Instructions to use VijayRam1812/content-classifier-gemma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VijayRam1812/content-classifier-gemma with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="VijayRam1812/content-classifier-gemma")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("VijayRam1812/content-classifier-gemma") model = AutoModelForSequenceClassification.from_pretrained("VijayRam1812/content-classifier-gemma") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 01c2631957db62a77e54024dbd8df50dcf8d13fe30cdea714599b1e322fa9f86
- Size of remote file:
- 33.4 MB
- SHA256:
- 8f1432efce542faeec45d3a12bca95be101095f974cc16a11232f92d929a024a
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