add conll2003 dataset

dataset_builders/pie/conll2003/README.md

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+# PIE Dataset Card for "conll2003"
+
+This is a [PyTorch-IE](https://github.com/ChristophAlt/pytorch-ie) wrapper for the
+[CoNLL 2003 Huggingface dataset loading script](https://huggingface.co/datasets/conll2003).
+
+## Data Schema
+
+The document type for this dataset is `CoNLL2003Document` which defines the following data fields:
+
+- `text` (str)
+- `id` (str, optional)
+- `metadata` (dictionary, optional)
+
+and the following annotation layers:
+
+- `entities` (annotation type: `LabeledSpan`, target: `text`)
+
+See [here](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/annotations.py) for the definitions of
+the annotation types.

dataset_builders/pie/conll2003/conll2003.py

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+from dataclasses import dataclass
+
+import datasets
+import pytorch_ie.data.builder
+from pytorch_ie.annotations import LabeledSpan
+from pytorch_ie.core import AnnotationList, annotation_field
+from pytorch_ie.documents import TextDocument, TextDocumentWithLabeledSpans
+from pytorch_ie.utils.span import tokens_and_tags_to_text_and_labeled_spans
+
+
+@dataclass
+class CoNLL2003Document(TextDocument):
+    entities: AnnotationList[LabeledSpan] = annotation_field(target="text")
+
+
+class Conll2003(pytorch_ie.data.builder.GeneratorBasedBuilder):
+    DOCUMENT_TYPE = CoNLL2003Document
+
+    BASE_DATASET_PATH = "conll2003"
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="conll2003", version=datasets.Version("1.0.0"), description="CoNLL2003 dataset"
+        ),
+    ]
+
+    DOCUMENT_CONVERTERS = {
+        TextDocumentWithLabeledSpans: {
+            # just rename the layer
+            "entities": "labeled_spans",
+        }
+    }
+
+    def _generate_document_kwargs(self, dataset):
+        return {"int_to_str": dataset.features["ner_tags"].feature.int2str}
+
+    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

tests/dataset_builders/pie/test_conll2003.py

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+import datasets
+import pytest
+from pytorch_ie import DatasetDict
+from pytorch_ie.core import Document
+from pytorch_ie.documents import TextDocumentWithLabeledSpans
+
+from dataset_builders.pie.conll2003.conll2003 import Conll2003
+from tests.dataset_builders.common import PIE_BASE_PATH
+
+DATASET_NAME = "conll2003"
+PIE_DATASET_PATH = PIE_BASE_PATH / DATASET_NAME
+HF_DATASET_PATH = Conll2003.BASE_DATASET_PATH
+SPLIT_NAMES = {"train", "validation", "test"}
+SPLIT_SIZES = {"train": 14041, "validation": 3250, "test": 3453}
+
+
+@pytest.fixture(params=[config.name for config in Conll2003.BUILDER_CONFIGS], scope="module")
+def dataset_name(request):
+    return request.param
+
+
+@pytest.fixture(scope="module")
+def hf_dataset(dataset_name):
+    return datasets.load_dataset(str(HF_DATASET_PATH), name=dataset_name)
+
+
+def test_hf_dataset(hf_dataset):
+    assert set(hf_dataset) == SPLIT_NAMES
+    split_sizes = {split_name: len(ds) for split_name, ds in hf_dataset.items()}
+    assert split_sizes == SPLIT_SIZES
+
+
+@pytest.fixture(scope="module")
+def hf_example(hf_dataset):
+    return hf_dataset["train"][0]
+
+
+def test_hf_example(hf_example, dataset_name):
+    if dataset_name == "conll2003":
+        assert hf_example == {
+            "chunk_tags": [11, 21, 11, 12, 21, 22, 11, 12, 0],
+            "id": "0",
+            "ner_tags": [3, 0, 7, 0, 0, 0, 7, 0, 0],
+            "pos_tags": [22, 42, 16, 21, 35, 37, 16, 21, 7],
+            "tokens": ["EU", "rejects", "German", "call", "to", "boycott", "British", "lamb", "."],
+        }
+    else:
+        raise ValueError(f"Unknown dataset name: {dataset_name}")
+
+
+@pytest.fixture(scope="module")
+def document(hf_example, hf_dataset):
+    conll2003 = Conll2003()
+    generate_document_kwargs = conll2003._generate_document_kwargs(hf_dataset["train"])
+    document = conll2003._generate_document(example=hf_example, **generate_document_kwargs)
+    return document
+
+
+def test_document(document, dataset_name):
+    assert isinstance(document, Document)
+    if dataset_name == "conll2003":
+        assert document.text == "EU rejects German call to boycott British lamb ."
+        entities = list(document.entities)
+        assert len(entities) == 3
+        assert str(entities[0]) == "EU"
+        assert str(entities[1]) == "German"
+        assert str(entities[2]) == "British"
+    else:
+        raise ValueError(f"Unknown dataset name: {dataset_name}")
+
+
+@pytest.fixture(scope="module")
+def pie_dataset(dataset_name):
+    return DatasetDict.load_dataset(str(PIE_DATASET_PATH), name=dataset_name)
+
+
+def test_pie_dataset(pie_dataset):
+    assert set(pie_dataset) == SPLIT_NAMES
+    split_sizes = {split_name: len(ds) for split_name, ds in pie_dataset.items()}
+    assert split_sizes == SPLIT_SIZES
+
+
+@pytest.fixture(scope="module", params=list(Conll2003.DOCUMENT_CONVERTERS))
+def converter_document_type(request):
+    return request.param
+
+
+@pytest.fixture(scope="module")
+def converted_pie_dataset(pie_dataset, converter_document_type):
+    pie_dataset_converted = pie_dataset.to_document_type(document_type=converter_document_type)
+    return pie_dataset_converted
+
+
+def test_converted_pie_dataset(converted_pie_dataset, converter_document_type):
+    assert set(converted_pie_dataset) == SPLIT_NAMES
+    split_sizes = {split_name: len(ds) for split_name, ds in converted_pie_dataset.items()}
+    assert split_sizes == SPLIT_SIZES
+    for ds in converted_pie_dataset.values():
+        for document in ds:
+            assert isinstance(document, converter_document_type)
+
+
+@pytest.fixture(scope="module")
+def converted_document(converted_pie_dataset):
+    return converted_pie_dataset["train"][0]
+
+
+def test_converted_document(converted_document, converter_document_type):
+    assert isinstance(converted_document, converter_document_type)
+    if converter_document_type == TextDocumentWithLabeledSpans:
+        assert converted_document.text == "EU rejects German call to boycott British lamb ."
+        entities = list(converted_document.labeled_spans)
+        assert len(entities) == 3
+        assert str(entities[0]) == "EU"
+        assert str(entities[1]) == "German"
+        assert str(entities[2]) == "British"
+    else:
+        raise ValueError(f"Unknown converter document type: {converter_document_type}")