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")# 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 pytorch_model.bin from OpenMatch/Web-Graph-Embedding: direct link, hf CLI and curl.
- Browser
- Download file 903 MB
-
https://huggingface.co/OpenMatch/Web-Graph-Embedding/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://OpenMatch/Web-Graph-Embedding/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/OpenMatch/Web-Graph-Embedding/resolve/main/pytorch_model.bin
903 MB
- Xet hash:
- 50ab4085bcaad3f13c621282df239e3aac38b3b0237be484fcb01f666b8eb14c
- Size of remote file:
- 903 MB
- SHA256:
- ea60c3995c451ed16877654df93580b6f93948e3a3f170d494568cec2f933e8a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.