Instructions to use ryota39/retriva-bert-preference-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ryota39/retriva-bert-preference-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ryota39/retriva-bert-preference-classifier", trust_remote_code=True)# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("ryota39/retriva-bert-preference-classifier", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1cda9fb31d671c7b73c94aad6b07e31bf80a26e6f10e4616da85b24fd2da79c0
- Size of remote file:
- 5.18 kB
- SHA256:
- ddb61849c08709a6eb8922610c0b71174ac8273f6151d49607e26609db230e6e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.