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language: |
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- en |
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<div align="center"> |
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<img src="https://github.com/SapienzaNLP/relik/blob/main/relik.png?raw=true" height="150"> |
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<img src="https://github.com/SapienzaNLP/relik/blob/main/Sapienza_Babelscape.png?raw=true" height="50"> |
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<div align="center"> |
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<h1>Retrieve, Read and LinK: Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget</h1> |
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</div> |
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<div style="display:flex; justify-content: center; align-items: center; flex-direction: row;"> |
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<a href="https://2024.aclweb.org/"><img src="http://img.shields.io/badge/ACL-2024-4b44ce.svg"></a> |
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<a href="https://aclanthology.org/"><img src="http://img.shields.io/badge/paper-ACL--anthology-B31B1B.svg"></a> |
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<a href="https://arxiv.org/abs/2408.00103"><img src="https://img.shields.io/badge/arXiv-2408.00103-b31b1b.svg"></a> |
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</div> |
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<div style="display:flex; justify-content: center; align-items: center; flex-direction: row;"> |
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<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> |
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<a href="https://github.com/SapienzaNLP/relik"><img src="https://img.shields.io/badge/GitHub-Repo-121013?logo=github&logoColor=white"></a> |
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<a href="https://github.com/SapienzaNLP/relik/releases"><img src="https://img.shields.io/github/v/release/SapienzaNLP/relik"></a> |
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</div> |
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A blazing fast and lightweight Information Extraction model for **Entity Linking** and **Relation Extraction**. |
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**This repository contains the weights for the ReLiK Retriever component fine-tuned on NYT dataset.** |
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## 🛠️ Installation |
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Installation from PyPI |
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```bash |
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pip install relik |
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``` |
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<details> |
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<summary>Other installation options</summary> |
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#### Install with optional dependencies |
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Install with all the optional dependencies. |
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```bash |
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pip install relik[all] |
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``` |
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Install with optional dependencies for training and evaluation. |
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```bash |
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pip install relik[train] |
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``` |
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Install with optional dependencies for [FAISS](https://github.com/facebookresearch/faiss) |
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FAISS PyPI package is only available for CPU. For GPU, install it from source or use the conda package. |
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For CPU: |
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```bash |
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pip install relik[faiss] |
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``` |
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For GPU: |
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```bash |
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conda create -n relik python=3.10 |
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conda activate relik |
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# install pytorch |
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conda install -y pytorch=2.1.0 pytorch-cuda=12.1 -c pytorch -c nvidia |
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# GPU |
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conda install -y -c pytorch -c nvidia faiss-gpu=1.8.0 |
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# or GPU with NVIDIA RAFT |
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conda install -y -c pytorch -c nvidia -c rapidsai -c conda-forge faiss-gpu-raft=1.8.0 |
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pip install relik |
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``` |
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Install with optional dependencies for serving the models with |
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[FastAPI](https://fastapi.tiangolo.com/) and [Ray](https://docs.ray.io/en/latest/serve/quickstart.html). |
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```bash |
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pip install relik[serve] |
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``` |
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#### Installation from source |
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```bash |
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git clone https://github.com/SapienzaNLP/relik.git |
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cd relik |
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pip install -e .[all] |
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``` |
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</details> |
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## 🚀 Quick Start |
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[//]: # (Write a short description of the model and how to use it with the `from_pretrained` method.) |
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ReLiK is a lightweight and fast model for **Entity Linking** and **Relation Extraction**. |
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It is composed of two main components: a retriever and a reader. |
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The retriever is responsible for retrieving relevant documents from a large collection, |
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while the reader is responsible for extracting entities and relations from the retrieved documents. |
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ReLiK can be used with the `from_pretrained` method to load a pre-trained pipeline. |
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Here is an example of how to use ReLiK for **Relation Extraction**: |
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```python |
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from relik import Relik |
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from relik.inference.data.objects import RelikOutput |
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relik = Relik.from_pretrained("sapienzanlp/relik-relation-extraction-nyt-large") |
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relik_out: RelikOutput = relik("Michael Jordan was one of the best players in the NBA.") |
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``` |
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RelikOutput( |
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text='Michael Jordan was one of the best players in the NBA.', |
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tokens=Michael Jordan was one of the best players in the NBA., |
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id=0, |
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spans=[ |
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Span(start=0, end=14, label='--NME--', text='Michael Jordan'), |
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Span(start=50, end=53, label='--NME--', text='NBA') |
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], |
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triplets=[ |
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Triplets( |
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subject=Span(start=0, end=14, label='--NME--', text='Michael Jordan'), |
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label='company', |
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object=Span(start=50, end=53, label='--NME--', text='NBA'), |
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confidence=1.0 |
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) |
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], |
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candidates=Candidates( |
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span=[], |
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triplet=[ |
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[ |
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[ |
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{"text": "company", "id": 4, "metadata": {"definition": "company of this person"}}, |
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{"text": "nationality", "id": 10, "metadata": {"definition": "nationality of this person or entity"}}, |
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{"text": "child", "id": 17, "metadata": {"definition": "child of this person"}}, |
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{"text": "founded by", "id": 0, "metadata": {"definition": "founder or co-founder of this organization, religion or place"}}, |
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{"text": "residence", "id": 18, "metadata": {"definition": "place where this person has lived"}}, |
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... |
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] |
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] |
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] |
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), |
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) |
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## 📊 Performance |
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The following table shows the results (Micro F1) of ReLiK Large on the NYT dataset: |
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| Model | NYT | NYT (Pretr) | AIT (m:s) | |
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|------------------------------------------|------|-------|------------| |
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| REBEL | 93.1 | 93.4 | 01:45 | |
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| UiE | 93.5 | -- | -- | |
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| USM | 94.0 | 94.1 | -- | |
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| ➡️ [ReLiK<sub>Large<sub>](https://huggingface.co/sapienzanlp/relik-relation-extraction-nyt-large) | **95.0** | **94.9** | 00:30 | |
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## 🤖 Models |
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Models can be found on [🤗 Hugging Face](https://huggingface.co/collections/sapienzanlp/relik-retrieve-read-and-link-665d9e4a5c3ecba98c1bef19). |
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## 💽 Cite this work |
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If you use any part of this work, please consider citing the paper as follows: |
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```bibtex |
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@inproceedings{orlando-etal-2024-relik, |
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title = "Retrieve, Read and LinK: Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget", |
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author = "Orlando, Riccardo and Huguet Cabot, Pere-Llu{\'\i}s and Barba, Edoardo and Navigli, Roberto", |
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booktitle = "Findings of the Association for Computational Linguistics: ACL 2024", |
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month = aug, |
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year = "2024", |
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address = "Bangkok, Thailand", |
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publisher = "Association for Computational Linguistics", |
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} |
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``` |
