Instructions to use castorini/wiki-all-6-3-multi-dpr2-query-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use castorini/wiki-all-6-3-multi-dpr2-query-encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="castorini/wiki-all-6-3-multi-dpr2-query-encoder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("castorini/wiki-all-6-3-multi-dpr2-query-encoder") model = AutoModel.from_pretrained("castorini/wiki-all-6-3-multi-dpr2-query-encoder") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
Dense passage retriever (DPR) is a dense retrieval method described in the following paper:
Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih. Dense Passage Retrieval for Open-Domain Question Answering. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6769-6781, 2020.
We have trained our own DPR models with our Wikipedia corpus variants using the Tevatron library.
Our own efforts are described in the paper entitled:
Pre-Processing Matters! Improved Wikipedia Corpora for Open-Domain Question Answering.
This is the query encoder portion of a 2nd iteration DPR model for the wiki-all-6-3 corpus variant trained on the amalgamation of the NQ, TriviaQA, WQ, and CuratedTREC datasets.
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