relik-ie/relik-reader-deberta-v3-small-re-wikipedia

<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> &nbsp; &nbsp; <a href="https://aclanthology.org/"><img src="http://img.shields.io/badge/paper-ACL--anthology-B31B1B.svg"></a> &nbsp; &nbsp; <a href="https://arxiv.org/abs/placeholder"><img src="https://img.shields.io/badge/arXiv-placeholder-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> &nbsp; &nbsp; <a href="https://github.com/SapienzaNLP/relik"><img src="https://img.shields.io/badge/GitHub-Repo-121013?logo=github&logoColor=white"></a> &nbsp; &nbsp; <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

pip install relik

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

Install with optional dependencies

Install with all the optional dependencies.

pip install relik[all]

Install with optional dependencies for training and evaluation.

pip install relik[train]

Install with optional dependencies for FAISS

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

For CPU:

pip install relik[faiss]

For GPU:

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 and Ray.

pip install relik[serve]

Installation from source

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:

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:

🤖 Models

Models can be found on 🤗 Hugging Face.

💽 Cite this work

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

@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",
}