Token Classification
Transformers
PyTorch
Nepali
distilbert
named-entity-recognition
nepali-language
Instructions to use debabrata-ai/NepNER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use debabrata-ai/NepNER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="debabrata-ai/NepNER")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("debabrata-ai/NepNER") model = AutoModelForTokenClassification.from_pretrained("debabrata-ai/NepNER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from debabrata-ai/NepNER: direct link, hf CLI and curl.
- Browser
- Download file 2.13 MB
-
https://huggingface.co/debabrata-ai/NepNER/resolve/main/tokenizer.json
- Command line
-
hf download hf://debabrata-ai/NepNER/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/debabrata-ai/NepNER/resolve/main/tokenizer.json
2.13 MB
File too large to display, you can check the raw version instead.