diff --git a/codecov.yml b/codecov.yml
index 4daa1dd8..3ac41467 100644
--- a/codecov.yml
+++ b/codecov.yml
@@ -2,7 +2,7 @@ coverage:
     status:
         project:
             default:
-                target: 70% # TODO: switch back to auto
+                target: 68% # TODO: switch back to auto
                 threshold: 1% # the leniency in hitting the target
         patch:
             default:
diff --git a/poetry.lock b/poetry.lock
index cd2b795c..009d8624 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -346,100 +346,64 @@ markers = {main = "platform_system == \"Windows\"", dev = "sys_platform == \"win
 
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 name = "coverage"
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+version = "7.5.2"
 description = "Code coverage measurement for Python"
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.8"
 groups = ["dev"]
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+    {file = "pandas-2.2.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:eee3a87076c0756de40b05c5e9a6069c035ba43e8dd71c379e68cab2c20f16ad"},
+    {file = "pandas-2.2.2-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:3e374f59e440d4ab45ca2fffde54b81ac3834cf5ae2cdfa69c90bc03bde04d76"},
+    {file = "pandas-2.2.2-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:43498c0bdb43d55cb162cdc8c06fac328ccb5d2eabe3cadeb3529ae6f0517c32"},
+    {file = "pandas-2.2.2-cp312-cp312-win_amd64.whl", hash = "sha256:d187d355ecec3629624fccb01d104da7d7f391db0311145817525281e2804d23"},
+    {file = "pandas-2.2.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:0ca6377b8fca51815f382bd0b697a0814c8bda55115678cbc94c30aacbb6eff2"},
+    {file = "pandas-2.2.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:9057e6aa78a584bc93a13f0a9bf7e753a5e9770a30b4d758b8d5f2a62a9433cd"},
+    {file = "pandas-2.2.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:001910ad31abc7bf06f49dcc903755d2f7f3a9186c0c040b827e522e9cef0863"},
+    {file = "pandas-2.2.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:66b479b0bd07204e37583c191535505410daa8df638fd8e75ae1b383851fe921"},
+    {file = "pandas-2.2.2-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:a77e9d1c386196879aa5eb712e77461aaee433e54c68cf253053a73b7e49c33a"},
+    {file = "pandas-2.2.2-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:92fd6b027924a7e178ac202cfbe25e53368db90d56872d20ffae94b96c7acc57"},
+    {file = "pandas-2.2.2-cp39-cp39-win_amd64.whl", hash = "sha256:640cef9aa381b60e296db324337a554aeeb883ead99dc8f6c18e81a93942f5f4"},
+    {file = "pandas-2.2.2.tar.gz", hash = "sha256:9e79019aba43cb4fda9e4d983f8e88ca0373adbb697ae9c6c43093218de28b54"},
 ]
 
 [package.dependencies]
@@ -1442,6 +1393,22 @@ files = [
 [package.dependencies]
 huggingface_hub = ">=0.23.4,<0.26.0"
 
+[[package]]
+name = "pie-documents"
+version = "0.1.0"
+description = "Python-IE document and annotation types as well as document processing utilities"
+optional = false
+python-versions = "<4.0,>=3.9"
+groups = ["main"]
+files = [
+    {file = "pie_documents-0.1.0-py3-none-any.whl", hash = "sha256:4802b8f4e58c8a97c26566818ee42597ec180ba49098453813891db5640ee20a"},
+    {file = "pie_documents-0.1.0.tar.gz", hash = "sha256:93b041dd7ca36ee246213e585a7ae10ae4bc6fc7aadc39bb049badc21b259457"},
+]
+
+[package.dependencies]
+pandas = ">=2.0.3,<3.0.0"
+pie-core = ">=0.2.0,<0.4.0"
+
 [[package]]
 name = "platformdirs"
 version = "4.2.2"
@@ -1535,14 +1502,14 @@ dev = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments
 
 [[package]]
 name = "pytest-cov"
-version = "6.2.1"
+version = "6.3.0"
 description = "Pytest plugin for measuring coverage."
 optional = false
 python-versions = ">=3.9"
 groups = ["dev"]
 files = [
-    {file = "pytest_cov-6.2.1-py3-none-any.whl", hash = "sha256:f5bc4c23f42f1cdd23c70b1dab1bbaef4fc505ba950d53e0081d0730dd7e86d5"},
-    {file = "pytest_cov-6.2.1.tar.gz", hash = "sha256:25cc6cc0a5358204b8108ecedc51a9b57b34cc6b8c967cc2c01a4e00d8a67da2"},
+    {file = "pytest_cov-6.3.0-py3-none-any.whl", hash = "sha256:440db28156d2468cafc0415b4f8e50856a0d11faefa38f30906048fe490f1749"},
+    {file = "pytest_cov-6.3.0.tar.gz", hash = "sha256:35c580e7800f87ce892e687461166e1ac2bcb8fb9e13aea79032518d6e503ff2"},
 ]
 
 [package.dependencies]
@@ -1638,30 +1605,30 @@ test = ["cloudpickle (>=1.3)", "coverage (==7.3.1)", "fastapi", "onnx (>=0.14.0)
 
 [[package]]
 name = "pytz"
-version = "2025.2"
+version = "2024.1"
 description = "World timezone definitions, modern and historical"
 optional = false
 python-versions = "*"
 groups = ["main"]
 files = [
-    {file = "pytz-2025.2-py2.py3-none-any.whl", hash = "sha256:5ddf76296dd8c44c26eb8f4b6f35488f3ccbf6fbbd7adee0b7262d43f0ec2f00"},
-    {file = "pytz-2025.2.tar.gz", hash = "sha256:360b9e3dbb49a209c21ad61809c7fb453643e048b38924c765813546746e81c3"},
+    {file = "pytz-2024.1-py2.py3-none-any.whl", hash = "sha256:328171f4e3623139da4983451950b28e95ac706e13f3f2630a879749e7a8b319"},
+    {file = "pytz-2024.1.tar.gz", hash = "sha256:2a29735ea9c18baf14b448846bde5a48030ed267578472d8955cd0e7443a9812"},
 ]
 
 [[package]]
 name = "pyupgrade"
-version = "3.20.0"
+version = "3.15.2"
 description = "A tool to automatically upgrade syntax for newer versions."
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.8.1"
 groups = ["dev"]
 files = [
-    {file = "pyupgrade-3.20.0-py2.py3-none-any.whl", hash = "sha256:cd5bf842b863f50adad324a01c30aef60b9f698a9814848094818659c92cd1f4"},
-    {file = "pyupgrade-3.20.0.tar.gz", hash = "sha256:dd6a16c13fc1a7db45796008689a9a35420bd364d681430f640c5e54a3d351ea"},
+    {file = "pyupgrade-3.15.2-py2.py3-none-any.whl", hash = "sha256:ce309e0ff8ecb73f56a45f12570be84bbbde9540d13697cacb261a7f595fb1f5"},
+    {file = "pyupgrade-3.15.2.tar.gz", hash = "sha256:c488d6896c546d25845712ef6402657123008d56c1063174e27aabe15bd6b4e5"},
 ]
 
 [package.dependencies]
-tokenize-rt = ">=6.1.0"
+tokenize-rt = ">=5.2.0"
 
 [[package]]
 name = "pyyaml"
@@ -2068,14 +2035,14 @@ files = [
 
 [[package]]
 name = "six"
-version = "1.17.0"
+version = "1.16.0"
 description = "Python 2 and 3 compatibility utilities"
 optional = false
-python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
 groups = ["main"]
 files = [
-    {file = "six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274"},
-    {file = "six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81"},
+    {file = "six-1.16.0-py2.py3-none-any.whl", hash = "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"},
+    {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
 ]
 
 [[package]]
@@ -2110,14 +2077,14 @@ files = [
 
 [[package]]
 name = "tokenize-rt"
-version = "6.2.0"
+version = "5.2.0"
 description = "A wrapper around the stdlib `tokenize` which roundtrips."
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.8"
 groups = ["dev"]
 files = [
-    {file = "tokenize_rt-6.2.0-py2.py3-none-any.whl", hash = "sha256:a152bf4f249c847a66497a4a95f63376ed68ac6abf092a2f7cfb29d044ecff44"},
-    {file = "tokenize_rt-6.2.0.tar.gz", hash = "sha256:8439c042b330c553fdbe1758e4a05c0ed460dbbbb24a606f11f0dee75da4cad6"},
+    {file = "tokenize_rt-5.2.0-py2.py3-none-any.whl", hash = "sha256:b79d41a65cfec71285433511b50271b05da3584a1da144a0752e9c621a285289"},
+    {file = "tokenize_rt-5.2.0.tar.gz", hash = "sha256:9fe80f8a5c1edad2d3ede0f37481cc0cc1538a2f442c9c2f9e4feacd2792d054"},
 ]
 
 [[package]]
@@ -2498,14 +2465,14 @@ files = [
 
 [[package]]
 name = "tzdata"
-version = "2025.2"
+version = "2024.1"
 description = "Provider of IANA time zone data"
 optional = false
 python-versions = ">=2"
 groups = ["main"]
 files = [
-    {file = "tzdata-2025.2-py2.py3-none-any.whl", hash = "sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8"},
-    {file = "tzdata-2025.2.tar.gz", hash = "sha256:b60a638fcc0daffadf82fe0f57e53d06bdec2f36c4df66280ae79bce6bd6f2b9"},
+    {file = "tzdata-2024.1-py2.py3-none-any.whl", hash = "sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252"},
+    {file = "tzdata-2024.1.tar.gz", hash = "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd"},
 ]
 
 [[package]]
@@ -2671,4 +2638,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.9,<4.0"
-content-hash = "41b9146c4a075dccea911a688d3b42819cdecf86459effc0f2b5073fc7d4db22"
+content-hash = "2b1197ef088543dadf4f2048b6ca32cbf781698ffe7f78d79c0e78de388e9f15"
diff --git a/pyproject.toml b/pyproject.toml
index 4d91a0e4..310c033e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -18,12 +18,11 @@ dynamic = [ "classifiers" ]
 requires-python = ">=3.9,<4.0"
 dependencies = [
     "pie-core >=0.2.1, <0.4.0",
+    "pie-documents >=0.1.0, <0.2.0",
     "torch >=1.10",
     "pytorch-lightning >=2, <3",
     "torchmetrics >1, <2",
     "transformers >=4.18, <5",
-    # required for metrics: f1, confusion_matrix, and statsistics
-    "pandas >=2.0.0, <3",
 ]
 
 [project.urls]
@@ -44,6 +43,10 @@ classifiers = [
     "License :: OSI Approved :: MIT License"
 ]
 
