pytorch-ie 0.3.0

State-of-the-art Information Extraction in PyTorch

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🚀️ Quickstart

$ pip install pytorch-ie

⚡️ Examples

Span-classification-based Named Entity Recognition

from pytorch_ie.taskmodules import TransformerSpanClassificationTaskModule
from pytorch_ie.models import TransformerSpanClassificationModel
from pytorch_ie import Pipeline, Document

model_name_or_path = "pie/example-ner-spanclf-conll03"
ner_taskmodule = TransformerSpanClassificationTaskModule.from_pretrained(model_name_or_path)
ner_model = TransformerSpanClassificationModel.from_pretrained(model_name_or_path)

ner_pipeline = Pipeline(model=ner_model, taskmodule=ner_taskmodule, device=-1)

document = Document("“Making a super tasty alt-chicken wing is only half of it,” said Po Bronson, general partner at SOSV and managing director of IndieBio.")

ner_pipeline(document, predict_field="entities")

for entity in document.predictions.spans["entities"]:
    entity_text = document.text[entity.start : entity.end]
    label = entity.label
    print(f"{entity_text} -> {label}")

# Result:
# IndieBio -> ORG
# Po Bronson -> PER
# SOSV -> ORG

Text-classification-based Relation Extraction

from pytorch_ie.taskmodules import TransformerRETextClassificationTaskModule
from pytorch_ie.models import TransformerTextClassificationModel
from pytorch_ie import Pipeline
from pytorch_ie.data import Document, LabeledSpan

model_name_or_path = "pie/example-re-textclf-tacred"
re_taskmodule = TransformerRETextClassificationTaskModule.from_pretrained(model_name_or_path)
re_model = TransformerTextClassificationModel.from_pretrained(model_name_or_path)

re_pipeline = Pipeline(model=re_model, taskmodule=re_taskmodule, device=-1)

document = Document("“Making a super tasty alt-chicken wing is only half of it,” said Po Bronson, general partner at SOSV and managing director of IndieBio.")

for start, end, label in [(65, 75, "PER"), (96, 100, "ORG"), (126, 134, "ORG")]:
    document.add_annotation("entities", LabeledSpan(start, end, label))

re_pipeline(document, predict_field="relations")

for relation in document.predictions.binary_relations["relations"]:
    head, tail = relation.head, relation.tail
    head_text = document.text[head.start : head.end]
    tail_text = document.text[tail.start : tail.end]
    label = relation.label
    print(f"({head_text} -> {tail_text}) -> {label}")

# Result:
# (Po Bronson -> SOSV) -> per:employee_of
# (Po Bronson -> IndieBio) -> per:employee_of
# (SOSV -> Po Bronson) -> org:top_members/employees
# (IndieBio -> Po Bronson) -> org:top_members/employees

✨📚✨ Read the full documentation

Development Setup

🏅 Acknowledgements

• This package is based on the sourcery-ai/python-best-practices-cookiecutter and cjolowicz/cookiecutter-hypermodern-python project templates.

📃 Citation

If you want to cite the framework feel free to use this:

@misc{alt2022pytorchie,
author={Christoph Alt, Arne Binder},
title = {PyTorch-IE: State-of-the-art Information Extraction in PyTorch},
year = {2022},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/ChristophAlt/pytorch-ie}}
}