Instructions to use ychu612/RSAVAV_FN_CLF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ychu612/RSAVAV_FN_CLF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ychu612/RSAVAV_FN_CLF")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ychu612/RSAVAV_FN_CLF") model = AutoModelForSequenceClassification.from_pretrained("ychu612/RSAVAV_FN_CLF") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f6165f5d441efc0ae223581ed01fcaa3ac6b90c009875217fb25cf1b1e828e10
- Size of remote file:
- 5.24 kB
- SHA256:
- 9f9982d57dc606c75980acbf9a0a5c242014622c4c556931af7bcab26fb5a88c
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