Feature Extraction
Transformers
Safetensors
English
bert
retrieval
question answering
custom_code
text-embeddings-inference
Instructions to use sapienzanlp/zebra-retriever-e5-base-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sapienzanlp/zebra-retriever-e5-base-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="sapienzanlp/zebra-retriever-e5-base-v2", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sapienzanlp/zebra-retriever-e5-base-v2", trust_remote_code=True) model = AutoModel.from_pretrained("sapienzanlp/zebra-retriever-e5-base-v2", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -145,21 +145,22 @@ If you use any part of this work, please consider citing the paper as follows:
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```bibtex
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@inproceedings{molfese-etal-2024-zebra,
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title = "ZEBRA: Zero-Shot Example-Based Retrieval Augmentation for Commonsense Question Answering",
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author = "Molfese, Francesco Maria and
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Conia, Simone and
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Orlando, Riccardo and
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Navigli, Roberto",
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editor = "
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booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
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month = nov,
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year = "2024",
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address = "Miami",
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publisher = "Association for Computational Linguistics",
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url = "",
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doi = "",
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pages = ""
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abstract = "",
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}
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```
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```bibtex
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@inproceedings{molfese-etal-2024-zebra,
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title = "{ZEBRA}: Zero-Shot Example-Based Retrieval Augmentation for Commonsense Question Answering",
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author = "Molfese, Francesco Maria and
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Conia, Simone and
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Orlando, Riccardo and
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Navigli, Roberto",
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editor = "Al-Onaizan, Yaser and
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Bansal, Mohit and
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Chen, Yun-Nung",
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booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
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month = nov,
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year = "2024",
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address = "Miami, Florida, USA",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/2024.emnlp-main.1251",
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doi = "10.18653/v1/2024.emnlp-main.1251",
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pages = "22429--22444"
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}
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```
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