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relik-ie/
relik-relation-extraction-small-wikipedia-ner

Name: relik-relation-extraction-small-wikipedia-ner
Availability: InStock

2408.00103

apache-2.0

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Retrieve, Read and LinK: Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget

This card is for a Relation Extraction model trained with Named Entity Recognition. The relation predictions are Wikidata properties. The entity types are taken from Babelscape/SREDFM and are still WIP.

This card is for the whole system, using a RE retriever and a Reader. The NER types use a dummy retriever that always gives the same candidates to the Reader. They are somewhat flexible (similar to GliNER) but this is still WIP and we are working to improve it.

python

from relik.inference.annotator import Relik
from relik.retriever.indexers.inmemory import InMemoryDocumentIndex
from relik.retriever.pytorch_modules.model import GoldenRetriever
from relik.retriever.pytorch_modules import RetrievedSample
from relik.retriever.indexers.document import Document, DocumentStore
from relik.retriever.indexers.base import BaseDocumentIndex

class GoldenSillyRetriever(GoldenRetriever):
    def __init__(self, documents: List[str], *args, **kwargs):
        self.documents = DocumentStore([Document(doc) for doc in documents])
        self.document_index = BaseDocumentIndex(self.documents)
    def retrieve(self,
                text: Optional[Union[str, List[str]]] = None,
                k: int = 100,
                *args,
                **kwargs,
    ) -> List[List[RetrievedSample]]:
        if isinstance(text, str):
            text = [text]
        elif text is None:
            text = []
        return [
            [RetrievedSample(score=1.0, document=doc) for doc in self.documents[:k]]
            for _ in text
        ]
    def index(self):
        pass
    def eval(self):
        pass
    def save_pretrained(self):
        pass
    def to(self, device):
        pass

# And pass this to the ReLiK module:
wikidata_retriever = GoldenRetriever("relik-ie/encoder-e5-small-v2-wikipedia-relations", device="cuda")
wikidata_index = InMemoryDocumentIndex.from_pretrained("relik-ie/encoder-e5-small-v2-wikipedia-relations-index", index_precision="bf16", device="cuda")

ner_type_retriever = GoldenSillyRetriever(
                documents=['media', 'disease', 'miscellaneous', 'event', 'person', 'location', 'time', 'celestial', 'organization', 'concept']
            )

relik = Relik.from_pretrained(
        "relik-ie/relik-relation-extraction-small-wikipedia-ner",
        index=wikidata_index,
        device="cuda",
        use_nme=True,
        retriever={
            TaskType.SPAN: ner_type_retriever,
            TaskType.TRIPLET: wikidata_retriever,
        },
        index={
            TaskType.SPAN: ner_type_retriever.document_index,
            TaskType.TRIPLET: wikidata_index,
        }
)

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

🛠️ Installation

Installation from PyPI

pip install relik

Other installation options

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:

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 and Ray.

pip install relik[serve]

Installation from source

bash

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 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. 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_Base	85.3	72.3	55.6	68.0	48.1	41.6	62.5	52.3	60.7	57.2	00:29
➡️ ReLiK_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.

💽 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",
}