pie-datasets 0.10.0

Building scripts for PyTorch-IE Datasets

pie-datasets

Dataset building scripts and utilities for PyTorch-IE. We parse all datasets into a common format that can be loaded directly from the Huggingface Hub. Taking advantage of Huggingface datasets, the documents are cached in an arrow table and serialized / deserialized on the fly. Any changes or preprocessing applied to the documents will be cached as well.

Setup

pip install pie-datasets

To install the latest version from GitHub:

pip install git+https://[e-mail]/ArneBinder/pie-datasets.git

Available datasets

See here for a list of available datasets. Note, that you can easily add your own datasets by following the instructions below.

Usage

General

from pie_datasets import load_dataset

# load the dataset from https://huggingface.co/datasets/pie/conll2003
dataset = load_dataset("pie/conll2003")

print(dataset["train"][0])
# >>> CoNLL2003Document(text='EU rejects German call to boycott British lamb .', id='0', metadata={})

dataset["train"][0].entities
# >>> AnnotationLayer([LabeledSpan(start=0, end=2, label='ORG', score=1.0), LabeledSpan(start=11, end=17, label='MISC', score=1.0), LabeledSpan(start=34, end=41, label='MISC', score=1.0)])

entity = dataset["train"][0].entities[1]

print(f"[{entity.start}, {entity.end}] {entity}")
# >>> [11, 17] German

{name: len(split) for name, split in dataset.items()}
# >>> {'train': 14041, 'validation': 3250, 'test': 3453}

Adjusting splits

Similar to Huggingface datasets, you can adjust the splits of a dataset in various ways. Here are some examples:

from pie_datasets import load_dataset

dataset = load_dataset("pie/conll2003")

# re-create a validation split from train split concatenated with the original validation split
dataset_with_new_val = dataset.concat_splits(
   ["train", "validation"], target="train"
).add_test_split(
   source_split="train", target_split="my_validation", test_size=0.2, seed=42
)
{name: len(split) for name, split in dataset_with_new_val.items()}
# >>> {'test': 3453, 'train': 13832, 'my_validation': 3459}

# drop the test split
dataset_without_test = dataset_with_new_val.drop_splits(["test"])
{name: len(split) for name, split in dataset_without_test.items()}
# >>> {'train': 13832, 'my_validation': 3459}

Adjusting dataset entries

Calling map on the dataset will apply the given function to all its documents. Internally, that relies on datasets.Dataset.map. Thus, the function can be any function that takes a document as input and returns a document as output. If the function returns a different document type, you need to provide it as result_document_type argument to map. Note, that the result is cached for each split, so that re-running the same function on the same dataset will be a no-op.

Example where the function returns the same document type:

from pie_datasets import load_dataset

def duplicate_entities(document):
    new_document = document.copy()
    for entity in document.entities:
        # we need to copy the entity because each annotation can only be part of one document
        new_document.entities.append(entity.copy())
    return new_document

dataset = load_dataset("pie/conll2003")
len(dataset["train"][0].entities)
# >>> 3

converted_dataset = dataset.map(duplicate_entities)
# Map: 100%|██████████| 14041/14041 [00:02<00:00, 4697.18 examples/s]
# Map: 100%|██████████| 3250/3250 [00:00<00:00, 4583.95 examples/s]
# Map: 100%|██████████| 3453/3453 [00:00<00:00, 4614.67 examples/s]
len(converted_dataset["train"][0].entities)
# >>> 6

Example where the function returns a different document type:

from dataclasses import dataclass

from pytorch_ie.core import AnnotationLayer, annotation_field
from pytorch_ie.documents import TextBasedDocument
from pytorch_ie.annotations import LabeledSpan, Span
from pie_datasets import load_dataset

