Instructions to use helenai/textattack-roberta-base-SST-2-ov with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use helenai/textattack-roberta-base-SST-2-ov with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="helenai/textattack-roberta-base-SST-2-ov")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("helenai/textattack-roberta-base-SST-2-ov") model = AutoModelForSequenceClassification.from_pretrained("helenai/textattack-roberta-base-SST-2-ov") - Notebooks
- Google Colab
- Kaggle
textattack/roberta-base-SST-2
This is the textattack/roberta-base-SST-2 model converted to OpenVINO, for accellerated inference.
An example of how to do inference on this model:
from optimum.intel.openvino import OVModelForSequenceClassification
from transformers import AutoTokenizer, pipeline
# model_id should be set to either a local directory or a model available on the HuggingFace hub.
model_id = "helenai/textattack-roberta-base-SST-2-ov"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForSequenceClassification.from_pretrained(model_id)
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
result = pipe("I like you. I love you")
print(result)
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