diff --git a/dataset_builders/pie/brat/README.md b/dataset_builders/pie/brat/README.md
index 979f321f..e57e2c57 100644
--- a/dataset_builders/pie/brat/README.md
+++ b/dataset_builders/pie/brat/README.md
@@ -1,8 +1,16 @@
-# PIE Dataset Card for "conll2003"
+# PIE Dataset Card for "brat"
 
 This is a [PyTorch-IE](https://github.com/ChristophAlt/pytorch-ie) wrapper for the
 [BRAT Huggingface dataset loading script](https://huggingface.co/datasets/DFKI-SLT/brat).
 
+## Dataset Variants
+
+The dataset provides the following variants:
+
+- `default`: The original dataset. Documents are of type `BratDocument` (with `LabeledMultiSpan` annotations, see below).
+- `merge_fragmented_spans`: Documents are of type `BratDocumentWithMergedSpans` (this variant merges spans that are
+  fragmented into simple `LabeledSpans`, see below).
+
 ## Data Schema
 
 The document type for this dataset is `BratDocument` or `BratDocumentWithMergedSpans`, depending on if the
@@ -19,6 +27,12 @@ and the following annotation layers:
 - `span_attributes` (annotation type: `Attribute`, target: `spans`)
 - `relation_attributes` (annotation type: `Attribute`, target: `relations`)
 
+The `LabeledMultiSpan` annotation type is defined as follows:
+
+- `slices` (type: `Tuple[Tuple[int, int], ...]`): the slices consisting if start (including) and end (excluding) indices of the spans
+- `label` (type: `str`)
+- `score` (type: `float`, optional, not included in comparison)
+
 The `Attribute` annotation type is defined as follows:
 
 - `annotation` (type: `Annotation`): the annotation to which the attribute is attached
@@ -31,50 +45,4 @@ See [here](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/a
 ## Document Converters
 
 The dataset provides no predefined document converters because the BRAT format is very flexible and can be used
-for many different tasks. You can add your own document converter by doing the following:
-
-```python
-import dataclasses
-from typing import Optional
-
-from pytorch_ie.core import AnnotationList, annotation_field
-from pytorch_ie.documents import TextBasedDocument
-from pytorch_ie.annotations import LabeledSpan
-
-from pie_datasets import DatasetDict
-
-# define your document class
-@dataclasses.dataclass
-class MyDocument(TextBasedDocument):
-    my_field: Optional[str] = None
-    my_span_annotations: AnnotationList[LabeledSpan] = annotation_field(target="text")
-
-# define your document converter
-def my_converter(document: BratDocumentWithMergedSpans) -> MyDocument:
-    # create your document with the data from the original document.
-    # The fields "text", "id" and "metadata" are derived from the TextBasedDocument.
-    my_document = MyDocument(id=document.id, text=document.text, metadata=document.metadata, my_field="my_value")
-
-    # create a new span annotation
-    new_span = LabeledSpan(label="my_label", start=2, end=10)
-    # add the new span annotation to your document
-    my_document.my_span_annotations.append(new_span)
-
-    # add annotations from the document to your document
-    for span in document.spans:
-        # we need to copy the span because an annotation can only be attached to one document
-        my_document.my_span_annotations.append(span.copy())
-
-    return my_document
-
-
-# load the dataset. We use the "merge_fragmented_spans" dataset variant here
-# because it provides documents of type BratDocumentWithMergedSpans.
-dataset = DatasetDict.load_dataset("pie/brat", name="merge_fragmented_spans", data_dir="path/to/brat/data")
-
-# attach your document converter to the dataset
-dataset.register_document_converter(my_converter)
-
-# convert the dataset
-converted_dataset = dataset.to_document_type(MyDocument)
-```
+for many different tasks.
diff --git a/dataset_builders/pie/brat/brat.py b/dataset_builders/pie/brat/brat.py
index 134e82f4..00d7fc64 100644
--- a/dataset_builders/pie/brat/brat.py
+++ b/dataset_builders/pie/brat/brat.py
@@ -2,4 +2,5 @@
 
 
 class Brat(BratBuilder):
-    pass
+    BASE_DATASET_PATH = "DFKI-SLT/brat"
+    BASE_DATASET_REVISION = "052163d34b4429d81003981bc10674cef54aa0b8"