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
File size: 941 Bytes
4117b7f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | {
"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"
}
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