Instructions to use wskhanh/pert-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wskhanh/pert-qa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="wskhanh/pert-qa")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("wskhanh/pert-qa") model = AutoModelForQuestionAnswering.from_pretrained("wskhanh/pert-qa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 420ab194004bfca4c3848fefb397e1972e3b842f5a3e8d5208b4acd85dcef0d6
- Size of remote file:
- 1.3 GB
- SHA256:
- 7c6b5c0799f197966cea38418f7e2afaa830de11ebc1cb9bd7951a0b71f7edcf
路
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