+[tool.poetry.urls]
+"Bug Tracker" = "https://github.com/christophalt/pytorch-ie/issues"
+"Changelog" = "https://github.com/christophalt/pytorch-ie/releases"
+
 [tool.poetry.group.dev]
 optional = true
 
diff --git a/src/pytorch_ie/annotations.py b/src/pytorch_ie/annotations.py
index 91b877de..9149a218 100644
--- a/src/pytorch_ie/annotations.py
+++ b/src/pytorch_ie/annotations.py
@@ -1,188 +1,13 @@
-from dataclasses import dataclass, field
-from typing import Any, Optional, Tuple
-
-from pie_core import Annotation
-
-
-def _post_init_single_label(self):
-    if not isinstance(self.label, str):
-        raise ValueError("label must be a single string.")
-
-    if not isinstance(self.score, float):
-        raise ValueError("score must be a single float.")
-
-
-def _post_init_multi_label(self):
-    if self.score is None:
-        score = tuple([1.0] * len(self.label))
-        object.__setattr__(self, "score", score)
-
-    if not isinstance(self.label, tuple):
-        object.__setattr__(self, "label", tuple(self.label))
-
-    if not isinstance(self.score, tuple):
-        object.__setattr__(self, "score", tuple(self.score))
-
-    if len(self.label) != len(self.score):
-        raise ValueError(
-            f"Number of labels ({len(self.label)}) and scores ({len(self.score)}) must be equal."
-        )
-
-
-def _post_init_multi_span(self):
-    if isinstance(self.slices, list):
-        object.__setattr__(self, "slices", tuple(tuple(s) for s in self.slices))
-
-
-def _post_init_arguments_and_roles(self):
-    if len(self.arguments) != len(self.roles):
-        raise ValueError(
-            f"Number of arguments ({len(self.arguments)}) and roles ({len(self.roles)}) must be equal"
-        )
-    if not isinstance(self.arguments, tuple):
-        object.__setattr__(self, "arguments", tuple(self.arguments))
-    if not isinstance(self.roles, tuple):
-        object.__setattr__(self, "roles", tuple(self.roles))
-
-
-@dataclass(eq=True, frozen=True)
-class Label(Annotation):
-    label: str
-    score: float = field(default=1.0, compare=False)
-
-    def __post_init__(self) -> None:
-        _post_init_single_label(self)
-
-    def resolve(self) -> Any:
-        return self.label
-
-
-@dataclass(eq=True, frozen=True)
-class MultiLabel(Annotation):
-    label: Tuple[str, ...]
-    score: Optional[Tuple[float, ...]] = field(default=None, compare=False)
-
-    def __post_init__(self) -> None:
-        _post_init_multi_label(self)
-
-    def resolve(self) -> Any:
-        return self.label
-
-
-@dataclass(eq=True, frozen=True)
-class Span(Annotation):
-    start: int
-    end: int
-
-    def __str__(self) -> str:
-        if not self.is_attached:
-            return super().__str__()
-        return str(self.target[self.start : self.end])
-
-    def resolve(self) -> Any:
-        if self.is_attached:
-            return self.target[self.start : self.end]
-        else:
-            raise ValueError(f"{self} is not attached to a target.")
-
-
-@dataclass(eq=True, frozen=True)
-class LabeledSpan(Span):
-    label: str
-    score: float = field(default=1.0, compare=False)
-
-    def __post_init__(self) -> None:
-        _post_init_single_label(self)
-
-    def resolve(self) -> Any:
-        return self.label, super().resolve()
-
-
-@dataclass(eq=True, frozen=True)
-class MultiLabeledSpan(Span):
-    label: Tuple[str, ...]
-    score: Optional[Tuple[float, ...]] = field(default=None, compare=False)
-
-    def __post_init__(self) -> None:
-        _post_init_multi_label(self)
-
-    def resolve(self) -> Any:
-        return self.label, super().resolve()
-
-
-@dataclass(eq=True, frozen=True)
-class MultiSpan(Annotation):
-    slices: Tuple[Tuple[int, int], ...]
-
-    def __post_init__(self) -> None:
-        _post_init_multi_span(self)
-
-    def __str__(self) -> str:
-        if not self.is_attached:
-            return super().__str__()
-        return str(tuple(self.target[start:end] for start, end in self.slices))
-
-    def resolve(self) -> Any:
-        if self.is_attached:
-            return tuple(self.target[start:end] for start, end in self.slices)
-        else:
-            raise ValueError(f"{self} is not attached to a target.")
-
-
-@dataclass(eq=True, frozen=True)
-class LabeledMultiSpan(MultiSpan):
-    label: str
-    score: float = field(default=1.0, compare=False)
-
-    def __post_init__(self) -> None:
-        super().__post_init__()
-        _post_init_single_label(self)
-
-    def resolve(self) -> Any:
-        return self.label, super().resolve()
-
-
-@dataclass(eq=True, frozen=True)
-class BinaryRelation(Annotation):
-    head: Annotation
-    tail: Annotation
-    label: str
-    score: float = field(default=1.0, compare=False)
-
-    def __post_init__(self) -> None:
-        _post_init_single_label(self)
-
-    def resolve(self) -> Any:
-        return self.label, (self.head.resolve(), self.tail.resolve())
-
-
-@dataclass(eq=True, frozen=True)
-class MultiLabeledBinaryRelation(Annotation):
-    head: Annotation
-    tail: Annotation
-    label: Tuple[str, ...]
-    score: Optional[Tuple[float, ...]] = field(default=None, compare=False)
-
-    def __post_init__(self) -> None:
-        _post_init_multi_label(self)
-
-    def resolve(self) -> Any:
-        return self.label, (self.head.resolve(), self.tail.resolve())
-
-
-@dataclass(eq=True, frozen=True)
-class NaryRelation(Annotation):
-    arguments: Tuple[Annotation, ...]
-    roles: Tuple[str, ...]
-    label: str
-    score: float = field(default=1.0, compare=False)
-
-    def __post_init__(self) -> None:
-        _post_init_arguments_and_roles(self)
-        _post_init_single_label(self)
-
-    def resolve(self) -> Any:
-        return (
-            self.label,
-            tuple((role, arg.resolve()) for arg, role in zip(self.arguments, self.roles)),
-        )
+# backward compatibility
+from pie_documents.annotations import (
+    BinaryRelation,
+    Label,
+    LabeledMultiSpan,
+    LabeledSpan,
+    MultiLabel,
+    MultiLabeledBinaryRelation,
+    MultiLabeledSpan,
+    MultiSpan,
+    NaryRelation,
+    Span,
+)
diff --git a/src/pytorch_ie/documents.py b/src/pytorch_ie/documents.py
index f1f0076b..69ae50ba 100644
--- a/src/pytorch_ie/documents.py
+++ b/src/pytorch_ie/documents.py
@@ -1,171 +1,31 @@
-import dataclasses
-from typing import Any, Dict, Optional, Tuple
-
-from pie_core import AnnotationLayer, Document, annotation_field
-from typing_extensions import TypeAlias
-
-from pytorch_ie.annotations import (
-    BinaryRelation,
-    Label,
-    LabeledMultiSpan,
-    LabeledSpan,
-    MultiLabel,
-    Span,
-)
-
-
-@dataclasses.dataclass
-class WithMetadata:
-    id: Optional[str] = None
-    metadata: Dict[str, Any] = dataclasses.field(default_factory=dict)
-
-
-@dataclasses.dataclass
-class WithTokens:
-    tokens: Tuple[str, ...]
-
-
-@dataclasses.dataclass
-class WithText:
-    text: str
-
-
-@dataclasses.dataclass
-class TextBasedDocument(WithMetadata, WithText, Document):
-    pass
-
-
-@dataclasses.dataclass
-class TokenBasedDocument(WithMetadata, WithTokens, Document):
-    def __post_init__(self) -> None:
-
-        # When used in a dataset, the document gets serialized to json like structure which does not know tuples,
-        # so they get converted to lists. This is a workaround to automatically convert the "tokens" back to tuples
-        # when the document is created from a dataset.
-        if isinstance(self.tokens, list):
-            object.__setattr__(self, "tokens", tuple(self.tokens))
-        elif not isinstance(self.tokens, tuple):
-            raise ValueError("tokens must be a tuple.")
-
-        # Call the default document construction code
-        super().__post_init__()
-
-
 # backwards compatibility
-TextDocument: TypeAlias = TextBasedDocument
-
-
-@dataclasses.dataclass
-class DocumentWithLabel(Document):
-    label: AnnotationLayer[Label] = annotation_field()
-
-
-@dataclasses.dataclass
-class DocumentWithMultiLabel(Document):
-    label: AnnotationLayer[MultiLabel] = annotation_field()
-
-
-@dataclasses.dataclass
-class TextDocumentWithLabel(DocumentWithLabel, TextBasedDocument):
-    pass
-
-
-@dataclasses.dataclass
-class TextDocumentWithMultiLabel(DocumentWithMultiLabel, TextBasedDocument):
-    pass
-
-
-@dataclasses.dataclass
-class TextDocumentWithLabeledPartitions(TextBasedDocument):
-    labeled_partitions: AnnotationLayer[LabeledSpan] = annotation_field(target="text")
-
-
-@dataclasses.dataclass
-class TextDocumentWithSentences(TextBasedDocument):
-    sentences: AnnotationLayer[Span] = annotation_field(target="text")
-
-
-@dataclasses.dataclass
-class TextDocumentWithSpans(TextBasedDocument):
-    spans: AnnotationLayer[Span] = annotation_field(target="text")
-
-
-@dataclasses.dataclass
-class TextDocumentWithLabeledSpans(TextBasedDocument):
-    labeled_spans: AnnotationLayer[LabeledSpan] = annotation_field(target="text")
-
-
-@dataclasses.dataclass
-class TextDocumentWithLabeledSpansAndLabeledPartitions(
-    TextDocumentWithLabeledSpans, TextDocumentWithLabeledPartitions
-):
-    pass
-
-
-@dataclasses.dataclass
-class TextDocumentWithLabeledSpansAndSentences(
-    TextDocumentWithLabeledSpans, TextDocumentWithSentences
-):
-    pass
-
-
-@dataclasses.dataclass
-class TextDocumentWithLabeledSpansAndBinaryRelations(TextDocumentWithLabeledSpans):
-    binary_relations: AnnotationLayer[BinaryRelation] = annotation_field(target="labeled_spans")
-
-
-@dataclasses.dataclass
-class TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions(
-    TextDocumentWithLabeledSpansAndLabeledPartitions,
-    TextDocumentWithLabeledSpansAndBinaryRelations,
+from pie_documents.documents import (
+    DocumentWithLabel,
+    DocumentWithMultiLabel,
+    TextBasedDocument,
+    TextDocumentWithLabel,
+    TextDocumentWithLabeledMultiSpans,
+    TextDocumentWithLabeledMultiSpansAndBinaryRelations,
+    TextDocumentWithLabeledMultiSpansAndLabeledPartitions,
+    TextDocumentWithLabeledMultiSpansBinaryRelationsAndLabeledPartitions,
     TextDocumentWithLabeledPartitions,
-):
-    pass
-
-
-@dataclasses.dataclass
-class TextDocumentWithSpansAndBinaryRelations(TextDocumentWithSpans):
-    binary_relations: AnnotationLayer[BinaryRelation] = annotation_field(target="spans")
-
-
-@dataclasses.dataclass
-class TextDocumentWithSpansAndLabeledPartitions(
-    TextDocumentWithSpans, TextDocumentWithLabeledPartitions
-):
-    pass
-
-
-@dataclasses.dataclass
-class TextDocumentWithSpansBinaryRelationsAndLabeledPartitions(
-    TextDocumentWithSpansAndLabeledPartitions,
+    TextDocumentWithLabeledSpans,
+    TextDocumentWithLabeledSpansAndBinaryRelations,
+    TextDocumentWithLabeledSpansAndLabeledPartitions,
+    TextDocumentWithLabeledSpansAndSentences,
+    TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions,
+    TextDocumentWithMultiLabel,
+    TextDocumentWithSentences,
+    TextDocumentWithSpans,
     TextDocumentWithSpansAndBinaryRelations,
-    TextDocumentWithLabeledPartitions,
-):
-    pass
-
-
-@dataclasses.dataclass
-class TextDocumentWithLabeledMultiSpans(TextBasedDocument):
-    labeled_multi_spans: AnnotationLayer[LabeledMultiSpan] = annotation_field(target="text")
-
-
-@dataclasses.dataclass
-class TextDocumentWithLabeledMultiSpansAndLabeledPartitions(
-    TextDocumentWithLabeledMultiSpans, TextDocumentWithLabeledPartitions
-):
-    pass
-
-
-@dataclasses.dataclass
-class TextDocumentWithLabeledMultiSpansAndBinaryRelations(TextDocumentWithLabeledMultiSpans):
-    binary_relations: AnnotationLayer[BinaryRelation] = annotation_field(
-        target="labeled_multi_spans"
-    )
-
+    TextDocumentWithSpansAndLabeledPartitions,
+    TextDocumentWithSpansBinaryRelationsAndLabeledPartitions,
+    TokenBasedDocument,
+    WithMetadata,
+    WithText,
+    WithTokens,
+)
+from typing_extensions import TypeAlias
 
