Instructions to use xraychen/mqa-cls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xraychen/mqa-cls with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="xraychen/mqa-cls")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("xraychen/mqa-cls") model = AutoModelForQuestionAnswering.from_pretrained("xraychen/mqa-cls") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f73a2cb6db8426679d1dd18ebdbedcfecb368aa639493388069de1213859bb8e
- Size of remote file:
- 436 MB
- SHA256:
- f59ac2756e1e3509ce2ea8eb9b58b5733776f07e07dca4048ef065766e0a5ef6
路
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