Instructions to use nadika/nepali_complaints_classification_muril with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nadika/nepali_complaints_classification_muril with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nadika/nepali_complaints_classification_muril")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nadika/nepali_complaints_classification_muril") model = AutoModelForSequenceClassification.from_pretrained("nadika/nepali_complaints_classification_muril") - Notebooks
- Google Colab
- Kaggle
Readme updated.
Browse files
README.md
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tags:
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- generated_from_trainer
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base_model: google/muril-base-cased
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model-index:
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- name: nepali_complaints_classification_muril
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results: []
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tags:
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- generated_from_trainer
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base_model: google/muril-base-cased
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- text: 'मेयरले उपभोक्ता समितिसँग ३० प्रतिशत कमिसन लिएपछि विकासको काम गुणस्तरहीन, दबाब झेल्न नसकेर प्रशासकीय अधिकृतको भागाभाग। बाह्रबिसेका मेयरको मनोमानी- दोहोरीमा रमाइलो गरेको बिलसमेत नगरपालिकाबाटै भुक्तानी गर्न दबाब मेयरले उपभोक्ता समितिसँग ३० प्रतिशत कमिसन लिएपछि विकासको काम गुणस्तरहीन, दबाब झेल्न नसकेर प्रशासकीय अधिकृतको भागाभाग। '
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model-index:
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- name: nepali_complaints_classification_muril
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results: []
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