Instructions to use Shitao/bge-stackques with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shitao/bge-stackques with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Shitao/bge-stackques")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Shitao/bge-stackques") model = AutoModel.from_pretrained("Shitao/bge-stackques", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f7028f4c19b8df3b28ae96f5077f451571c82ccbd64afacb1958b9d500d0d47d
- Size of remote file:
- 438 MB
- SHA256:
- 767a9ea99a1720ea416e138b502384a8a77911cea2e9cf868865c527140c7c91
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