Instructions to use nadika/nepali_complaints_classification_muril2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nadika/nepali_complaints_classification_muril2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nadika/nepali_complaints_classification_muril2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nadika/nepali_complaints_classification_muril2") model = AutoModelForSequenceClassification.from_pretrained("nadika/nepali_complaints_classification_muril2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5b7deebd62d040386babbf6ec3bc911d58c65a1a4480c5d3b67b24a01314b35e
- Size of remote file:
- 950 MB
- SHA256:
- f39bb5b8f4f6f5dcbb5b762c501b50ab0a8e9ec849d74230c36bce0802e0704e
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