Instructions to use dpkrm/NepaliSentimentAnalysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dpkrm/NepaliSentimentAnalysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dpkrm/NepaliSentimentAnalysis")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dpkrm/NepaliSentimentAnalysis") model = AutoModelForSequenceClassification.from_pretrained("dpkrm/NepaliSentimentAnalysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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license: apache-2.0
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# Sentiment Analysis with BERT: Nepali Sentiment Dataset
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**Note:** The code provided is a simplified version for demonstration purposes. Additional error handling, logging, and hyperparameter tuning can be added for production use.
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license: apache-2.0
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pipeline_tag: text-classification
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# Sentiment Analysis with BERT: Nepali Sentiment Dataset
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**Note:** The code provided is a simplified version for demonstration purposes. Additional error handling, logging, and hyperparameter tuning can be added for production use.
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