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
|
Download README.md from Xenserv/NepaliSentimentAnalysis: direct link, hf CLI and curl.
- Browser
- Download file 580 Bytes
-
https://huggingface.co/Xenserv/NepaliSentimentAnalysis/resolve/main/README.md
- Command line
-
hf download hf://Xenserv/NepaliSentimentAnalysis/README.md
-
curl -L -o README.md https://huggingface.co/Xenserv/NepaliSentimentAnalysis/resolve/main/README.md
580 Bytes
| 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) | |
| ``` |