Instructions to use hfl/chinese-macbert-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hfl/chinese-macbert-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hfl/chinese-macbert-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hfl/chinese-macbert-large") model = AutoModelForMaskedLM.from_pretrained("hfl/chinese-macbert-large", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
-
upload flax model
1cf2677 -
allow flax
ea4a197
-
add fast tokenizer config
hfl-rc7534575
-
Update README.md
Joint Laboratory of HIT and iFLYTEK Research (HFL)becfff0 -
Create README.md
Joint Laboratory of HIT and iFLYTEK Research (HFL)7a183ca
-
Update config.json
3f6b8ab -
Update pytorch_model.bin
afca34c -
Update tf_model.h5
e86f68a -
Update added_tokens.json
2b40150 -
Update config.json
eb24706 -
Update special_tokens_map.json
6764bbf -
Update tokenizer_config.json
a1c1bd9 -
Update vocab.txt
bcb853f -
initial commit
7a616e4