-@dataclasses.dataclass
-class TextDocumentWithLabeledMultiSpansBinaryRelationsAndLabeledPartitions(
-    TextDocumentWithLabeledMultiSpansAndLabeledPartitions,
-    TextDocumentWithLabeledMultiSpansAndBinaryRelations,
-):
-    pass
+# backwards compatibility
+TextDocument: TypeAlias = TextBasedDocument
diff --git a/src/pytorch_ie/metrics/__init__.py b/src/pytorch_ie/metrics/__init__.py
index 49b3a056..5885a047 100644
--- a/src/pytorch_ie/metrics/__init__.py
+++ b/src/pytorch_ie/metrics/__init__.py
@@ -1,4 +1,2 @@
-from .confusion_matrix import ConfusionMatrix
-from .f1 import F1Metric
-
-__all__ = ["F1Metric", "ConfusionMatrix"]
+# backwards compatibility
+from pie_documents.metrics import ConfusionMatrix, F1Metric, SQuADF1
diff --git a/src/pytorch_ie/metrics/confusion_matrix.py b/src/pytorch_ie/metrics/confusion_matrix.py
deleted file mode 100644
index 4d1df92a..00000000
--- a/src/pytorch_ie/metrics/confusion_matrix.py
+++ /dev/null
@@ -1,157 +0,0 @@
-import logging
-from collections import defaultdict
-from typing import Callable, Dict, Optional, Tuple, Union
-
-import pandas as pd
-from pie_core import Annotation, Document, DocumentMetric
-
-from pytorch_ie.utils.hydra import resolve_target
-
-logger = logging.getLogger(__name__)
-
-
-class ConfusionMatrix(DocumentMetric):
-    """Computes the confusion matrix for a given annotation layer that contains labeled annotations.
-
-    Args:
-        layer: The layer to compute the confusion matrix for.
-        label_field: The field to use for the label. Defaults to "label".
-        unassignable_label: The label to use for false negative annotations. Defaults to "UNASSIGNABLE".
-        undetected_label: The label to use for false positive annotations. Defaults to "UNDETECTED".
-        strict: If True, raises an error if a base annotation has multiple gold labels. If False, logs a warning.
-        show_as_markdown: If True, logs the confusion matrix as markdown on the console when calling compute().
-        annotation_processor: A callable that processes the annotations before calculating the confusion matrix.
-    """
-
-    def __init__(
-        self,
-        layer: str,
-        label_field: str = "label",
-        show_as_markdown: bool = False,
-        unassignable_label: str = "UNASSIGNABLE",
-        undetected_label: str = "UNDETECTED",
-        strict: bool = True,
-        annotation_processor: Optional[Union[Callable[[Annotation], Annotation], str]] = None,
-    ):
-        super().__init__()
-        self.layer = layer
-        self.label_field = label_field
-        self.unassignable_label = unassignable_label
-        self.undetected_label = undetected_label
-        self.strict = strict
-        self.show_as_markdown = show_as_markdown
-        self.annotation_processor: Optional[Callable[[Annotation], Annotation]]
-        if isinstance(annotation_processor, str):
-            self.annotation_processor = resolve_target(annotation_processor)
-        else:
-            self.annotation_processor = annotation_processor
-
-    def reset(self):
-        self.counts: Dict[Tuple[str, str], int] = defaultdict(int)
-
-    def calculate_counts(
-        self,
-        document: Document,
-        annotation_filter: Optional[Callable[[Annotation], bool]] = None,
-        annotation_processor: Optional[Callable[[Annotation], Annotation]] = None,
-    ) -> Dict[Tuple[str, str], int]:
-        annotation_processor = annotation_processor or (lambda ann: ann)
-        annotation_filter = annotation_filter or (lambda ann: True)
-        predicted_annotations = {
-            annotation_processor(ann)
-            for ann in document[self.layer].predictions
-            if annotation_filter(ann)
-        }
-        gold_annotations = {
-            annotation_processor(ann) for ann in document[self.layer] if annotation_filter(ann)
-        }
-        base2gold = defaultdict(list)
-        for ann in gold_annotations:
-            base_ann_kwargs = {self.label_field: "DUMMY_LABEL"}
-            base_ann = ann.copy(**base_ann_kwargs)
-            base2gold[base_ann].append(ann)
-        base2pred = defaultdict(list)
-        for ann in predicted_annotations:
-            base_ann_kwargs = {self.label_field: "DUMMY_LABEL"}
-            base_ann = ann.copy(**base_ann_kwargs)
-            base2pred[base_ann].append(ann)
-
-        # (gold_label, pred_label) -> count
-        counts: Dict[Tuple[str, str], int] = defaultdict(int)
-        for base_ann in set(base2gold) | set(base2pred):
-            gold_labels = [getattr(ann, self.label_field) for ann in base2gold[base_ann]]
-            pred_labels = [getattr(ann, self.label_field) for ann in base2pred[base_ann]]
-
-            if self.undetected_label in gold_labels:
-                raise ValueError(
-                    f"The gold annotation has the label '{self.undetected_label}' for undetected instances. "
-                    f"Set a different undetected_label."
-                )
-            if self.unassignable_label in pred_labels:
-                raise ValueError(
-                    f"The predicted annotation has the label '{self.unassignable_label}' for unassignable predictions. "
-                    f"Set a different unassignable_label."
-                )
-
-            if len(gold_labels) > 1:
-                msg = f"The base annotation {base_ann} has multiple gold labels: {gold_labels}."
-                if self.strict:
-                    raise ValueError(msg)
-                else:
-                    logger.warning(msg + " Skip this base annotation.")
-                    continue
-
-            # use placeholder labels for empty gold or prediction labels
-            if len(gold_labels) == 0:
-                gold_labels.append(self.undetected_label)
-            if len(pred_labels) == 0:
-                pred_labels.append(self.unassignable_label)
-
-            # main logic
-            for gold_label in gold_labels:
-                for pred_label in pred_labels:
-                    counts[(gold_label, pred_label)] += 1
-
-        return counts
-
-    def add_counts(self, counts: Dict[Tuple[str, str], int]):
-        for key, value in counts.items():
-            self.counts[key] += value
-
-    def _update(self, document: Document):
-        new_counts = self.calculate_counts(
-            document=document,
-            annotation_processor=self.annotation_processor,
-        )
-        self.add_counts(new_counts)
-
-    def _compute(self) -> Dict[str, Dict[str, int]]:
-
-        res: Dict[str, Dict[str, int]] = defaultdict(dict)
-        for gold_label, pred_label in sorted(self.counts):
-            res[gold_label][pred_label] = self.counts[(gold_label, pred_label)]
-
-        if self.show_as_markdown:
-            res_df = pd.DataFrame(res).fillna(0)
-            # index is prediction, columns is gold
-            gold_labels = res_df.columns
-            pred_labels = res_df.index
-
-            # re-arrange index and columns: sort and put undetected_label and unassignable_label at the end
-            gold_labels_sorted = sorted(
-                [gold_label for gold_label in gold_labels if gold_label != self.undetected_label]
-            )
-            # re-add undetected_label at the end, if it was in the gold labels
-            if self.undetected_label in gold_labels:
-                gold_labels_sorted = gold_labels_sorted + [self.undetected_label]
-            pred_labels_sorted = sorted(
-                [pred_label for pred_label in pred_labels if pred_label != self.unassignable_label]
-            )
-            # re-add unassignable_label at the end, if it was in the pred labels
-            if self.unassignable_label in pred_labels:
-                pred_labels_sorted = pred_labels_sorted + [self.unassignable_label]
-            res_df_sorted = res_df.loc[pred_labels_sorted, gold_labels_sorted]
-
-            # transpose and show as markdown: index is now gold, columns is prediction
-            logger.info(f"\n{self.layer}:\n{res_df_sorted.T.to_markdown()}")
-        return res
diff --git a/src/pytorch_ie/metrics/f1.py b/src/pytorch_ie/metrics/f1.py
deleted file mode 100644
index 7d4a02e8..00000000
--- a/src/pytorch_ie/metrics/f1.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import logging
-from collections import defaultdict
-from functools import partial
-from typing import Callable, Collection, Dict, Hashable, Optional, Tuple, Union
-
-import pandas as pd
-from pie_core import Annotation, Document, DocumentMetric
-
-from pytorch_ie.utils.hydra import resolve_target
-
-logger = logging.getLogger(__name__)
-
-
-def has_one_of_the_labels(ann: Annotation, label_field: str, labels: Collection[str]) -> bool:
-    return getattr(ann, label_field) in labels
-
-
-def has_this_label(ann: Annotation, label_field: str, label: str) -> bool:
-    return getattr(ann, label_field) == label
-
-
-class F1Metric(DocumentMetric):
-    """Computes the (micro aggregated) F1 score for a given layer. If labels are provided,
-    it also computes the F1 score for each label separately and the macro F1 score.
-
-    Args:
-        layer: The layer to compute the F1 score for.
-        labels: If provided, calculate F1 score for each label.
-        label_field: The field to use for the label. Defaults to "label".
-        show_as_markdown: If True, logs the F1 score as markdown on the console when calling compute().
-    """
-
-    def __init__(
-        self,
-        layer: str,
-        labels: Optional[Union[Collection[str], str]] = None,
-        label_field: str = "label",
-        show_as_markdown: bool = False,
-        annotation_processor: Optional[Union[Callable[[Annotation], Hashable], str]] = None,
-    ):
-        super().__init__()
-        self.layer = layer
-        self.label_field = label_field
-        self.show_as_markdown = show_as_markdown
-        self.annotation_processor: Optional[Callable[[Annotation], Hashable]]
-        if isinstance(annotation_processor, str):
-            self.annotation_processor = resolve_target(annotation_processor)
-        else:
-            self.annotation_processor = annotation_processor
-
-        self.per_label = labels is not None
-        self.infer_labels = False
-        if self.per_label:
-            if isinstance(labels, str):
-                if labels != "INFERRED":
-                    raise ValueError(
-                        "labels can only be 'INFERRED' if per_label is True and labels is a string"
-                    )
-                self.labels = []
-                self.infer_labels = True
-            elif isinstance(labels, Collection):
-                if not all(isinstance(label, str) for label in labels):
-                    raise ValueError("labels must be a collection of strings")
-                if "MICRO" in labels or "MACRO" in labels:
-                    raise ValueError(
-                        "labels cannot contain 'MICRO' or 'MACRO' because they are used to capture aggregated metrics"
-                    )
-                if len(labels) == 0:
-                    raise ValueError("labels cannot be empty")
-                self.labels = list(labels)
-            else:
-                raise ValueError("labels must be a string or a collection of strings")
-
-    def reset(self):
