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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 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.

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)

Example output:

[{'label': 'POSITIVE', 'score': 0.9998594522476196}]
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Text Classification
This model can be loaded on the Inference API on-demand.

Datasets used to train helenai/distilbert-base-uncased-finetuned-sst-2-english-ov-int8