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--- |
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language: |
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- en |
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--- |
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# InRanker-3B (3B parameters) |
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InRanker is a version of monoT5 self-distilled from [monoT5-3B](https://huggingface.co/castorini/monot5-3b-msmarco-10k) with increased effectiveness on out-of-domain scenarios. |
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Our key insight were to use language models and rerankers to generate as much as possible |
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synthetic "in-domain" training data, i.e., data that closely resembles |
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the data that will be seen at retrieval time. The pipeline used for training consists of |
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two distillation phases that do not require additional user queries |
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or manual annotations: (1) training on existing supervised soft |
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teacher labels, and (2) training on teacher soft labels for synthetic |
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queries generated using a large language model. |
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The paper with further details can be found [here](https://arxiv.org/abs/2401.06910). The code and library are available at |
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https://github.com/unicamp-dl/InRanker |
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## Usage |
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The library was tested using python 3.10 and is installed with: |
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```bash |
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pip install inranker |
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``` |
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The code for inference is: |
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```python |
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from inranker import T5Ranker |
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model = T5Ranker(model_name_or_path="unicamp-dl/InRanker-3B") |
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docs = [ |
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"The capital of France is Paris", |
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"Learn deep learning with InRanker and transformers" |
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] |
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scores = model.get_scores( |
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query="What is the best way to learn deep learning?", |
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docs=docs |
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) |
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# Scores are sorted in descending order (most relevant to least) |
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# scores -> [0, 1] |
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sorted_scores = sorted(zip(scores, docs), key=lambda x: x[0], reverse=True) |
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``` |
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## How to Cite |
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``` |
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@misc{laitz2024inranker, |
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title={InRanker: Distilled Rankers for Zero-shot Information Retrieval}, |
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author={Thiago Laitz and Konstantinos Papakostas and Roberto Lotufo and Rodrigo Nogueira}, |
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year={2024}, |
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eprint={2401.06910}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.IR} |
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} |
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``` |