-        self.counts = defaultdict(lambda: (0, 0, 0))
-
-    def calculate_counts(
-        self,
-        document: Document,
-        annotation_filter: Optional[Callable[[Annotation], bool]] = None,
-        annotation_processor: Optional[Callable[[Annotation], Hashable]] = None,
-    ) -> Tuple[int, int, int]:
-        annotation_processor = annotation_processor or (lambda ann: ann)
-        annotation_filter = annotation_filter or (lambda ann: True)
-        predicted_annotations = {
-            annotation_processor(ann)
-            for ann in document[self.layer].predictions
-            if annotation_filter(ann)
-        }
-        gold_annotations = {
-            annotation_processor(ann) for ann in document[self.layer] if annotation_filter(ann)
-        }
-        tp = len([ann for ann in predicted_annotations & gold_annotations])
-        fn = len([ann for ann in gold_annotations - predicted_annotations])
-        fp = len([ann for ann in predicted_annotations - gold_annotations])
-        return tp, fp, fn
-
-    def add_counts(self, counts: Tuple[int, int, int], label: str):
-        self.counts[label] = (
-            self.counts[label][0] + counts[0],
-            self.counts[label][1] + counts[1],
-            self.counts[label][2] + counts[2],
-        )
-
-    def _update(self, document: Document):
-        new_counts = self.calculate_counts(
-            document=document,
-            annotation_filter=(
-                partial(has_one_of_the_labels, label_field=self.label_field, labels=self.labels)
-                if self.per_label and not self.infer_labels
-                else None
-            ),
-            annotation_processor=self.annotation_processor,
-        )
-        self.add_counts(new_counts, label="MICRO")
-        if self.infer_labels:
-            layer = document[self.layer]
-            # collect labels from gold data and predictions
-            for ann in list(layer) + list(layer.predictions):
-                label = getattr(ann, self.label_field)
-                if label not in self.labels:
-                    self.labels.append(label)
-        if self.per_label:
-            for label in self.labels:
-                new_counts = self.calculate_counts(
-                    document=document,
-                    annotation_filter=partial(
-                        has_this_label, label_field=self.label_field, label=label
-                    ),
-                    annotation_processor=self.annotation_processor,
-                )
-                self.add_counts(new_counts, label=label)
-
-    def _compute(self) -> Dict[str, Dict[str, float]]:
-        res = dict()
-        if self.per_label:
-            res["MACRO"] = {"f1": 0.0, "p": 0.0, "r": 0.0}
-        for label, counts in self.counts.items():
-            tp, fp, fn = counts
-            if tp == 0:
-                p, r, f1 = 0.0, 0.0, 0.0
-            else:
-                p = tp / (tp + fp)
-                r = tp / (tp + fn)
-                f1 = 2 * p * r / (p + r)
-            res[label] = {"f1": f1, "p": p, "r": r, "s": tp + fn}
-            if self.per_label and label in self.labels:
-                res["MACRO"]["f1"] += f1 / len(self.labels)
-                res["MACRO"]["p"] += p / len(self.labels)
-                res["MACRO"]["r"] += r / len(self.labels)
-        if self.show_as_markdown:
-            logger.info(f"\n{self.layer}:\n{pd.DataFrame(res).round(3).T.to_markdown()}")
-        return res
diff --git a/src/pytorch_ie/metrics/statistics.py b/src/pytorch_ie/metrics/statistics.py
deleted file mode 100644
index b4a9c134..00000000
--- a/src/pytorch_ie/metrics/statistics.py
+++ /dev/null
@@ -1,135 +0,0 @@
-import logging
-from collections import defaultdict
-from typing import Any, Callable, Dict, List, Optional, Type, Union
-
-from pie_core import Document, DocumentStatistic
-from transformers import AutoTokenizer, PreTrainedTokenizer
-
-from pytorch_ie.documents import TextBasedDocument
-
-logger = logging.getLogger(__name__)
-
-
-class TokenCountCollector(DocumentStatistic):
-    """Collects the token count of a field when tokenizing its content with a Huggingface
-    tokenizer.
-
-    The content of the field should be a string.
-    """
-
-    def __init__(
-        self,
-        tokenizer: Union[str, PreTrainedTokenizer],
-        text_field: str = "text",
-        tokenizer_kwargs: Optional[Dict[str, Any]] = None,
-        document_type: Optional[Type[Document]] = None,
-        **kwargs,
-    ):
-        if document_type is None and text_field == "text":
-            document_type = TextBasedDocument
-        super().__init__(document_type=document_type, **kwargs)
-        self.tokenizer = (
-            AutoTokenizer.from_pretrained(tokenizer) if isinstance(tokenizer, str) else tokenizer
-        )
-        self.tokenizer_kwargs = tokenizer_kwargs or {}
-        self.text_field = text_field
-
-    def _collect(self, doc: Document) -> int:
-        text = getattr(doc, self.text_field)
-        encodings = self.tokenizer(text, **self.tokenizer_kwargs)
-        tokens = encodings.tokens()
-        return len(tokens)
-
-
-class FieldLengthCollector(DocumentStatistic):
-    """Collects the length of a field, e.g. to collect the number the characters in the input text.
-
-    The field should be a list of sized elements.
-    """
-
-    def __init__(self, field: str, **kwargs):
-        super().__init__(**kwargs)
-        self.field = field
-
-    def _collect(self, doc: Document) -> int:
-        field_obj = getattr(doc, self.field)
-        return len(field_obj)
-
-
-class SubFieldLengthCollector(DocumentStatistic):
-    """Collects the length of a subfield in a field, e.g. to collect the number of arguments of
-    N-ary relations."""
-
-    def __init__(self, field: str, subfield: str, **kwargs):
-        super().__init__(**kwargs)
-        self.field = field
-        self.subfield = subfield
-
-    def _collect(self, doc: Document) -> List[int]:
-        field_obj = getattr(doc, self.field)
-        lengths = []
-        for entry in field_obj:
-            subfield_obj = getattr(entry, self.subfield)
-            lengths.append(len(subfield_obj))
-        return lengths
-
-
-class DummyCollector(DocumentStatistic):
-    """A dummy collector that always returns 1, e.g. to count the number of documents.
-
-    Can be used to count the number of documents.
-    """
-
-    DEFAULT_AGGREGATION_FUNCTIONS = ["sum"]
-
-    def _collect(self, doc: Document) -> int:
-        return 1
-
-
-class LabelCountCollector(DocumentStatistic):
-    """Collects the number of field entries per label, e.g. to collect the number of entities per
-    type.
-
-    The field should be a list of elements with a label attribute.
-
-    Important: To make correct use of the result data, missing values need to be filled with 0, e.g.:
-        {("ORG",): [2, 3], ("LOC",): [2]} -> {("ORG",): [2, 3], ("LOC",): [2, 0]}
-    """
-
-    DEFAULT_AGGREGATION_FUNCTIONS = ["mean", "std", "min", "max", "len", "sum"]
-
-    def __init__(
-        self, field: str, labels: Union[List[str], str], label_attribute: str = "label", **kwargs
-    ):
-        super().__init__(**kwargs)
-        self.field = field
-        self.label_attribute = label_attribute
-        if not (isinstance(labels, list) or labels == "INFERRED"):
-            raise ValueError("labels must be a list of strings or 'INFERRED'")
-        if labels == "INFERRED":
-            logger.warning(
-                f"Inferring labels with {self.__class__.__name__} from data produces wrong results "
-                f"for certain aggregation functions (e.g. 'mean', 'std', 'min') because zero values "
-                f"are not included in the calculation. We remove these aggregation functions from "
-                f"this collector, but be aware that the results may be wrong for your own aggregation "
-                f"functions that rely on zero values."
-            )
-            self.aggregation_functions: Dict[str, Callable[[List], Any]] = {
-                name: func
-                for name, func in self.aggregation_functions.items()
-                if name not in ["mean", "std", "min"]
-            }
-
-        self.labels = labels
-
-    def _collect(self, doc: Document) -> Dict[str, int]:
-        field_obj = getattr(doc, self.field)
-        counts: Dict[str, int]
-        if self.labels == "INFERRED":
-            counts = defaultdict(int)
-        else:
-            counts = {label: 0 for label in self.labels}
-        for elem in field_obj:
-            label = getattr(elem, self.label_attribute)
-            counts[label] += 1
-        return dict(counts)
diff --git a/src/pytorch_ie/metrics/statistics/__init__.py b/src/pytorch_ie/metrics/statistics/__init__.py
new file mode 100644
index 00000000..be9c913e
--- /dev/null
+++ b/src/pytorch_ie/metrics/statistics/__init__.py
@@ -0,0 +1,8 @@
+from pie_documents.metrics.statistics import (
+    DummyCollector,
+    FieldLengthCollector,
+    LabelCountCollector,
+    SubFieldLengthCollector,
+)
+
+from .token_count_collector import TokenCountCollector
diff --git a/src/pytorch_ie/metrics/statistics/token_count_collector.py b/src/pytorch_ie/metrics/statistics/token_count_collector.py
new file mode 100644
index 00000000..91545a84
--- /dev/null
+++ b/src/pytorch_ie/metrics/statistics/token_count_collector.py
@@ -0,0 +1,40 @@
+import logging
+from typing import Any, Dict, Optional, Type, Union
+
+from pie_core import Document, DocumentStatistic
+from transformers import AutoTokenizer, PreTrainedTokenizer
+
+from pytorch_ie.documents import TextBasedDocument
+
+logger = logging.getLogger(__name__)
+
+
+class TokenCountCollector(DocumentStatistic):
+    """Collects the token count of a field when tokenizing its content with a Huggingface
+    tokenizer.
+
+    The content of the field should be a string.
+    """
+
+    def __init__(
+        self,
+        tokenizer: Union[str, PreTrainedTokenizer],
+        text_field: str = "text",
+        tokenizer_kwargs: Optional[Dict[str, Any]] = None,
+        document_type: Optional[Type[Document]] = None,
+        **kwargs,
+    ):
+        if document_type is None and text_field == "text":
+            document_type = TextBasedDocument
+        super().__init__(document_type=document_type, **kwargs)
+        self.tokenizer = (
+            AutoTokenizer.from_pretrained(tokenizer) if isinstance(tokenizer, str) else tokenizer
+        )
+        self.tokenizer_kwargs = tokenizer_kwargs or {}
+        self.text_field = text_field
+
+    def _collect(self, doc: Document) -> int:
+        text = getattr(doc, self.text_field)
+        encodings = self.tokenizer(text, **self.tokenizer_kwargs)
+        tokens = encodings.tokens()
+        return len(tokens)
diff --git a/src/pytorch_ie/utils/hydra.py b/src/pytorch_ie/utils/hydra.py
index 23bb51f6..2c3d562b 100644
--- a/src/pytorch_ie/utils/hydra.py
+++ b/src/pytorch_ie/utils/hydra.py
@@ -1,3 +1,4 @@
+# backwards compatibility
 from pie_core.utils.hydra import (
     InstantiationException,
     resolve_optional_document_type,
diff --git a/src/pytorch_ie/utils/span.py b/src/pytorch_ie/utils/span.py
index ddef0d4c..b8f9bd8d 100644
--- a/src/pytorch_ie/utils/span.py
+++ b/src/pytorch_ie/utils/span.py
@@ -13,11 +13,14 @@
     Tuple,
 )
 
