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 openmatch_config.json from OpenMatch/Web-Graph-Embedding: direct link, hf CLI and curl.
- Browser
- Download file 189 Bytes
-
https://huggingface.co/OpenMatch/Web-Graph-Embedding/resolve/refs%2Fpr%2F1/openmatch_config.json
- Command line
-
hf download hf://OpenMatch/Web-Graph-Embedding@refs/pr/1/openmatch_config.json
-
curl -L -o openmatch_config.json https://huggingface.co/OpenMatch/Web-Graph-Embedding/resolve/refs%2Fpr%2F1/openmatch_config.json
189 Bytes
| { | |
| "tied": true, | |
| "plm_backbone": { | |
| "type": "T5Model", | |
| "feature": "last_hidden_state" | |
| }, | |
| "pooling": "first", | |
| "linear_head": false, | |
| "normalize": false | |
| } |