Instructions to use Cournane/roberta-base-reduced-Hat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cournane/roberta-base-reduced-Hat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cournane/roberta-base-reduced-Hat")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cournane/roberta-base-reduced-Hat") model = AutoModelForSequenceClassification.from_pretrained("Cournane/roberta-base-reduced-Hat") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b6ddbbec9ef969aa9f086ac8dd6ea27349411648ef0a147af24fc9e2ea7dfe5
- Size of remote file:
- 499 MB
- SHA256:
- 5947cd917e4c57d3f5a41b29e768eaff1d0af62830dfe3698b6c4d2a8ec7e5d8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.