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
Commit History
Yesterday
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Release Nep-Tokenizer v2 (64k): Native Hugging Face BPE with Mark-Aware Pre-tokenization and 100% Devanagari Block Coverage
- Architecture: Native Rust BPE (PreTrainedTokenizerFast) - Pre-tokenization: Mark-Aware Regex [\p{L}\p{M}]+ & Digit Grouping [0-9०-९]{1,3} - Full 17-Model Benchmark Evaluation and Publication Figures - Affiliation: DOAI & ILPRL, Kathmandu Universitya4c3ef3