Create README.md

README.md

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+---
+language:
+- en
+---
+
+<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**.
+
+**This repository contains the weights and the index for the Entity Linking ReLiK pipeline.**
+
+## 🛠️ 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 **Entity Linking**:
+
+```python
+from relik import Relik
+from relik.inference.data.objects import RelikOutput
+
+relik = Relik.from_pretrained("sapienzanlp/relik-entity-linking-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="Michael Jordan", text="Michael Jordan"),
+          Span(start=50, end=53, label="National Basketball Association", text="NBA"),
+      ],
+      triples=[],
+      candidates=Candidates(
+          span=[
+              [
+                  [
+                      {"text": "Michael Jordan", "id": 4484083},
+                      {"text": "National Basketball Association", "id": 5209815},
+                      {"text": "Walter Jordan", "id": 2340190},
+                      {"text": "Jordan", "id": 3486773},
+                      {"text": "50 Greatest Players in NBA History", "id": 1742909},
+                      ...
+                  ]
+              ]
+          ]
+      ),
+    )
+
+## 📊 Performance
+
+We evaluate the performance of ReLiK on Entity Linking using [GERBIL](http://gerbil-qa.aksw.org/gerbil/). The following table shows the results (InKB Micro F1) of ReLiK Large and Base:
+
+| Model                                    | AIDA | MSNBC | Der  | K50  | R128 | R500 | O15  | O16  | Tot  | OOD  | AIT (m:s) |
+|------------------------------------------|------|-------|------|------|------|------|------|------|------|------|------------|
+| GENRE                                    | 83.7 | 73.7  | 54.1 | 60.7 | 46.7 | 40.3 | 56.1 | 50.0 | 58.2 | 54.5 | 38:00      |
+| EntQA                                    | 85.8 | 72.1  | 52.9 | 64.5 | **54.1** | 41.9 | 61.1 | 51.3 | 60.5 | 56.4 | 20:00      |
+| [ReLiK<sub>Base<sub>](https://huggingface.co/sapienzanlp/relik-entity-linking-base)                      | 85.3 | 72.3  | 55.6 | 68.0 | 48.1 | 41.6 | 62.5 | 52.3 | 60.7 | 57.2 | 00:29      |
+| ➡️ [ReLiK<sub>Large<sub>](https://huggingface.co/sapienzanlp/relik-entity-linking-large)                     | **86.4** | **75.0**  | **56.3** | **72.8** | 51.7 | **43.0** | **65.1** | **57.2** | **63.4** | **60.2** | 01:46      |
+
+Comparison systems' evaluation (InKB Micro F1) on the *in-domain* AIDA test set and *out-of-domain* MSNBC (MSN), Derczynski (Der), KORE50 (K50), N3-Reuters-128 (R128), 
+N3-RSS-500 (R500), OKE-15 (O15), and OKE-16 (O16) test sets. **Bold** indicates the best model. 
+GENRE uses mention dictionaries. 
+The AIT column shows the time in minutes and seconds (m:s) that the systems need to process the whole AIDA test set using an NVIDIA RTX 4090, 
+except for EntQA which does not fit in 24GB of RAM and for which an A100 is used.
+
+## 🤖 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",
+}
+```