Instructions to use OpenMatch/Web-Graph-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMatch/Web-Graph-Embedding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="OpenMatch/Web-Graph-Embedding")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("OpenMatch/Web-Graph-Embedding") model = AutoModel.from_pretrained("OpenMatch/Web-Graph-Embedding", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download sp.model from OpenMatch/Web-Graph-Embedding: direct link, hf CLI and curl.
- Browser
- Download file 825 kB
-
https://huggingface.co/OpenMatch/Web-Graph-Embedding/resolve/main/sp.model
- Command line
-
hf download hf://OpenMatch/Web-Graph-Embedding/sp.model
-
curl -L -o sp.model https://huggingface.co/OpenMatch/Web-Graph-Embedding/resolve/main/sp.model
825 kB
- Xet hash:
- 0bdc6ba9d9ebe22cf2a7a8e134afb8b1ebabcd8485ab3281c42799f9ee2d3a14
- Size of remote file:
- 825 kB
- SHA256:
- 1f7dda6f68d6676a944418205def0e463eaa7b9aa3868f99b1ac122c849af152
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