Instructions to use sparsh35/trained_gemma_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sparsh35/trained_gemma_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sparsh35/trained_gemma_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sparsh35/trained_gemma_classifier") model = AutoModelForSequenceClassification.from_pretrained("sparsh35/trained_gemma_classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b4b4adaa5a90275143382e3b45630d57f240b1709c013f8665a4dfb579116b96
- Size of remote file:
- 33.4 MB
- SHA256:
- c64e40588e94d5d8686c0b941c0ee0e1dcc12b850e04a72ca80661da653015a5
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