+# backwards compatibility imports from pie_documents.utils.span
+from pie_documents.utils.span import have_overlap as has_overlap
+from pie_documents.utils.span import is_contained_in
 from transformers import PreTrainedTokenizer
 
 from pytorch_ie.annotations import LabeledSpan, Span
 
-# TODO: most of this should be superseded by pie_modules.utils.span and pie_modules.utils.sequence_tagging,
+# TODO: most of this should be superseded by pie_documents.utils.sequence_tagging,
 #  remove respective content
 
 TypedSpan = Tuple[int, Tuple[int, int]]
@@ -229,19 +232,6 @@ def get_token_slice(
     return start, before_end + 1
 
 
-def is_contained_in(start_end: Tuple[int, int], other_start_end: Tuple[int, int]) -> bool:
-    return other_start_end[0] <= start_end[0] and start_end[1] <= other_start_end[1]
-
-
-def has_overlap(start_end: Tuple[int, int], other_start_end: Tuple[int, int]):
-    return (
-        start_end[0] <= other_start_end[0] < start_end[1]
-        or start_end[0] < other_start_end[1] <= start_end[1]
-        or other_start_end[0] <= start_end[0] < other_start_end[1]
-        or other_start_end[0] < start_end[1] <= other_start_end[1]
-    )
-
-
 def _char_to_token_mapper(
     char_idx: int,
     char_to_token_mapping: Dict[int, int],
diff --git a/tests/conftest.py b/tests/conftest.py
index c6f71c97..5417bbc5 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -1,9 +1,8 @@
 import dataclasses
 import json
-from typing import Dict, Optional
 
 import pytest
-from pie_core import Annotation, AnnotationLayer, annotation_field
+from pie_core import AnnotationLayer, annotation_field
 
 from pytorch_ie.annotations import BinaryRelation, LabeledSpan, Span
 from pytorch_ie.documents import TextDocument
@@ -61,13 +60,3 @@ def documents(document_dataset):
 def test_documents(documents):
     assert len(documents) == 8
     assert all(isinstance(doc, TestDocument) for doc in documents)
-
-
-def _test_annotation_reconstruction(
-    annotation: Annotation, annotation_store: Optional[Dict[int, Annotation]] = None
-):
-    ann_str = json.dumps(annotation.asdict())
-    annotation_reconstructed = type(annotation).fromdict(
-        json.loads(ann_str), annotation_store=annotation_store
-    )
-    assert annotation_reconstructed == annotation
diff --git a/tests/metrics/test_confusion_matrix.py b/tests/metrics/test_confusion_matrix.py
deleted file mode 100644
index cf09d6c4..00000000
--- a/tests/metrics/test_confusion_matrix.py
+++ /dev/null
@@ -1,60 +0,0 @@
-from dataclasses import dataclass
-
-import pytest
-from pie_core import AnnotationLayer, annotation_field
-
-from pytorch_ie.annotations import LabeledSpan
-from pytorch_ie.documents import TextBasedDocument
-from pytorch_ie.metrics import ConfusionMatrix
-
-
-@pytest.fixture
-def documents():
-    @dataclass
-    class TextDocumentWithEntities(TextBasedDocument):
-        entities: AnnotationLayer[LabeledSpan] = annotation_field(target="text")
-
-    # a test sentence with two entities
-    doc1 = TextDocumentWithEntities(
-        text="The quick brown fox jumps over the lazy dog.",
-    )
-    doc1.entities.append(LabeledSpan(start=4, end=19, label="animal"))
-    doc1.entities.append(LabeledSpan(start=35, end=43, label="animal"))
-    assert str(doc1.entities[0]) == "quick brown fox"
-    assert str(doc1.entities[1]) == "lazy dog"
-
-    # a second test sentence with a different text and a single entity (a company)
-    doc2 = TextDocumentWithEntities(text="Apple is a great company.")
-    doc2.entities.append(LabeledSpan(start=0, end=5, label="company"))
-    assert str(doc2.entities[0]) == "Apple"
-
-    documents = [doc1, doc2]
-
-    # add predictions
-    # correct
-    documents[0].entities.predictions.append(LabeledSpan(start=4, end=19, label="animal"))
-    # wrong label
-    documents[0].entities.predictions.append(LabeledSpan(start=35, end=43, label="cat"))
-    # correct
-    documents[1].entities.predictions.append(LabeledSpan(start=0, end=5, label="company"))
-    # wrong span
-    documents[1].entities.predictions.append(LabeledSpan(start=10, end=15, label="company"))
-
-    return documents
-
-
-def test_confusion_matrix(documents):
-    metric = ConfusionMatrix(layer="entities")
-    metric(documents)
-    # (gold_label, predicted_label): count
-    assert dict(metric.counts) == {
-        ("animal", "animal"): 1,
-        ("animal", "cat"): 1,
-        ("UNDETECTED", "company"): 1,
-        ("company", "company"): 1,
-    }
-    assert metric.compute() == {
-        "animal": {"animal": 1, "cat": 1},
-        "UNDETECTED": {"company": 1},
-        "company": {"company": 1},
-    }
diff --git a/tests/metrics/test_f1.py b/tests/metrics/test_f1.py
deleted file mode 100644
index 5e6e5539..00000000
--- a/tests/metrics/test_f1.py
+++ /dev/null
@@ -1,128 +0,0 @@
-from dataclasses import dataclass
-
-import pytest
-from pie_core import AnnotationLayer, annotation_field
-
-from pytorch_ie.annotations import LabeledSpan
-from pytorch_ie.documents import TextBasedDocument
-from pytorch_ie.metrics import F1Metric
-
-
-@pytest.fixture
-def documents():
-    @dataclass
-    class TextDocumentWithEntities(TextBasedDocument):
-        entities: AnnotationLayer[LabeledSpan] = annotation_field(target="text")
-
-    # a test sentence with two entities
-    doc1 = TextDocumentWithEntities(
-        text="The quick brown fox jumps over the lazy dog.",
-    )
-    doc1.entities.append(LabeledSpan(start=4, end=19, label="animal"))
-    doc1.entities.append(LabeledSpan(start=35, end=43, label="animal"))
-    assert str(doc1.entities[0]) == "quick brown fox"
-    assert str(doc1.entities[1]) == "lazy dog"
-
-    # a second test sentence with a different text and a single entity (a company)
-    doc2 = TextDocumentWithEntities(text="Apple is a great company.")
-    doc2.entities.append(LabeledSpan(start=0, end=5, label="company"))
-    assert str(doc2.entities[0]) == "Apple"
-
-    documents = [doc1, doc2]
-
-    # add predictions
-    # correct
-    documents[0].entities.predictions.append(LabeledSpan(start=4, end=19, label="animal"))
-    # correct, but duplicate, this should not be counted
-    documents[0].entities.predictions.append(LabeledSpan(start=4, end=19, label="animal"))
-    # correct
-    documents[0].entities.predictions.append(LabeledSpan(start=35, end=43, label="animal"))
-    # wrong label
-    documents[0].entities.predictions.append(LabeledSpan(start=35, end=43, label="cat"))
-    # correct
-    documents[1].entities.predictions.append(LabeledSpan(start=0, end=5, label="company"))
-    # wrong span
-    documents[1].entities.predictions.append(LabeledSpan(start=10, end=15, label="company"))
-
-    return documents
-
-
-def test_f1(documents):
-    metric = F1Metric(layer="entities")
-    metric(documents)
-    # tp, fp, fn for micro
-    assert dict(metric.counts) == {"MICRO": (3, 2, 0)}
-    assert metric.compute() == {"MICRO": {"f1": 0.7499999999999999, "p": 0.6, "r": 1.0, "s": 3}}
-
-
-def test_f1_per_label(documents):
-    metric = F1Metric(layer="entities", labels=["animal", "company", "cat"])
-    metric(documents)
-    # tp, fp, fn for micro and per label
-    assert dict(metric.counts) == {
-        "MICRO": (3, 2, 0),
-        "cat": (0, 1, 0),
-        "company": (1, 1, 0),
-        "animal": (2, 0, 0),
-    }
-    assert metric.compute() == {
-        "MACRO": {"f1": 0.5555555555555556, "p": 0.5, "r": 0.6666666666666666},
-        "MICRO": {"f1": 0.7499999999999999, "p": 0.6, "r": 1.0, "s": 3},
-        "animal": {"f1": 1.0, "p": 1.0, "r": 1.0, "s": 2},
-        "cat": {"f1": 0.0, "p": 0.0, "r": 0.0, "s": 0},
-        "company": {"f1": 0.6666666666666666, "p": 0.5, "r": 1.0, "s": 1},
-    }
-
-
-def test_f1_per_label_inferred(documents):
-    metric = F1Metric(layer="entities", labels="INFERRED")
-    metric(documents)
-    # tp, fp, fn for micro and per label
-    assert dict(metric.counts) == {
-        "MICRO": (3, 2, 0),
-        "animal": (2, 0, 0),
-        "company": (1, 1, 0),
-        "cat": (0, 1, 0),
-    }
-    assert metric.compute() == {
-        "MACRO": {"f1": 0.5555555555555556, "p": 0.5, "r": 0.6666666666666666},
-        "MICRO": {"f1": 0.7499999999999999, "p": 0.6, "r": 1.0, "s": 3},
-        "animal": {"f1": 1.0, "p": 1.0, "r": 1.0, "s": 2},
-        "cat": {"f1": 0.0, "p": 0.0, "r": 0.0, "s": 0},
-        "company": {"f1": 0.6666666666666666, "p": 0.5, "r": 1.0, "s": 1},
-    }
-
-
-def test_f1_per_label_no_labels(documents):
-    with pytest.raises(ValueError) as excinfo:
-        F1Metric(layer="entities", labels=[])
-    assert str(excinfo.value) == "labels cannot be empty"
-
-
-def test_f1_per_label_not_allowed():
-    with pytest.raises(ValueError) as excinfo:
-        F1Metric(layer="entities", labels=["animal", "MICRO"])
-    assert (
-        str(excinfo.value)
-        == "labels cannot contain 'MICRO' or 'MACRO' because they are used to capture aggregated metrics"
-    )
-
-
-# def test_f1_show_as_markdown(documents, caplog):
-#    metric = F1Metric(layer="entities", labels=["animal", "company", "cat"], show_as_markdown=True)
-#    metric(documents)
-#    caplog.set_level(logging.INFO)
-#    caplog.clear()
-#    metric.compute()
-#    assert len(caplog.records) == 1
-#    assert (
-#        caplog.records[0].message == "\n"
-#        "entities:\n"
-#        "|         |    f1 |   p |     r |\n"
-#        "|:--------|------:|----:|------:|\n"
-#        "| MACRO   | 0.556 | 0.5 | 0.667 |\n"
-#        "| MICRO   | 0.75  | 0.6 | 1     |\n"
-#        "| animal  | 1     | 1   | 1     |\n"
-#        "| company | 0.667 | 0.5 | 1     |\n"
-#        "| cat     | 0     | 0   | 0     |"
-#    )
diff --git a/tests/metrics/test_statistics.py b/tests/metrics/test_statistics.py
index 8035b462..d48dd767 100644
--- a/tests/metrics/test_statistics.py
+++ b/tests/metrics/test_statistics.py
@@ -1,76 +1,4 @@
-from pytorch_ie.metrics.statistics import (
-    DummyCollector,
-    FieldLengthCollector,
-    LabelCountCollector,
-    SubFieldLengthCollector,
-    TokenCountCollector,
-)
-
-
-def test_statistics(document_dataset):
-    statistic = DummyCollector()
-    values = statistic(document_dataset)
-    assert values == {"test": {"sum": 2}, "train": {"sum": 8}, "val": {"sum": 2}}
-
-    # note that we check for labels=["LOC", "PER", "ORG"], but the actual labels in the data are just ["PER", "ORG"]
-    statistic = LabelCountCollector(field="entities", labels=["LOC", "PER", "ORG"])
-    values = statistic(document_dataset)
-    assert values == {
-        "test": {
-            "LOC": {"mean": 0.0, "std": 0.0, "min": 0, "max": 0, "len": 2, "sum": 0},
-            "PER": {"mean": 0.5, "std": 0.5, "min": 0, "max": 1, "len": 2, "sum": 1},
-            "ORG": {"mean": 1.0, "std": 1.0, "min": 0, "max": 2, "len": 2, "sum": 2},
-        },
-        "val": {
-            "LOC": {"mean": 0.0, "std": 0.0, "min": 0, "max": 0, "len": 2, "sum": 0},
-            "PER": {"mean": 0.5, "std": 0.5, "min": 0, "max": 1, "len": 2, "sum": 1},
-            "ORG": {"mean": 1.0, "std": 1.0, "min": 0, "max": 2, "len": 2, "sum": 2},
-        },
-        "train": {
-            "LOC": {"mean": 0.0, "std": 0.0, "min": 0, "max": 0, "len": 8, "sum": 0},
-            "PER": {
-                "mean": 0.875,
-                "std": 0.5994789404140899,
-                "min": 0,
-                "max": 2,
-                "len": 8,
-                "sum": 7,
-            },
-            "ORG": {
-                "mean": 1.125,
-                "std": 0.7806247497997998,
-                "min": 0,
-                "max": 2,
-                "len": 8,
-                "sum": 9,
-            },
-        },
-    }
-
-    statistic = LabelCountCollector(field="entities", labels="INFERRED")
-    values = statistic(document_dataset)
-    assert values == {
-        "test": {"PER": {"max": 1, "len": 1, "sum": 1}, "ORG": {"max": 2, "len": 1, "sum": 2}},
-        "val": {"PER": {"max": 1, "len": 1, "sum": 1}, "ORG": {"max": 2, "len": 1, "sum": 2}},
-        "train": {"PER": {"max": 2, "len": 6, "sum": 7}, "ORG": {"max": 2, "len": 6, "sum": 9}},
-    }
-
-    statistic = FieldLengthCollector(field="text")
-    values = statistic(document_dataset)
-    assert values == {
-        "test": {"max": 51, "mean": 34.5, "min": 18, "std": 16.5},
-        "train": {"max": 54, "mean": 28.25, "min": 15, "std": 14.694812009685595},
-        "val": {"max": 51, "mean": 34.5, "min": 18, "std": 16.5},
-    }
-
-    # this is not super useful, we just collect the lengths of the labels, but it is enough to test the code
-    statistic = SubFieldLengthCollector(field="entities", subfield="label")
-    values = statistic(document_dataset)
-    assert values == {
-        "test": {"max": 3, "mean": 3.0, "min": 3, "std": 0.0},
-        "train": {"max": 3, "mean": 3.0, "min": 3, "std": 0.0},
-        "val": {"max": 3, "mean": 3.0, "min": 3, "std": 0.0},
-    }
+from pytorch_ie.metrics.statistics import TokenCountCollector
 
