bert-base-uncased-sst2-PTQ

This model conducts simple post training quantization of textattack/bert-base-uncased-SST-2 on the GLUE SST2 dataset. It achieves the following results on the evaluation set:

• torch loss: 0.2140
• torch accuracy: 0.9243
• OpenVINO IR accuracy: 0.9174

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: 1e-05
• train_batch_size: 32
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3.0

Framework versions

• Transformers 4.26.0
• Pytorch 1.13.1+cu116
• Datasets 2.8.0
• Tokenizers 0.13.2