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")# 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
- Xet hash:
- 26f9d6aa0d0bfc57e6b6d979e5cfcf39bd92d83a0b6a03bcffeb999c6fe747bd
- Size of remote file:
- 438 MB
- SHA256:
- 00de4aecbec3d003d7d94334a955fa58f25e68ecc2910bd30b98dd9c79c85172
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