Instructions to use Cournane/roberta-base-labels-Hat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cournane/roberta-base-labels-Hat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cournane/roberta-base-labels-Hat")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cournane/roberta-base-labels-Hat") model = AutoModelForSequenceClassification.from_pretrained("Cournane/roberta-base-labels-Hat") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d1eda54e020fbca6069fbc1a22a0768088a8b3430e053146416c7e5b29ff3a62
- Size of remote file:
- 3.9 kB
- SHA256:
- 526483561a233a52b591c4270f7fae26af3f41770bbfc6d87e71870ce53d87ae
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.