Instructions to use Cournane/roberta-base-labels-Neckline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cournane/roberta-base-labels-Neckline with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cournane/roberta-base-labels-Neckline")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cournane/roberta-base-labels-Neckline") model = AutoModelForSequenceClassification.from_pretrained("Cournane/roberta-base-labels-Neckline") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9d72990cd2127d8f1272180182166ba74b6bee134cdfaa2f1d5fc4006735c919
- Size of remote file:
- 499 MB
- SHA256:
- 43d7e5f8864bba6883e02dafcd9b5e7e744b0d3a6750648d493743335def6c3a
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