 
 def test_statistics_with_tokenize(document_dataset):
diff --git a/tests/test_annotations.py b/tests/test_annotations.py
deleted file mode 100644
index 03116aa9..00000000
--- a/tests/test_annotations.py
+++ /dev/null
@@ -1,377 +0,0 @@
-import dataclasses
-import re
-
-import pytest
-
-from pytorch_ie import AnnotationLayer, annotation_field
-from pytorch_ie.annotations import (
-    BinaryRelation,
-    Label,
-    LabeledMultiSpan,
-    LabeledSpan,
-    MultiLabel,
-    MultiLabeledBinaryRelation,
-    MultiLabeledSpan,
-    NaryRelation,
-    Span,
-)
-from pytorch_ie.documents import TextBasedDocument
-from tests.conftest import _test_annotation_reconstruction
-
-
-def test_label():
-    label1 = Label(label="label1")
-    assert label1.label == "label1"
-    assert label1.score == pytest.approx(1.0)
-    assert label1.resolve() == "label1"
-
-    label2 = Label(label="label2", score=0.5)
-    assert label2.label == "label2"
-    assert label2.score == pytest.approx(0.5)
-
-    assert label2.asdict() == {
-        "_id": label2._id,
-        "label": "label2",
-        "score": 0.5,
-    }
-
-    _test_annotation_reconstruction(label2)
-
-
-def test_multilabel():
-    multilabel1 = MultiLabel(label=("label1", "label2"))
-    assert multilabel1.label == ("label1", "label2")
-    assert multilabel1.score == pytest.approx((1.0, 1.0))
-    assert multilabel1.resolve() == ("label1", "label2")
-
-    multilabel2 = MultiLabel(label=("label3", "label4"), score=(0.4, 0.5))
-    assert multilabel2.label == ("label3", "label4")
-    assert multilabel2.score == pytest.approx((0.4, 0.5))
-
-    assert multilabel2.asdict() == {
-        "_id": multilabel2._id,
-        "label": ("label3", "label4"),
-        "score": (0.4, 0.5),
-    }
-
-    _test_annotation_reconstruction(multilabel2)
-
-    with pytest.raises(
-        ValueError, match=re.escape("Number of labels (2) and scores (3) must be equal.")
-    ):
-        MultiLabel(label=("label5", "label6"), score=(0.1, 0.2, 0.3))
-
-
-def test_span():
-    span = Span(start=1, end=2)
-    assert span.start == 1
-    assert span.end == 2
-
-    assert span.asdict() == {
-        "_id": span._id,
-        "start": 1,
-        "end": 2,
-    }
-
-    _test_annotation_reconstruction(span)
-
-    with pytest.raises(ValueError) as excinfo:
-        span.resolve()
-    assert str(excinfo.value) == "Span(start=1, end=2) is not attached to a target."
-
-    @dataclasses.dataclass
-    class TestDocument(TextBasedDocument):
-        spans: AnnotationLayer[Span] = annotation_field(target="text")
-
-    doc = TestDocument(text="Hello, world!")
-    span = Span(start=7, end=12)
-    doc.spans.append(span)
-    assert span.resolve() == "world"
-
-
-def test_labeled_span():
-    labeled_span1 = LabeledSpan(start=1, end=2, label="label1")
-    assert labeled_span1.start == 1
-    assert labeled_span1.end == 2
-    assert labeled_span1.label == "label1"
-    assert labeled_span1.score == pytest.approx(1.0)
-
-    labeled_span2 = LabeledSpan(start=3, end=4, label="label2", score=0.5)
-    assert labeled_span2.start == 3
-    assert labeled_span2.end == 4
-    assert labeled_span2.label == "label2"
-    assert labeled_span2.score == pytest.approx(0.5)
-
-    assert labeled_span2.asdict() == {
-        "_id": labeled_span2._id,
-        "start": 3,
-        "end": 4,
-        "label": "label2",
-        "score": 0.5,
-    }
-
-    _test_annotation_reconstruction(labeled_span2)
-
-    with pytest.raises(ValueError) as excinfo:
-        labeled_span1.resolve()
-    assert (
-        str(excinfo.value)
-        == "LabeledSpan(start=1, end=2, label='label1', score=1.0) is not attached to a target."
-    )
-
-    @dataclasses.dataclass
-    class TestDocument(TextBasedDocument):
-        spans: AnnotationLayer[LabeledSpan] = annotation_field(target="text")
-
-    doc = TestDocument(text="Hello, world!")
-    labeled_span = LabeledSpan(start=7, end=12, label="LOC")
-    doc.spans.append(labeled_span)
-    assert labeled_span.resolve() == ("LOC", "world")
-
-
-def test_multilabeled_span():
-    multilabeled_span1 = MultiLabeledSpan(start=1, end=2, label=("label1", "label2"))
-    assert multilabeled_span1.start == 1
-    assert multilabeled_span1.end == 2
-    assert multilabeled_span1.label == ("label1", "label2")
-    assert multilabeled_span1.score == pytest.approx((1.0, 1.0))
-
-    multilabeled_span2 = MultiLabeledSpan(
-        start=3, end=4, label=("label3", "label4"), score=(0.4, 0.5)
-    )
-    assert multilabeled_span2.start == 3
-    assert multilabeled_span2.end == 4
-    assert multilabeled_span2.label == ("label3", "label4")
-    assert multilabeled_span2.score == pytest.approx((0.4, 0.5))
-
-    assert multilabeled_span2.asdict() == {
-        "_id": multilabeled_span2._id,
-        "start": 3,
-        "end": 4,
-        "label": ("label3", "label4"),
-        "score": (0.4, 0.5),
-    }
-
-    _test_annotation_reconstruction(multilabeled_span2)
-
-    with pytest.raises(
-        ValueError, match=re.escape("Number of labels (2) and scores (3) must be equal.")
-    ):
-        MultiLabeledSpan(start=5, end=6, label=("label5", "label6"), score=(0.1, 0.2, 0.3))
-
-    with pytest.raises(ValueError) as excinfo:
-        multilabeled_span1.resolve()
-    assert (
-        str(excinfo.value)
-        == "MultiLabeledSpan(start=1, end=2, label=('label1', 'label2'), score=(1.0, 1.0)) is not attached to a target."
-    )
-
-    @dataclasses.dataclass
-    class TestDocument(TextBasedDocument):
-        spans: AnnotationLayer[MultiLabeledSpan] = annotation_field(target="text")
-
-    doc = TestDocument(text="Hello, world!")
-    multilabeled_span = MultiLabeledSpan(start=7, end=12, label=("LOC", "ORG"))
-    doc.spans.append(multilabeled_span)
-    assert multilabeled_span.resolve() == (("LOC", "ORG"), "world")
-
-
-def test_labeled_multi_span():
-    labeled_multi_span1 = LabeledMultiSpan(slices=((1, 2), (3, 4)), label="label1")
-    assert labeled_multi_span1.slices == ((1, 2), (3, 4))
-    assert labeled_multi_span1.label == "label1"
-    assert labeled_multi_span1.score == pytest.approx(1.0)
-
-    labeled_multi_span2 = LabeledMultiSpan(
-        slices=((5, 6), (7, 8)),
-        label="label2",
-        score=0.5,
-    )
-    assert labeled_multi_span2.slices == ((5, 6), (7, 8))
-    assert labeled_multi_span2.label == "label2"
-    assert labeled_multi_span2.score == pytest.approx(0.5)
-
-    assert labeled_multi_span2.asdict() == {
-        "_id": labeled_multi_span2._id,
-        "slices": ((5, 6), (7, 8)),
-        "label": "label2",
-        "score": 0.5,
-    }
-
-    _test_annotation_reconstruction(labeled_multi_span2)
-
-
-def test_binary_relation():
-    head = Span(start=1, end=2)
-    tail = Span(start=3, end=4)
-
-    binary_relation1 = BinaryRelation(head=head, tail=tail, label="label1")
-    assert binary_relation1.head == head
-    assert binary_relation1.tail == tail
-    assert binary_relation1.label == "label1"
-    assert binary_relation1.score == pytest.approx(1.0)
-
-    binary_relation2 = BinaryRelation(head=head, tail=tail, label="label2", score=0.5)
-    assert binary_relation2.head == head
-    assert binary_relation2.tail == tail
-    assert binary_relation2.label == "label2"
-    assert binary_relation2.score == pytest.approx(0.5)
-
-    assert binary_relation2.asdict() == {
-        "_id": binary_relation2._id,
-        "head": head._id,
-        "tail": tail._id,
-        "label": "label2",
-        "score": 0.5,
-    }
-
-    annotation_store = {
-        head._id: head,
-        tail._id: tail,
-    }
-    _test_annotation_reconstruction(binary_relation2, annotation_store=annotation_store)
-
-    with pytest.raises(
-        ValueError,
-        match=re.escape("Unable to resolve the annotation id without annotation_store."),
-    ):
-        BinaryRelation.fromdict(binary_relation2.asdict())
-
-    with pytest.raises(ValueError) as excinfo:
-        binary_relation1.resolve()
-    assert str(excinfo.value) == "Span(start=1, end=2) is not attached to a target."
