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--- |
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license: mit |
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--- |
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# Cross-Encoder for MS Marco |
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This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task. |
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The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See our paper [R2ANKER](https://arxiv.org/pdf/2206.08063.pdf) for more details. |
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## Usage with Transformers |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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tokenizer = AutoTokenizer.from_pretrained("YCZhou/R2ANKER") |
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model = AutoModelForSequenceClassification.from_pretrained("YCZhou/R2ANKER") |
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features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'], padding=True, truncation=True, return_tensors="pt") |
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model.eval() |
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with torch.no_grad(): |
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scores = model(**features).logits |
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print(scores) |
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``` |
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## Citation |
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``` |
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@inproceedings{DBLP:conf/acl/Zhou0GTXLJJ23, |
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author = {Yucheng Zhou and |
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Tao Shen and |
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Xiubo Geng and |
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Chongyang Tao and |
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Can Xu and |
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Guodong Long and |
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Binxing Jiao and |
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Daxin Jiang}, |
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title = {Towards Robust Ranker for Text Retrieval}, |
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booktitle = {Findings of the Association for Computational Linguistics: {ACL} 2023, |
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Toronto, Canada, July 9-14, 2023}, |
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pages = {5387--5401}, |
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publisher = {Association for Computational Linguistics}, |
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year = {2023}, |
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url = {https://doi.org/10.18653/v1/2023.findings-acl.332}, |
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doi = {10.18653/V1/2023.FINDINGS-ACL.332}, |
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timestamp = {Sat, 30 Sep 2023 09:33:34 +0200}, |
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biburl = {https://dblp.org/rec/conf/acl/Zhou0GTXLJJ23.bib}, |
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bibsource = {dblp computer science bibliography, https://dblp.org} |
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} |
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``` |
