Instructions to use dakkulanthu/nepali-sentiment-analysis-short with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dakkulanthu/nepali-sentiment-analysis-short with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dakkulanthu/nepali-sentiment-analysis-short")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dakkulanthu/nepali-sentiment-analysis-short") model = AutoModelForSequenceClassification.from_pretrained("dakkulanthu/nepali-sentiment-analysis-short") - Notebooks
- Google Colab
- Kaggle
Pre-trained sentiment analysis model for Nepali language.
Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="dakkulanthu/nepali-sentiment-analysis-short")
Example text data
text_data = """ बोलेरै कांग्रेसको मागको दोकान बन्द गर्छु भन्नेले विशेष पत्रकार सम्मेलन गरेर बोल्नुस जनताले बुझ्छन्। कांग्रेसले संसद बन्द गरिराखोस्। तपाईले बोल्न बाकी के के रैछ हेरौ। """
Use the pipeline for sentiment analysis
sentiment_result = pipe(text_data)
sentiment_result
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