afriberta_base
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Explore AI models 21
afriberta_large
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View details ↗afriberta_small
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View details ↗afriteva_v2_base
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View details ↗ance-msmarco-passage
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View details ↗cosdpr-distil
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View details ↗doc2query-t5-base-msmarco
castorini基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗mdpr-tied-pft-msmarco
castorini基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗monot5-3b-msmarco-10k
castorini基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗monot5-base-msmarco
castoriniThis model is a T5-base reranker fine-tuned on the MS MARCO passage dataset for 100k steps (or 10 epochs). For better zero-shot performance (i.e., inference on other datasets), we recommend using `castorini/monot5-base-msmarco-10k`. For more details on how to use it, check the following links: - A simple reranking example - Rerank MS MARCO passages - Rerank Robust04 documents
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View details ↗monot5-base-msmarco-10k
castoriniThis model is a T5-base reranker fine-tuned on the MS MARCO passage dataset for 10k steps (or 1 epoch). This model usually has a better zero-shot performance than `monot5-base-msmarco`, i.e., it performs better on datasets different from MS MARCO. For more details on how to use it, check the following links: - A simple reranking example - Rerank MS MARCO passages - Rerank Robust04 documents
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View details ↗monot5-small-msmarco-10k
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View details ↗rank_zephyr_7b_v1_full
castorini基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗splade-pp-onnx
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View details ↗t5-base-canard
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View details ↗tct_colbert-msmarco
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View details ↗tct_colbert-v2-hnp-msmarco
castorini基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗unicoil-msmarco-passage
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View details ↗> 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. Our own efforts are described in the paper entitled: > Pre-Processing Matters! Improved Wikipedia Corpora for Open-Domain Question Answering. This is the passage encoder portion of a 2nd iteration DPR model for the wiki-all-8-4 corpus variant trained on the amalgamation of the NQ, TriviaQA, WQ, and CuratedTREC datasets.
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View details ↗> 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. 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-8-4 corpus variant trained on the amalgamation of the NQ, TriviaQA, WQ, and CuratedTREC datasets.
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