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") - Notebooks
- Google Colab
- Kaggle
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license: mit
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license: mit
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Embedding Model in Paper: Distributionally Robust Unsupervised Dense Retrieval Training on Web Graphs
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This model is trained by predicting links between web pages. It serves as an encoder to generate dense vectors that are later used for clustering documents.
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See more about this model and our work at https://github.com/OpenMatch/Web-DRO .
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