Token Classification
Transformers
PyTorch
Nepali
xlm-roberta
named-entity-recognition
nepali-language
Instructions to use debabrata-ai/Nepali-Named-Entity-Tagger-XLM-R with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use debabrata-ai/Nepali-Named-Entity-Tagger-XLM-R with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="debabrata-ai/Nepali-Named-Entity-Tagger-XLM-R")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("debabrata-ai/Nepali-Named-Entity-Tagger-XLM-R") model = AutoModelForTokenClassification.from_pretrained("debabrata-ai/Nepali-Named-Entity-Tagger-XLM-R", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
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by SFconvertbot - opened
- model.safetensors +3 -0
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