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:
- 55fed90a78551deede9364d65f4dbee1fdf91246bb969e80d1a797b5d7d631ca
- Size of remote file:
- 4.86 kB
- SHA256:
- d1a8260028c8a30e9a2d80c7042fd7c93f404278aec817274cf7775e7652406c
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