Feature Extraction
Transformers
Nepali
English
Hindi
tokenizer
nepali
devanagari
bpe
byte-level-bpe
mark-aware
doai-ku
ilprl
fast-tokenizer
Instructions to use DOAIxILPRL-KU/nep-tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DOAIxILPRL-KU/nep-tokenizer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="DOAIxILPRL-KU/nep-tokenizer")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DOAIxILPRL-KU/nep-tokenizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Release Nep-Tokenizer v2 (64k): Native Hugging Face BPE with Mark-Aware Pre-tokenization and 100% Devanagari Block Coverage
a4c3ef3 Download tokenizer.json from DOAIxILPRL-KU/nep-tokenizer: direct link, hf CLI and curl.
- Browser
- Download file 6.63 MB
-
https://huggingface.co/DOAIxILPRL-KU/nep-tokenizer/resolve/main/tokenizer.json
- Command line
-
hf download hf://DOAIxILPRL-KU/nep-tokenizer/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/DOAIxILPRL-KU/nep-tokenizer/resolve/main/tokenizer.json
6.63 MB
File too large to display, you can check the raw version instead.