Instructions to use Xenserv/NepaliSentimentAnalysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Xenserv/NepaliSentimentAnalysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Xenserv/NepaliSentimentAnalysis")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Xenserv/NepaliSentimentAnalysis") model = AutoModelForSequenceClassification.from_pretrained("Xenserv/NepaliSentimentAnalysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Xenserv/NepaliSentimentAnalysis: direct link, hf CLI and curl.
- Browser
- Download file 580 Bytes
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https://huggingface.co/Xenserv/NepaliSentimentAnalysis/resolve/main/README.md
- Command line
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hf download hf://Xenserv/NepaliSentimentAnalysis/README.md
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curl -L -o README.md https://huggingface.co/Xenserv/NepaliSentimentAnalysis/resolve/main/README.md
580 Bytes
metadata
language:
- ne
base_model:
- Shushant/nepaliBERT
pipeline_tag: text-classification
library_name: transformers
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)