Instructions to use kuramot6f/classical-ja-tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kuramot6f/classical-ja-tokenizer with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("kuramot6f/classical-ja-tokenizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download verify.log from kuramot6f/classical-ja-tokenizer: direct link, hf CLI and curl.
- Browser
- Download file 1.84 kB
-
https://huggingface.co/kuramot6f/classical-ja-tokenizer/resolve/main/verify.log
- Command line
-
hf download hf://kuramot6f/classical-ja-tokenizer/verify.log
-
curl -L -o verify.log https://huggingface.co/kuramot6f/classical-ja-tokenizer/resolve/main/verify.log
1.84 kB
| { | |
| "passed": true, | |
| "verified": "2026-10-20T10:05:10.712512+00:00", | |
| "vocab_size": 32000, | |
| "byte_pieces": 256, | |
| "special_ids": { | |
| "unk": 0, | |
| "bos": 1, | |
| "eos": 2, | |
| "pad": 3 | |
| }, | |
| "by_source": { | |
| "kanripo": { | |
| "samples": 128, | |
| "characters": 244506, | |
| "tokens": 197796, | |
| "unknown_tokens": 0, | |
| "byte_tokens": 13491, | |
| "roundtrip_matches": 128, | |
| "characters_per_token": 1.236152399441849, | |
| "byte_token_fraction": 0.0682066371412971 | |
| }, | |
| "minhon": { | |
| "samples": 128, | |
| "characters": 38835, | |
| "tokens": 26888, | |
| "unknown_tokens": 0, | |
| "byte_tokens": 3017, | |
| "roundtrip_matches": 128, | |
| "characters_per_token": 1.4443246057720915, | |
| "byte_token_fraction": 0.11220618863433501 | |
| }, | |
| "ndl": { | |
| "samples": 128, | |
| "characters": 213716, | |
| "tokens": 156139, | |
| "unknown_tokens": 0, | |
| "byte_tokens": 677, | |
| "roundtrip_matches": 128, | |
| "characters_per_token": 1.368754763383908, | |
| "byte_token_fraction": 0.004335880209300687 | |
| }, | |
| "wikisource": { | |
| "samples": 128, | |
| "characters": 219399, | |
| "tokens": 155118, | |
| "unknown_tokens": 0, | |
| "byte_tokens": 3874, | |
| "roundtrip_matches": 128, | |
| "characters_per_token": 1.414400649827873, | |
| "byte_token_fraction": 0.024974535514898335 | |
| }, | |
| "_probes": { | |
| "samples": 9, | |
| "characters": 102, | |
| "tokens": 126, | |
| "unknown_tokens": 0, | |
| "byte_tokens": 57, | |
| "roundtrip_matches": 9, | |
| "characters_per_token": 0.8095238095238095, | |
| "byte_token_fraction": 0.4523809523809524 | |
| } | |
| }, | |
| "failures": [], | |
| "native_u2581_roundtrip": false, | |
| "required_wrapper": "scripts/sentencepiece_unigram.py:Tokenizer for literal U+2581", | |
| "evaluation_limit": "Diagnostic training-distribution sample, not held-out evaluation" | |
| } | |