Instructions to use uzabase/luke-japanese-wordpiece-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use uzabase/luke-japanese-wordpiece-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="uzabase/luke-japanese-wordpiece-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("uzabase/luke-japanese-wordpiece-base") model = AutoModelForMaskedLM.from_pretrained("uzabase/luke-japanese-wordpiece-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "auto_map": { | |
| "AutoTokenizer": [ | |
| "tokenization_luke_bert_japanese.LukeBertJapaneseTokenizer", | |
| null | |
| ] | |
| }, | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "[CLS]", | |
| "do_lower_case": false, | |
| "do_subword_tokenize": true, | |
| "do_word_tokenize": true, | |
| "entity_mask2_token": "[MASK2]", | |
| "entity_mask_token": "[MASK]", | |
| "entity_pad_token": "[PAD]", | |
| "entity_token_1": "<ent>", | |
| "entity_token_2": "<ent2>", | |
| "entity_unk_token": "[UNK]", | |
| "jumanpp_kwargs": null, | |
| "mask_token": "[MASK]", | |
| "max_entity_length": 32, | |
| "max_mention_length": 30, | |
| "mecab_kwargs": { | |
| "mecab_dic": "unidic_lite" | |
| }, | |
| "model_max_length": 512, | |
| "never_split": null, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "spm_file": null, | |
| "subword_tokenizer_type": "wordpiece", | |
| "sudachi_kwargs": null, | |
| "task": null, | |
| "tokenizer_class": "LukeBertJapaneseTokenizer", | |
| "unk_token": "[UNK]", | |
| "word_tokenizer_type": "mecab" | |
| } | |