Instructions to use ckiplab/bert-base-chinese-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ckiplab/bert-base-chinese-qa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="ckiplab/bert-base-chinese-qa")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("ckiplab/bert-base-chinese-qa") model = AutoModelForQuestionAnswering.from_pretrained("ckiplab/bert-base-chinese-qa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ff585252c38fca4657ca9dabb012d221438fac3352a94bd05c6406b1e2026b37
- Size of remote file:
- 407 MB
- SHA256:
- b97fee98374da90086cf6dc7e49ffa6c35057e5900bec2bbb7125ea81a807088
Β·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.