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# bert-base-uncased-sst2-unstructured80-PTQ

This model conducts simple post training quantization of [yujiepan/bert-base-uncased-sst2-unstructured-sparsity-80](https://huggingface.co/yujiepan/bert-base-uncased-sst2-unstructured-sparsity-80) on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- torch loss: 0.4029
- torch accuracy: 0.9128
- OpenVINO IR accuracy: 0.9117
- Sparsity in transformer block linear layers: 0.80

## 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: 64
- eval_batch_size: 8
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- num_epochs: 12.0
- mixed_precision_training: Native AMP

### Framework versions

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

{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "model-index": [{"name": "bert-base-uncased-sst2-unstructured80-PTQ", "results": []}]}
transformers pytorch openvino bert generated_from_trainer en dataset:glue license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #openvino #bert #generated_from_trainer #en #dataset-glue #license-apache-2.0 #endpoints_compatible #region-us 




# bert-base-uncased-sst2-unstructured80-PTQ

This model conducts simple post training quantization of yujiepan/bert-base-uncased-sst2-unstructured-sparsity-80 on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- torch loss: 0.4029
- torch accuracy: 0.9128
- OpenVINO IR accuracy: 0.9117
- Sparsity in transformer block linear layers: 0.80

## 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: 64
- eval_batch_size: 8
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- num_epochs: 12.0
- mixed_precision_training: Native AMP

### Framework versions

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

# bert-base-uncased-sst2-unstructured80-PTQ

This model conducts simple post training quantization of yujiepan/bert-base-uncased-sst2-unstructured-sparsity-80 on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- torch loss: 0.4029
- torch accuracy: 0.9128
- OpenVINO IR accuracy: 0.9117
- Sparsity in transformer block linear layers: 0.80 ## 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: 64
- eval_batch_size: 8
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- num_epochs: 12.0
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #openvino #bert #generated_from_trainer #en #dataset-glue #license-apache-2.0 #endpoints_compatible #region-us 
 # bert-base-uncased-sst2-unstructured80-PTQ

This model conducts simple post training quantization of yujiepan/bert-base-uncased-sst2-unstructured-sparsity-80 on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- torch loss: 0.4029
- torch accuracy: 0.9128
- OpenVINO IR accuracy: 0.9117
- Sparsity in transformer block linear layers: 0.80 ## 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: 64
- eval_batch_size: 8
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- num_epochs: 12.0
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #openvino #bert #generated_from_trainer #en #dataset-glue #license-apache-2.0 #endpoints_compatible #region-us 
# bert-base-uncased-sst2-unstructured80-PTQ

This model conducts simple post training quantization of yujiepan/bert-base-uncased-sst2-unstructured-sparsity-80 on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- torch loss: 0.4029
- torch accuracy: 0.9128
- OpenVINO IR accuracy: 0.9117
- Sparsity in transformer block linear layers: 0.80## 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: 64
- eval_batch_size: 8
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- num_epochs: 12.0
- mixed_precision_training: Native AMP### Framework versions

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