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# bert-base-uncased-sst2

This model is **exactly the same** as [textattack/bert-base-uncased-SST-2](https://huggingface.co/textattack/bert-base-uncased-SST-2) but is also converted to OpenVINO IR format.
It achieves the following results on the evaluation set:
- torch loss: 0.2819
- torch accuracy: 0.9243
- OpenVINO IR accuracy: 0.9243


### Framework versions

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

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




# bert-base-uncased-sst2

This model is exactly the same as textattack/bert-base-uncased-SST-2 but is also converted to OpenVINO IR format.
It achieves the following results on the evaluation set:
- torch loss: 0.2819
- torch accuracy: 0.9243
- OpenVINO IR accuracy: 0.9243


### Framework versions

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

# bert-base-uncased-sst2

This model is exactly the same as textattack/bert-base-uncased-SST-2 but is also converted to OpenVINO IR format.
It achieves the following results on the evaluation set:
- torch loss: 0.2819
- torch accuracy: 0.9243
- OpenVINO IR accuracy: 0.9243 ### 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 #endpoints_compatible #region-us 
 # bert-base-uncased-sst2

This model is exactly the same as textattack/bert-base-uncased-SST-2 but is also converted to OpenVINO IR format.
It achieves the following results on the evaluation set:
- torch loss: 0.2819
- torch accuracy: 0.9243
- OpenVINO IR accuracy: 0.9243 ### 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 #endpoints_compatible #region-us 
# bert-base-uncased-sst2

This model is exactly the same as textattack/bert-base-uncased-SST-2 but is also converted to OpenVINO IR format.
It achieves the following results on the evaluation set:
- torch loss: 0.2819
- torch accuracy: 0.9243
- OpenVINO IR accuracy: 0.9243### Framework versions

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