Instructions to use helenai/philschmid-roberta-large-sst2-ov with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use helenai/philschmid-roberta-large-sst2-ov with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="helenai/philschmid-roberta-large-sst2-ov")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("helenai/philschmid-roberta-large-sst2-ov") model = AutoModelForSequenceClassification.from_pretrained("helenai/philschmid-roberta-large-sst2-ov") - Notebooks
- Google Colab
- Kaggle
| 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/philschmid-roberta-large-sst2-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) | |