Instructions to use anishreddy91/Gemma-Quantized-Model-Emotion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anishreddy91/Gemma-Quantized-Model-Emotion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="anishreddy91/Gemma-Quantized-Model-Emotion")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("anishreddy91/Gemma-Quantized-Model-Emotion") model = AutoModelForSequenceClassification.from_pretrained("anishreddy91/Gemma-Quantized-Model-Emotion") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 922b7ca7ba4d2f66d92f0b1191fe3a4010a67fe8265e9c69b1d65f60f9970339
- Size of remote file:
- 34.4 MB
- SHA256:
- f559f2189f392b4555613965f089e7c4d300b41fbe080bf79da0d676e33ee7f0
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