@dataclass
class CoNLL2003DocumentWithWords(TextBasedDocument):
    entities: AnnotationLayer[LabeledSpan] = annotation_field(target="text")
    words: AnnotationLayer[Span] = annotation_field(target="text")

def add_words(document) -> CoNLL2003DocumentWithWords:
    new_document = CoNLL2003DocumentWithWords(text=document.text, id=document.id)
    for entity in document.entities:
        new_document.entities.append(entity.copy())
    start = 0
    for word in document.text.split():
        word_start = document.text.index(word, start)
        word_annotation = Span(start=word_start, end=word_start + len(word))
        new_document.words.append(word_annotation)
    return new_document

dataset = load_dataset("pie/conll2003")
dataset.document_type
# >>> <class 'datasets_modules.datasets.pie--conll2003.821bfce48d2ebc3533db067c4d8e89396155c65cd311d2341a82acf81f561885.conll2003.CoNLL2003Document'>

converted_dataset = dataset.map(add_words, result_document_type=CoNLL2003DocumentWithWords)
# Map: 100%|██████████| 14041/14041 [00:03<00:00, 3902.00 examples/s]
# Map: 100%|██████████| 3250/3250 [00:00<00:00, 3929.52 examples/s]
# Map: 100%|██████████| 3453/3453 [00:00<00:00, 3947.49 examples/s]

converted_dataset.document_type
# >>> <class '__main__.CoNLL2003DocumentWithWords'>

converted_dataset["train"][0].words
# >>> AnnotationLayer([Span(start=0, end=2), Span(start=3, end=10), Span(start=11, end=17), Span(start=18, end=22), Span(start=23, end=25), Span(start=26, end=33), Span(start=34, end=41), Span(start=42, end=46), Span(start=47, end=48)])

[str(word) for word in converted_dataset["train"][0].words]
# >>> ['EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'lamb', '.']

We can also register a document converter for a specific document type. This will be used when calling to_document_type with the respective document type. The following code will produce the same result as the previous one:

dataset = load_dataset("pie/conll2003")

# Register add_words as a converter function for the target document type CoNLL2003DocumentWithWords.
# Since add_words specifies the return type, we can omit the document type here.
dataset.register_document_converter(add_words)

# Determine the matching converter entry for the target document type and apply it with dataset.map.
converted_dataset = dataset.to_document_type(CoNLL2003DocumentWithWords)

Note, that some of the PIE datasets come with default document converters. For instance, the PIE conll2003 dataset comes with one that converts the dataset to pytorch_ie.documents.TextDocumentWithLabeledSpans. These documents work with the PIE taskmodules for token classification and span classification out-of-the-box. The following code will load the dataset and convert it to the required document type:

from pie_datasets import load_dataset
from pytorch_ie.taskmodules import TransformerTokenClassificationTaskModule

taskmodule = TransformerTokenClassificationTaskModule(tokenizer_name_or_path="bert-base-cased")
# the taskmodule expects TextDocumentWithLabeledSpans as input and the conll2003 dataset comes with a
# default converter for that document type. Thus, we can directly load the dataset and convert it.
dataset = load_dataset("pie/conll2003").to_document_type(taskmodule.document_type)
...

How to create your own PIE dataset

PIE datasets are built on top of Huggingface datasets. For instance, consider conll2003 at the Huggingface Hub and especially its respective dataset loading script. To create a PIE dataset from that, you have to implement:

1. A Document class. This will be the type of individual dataset examples.

from dataclasses import dataclass

from pytorch_ie.annotations import LabeledSpan
from pytorch_ie.core import AnnotationLayer, annotation_field
from pytorch_ie.documents import TextBasedDocument

@dataclass
class CoNLL2003Document(TextBasedDocument):
    entities: AnnotationLayer[LabeledSpan] = annotation_field(target="text")

Here we derive from TextBasedDocument that has a simple text string as base annotation target. The CoNLL2003Document adds one single annotation layer called entities that consists of LabeledSpans which reference the text field of the document. You can add further annotation types by adding AnnotationLayer fields that may also reference (i.e. target) other annotations as you like. The package pytorch_ie.annotations contains some predefined annotation types and the package pytorch_ie.documents defines some document types that you can use as base classes.

