Instructions to use RAYZ/macbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RAYZ/macbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="RAYZ/macbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("RAYZ/macbert") model = AutoModel.from_pretrained("RAYZ/macbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 498 Bytes
c268a59 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | {
"cls_token": "[CLS]",
"do_lower_case": true,
"mask_token": "[MASK]",
"name_or_path": "hfl/chinese-macbert-base",
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"special_tokens_map_file": "/home/rayz/.cache/huggingface/transformers/9f382d012cf0761684535742ec8cf1096e0be52359ee0389a418eaada1265367.dd8bd9bfd3664b530ea4e645105f557769387b3da9f79bdb55ed556bdd80611d",
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "BertTokenizer",
"unk_token": "[UNK]"
}
|