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	---
	language:
	- en
	tags:
	- relik
	---

	<div align="center">
	<img src="https://github.com/SapienzaNLP/relik/blob/main/relik.png?raw=true" height="150">
	<img src="https://github.com/SapienzaNLP/relik/blob/main/Sapienza_Babelscape.png?raw=true" height="50">
	</div>

	<div align="center">
	<h1>Retrieve, Read and LinK: Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget</h1>
	</div>

	<div style="display:flex; justify-content: center; align-items: center; flex-direction: row;">
	<a href="https://2024.aclweb.org/"><img src="http://img.shields.io/badge/ACL-2024-4b44ce.svg"></a>
	<a href="https://aclanthology.org/"><img src="http://img.shields.io/badge/paper-ACL--anthology-B31B1B.svg"></a>
	<a href="https://arxiv.org/abs/2408.00103"><img src="https://img.shields.io/badge/arXiv-2408.00103-b31b1b.svg"></a>
	</div>
	<div style="display:flex; justify-content: center; align-items: center; flex-direction: row;">
	<a href="https://huggingface.co/collections/sapienzanlp/relik-retrieve-read-and-link-665d9e4a5c3ecba98c1bef19"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Collection-FCD21D"></a>
	<a href="https://github.com/SapienzaNLP/relik"><img src="https://img.shields.io/badge/GitHub-Repo-121013?logo=github&logoColor=white"></a>
	<a href="https://github.com/SapienzaNLP/relik/releases"><img src="https://img.shields.io/github/v/release/SapienzaNLP/relik"></a>
	</div>


	A blazing fast and lightweight Information Extraction model for Entity Linking and Relation Extraction.

	## 🛠️ Installation

	Installation from PyPI

	```bash
	pip install relik
	```

	<details>
	<summary>Other installation options</summary>

	#### Install with optional dependencies

	Install with all the optional dependencies.

	```bash
	pip install relik[all]
	```

	Install with optional dependencies for training and evaluation.

	```bash
	pip install relik[train]
	```

	Install with optional dependencies for [FAISS](https://github.com/facebookresearch/faiss)

	FAISS PyPI package is only available for CPU. For GPU, install it from source or use the conda package.

	For CPU:

	```bash
	pip install relik[faiss]
	```

	For GPU:

	```bash
	conda create -n relik python=3.10
	conda activate relik

	# install pytorch
	conda install -y pytorch=2.1.0 pytorch-cuda=12.1 -c pytorch -c nvidia

	# GPU
	conda install -y -c pytorch -c nvidia faiss-gpu=1.8.0
	# or GPU with NVIDIA RAFT
	conda install -y -c pytorch -c nvidia -c rapidsai -c conda-forge faiss-gpu-raft=1.8.0

	pip install relik
	```

	Install with optional dependencies for serving the models with
	[FastAPI](https://fastapi.tiangolo.com/) and [Ray](https://docs.ray.io/en/latest/serve/quickstart.html).

	```bash
	pip install relik[serve]
	```

	#### Installation from source

	```bash
	git clone https://github.com/SapienzaNLP/relik.git
	cd relik
	pip install -e .[all]
	```

	</details>

	## 🚀 Quick Start

	[//]: # (Write a short description of the model and how to use it with the `from_pretrained` method.)

	ReLiK is a lightweight and fast model for Entity Linking and Relation Extraction.
	It is composed of two main components: a retriever and a reader.
	The retriever is responsible for retrieving relevant documents from a large collection,
	while the reader is responsible for extracting entities and relations from the retrieved documents.
	ReLiK can be used with the `from_pretrained` method to load a pre-trained pipeline.

	Here is an example of how to use ReLiK for Relation Extraction:

	```python
	from relik import Relik
	from relik.inference.data.objects import RelikOutput

	relik = Relik.from_pretrained("sapienzanlp/relik-relation-extraction-nyt-large")
	relik_out: RelikOutput = relik("Michael Jordan was one of the best players in the NBA.")
	```


	RelikOutput(
	text='Michael Jordan was one of the best players in the NBA.',
	tokens=Michael Jordan was one of the best players in the NBA.,
	id=0,
	spans=[
	Span(start=0, end=14, label='--NME--', text='Michael Jordan'),
	Span(start=50, end=53, label='--NME--', text='NBA')
	],
	triplets=[
	Triplets(
	subject=Span(start=0, end=14, label='--NME--', text='Michael Jordan'),
	label='company',
	object=Span(start=50, end=53, label='--NME--', text='NBA'),
	confidence=1.0
	)
	],
	candidates=Candidates(
	span=[],
	triplet=[
	[
	[
	{"text": "company", "id": 4, "metadata": {"definition": "company of this person"}},
	{"text": "nationality", "id": 10, "metadata": {"definition": "nationality of this person or entity"}},
	{"text": "child", "id": 17, "metadata": {"definition": "child of this person"}},
	{"text": "founded by", "id": 0, "metadata": {"definition": "founder or co-founder of this organization, religion or place"}},
	{"text": "residence", "id": 18, "metadata": {"definition": "place where this person has lived"}},
	...
	]
	]
	]
	),
	)


	## 📊 Performance

	The following table shows the results (Micro F1) of ReLiK Large on the NYT dataset:

	\| Model \| NYT \| NYT (Pretr) \| AIT (m:s) \|
	\|------------------------------------------\|------\|-------\|------------\|
	\| REBEL \| 93.1 \| 93.4 \| 01:45 \|
	\| UiE \| 93.5 \| -- \| -- \|
	\| USM \| 94.0 \| 94.1 \| -- \|
	\| ➡️ [ReLiK<sub>Large<sub>](https://huggingface.co/sapienzanlp/relik-relation-extraction-nyt-large) \| 95.0 \| 94.9 \| 00:30 \|


	## 🤖 Models

	Models can be found on [🤗 Hugging Face](https://huggingface.co/collections/sapienzanlp/relik-retrieve-read-and-link-665d9e4a5c3ecba98c1bef19).

	## 💽 Cite this work

	If you use any part of this work, please consider citing the paper as follows:

	```bibtex
	@inproceedings{orlando-etal-2024-relik,
	title = "Retrieve, Read and LinK: Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget",
	author = "Orlando, Riccardo and Huguet Cabot, Pere-Llu{\'\i}s and Barba, Edoardo and Navigli, Roberto",
	booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
	month = aug,
	year = "2024",
	address = "Bangkok, Thailand",
	publisher = "Association for Computational Linguistics",
	}
	```