-
-    @dataclasses.dataclass
-    class TestDocument(TextBasedDocument):
-        spans: AnnotationLayer[Span] = annotation_field(target="text")
-        relations: AnnotationLayer[BinaryRelation] = annotation_field(target="spans")
-
-    doc = TestDocument(text="Hello, world!")
-    head = Span(start=0, end=5)
-    tail = Span(start=7, end=12)
-    doc.spans.extend([head, tail])
-    relation = BinaryRelation(head=head, tail=tail, label="LABEL")
-    doc.relations.append(relation)
-    assert relation.resolve() == ("LABEL", ("Hello", "world"))
-
-
-def test_multilabeled_binary_relation():
-    head = Span(start=1, end=2)
-    tail = Span(start=3, end=4)
-
-    binary_relation1 = MultiLabeledBinaryRelation(head=head, tail=tail, label=("label1", "label2"))
-    assert binary_relation1.head == head
-    assert binary_relation1.tail == tail
-    assert binary_relation1.label == ("label1", "label2")
-    assert binary_relation1.score == pytest.approx((1.0, 1.0))
-
-    binary_relation2 = MultiLabeledBinaryRelation(
-        head=head, tail=tail, label=("label3", "label4"), score=(0.4, 0.5)
-    )
-    assert binary_relation2.head == head
-    assert binary_relation2.tail == tail
-    assert binary_relation2.label == ("label3", "label4")
-    assert binary_relation2.score == pytest.approx((0.4, 0.5))
-
-    assert binary_relation2.asdict() == {
-        "_id": binary_relation2._id,
-        "head": head._id,
-        "tail": tail._id,
-        "label": ("label3", "label4"),
-        "score": (0.4, 0.5),
-    }
-
-    annotation_store = {
-        head._id: head,
-        tail._id: tail,
-    }
-    _test_annotation_reconstruction(binary_relation2, annotation_store=annotation_store)
-
-    with pytest.raises(
-        ValueError,
-        match=re.escape("Unable to resolve the annotation id without annotation_store."),
-    ):
-        MultiLabeledBinaryRelation.fromdict(binary_relation2.asdict())
-
-    with pytest.raises(
-        ValueError, match=re.escape("Number of labels (2) and scores (3) must be equal.")
-    ):
-        MultiLabeledBinaryRelation(
-            head=head, tail=tail, label=("label5", "label6"), score=(0.1, 0.2, 0.3)
-        )
-
-    with pytest.raises(ValueError) as excinfo:
-        binary_relation1.resolve()
-    assert str(excinfo.value) == "Span(start=1, end=2) is not attached to a target."
-
-    @dataclasses.dataclass
-    class TestDocument(TextBasedDocument):
-        spans: AnnotationLayer[Span] = annotation_field(target="text")
-        relations: AnnotationLayer[MultiLabeledBinaryRelation] = annotation_field(target="spans")
-
-    doc = TestDocument(text="Hello, world!")
-    head = Span(start=0, end=5)
-    tail = Span(start=7, end=12)
-    doc.spans.extend([head, tail])
-    relation = MultiLabeledBinaryRelation(head=head, tail=tail, label=("LABEL1", "LABEL2"))
-    doc.relations.append(relation)
-    assert relation.resolve() == (("LABEL1", "LABEL2"), ("Hello", "world"))
-
-
-def test_nary_relation():
-    arg1 = Span(start=1, end=2)
-    arg2 = Span(start=3, end=4)
-    arg3 = Span(start=5, end=6)
-
-    nary_relation1 = NaryRelation(
-        arguments=(arg1, arg2, arg3), roles=("role1", "role2", "role3"), label="label1"
-    )
-
-    assert nary_relation1.arguments == (arg1, arg2, arg3)
-    assert nary_relation1.roles == ("role1", "role2", "role3")
-    assert nary_relation1.label == "label1"
-    assert nary_relation1.score == pytest.approx(1.0)
-
-    assert nary_relation1.asdict() == {
-        "_id": nary_relation1._id,
-        "arguments": [arg1._id, arg2._id, arg3._id],
-        "roles": ("role1", "role2", "role3"),
-        "label": "label1",
-        "score": 1.0,
-    }
-
-    annotation_store = {
-        arg1._id: arg1,
-        arg2._id: arg2,
-        arg3._id: arg3,
-    }
-    _test_annotation_reconstruction(nary_relation1, annotation_store=annotation_store)
-
-    with pytest.raises(
-        ValueError,
-        match=re.escape("Unable to resolve the annotation id without annotation_store."),
-    ):
-        NaryRelation.fromdict(nary_relation1.asdict())
-
-    with pytest.raises(ValueError) as excinfo:
-        nary_relation1.resolve()
-    assert str(excinfo.value) == "Span(start=1, end=2) is not attached to a target."
-
-    @dataclasses.dataclass
-    class TestDocument(TextBasedDocument):
-        spans: AnnotationLayer[Span] = annotation_field(target="text")
-        relations: AnnotationLayer[NaryRelation] = annotation_field(target="spans")
-
-    doc = TestDocument(text="Hello, world A and B!")
-    arg1 = Span(start=0, end=5)
-    arg2 = Span(start=7, end=14)
-    arg3 = Span(start=19, end=20)
-    doc.spans.extend([arg1, arg2, arg3])
-    relation = NaryRelation(
-        arguments=(arg1, arg2, arg3), roles=("ARG1", "ARG2", "ARG3"), label="LABEL"
-    )
-    doc.relations.append(relation)
-    assert relation.resolve() == (
-        "LABEL",
-        (("ARG1", "Hello"), ("ARG2", "world A"), ("ARG3", "B")),
-    )
diff --git a/tests/utils/test_span.py b/tests/utils/test_span.py
deleted file mode 100644
index 555da4f0..00000000
--- a/tests/utils/test_span.py
+++ /dev/null
@@ -1,442 +0,0 @@
-# import pytest
-
-# from pytorch_ie.utils.span import (
-#     convert_span_annotations_to_tag_sequence,
-#     get_char_to_token_mapper,
-#     has_overlap,
-# )
-# from tests.fixtures.document import get_doc1, get_doc2, get_doc3
-
-
-# @pytest.fixture
-# def documents():
-#     doc_kwargs = dict(
-#         assert_span_text=True,
-#     )
-#     documents = [get_doc1(**doc_kwargs), get_doc2(**doc_kwargs), get_doc3(**doc_kwargs)]
-#     return documents
-
-
-# @pytest.mark.skip
-# def test_get_char_to_token_mapper():
-#     # TODO: implement!
-#     pass
-
-
-# @pytest.mark.skip
-# def test_get_special_token_mask():
-#     # TODO: implement!
-#     pass
-
-
-# def test_convert_span_annotations_to_tag_sequence(documents):
-#     doc = documents[0]
-#     entities = doc.annotations.spans["entities"]
-#     assert len(entities) == 3
-#     char_to_token_mapping = {
-#         0: 1,
-#         1: 1,
-#         2: 1,
-#         3: 1,
-#         5: 2,
-#         6: 2,
-#         7: 2,
-#         8: 2,
-#         9: 2,
-#         11: 3,
-#         12: 3,
-#         14: 4,
-#         15: 4,
-#         16: 4,
-#         17: 4,
-#         18: 4,
-#         19: 4,
-#         20: 5,
-#         22: 6,
-#         23: 6,
-#         24: 6,
-#         25: 6,
-#         27: 7,
-#         28: 7,
-#         30: 8,
-#         31: 8,
-#         33: 9,
-#         34: 9,
-#         35: 9,
-#         36: 9,
-#         37: 9,
-#         38: 9,
-#         39: 9,
-#         40: 9,
-#         42: 10,
-#         43: 10,
-#         44: 10,
-#         45: 10,
-#         46: 10,
-#         48: 11,
-#         49: 11,
-#         50: 11,
-#         51: 11,
-#     }
-#     char_to_token_mapper = get_char_to_token_mapper(
-#         char_to_token_mapping=char_to_token_mapping,
-#     )
-#     special_tokens_mask = [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]
-#     tag_sequence = convert_span_annotations_to_tag_sequence(
-#         spans=entities,
-#         special_tokens_mask=special_tokens_mask,
-#         char_to_token_mapper=char_to_token_mapper,
-#     )
-#     assert tag_sequence == [
-#         None,
-#         "B-person",
-#         "O",
-#         "O",
-#         "B-city",
-#         "O",
-#         "O",
-#         "O",
-#         "O",
-#         "O",
-#         "O",
-#         "B-person",
-#         None,
-#     ]
-
-#     doc = documents[1]
-#     entities = doc.annotations.spans["entities"]
-#     assert len(entities) == 2
-#     char_to_token_mapping = {
-#         0: 1,
-#         1: 1,
-#         2: 1,
-#         3: 1,
-#         4: 1,
-#         5: 1,
-#         6: 1,
-#         8: 2,
-#         9: 2,
-#         11: 3,
-#         13: 4,
-#         14: 4,
-#         15: 4,
-#         16: 4,
-#         17: 4,
-#         19: 5,
-#         20: 5,
-#         21: 5,
-#         22: 5,
-#         23: 6,
-#         25: 7,
-#         26: 7,