2. A dataset config. This is similar to creating a Huggingface dataset config.

import datasets

class CoNLL2003Config(datasets.BuilderConfig):
    """BuilderConfig for CoNLL2003"""

    def __init__(self, **kwargs):
        """BuilderConfig for CoNLL2003.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super().__init__(**kwargs)

3. A dataset builder class. This should inherit from pie_datasets.GeneratorBasedBuilder which is a wrapper around the Huggingface dataset builder class with some utility functionality to work with PyTorch-IE Documents. The key elements to implement are: DOCUMENT_TYPE, BASE_DATASET_PATH, and _generate_document.

from pytorch_ie.documents import TextDocumentWithLabeledSpans
from pytorch_ie.utils.span import tokens_and_tags_to_text_and_labeled_spans
from pie_datasets import GeneratorBasedBuilder

class Conll2003(GeneratorBasedBuilder):
    # Specify the document type. This will be the class of individual dataset examples.
    DOCUMENT_TYPE = CoNLL2003Document

    # The Huggingface identifier that points to the base dataset. This may be any string that works
    # as path with Huggingface `datasets.load_dataset`.
    BASE_DATASET_PATH = "conll2003"
    # It is strongly recommended to also specify the revision (tag name, or branch name, or commit hash)
    # of the base dataset. This ensures that the dataset will not change unexpectedly when the base dataset
    # is updated.
    BASE_DATASET_REVISION = "01ad4ad271976c5258b9ed9b910469a806ff3288"

    # The builder configs, see https://huggingface.co/docs/datasets/dataset_script for further information.
    BUILDER_CONFIGS = [
        CoNLL2003Config(
            name="conll2003", version=datasets.Version("1.0.0"), description="CoNLL2003 dataset"
        ),
    ]

    # [Optional] Define additional keyword arguments which will be passed to `_generate_document` below.
    def _generate_document_kwargs(self, dataset):
        return {"int_to_str": dataset.features["ner_tags"].feature.int2str}

    # Define how a Pytorch-IE Document will be created from a Huggingface dataset example.
    def _generate_document(self, example, int_to_str):
        doc_id = example["id"]
        tokens = example["tokens"]
        ner_tags = [int_to_str(tag) for tag in example["ner_tags"]]

        text, ner_spans = tokens_and_tags_to_text_and_labeled_spans(tokens=tokens, tags=ner_tags)

        document = CoNLL2003Document(text=text, id=doc_id)

        for span in sorted(ner_spans, key=lambda span: span.start):
            document.entities.append(span)

        return document

    # [OPTIONAL] Define how the dataset will be converted to a different document type. Here, we add a
    # converter for the generic document type `TextDocumentWithLabeledSpans` that is used by the PIE
    # taskmodules for token and span classification. This allows to directly call
    # `pie_datasets.load_dataset("pie/conll2003").to_document_type(TextDocumentWithLabeledSpans)`.
    DOCUMENT_CONVERTERS = {
        TextDocumentWithLabeledSpans: {
            # if the converter is a simple dictionary, just rename the layer according that
            "entities": "labeled_spans",
        }
    }

The full script can be found here: dataset_builders/pie/conll2003/conll2003.py. Note, that to load the dataset with pie_datasets.load_dataset, the script has to be located in a directory with the same name (as it is the case for standard Huggingface dataset loading scripts).

Development

Setup

1. This project is build with Poetry. See here for installation instructions.
2. Get the code and switch into the project directory:

   git clone https://github.com/ArneBinder/pie-datasets
   cd pie-datasets
   

3. Create a virtual environment and install the dependencies:

   poetry install
   

Finally, to run any of the below commands, you need to activate the virtual environment:

poetry shell

Note: You can also run commands in the virtual environment without activating it first: poetry run <command>.

Code Formatting, Linting and Static Type Checking

pre-commit run -a

Testing

run all tests with coverage:

pytest --cov --cov-report term-missing

Releasing

1. Create the release branch: git switch --create release main
2. Increase the project version: poetry version <PATCH|MINOR|MAJOR>, e.g. poetry version patch for a patch release. If the release contains new features, or breaking changes, bump the minor version (this project has no main release yet). If the release contains only bugfixes, bump the patch version. See Semantic Versioning for more information.
3. Increase the maximum version in all dataset requirements.txt files, if not a patch release.
4. Commit the changes: git commit --message="release <NEW VERSION>" -a, e.g. git commit --message="release 0.13.0" -a
5. Push the changes to GitHub: git push origin release
6. Create a PR for that release branch on GitHub.
7. Wait until checks passed successfully.
8. Merge the PR into the main branch. This triggers the GitHub Action that creates all relevant release artefacts and also uploads them to PyPI.
9. Cleanup: Delete the release branch. This is important, because otherwise the next release will fail.
10. Update the scripts at huggingface.co/pie with the new versions.