Instructions to use frett/chinese_extract_longbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use frett/chinese_extract_longbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="frett/chinese_extract_longbert", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("frett/chinese_extract_longbert", trust_remote_code=True) model = AutoModelForQuestionAnswering.from_pretrained("frett/chinese_extract_longbert", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bc21087b46ff54609de5687457f28e257dc69f2680f8b604655ed6246aa5079a
- Size of remote file:
- 5.24 kB
- SHA256:
- c4ea58a9c9a4a9d3ee7edc102576859985898ca9af4b757ca386aa741f63081c
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.