diff --git a/src/pie_datasets/core/dataset.py b/src/pie_datasets/core/dataset.py
index ca18f0b0..d0d81ab7 100644
--- a/src/pie_datasets/core/dataset.py
+++ b/src/pie_datasets/core/dataset.py
@@ -292,6 +292,36 @@ def from_hf_dataset(
         )
         return document_dataset
 
+    @classmethod
+    def from_documents(
+        cls,
+        documents: List[Document],
+        document_converters: Optional[DocumentConvertersType] = None,
+        **dataset_kwargs,
+    ) -> "Dataset":
+        """Create a Dataset from a list of documents. It wraps the Huggingface
+        datasets.Dataset.from_list method, see the documentation for more details.
+
+        Args:
+            documents (List[Document]): A list of documents.
+            document_converters (Optional[DocumentConvertersType], optional): A dictionary of document
+                converters. Defaults to None.
+            **dataset_kwargs: Additional arguments for the Huggingface dataset creation.
+
+        Returns:
+            Dataset: The created dataset.
+        """
+
+        if len(documents) == 0:
+            raise ValueError("No documents to create dataset from")
+        document_type = type(documents[0])
+        data = [doc.asdict() for doc in documents]
+        hf_dataset = datasets.Dataset.from_list(mapping=data, **dataset_kwargs)
+        dataset = cls.from_hf_dataset(
+            hf_dataset, document_type=document_type, document_converters=document_converters
+        )
+        return dataset
+
     def apply_hf_func(self, func, **kwargs) -> "Dataset":
         return Dataset.from_hf_dataset(
             func(self, **kwargs),
@@ -470,6 +500,50 @@ def from_hf_dataset(
         )
         return dataset
 
+    @classmethod
+    def from_documents(
+        cls,
+        documents: Callable,
+        document_converters: Optional[DocumentConvertersType] = None,
+        **dataset_kwargs,
+    ) -> "IterableDataset":
+        """Create an Iterable Dataset from a generator that yields documents. It wraps the
+        Huggingface datasets.IterableDataset.from_generator method, see the documentation for more
+        details.
+
+        Args:
+            documents (Callable): A generator function that `yields` documents.
+            document_converters (Optional[DocumentConvertersType], optional): A dictionary of document
+                converters. Defaults to None.
+            **dataset_kwargs: Additional arguments for the Huggingface dataset creation.
+
+        Returns:
+            IterableDataset: The created iterable dataset.
+        """
+
+        # get first document to infer the document type
+        try:
+            gen_kwargs = dataset_kwargs.get("gen_kwargs", {})
+            first_doc = next(documents(**gen_kwargs))
+        except StopIteration:
+            raise ValueError("No documents to create dataset from")
+        document_type = type(first_doc)
+
+        # wrap the generator to yield dictionaries
+        def wrapped_documents_generator(**kwargs):
+            for doc in documents(**kwargs):
+                yield doc.asdict()
+
+        hf_dataset = datasets.IterableDataset.from_generator(
+            wrapped_documents_generator, **dataset_kwargs
+        )
+        dataset = cls.from_hf_dataset(
+            hf_dataset,
+            document_type=document_type,
+            document_converters=document_converters,
+        )
+        return dataset
+
     def __iter__(self):
         for example in iter(super().__iter__()):
             yield self.document_type.fromdict(example)
diff --git a/tests/unit/core/test_dataset.py b/tests/unit/core/test_dataset.py
index 3481d191..789b5d3a 100644
--- a/tests/unit/core/test_dataset.py
+++ b/tests/unit/core/test_dataset.py
@@ -6,6 +6,7 @@
 import numpy
 import pytest
 import torch
+from pytorch_ie import Document
 from pytorch_ie.annotations import BinaryRelation, Label, LabeledSpan, Span
 from pytorch_ie.core import AnnotationList, annotation_field
 from pytorch_ie.core.taskmodule import (
@@ -431,3 +432,50 @@ def test_dataset_with_taskmodule(
 
     for document in train_dataset:
         assert not document["entities"].predictions
+
+
+@pytest.mark.parametrize("as_iterable_dataset", [False, True])
+def test_pie_dataset_from_documents(documents, as_iterable_dataset):
+    if as_iterable_dataset:
+        dataset_class = IterableDataset
+
+        # make generators from list
+        def _documents():
+            yield from documents
+
+        def _empty_docs():
+            return iter([])
+
+    else:
+        dataset_class = Dataset
+        _documents = documents
+        _empty_docs = list[Document]()
+
+    dataset_from_documents = dataset_class.from_documents(_documents)
+
+    assert isinstance(dataset_from_documents, dataset_class)
+
+    assert all(isinstance(doc, TextBasedDocument) for doc in dataset_from_documents)
+    assert all(
+        doc1.asdict() == doc2.asdict() for doc1, doc2 in zip(documents, dataset_from_documents)
+    )
+    assert hasattr(dataset_from_documents, "document_type")
+
+    # Test dataset creation with document converter
+    dataset_from_documents_with_converter = dataset_class.from_documents(
+        _documents, document_converters={TestDocumentWithLabel: convert_to_document_with_label}
+    )
+
+    assert isinstance(dataset_from_documents_with_converter, dataset_class)
+
+    assert len(dataset_from_documents_with_converter.document_converters) == 1
+    assert TestDocumentWithLabel in dataset_from_documents_with_converter.document_converters
+    assert (
+        dataset_from_documents_with_converter.document_converters[TestDocumentWithLabel]
+        == convert_to_document_with_label
+    )
+
+    # Test dataset creation with empty list / generator
+    with pytest.raises(ValueError) as excinfo:
+        dataset_class.from_documents(_empty_docs)
+    assert str(excinfo.value) == "No documents to create dataset from"