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 model.safetensors from OpenMatch/Web-Graph-Embedding: direct link, hf CLI and curl.
- Browser
- Download file 903 MB
-
https://huggingface.co/OpenMatch/Web-Graph-Embedding/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://OpenMatch/Web-Graph-Embedding@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/OpenMatch/Web-Graph-Embedding/resolve/refs%2Fpr%2F1/model.safetensors
903 MB
- Xet hash:
- 6d54a12de08a8850a557497ea516dd124862768b1d9e4ccc7c18f59664c1da9a
- Size of remote file:
- 903 MB
- SHA256:
- c5a3389af594da7419195fc12cad061c544f5f3e332398ffc4fc0b044c2963d0
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