Instructions to use bond005/rubert-entity-embedder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bond005/rubert-entity-embedder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="bond005/rubert-entity-embedder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("bond005/rubert-entity-embedder") model = AutoModel.from_pretrained("bond005/rubert-entity-embedder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1006ad382237b94e12f58ffbb756afb8257c77d417555cfb5cb030e1f41ddc87
- Size of remote file:
- 711 MB
- SHA256:
- 2b411ff958b34bdf492f8408ada2cf960a0b1cede5cf0f3ba7708b896aa375de
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