Instructions to use frett/chinese_extract_pert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use frett/chinese_extract_pert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="frett/chinese_extract_pert")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("frett/chinese_extract_pert") model = AutoModelForQuestionAnswering.from_pretrained("frett/chinese_extract_pert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9667738c3185120ec033b552eb322d006e9dcff2675e696dfe6a97b937652782
- Size of remote file:
- 407 MB
- SHA256:
- 2b99b62a62c8004ec730b04eb0f7a4992d06b874fa309f487c05bfed0d68c577
路
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