sapienzanlp/relik repository overview

pip install relik

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},
                  ...
              ]
          ]
      ]
  ),
)

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"}},
                  ...
              ]
          ]
      ]
  ),
)

docker pull sapienzanlp/relik:latest

docker run -p 12345:8000 sapienzanlp/relik:latest --config relik-ie/relik-cie-small

docker run -p 12345:8000 --gpus all sapienzanlp/relik:latest --config relik-ie/relik-cie-small --device cuda

docker run -p 12345:8000 sapienzanlp/relik:1.0.5-cpu-fastapi --config relik-ie/relik-cie-small

docker run -p 12345:8000 -v ~/.cache/huggingface:/home/relik-user/.cache/huggingface sapienzanlp/relik:latest --config relik-ie/relik-cie-small

curl -X 'GET' \
  'http://127.0.0.1:12345/api/relik?text=Michael%20Jordan%20was%20one%20of%20the%20best%20players%20in%20the%20NBA.&is_split_into_words=false&retriever_batch_size=32&reader_batch_size=32&return_windows=false&use_doc_topic=false&annotation_type=char&relation_threshold=0.5' \
  -H 'accept: application/json'

docker run sapienzanlp/relik:latest -h

Usage: relik [-h --help] [-c --config] [-p --precision] [-d --device] [--retriever] [--retriever-device] 
[--retriever-precision] [--index-device] [--index-precision] [--reader] [--reader-device] [--reader-precision] 
[--annotation-type] [--frontend] [--workers] -- start the FastAPI server for the RElik model

where:
    -h --help               Show this help text
    -c --config             Pretrained ReLiK config name (from HuggingFace) or path
    -p --precision          Precision, default '32'.
    -d --device             Device to use, default 'cpu'.
    --retriever             Override retriever model name.
    --retriever-device      Override retriever device.
    --retriever-precision   Override retriever precision.
    --index-device          Override index device.
    --index-precision       Override index precision.
    --reader                Override reader model name.
    --reader-device         Override reader device.
    --reader-precision      Override reader precision.
    --annotation-type       Annotation type ('char', 'word'), default 'char'.
    --frontend              Whether to start the frontend server.
    --workers               Number of workers to use.

cd gerbil && ./start.sh

cd gerbil-SpotWrapNifWS4Test && mvn clean -Dmaven.tomcat.port=1235 tomcat:run

python relik/reader/utils/gerbil.py --relik-model-name sapienzanlp/relik-entity-linking-large

python relik/reader/trainer/predict_re.py --model_path sapienzanlp/relik-reader-deberta-v3-large-nyt --data_path /Users/perelluis/Documents/relik/data/debug/test.window.candidates.jsonl --is-eval

python relik/reader/trainer/predict_re.py --model_path sapienzanlp/relik-reader-deberta-v3-large-nyt --data_path /Users/perelluis/Documents/relik/data/debug/dev.window.candidates.jsonl --is-eval --compute-threshold

@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",
}