Instructions to use Cournane/roberta-base-labels-SleeveLength with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cournane/roberta-base-labels-SleeveLength with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cournane/roberta-base-labels-SleeveLength")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cournane/roberta-base-labels-SleeveLength") model = AutoModelForSequenceClassification.from_pretrained("Cournane/roberta-base-labels-SleeveLength") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 44987dc79147282912d8e79ab9a70decca3b48a5cf23c50baa981b1b2c62025b
- Size of remote file:
- 3.9 kB
- SHA256:
- 3d0058b9fc014f7fa543fc2dc5c340e513b3b980ce702e4b2e48d635edb5ff09
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.