Instructions to use Yale-LILY/a2cu-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Yale-LILY/a2cu-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Yale-LILY/a2cu-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Yale-LILY/a2cu-classifier") model = AutoModelForSequenceClassification.from_pretrained("Yale-LILY/a2cu-classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 68a926d282361c8b436f678a9679a9652a1c60ca429d26e9085c4bfa2fcfc717
- Size of remote file:
- 3.04 GB
- SHA256:
- e0105b96c2917094f3157c190a65a6f113b9dff91a13c2f6db71294ee31a417e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.