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_config.json from DOAIxILPRL-KU/nep-tokenizer: direct link, hf CLI and curl.
- Browser
- Download file 272 Bytes
-
https://huggingface.co/DOAIxILPRL-KU/nep-tokenizer/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://DOAIxILPRL-KU/nep-tokenizer/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/DOAIxILPRL-KU/nep-tokenizer/resolve/main/tokenizer_config.json
272 Bytes
| { | |
| "backend": "tokenizers", | |
| "bos_token": "<|bos|>", | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "<|eos|>", | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "<|pad|>", | |
| "tokenizer_class": "TokenizersBackend", | |
| "unk_token": "<|unk|>" | |
| } | |