Instructions to use Kdogs/dapt_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kdogs/dapt_bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Kdogs/dapt_bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Kdogs/dapt_bert") model = AutoModelForMaskedLM.from_pretrained("Kdogs/dapt_bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 530 Bytes
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"cls_token": "[CLS]",
"do_basic_tokenize": true,
"do_lower_case": false,
"mask_token": "[MASK]",
"model_max_length": 512,
"name_or_path": "klue/bert-base",
"never_split": null,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"special_tokens_map_file": "/home/sifter/.cache/huggingface/hub/models--klue--bert-base/snapshots/34b965303f98bc5214daca7f76b7fb82d2dc6183/special_tokens_map.json",
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "BertTokenizer",
"unk_token": "[UNK]"
}
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