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
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