Instructions to use vespa-engine/colbert-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vespa-engine/colbert-medium with Transformers:
# Load model directly from transformers import AutoTokenizer, ColBERT tokenizer = AutoTokenizer.from_pretrained("vespa-engine/colbert-medium") model = ColBERT.from_pretrained("vespa-engine/colbert-medium") - Notebooks
- Google Colab
- Kaggle
Jo Kristian Bergum commited on
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README.md
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# MS Marco Ranking with ColBERT on Vespa.ai
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# MS Marco Ranking with ColBERT on Vespa.ai
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This is work in progress.
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Model is based on [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://arxiv.org/abs/2004.12832).
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This BERT model is based on BERT Medium uncased [google/bert_uncased_L-8_H-512_A-8](https://huggingface.co/google/bert_uncased_L-8_H-512_A-8)
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To use this model with vespa.ai for MS Marco Passage Ranking, see
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[MS Marco Ranking using Vespa.ai sample app](https://github.com/vespa-engine/sample-apps/tree/master/msmarco-ranking)
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