5
  • mixed_precision_training: Native AMP
Training results
Training Loss Epoch Step Validation Loss Accuracy
0.4734 1.0 264 0.2046 0.9243
0.2399 2.0 528 0.1912 0.9346
0.1791 3.0 792 0.1943 0.9335
0.1442 4.0 1056 0.2103 0.9369
0.1217 5.0 1320 0.2013 0.9427
Framework versions
  • Transformers 4.17.0
  • Pytorch 1.10.2+cu113
  • Datasets 1.18.4
  • Tokenizers 0.11.6

Runs of philschmid MiniLMv2-L6-H768-sst2 on huggingface.co

4
Total runs
0
24-hour runs
0
3-day runs
2
7-day runs
1
30-day runs

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