A blazing fast and lightweight Information Extraction model for Entity Linking and Relation Extraction.
This repository contains the weights for the ReLiK Reader component.
🛠️ Installation
Installation from PyPI
pip install relik
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]
🚀 Quick Start
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:
| Model | NYT | NYT (Pretr) | AIT (m:s) |
|---|---|---|---|
| REBEL | 93.1 | 93.4 | 01:45 |
| UiE | 93.5 | -- | -- |
| USM | 94.0 | 94.1 | -- |
| ➡️ ReLiKLarge | 95.0 | 94.9 | 00:30 |
🤖 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",
}