pytorch-ie 0.6.0

State-of-the-art Information Extraction in PyTorch

---

🚀️ Quickstart

$ pip install pytorch-ie

⚡️ Examples

Note: Setting num_workers=0 in the pipeline is only necessary when running an example in an interactive python session. The reason is that multiprocessing doesn’t play well with the interactive python interpreter, see here for details.

Span-classification-based Named Entity Recognition

from dataclasses import dataclass

from pytorch_ie.annotations import LabeledSpan
from pytorch_ie.auto import AutoPipeline
from pytorch_ie.core import AnnotationList, annotation_field
from pytorch_ie.documents import TextDocument

@dataclass
class ExampleDocument(TextDocument):
    entities: AnnotationList[LabeledSpan] = annotation_field(target="text")

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

# see below for the long version
ner_pipeline = AutoPipeline.from_pretrained("pie/example-ner-spanclf-conll03", device=-1, num_workers=0)

ner_pipeline(document, predict_field="entities")

for entity in document.entities.predictions:
    print(f"{entity} -> {entity.label}")

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

To create the same pipeline as above without AutoPipeline:

from pytorch_ie.auto import AutoTaskModule, AutoModel
from pytorch_ie.pipeline import Pipeline

model_name_or_path = "pie/example-ner-spanclf-conll03"
ner_taskmodule = AutoTaskModule.from_pretrained(model_name_or_path)
ner_model = AutoModel.from_pretrained(model_name_or_path)
ner_pipeline = Pipeline(model=ner_model, taskmodule=ner_taskmodule, device=-1, num_workers=0)

Or, without Auto classes at all:

from pytorch_ie.pipeline import Pipeline
from pytorch_ie.models import TransformerSpanClassificationModel
from pytorch_ie.taskmodules import TransformerSpanClassificationTaskModule

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, num_workers=0)

Text-classification-based Relation Extraction

from dataclasses import dataclass

from pytorch_ie.annotations import BinaryRelation, LabeledSpan
from pytorch_ie.auto import AutoPipeline
from pytorch_ie.core import AnnotationList, annotation_field
from pytorch_ie.documents import TextDocument


@dataclass
class ExampleDocument(TextDocument):
    entities: AnnotationList[LabeledSpan] = annotation_field(target="text")
    relations: AnnotationList[BinaryRelation] = annotation_field(target="entities")

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

re_pipeline = AutoPipeline.from_pretrained("pie/example-re-textclf-tacred", device=-1, num_workers=0)

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

re_pipeline(document, predict_field="relations", batch_size=2)

for relation in document.relations.predictions:
    print(f"({relation.head} -> {relation.tail}) -> {relation.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}}
}