Feature Extraction
Safetensors
Transformers
English
hierarchy-transformers
mpnet
hierarchy-encoding
subsumption-relationships
text-embeddings-inference
Instructions to use Hierarchy-Transformers/HiT-MPNet-WordNetNoun with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hierarchy-Transformers/HiT-MPNet-WordNetNoun with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Hierarchy-Transformers/HiT-MPNet-WordNetNoun")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Hierarchy-Transformers/HiT-MPNet-WordNetNoun") model = AutoModel.from_pretrained("Hierarchy-Transformers/HiT-MPNet-WordNetNoun") - Notebooks
- Google Colab
- Kaggle
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*Yuan He, Zhangdie Yuan, Jiaoyan Chen, Ian Horrocks.* **Language Models as Hierarchy Encoders.** Advances in Neural Information Processing Systems 37 (NeurIPS 2024).
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url = {https://proceedings.neurips.cc/paper_files/paper/2024/file/1a970a3e62ac31c76ec3cea3a9f68fdf-Paper-Conference.pdf},
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volume = {37},
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year = {2024}
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}
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```
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*Yuan He, Zhangdie Yuan, Jiaoyan Chen, Ian Horrocks.* **Language Models as Hierarchy Encoders.** Advances in Neural Information Processing Systems 37 (NeurIPS 2024).
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@article{he2024language,
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title={Language models as hierarchy encoders},
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author={He, Yuan and Yuan, Moy and Chen, Jiaoyan and Horrocks, Ian},
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journal={Advances in Neural Information Processing Systems},
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volume={37},
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pages={14690--14711},
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year={2024}
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}
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```
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