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README.md

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+---
+license: mit
+language:
+- en
+library_name: transformers
+base_model: microsoft/deberta-v3-base
+tags:
+- question-answering
+- information-retrieval
+- reranking
+- query-expansion
+- bm25
+- deberta-v3
+---
+
+# EAR query reranker checkpoints
+
+Official scorer checkpoints for **Expand, Rerank, and Retrieve: Query
+Reranking for Open-Domain Question Answering** (Findings of ACL 2023).
+
+EAR is a query Expansion And Reranking method. It samples diverse query
+expansions and trains a reranker to select expansions that improve passage
+retrieval.
+
+## Contents
+
+The repository preserves the original experiment directory names:
+
+| Path | Dataset | Variant | Files |
+| --- | --- | --- | ---: |
+| best_nq/vanilla | Natural Questions | EAR-RI | 3 |
+| best_nq/wtop1 | Natural Questions | EAR-RD | 3 |
+| best_trivia/vanilla | TriviaQA | EAR-RI | 3 |
+| best_trivia/wtop1 | TriviaQA | EAR-RD | 4 |
+
+Each directory contains answer, sentence, and title scorer checkpoints.
+best_trivia/wtop1 additionally preserves the historical
+scorer-answer-best.bin file from the original release.
+
+These are raw PyTorch checkpoint/state-dict files used by the EAR codebase.
+They are not standalone AutoModel.from_pretrained repositories.
+
+## Usage
+
+Use these checkpoints with the official code:
+
+    git clone https://github.com/voidism/EAR
+    cd EAR
+
+For EAR-RI:
+
+    bash one_pass_eval_ri.sh nq /path/to/best_nq/vanilla
+    bash one_pass_eval_ri.sh trivia /path/to/best_trivia/vanilla
+
+For EAR-RD:
+
+    bash one_pass_eval_rd.sh nq /path/to/best_nq/wtop1
+    bash one_pass_eval_rd.sh trivia /path/to/best_trivia/wtop1
+
+The original release used Python 3.7.13, PyTorch 1.10.1,
+Transformers 4.24.0, Tokenizers 0.11.1, Pyserini, and Weights & Biases.
+The rerankers were trained from microsoft/deberta-v3-base.
+
+SOURCE_MANIFEST.sha256 contains a SHA-256 checksum for every checkpoint.
+The source archive was models_ear.tar.gz with SHA-256
+d756d111404fe8859fd094e313f1e2b95c489691228dfb05044c6471fe31c819.
+
+No training or evaluation dataset is included in this model repository.
+
+## Links
+
+- Paper: https://aclanthology.org/2023.findings-acl.768/
+- Code: https://github.com/voidism/EAR
+- arXiv: https://arxiv.org/abs/2305.17080
+
+## Citation
+
+    @inproceedings{chuang-etal-2023-expand,
+      title = {Expand, Rerank, and Retrieve: Query Reranking for Open-Domain Question Answering},
+      author = {Chuang, Yung-Sung and Fang, Wei and Li, Shang-Wen and Yih, Wen-tau and Glass, James},
+      booktitle = {Findings of the Association for Computational Linguistics: ACL 2023},
+      year = {2023},
+      pages = {12131--12147},
+      doi = {10.18653/v1/2023.findings-acl.768},
+      url = {https://aclanthology.org/2023.findings-acl.768/}
+    }

SOURCE_MANIFEST.sha256

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