Instructions to use nadika/nepali_complaints_classification_muril3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nadika/nepali_complaints_classification_muril3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nadika/nepali_complaints_classification_muril3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nadika/nepali_complaints_classification_muril3") model = AutoModelForSequenceClassification.from_pretrained("nadika/nepali_complaints_classification_muril3") - Notebooks
- Google Colab
- Kaggle
updated readme
Browse files
README.md
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@@ -3,6 +3,13 @@ license: apache-2.0
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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_muril3
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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: >-
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मेयरले उपभोक्ता समितिसँग ३० प्रतिशत कमिसन लिएपछि विकासको काम गुणस्तरहीन,
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दबाब झेल्न नसकेर प्रशासकीय अधिकृतको भागाभाग। बाह्रबिसेका मेयरको मनोमानी-
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दोहोरीमा रमाइलो गरेको बिलसमेत नगरपालिकाबाटै भुक्तानी गर्न दबाब मेयरले
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उपभोक्ता समितिसँग ३० प्रतिशत कमिसन लिएपछि विकासको काम गुणस्तरहीन, दबाब
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झेल्न नसकेर प्रशासकीय अधिकृतको भागाभाग।
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model-index:
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- name: nepali_complaints_classification_muril3
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results: []
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