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 training_args.bin from Xenserv/NepaliSentimentAnalysis: direct link, hf CLI and curl.
- Browser
- Download file 5.24 kB
-
https://huggingface.co/Xenserv/NepaliSentimentAnalysis/resolve/main/training_args.bin
- Command line
-
hf download hf://Xenserv/NepaliSentimentAnalysis/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Xenserv/NepaliSentimentAnalysis/resolve/main/training_args.bin
5.24 kB
- Xet hash:
- 007b60a9a23a0dc95f53c4e6f5a8eb0f945e0038ac2915defa29bd1a4ea62407
- Size of remote file:
- 5.24 kB
- SHA256:
- 18f0fc93b206b8a1ddd8063cb29bb1f56e0fda4c6acfead803640582af243860
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