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README.md

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## Installation
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We're currently setting up the package on PyPI. In the meantime, you can install the package from source:
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Lightning IR can be installed using pip:
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```
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pip install git+https://github.com/webis-de/lightning-ir.git
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pip install lightning-ir
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```
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## Getting Started
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See the [Quickstart](https://webis-de.github.io/lightning-ir/quickstart.html) guide for an introduction to Lightning IR. The [Documentation](https://webis-de.github.io/lightning-ir/) provides a detailed overview of the library's functionality.
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The easiest way to use Lightning IR is via the CLI. It uses the [PyTorch Lightning CLI](https://lightning.ai/docs/pytorch/stable/cli/lightning_cli.html#lightning-cli) and adds additional options to provide a unified interface for fine-tuning and running neural ranking models.
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The behavior of the CLI can be customized using yaml configuration files. See the [configs](configs) directory for several example configuration files. For example, the following command can be used to re-rank the official TREC DL 19/20 re-ranking set with a pre-finetuned cross-encoder model. It will automatically download the model and data, run the re-ranking, write the results to a TREC-style run file, and report the nDCG@10 score.
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For more details, see the [Usage](#usage) section.
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## Model Zoo
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### Cross-encoders
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| Model Name | TREC DL 19 | TREC DL 20 |
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| ------------------------------------------------------------------- | ---------- | ---------- |
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| [monoelectra-base](https://huggingface.co/webis/monoelectra-base) | 0.75 | 0.77 |
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| [monoelectra-large](https://huggingface.co/webis/monoelectra-large) | 0.75 | 0.79 |
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| monoT5 (Coming soon) | -- | -- |
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### Bi-encoders
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| Model Name | TREC DL 19/20 nDCG@10 |
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| ----------------------------- | --------------------- |
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| BERT Bi-encoder (Coming soon) | -- |
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| ColBERT (Coming soon) | -- |
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| SPLADE (Coming soon) | -- |
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| XTR (Coming soon) | -- |
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## Usage
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### Command Line Interface

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