readme: ---
  language: en
  license: apache-2.0
  datasets:
  - sst2
  - glue
  tags:
  - openvino
  ---
  
  ## distilbert-base-uncased-finetuned-sst-2-english
  
  [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) quantized with NNCF PTQ and exported to OpenVINO IR.
  
  **Model Description:** This model reaches an accuracy of 90.0 on the validation set. See [ov\_config.json](./ov_config.json) for the quantization config.
  
  ## Usage example
  
  To install the requirements for using the OpenVINO backend, do: 
  
  ```
  pip install optimum[openvino]
  ```
  
  This installs all necessary dependencies, including Transformers and OpenVINO.
  
  *NOTE: Python 3.7-3.9 are supported. A virtualenv is recommended.*
  
  You can use this model with a Transformers *pipeline*.
  
  ```python
  from transformers import AutoTokenizer, pipeline
  from optimum.intel.openvino import OVModelForSequenceClassification
  
  model_id = "helenai/distilbert-base-uncased-finetuned-sst-2-english-ov-int8"
  model = OVModelForSequenceClassification.from_pretrained(model_id)
  tokenizer = AutoTokenizer.from_pretrained(model_id)
  cls_pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
  text = "OpenVINO is awesome!"
  outputs = cls_pipe(text)
  print(outputs)
  ```
  
  Example output:
  
  ```sh
  [{'label': 'POSITIVE', 'score': 0.9998594522476196}]
  ```
  