# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Xenserv/NepaliSentimentAnalysis")
model = AutoModelForSequenceClassification.from_pretrained("Xenserv/NepaliSentimentAnalysis")Quick Links
Nepali Sentiment Analysis
This is a finetuned version of Sushant/NepaliBERT for sentiment classification. It classifies sentiment into 3 categories: Positive, Neutral and Negative.
Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Xenserv/NepaliSentimentAnalysis")
model = AutoModelForSequenceClassification.from_pretrained('Xenserv/NepaliSentimentAnalysis', num_labels=3)
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Model tree for Xenserv/NepaliSentimentAnalysis
Base model
Shushant/nepaliBERT
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Xenserv/NepaliSentimentAnalysis")