-#         27: 7,
-#         28: 7,
-#         29: 7,
-#         31: 8,
-#         32: 8,
-#         33: 9,
-#         34: 9,
-#         35: 9,
-#         36: 10,
-#         38: 11,
-#         39: 11,
-#         41: 12,
-#         42: 12,
-#         43: 12,
-#         45: 13,
-#         46: 13,
-#         47: 13,
-#         48: 13,
-#         49: 14,
-#         50: 15,
-#         52: 16,
-#         53: 16,
-#         54: 16,
-#         55: 16,
-#         56: 16,
-#         57: 17,
-#     }
-#     char_to_token_mapper = get_char_to_token_mapper(
-#         char_to_token_mapping=char_to_token_mapping,
-#     )
-#     special_tokens_mask = [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]
-#     tag_sequence = convert_span_annotations_to_tag_sequence(
-#         spans=entities,
-#         special_tokens_mask=special_tokens_mask,
-#         char_to_token_mapper=char_to_token_mapper,
-#     )
-#     assert tag_sequence == [
-#         None,
-#         "B-city",
-#         "O",
-#         "O",
-#         "O",
-#         "O",
-#         "O",
-#         "B-person",
-#         "I-person",
-#         "I-person",
-#         "I-person",
-#         "O",
-#         "O",
-#         "O",
-#         "O",
-#         "O",
-#         "O",
-#         "O",
-#         None,
-#     ]
-
-#     doc = documents[2]
-#     entities = doc.annotations.spans["entities"]
-#     assert len(entities) == 2
-#     char_to_token_mapping = {
-#         0: 1,
-#         1: 1,
-#         2: 1,
-#         3: 1,
-#         5: 2,
-#         6: 2,
-#         7: 2,
-#         8: 2,
-#         9: 2,
-#         10: 2,
-#         12: 3,
-#         13: 3,
-#         14: 3,
-#         15: 3,
-#         16: 3,
-#         18: 4,
-#         19: 4,
-#         20: 4,
-#         21: 4,
-#         23: 5,
-#         24: 5,
-#         26: 6,
-#         27: 6,
-#         28: 6,
-#         29: 6,
-#         30: 6,
-#         31: 6,
-#         32: 7,
-#     }
-#     char_to_token_mapper = get_char_to_token_mapper(
-#         char_to_token_mapping=char_to_token_mapping,
-#     )
-#     special_tokens_mask = [1, 0, 0, 0, 0, 0, 0, 0, 1]
-#     tag_sequence = convert_span_annotations_to_tag_sequence(
-#         spans=entities,
-#         special_tokens_mask=special_tokens_mask,
-#         char_to_token_mapper=char_to_token_mapper,
-#     )
-#     assert tag_sequence == [None, "B-person", "O", "O", "O", "O", "B-city", "O", None]
-
-
-# def test_convert_span_annotations_to_tag_sequence_with_partition(documents):
-#     doc = documents[0]
-#     entities = doc.annotations.spans["entities"]
-#     assert len(entities) == 3
-#     partitions = doc.annotations.spans["sentences"]
-#     assert len(partitions) == 1
-#     partition = partitions[0]
-#     char_to_token_mapping = {
-#         0: 1,
-#         1: 1,
-#         2: 1,
-#         3: 1,
-#         5: 2,
-#         6: 2,
-#         7: 2,
-#         8: 2,
-#         9: 2,
-#         11: 3,
-#         12: 3,
-#         14: 4,
-#         15: 4,
-#         16: 4,
-#         17: 4,
-#         18: 4,
-#         19: 4,
-#         20: 5,
-#     }
-#     special_tokens_mask = [1, 0, 0, 0, 0, 0, 1]
-#     char_to_token_mapper = get_char_to_token_mapper(
-#         char_to_token_mapping=char_to_token_mapping,
-#     )
-#     tag_sequence = convert_span_annotations_to_tag_sequence(
-#         spans=entities,
-#         special_tokens_mask=special_tokens_mask,
-#         char_to_token_mapper=char_to_token_mapper,
-#         partition=partition,
-#     )
-#     assert tag_sequence == [None, "B-person", "O", "O", "B-city", "O", None]
-
-#     doc = documents[1]
-#     entities = doc.annotations.spans["entities"]
-#     assert len(entities) == 2
-#     partitions = doc.annotations.spans["sentences"]
-#     assert len(partitions) == 2
-#     partition = partitions[0]
-#     char_to_token_mapping = {
-#         0: 1,
-#         1: 1,
-#         2: 1,
-#         3: 1,
-#         4: 1,
-#         5: 1,
-#         6: 1,
-#         8: 2,
-#         9: 2,
-#         11: 3,
-#         13: 4,
-#         14: 4,
-#         15: 4,
-#         16: 4,
-#         17: 4,
-#         19: 5,
-#         20: 5,
-#         21: 5,
-#         22: 5,
-#         23: 6,
-#     }
-#     special_tokens_mask = [1, 0, 0, 0, 0, 0, 0, 1]
-#     char_to_token_mapper = get_char_to_token_mapper(
-#         char_to_token_mapping=char_to_token_mapping,
-#     )
-#     tag_sequence = convert_span_annotations_to_tag_sequence(
-#         spans=entities,
-#         special_tokens_mask=special_tokens_mask,
-#         char_to_token_mapper=char_to_token_mapper,
-#         partition=partition,
-#     )
-#     assert tag_sequence == [None, "B-city", "O", "O", "O", "O", "O", None]
-#     partition = partitions[1]
-#     char_to_token_mapping = {
-#         0: 1,
-#         1: 1,
-#         2: 1,
-#         3: 1,
-#         4: 1,
-#         6: 2,
-#         7: 2,
-#         8: 3,
-#         9: 3,
-#         10: 3,
-#         11: 4,
-#         13: 5,
-#         14: 5,
-#         16: 6,
-#         17: 6,
-#         18: 6,
-#         20: 7,
-#         21: 7,
-#         22: 7,
-#         23: 7,
-#         24: 8,
-#         25: 9,
-#         27: 10,
-#         28: 10,
-#         29: 10,
-#         30: 10,
-#         31: 10,
-#         32: 11,
-#     }
-#     special_tokens_mask = [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]
-#     char_to_token_mapper = get_char_to_token_mapper(
-#         char_to_token_mapping=char_to_token_mapping,
-#     )
-#     tag_sequence = convert_span_annotations_to_tag_sequence(
-#         spans=entities,
-#         special_tokens_mask=special_tokens_mask,
-#         char_to_token_mapper=char_to_token_mapper,
-#         partition=partition,
-#     )
-#     assert tag_sequence == [
-#         None,
-#         "B-person",
-#         "I-person",
-#         "I-person",
-#         "I-person",
-#         "O",
-#         "O",
-#         "O",
-#         "O",
-#         "O",
-#         "O",
-#         "O",
-#         None,
-#     ]
-
-#     doc = documents[2]
-#     entities = doc.annotations.spans["entities"]
-#     assert len(entities) == 2
-#     partitions = doc.annotations.spans["sentences"]
-#     assert len(partitions) == 1
-#     partition = partitions[0]
-#     char_to_token_mapping = {
-#         0: 1,
-#         1: 1,
-#         2: 1,
-#         3: 1,
-#         5: 2,
-#         6: 2,
-#         7: 2,
-#         8: 2,
-#         9: 2,
-#         10: 2,
-#         12: 3,
-#         13: 3,
-#         14: 3,
-#         15: 3,
-#         16: 3,
-#         18: 4,
-#         19: 4,
-#         20: 4,
-#         21: 4,
-#         23: 5,
-#         24: 5,
-#         26: 6,
-#         27: 6,
-#         28: 6,
-#         29: 6,
-#         30: 6,
-#         31: 6,
-#         32: 7,
-#     }
-#     special_tokens_mask = [1, 0, 0, 0, 0, 0, 0, 0, 1]
-#     char_to_token_mapper = get_char_to_token_mapper(
-#         char_to_token_mapping=char_to_token_mapping,
-#     )
-#     tag_sequence = convert_span_annotations_to_tag_sequence(
-#         spans=entities,
-#         special_tokens_mask=special_tokens_mask,
-#         char_to_token_mapper=char_to_token_mapper,
-#         partition=partition,
-#     )
-#     assert tag_sequence == [None, "B-person", "O", "O", "O", "O", "B-city", "O", None]
-
-
-# def test_has_overlap():
-#     # no overlap - not touching
-#     assert not has_overlap((3, 5), (6, 10))
-#     assert not has_overlap((6, 10), (3, 5))
-
-#     # no overlap - touching
-#     assert not has_overlap((5, 10), (3, 5))
-#     assert not has_overlap((3, 5), (5, 10))
-
-#     # partly overlap
-#     assert has_overlap((3, 5), (4, 10))
-#     assert has_overlap((4, 10), (3, 5))
-
-#     # partly overlap - same start
-#     assert has_overlap((3, 5), (3, 10))
-#     assert has_overlap((3, 10), (3, 5))
-
-#     # partly overlap - same end
-#     assert has_overlap((3, 5), (2, 5))
-#     assert has_overlap((2, 5), (3, 5))
-
-#     # total overlap (containing)
-#     assert has_overlap((3, 5), (2, 10))
-#     assert has_overlap((2, 10), (3, 5))