Instructions to use ychu612/RSAVAV_SQ_CLF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ychu612/RSAVAV_SQ_CLF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ychu612/RSAVAV_SQ_CLF")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ychu612/RSAVAV_SQ_CLF") model = AutoModelForSequenceClassification.from_pretrained("ychu612/RSAVAV_SQ_CLF") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1974331ece090eb4c6c2c1b48b8d10e36313787e7c8e6fa255a6c7382d262a6e
- Size of remote file:
- 5.24 kB
- SHA256:
- 1d224b52f9cab8fefda73cd8e91580e83d1fb22dfe02b8dde37bd58dcc9b9928
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