update to pie-datasets 0.3.3

README.md

@@ -0,0 +1,28 @@
+# PIE Dataset Card for "SciDTB Argmin"
+
+This is a [PyTorch-IE](https://github.com/ChristophAlt/pytorch-ie) wrapper for the
+[SciDTB ArgMin Huggingface dataset loading script](https://huggingface.co/datasets/DFKI-SLT/scidtb_argmin).
+
+## Data Schema
+
+The document type for this dataset is `SciDTBArgminDocument` which defines the following data fields:
+
+- `tokens` (Tuple of string)
+- `id` (str, optional)
+- `metadata` (dictionary, optional)
+
+and the following annotation layers:
+
+- `units` (annotation type: `LabeledSpan`, target: `tokens`)
+- `relations` (annotation type: `BinaryRelation`, target: `units`)
+
+See [here](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/annotations.py) for the annotation type definitions.
+
+## Document Converters
+
+The dataset provides document converters for the following target document types:
+
+- `pytorch_ie.documents.TextDocumentWithLabeledSpansAndBinaryRelations`
+
+See [here](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py) for the document type
+definitions.

requirements.txt

@@ -0,0 +1,2 @@
+pie-datasets>=0.3.3
+pytorch-ie>=0.29.1

scidtb_argmin.py

@@ -3,11 +3,17 @@ import logging
 from typing import Any, Callable, Dict, List, Optional, Tuple
 
 import datasets
-import pytorch_ie.data.builder
 from pytorch_ie.annotations import BinaryRelation, LabeledSpan
 from pytorch_ie.core import AnnotationList, Document, annotation_field
+from pytorch_ie.documents import (
+    TextDocumentWithLabeledSpansAndBinaryRelations,
+    TokenBasedDocument,
+)
 from pytorch_ie.utils.span import bio_tags_to_spans
 
+from pie_datasets import GeneratorBasedBuilder
+from pie_datasets.document.conversion import token_based_document_to_text_based
+
 log = logging.getLogger(__name__)
 
 
@@ -23,25 +29,27 @@ def labels_and_spans_to_bio_tags(
 
 
 @dataclasses.dataclass
-class SciDTBArgminDocument(Document):
-    tokens: Tuple[str, ...]
-    id: Optional[str] = None
-    metadata: Dict[str, Any] = dataclasses.field(default_factory=dict)
+class SciDTBArgminDocument(TokenBasedDocument):
     units: AnnotationList[LabeledSpan] = annotation_field(target="tokens")
     relations: AnnotationList[BinaryRelation] = annotation_field(target="units")
 
 
+@dataclasses.dataclass
+class SimplifiedSciDTBArgminDocument(TokenBasedDocument):
+    labeled_spans: AnnotationList[LabeledSpan] = annotation_field(target="tokens")
+    binary_relations: AnnotationList[BinaryRelation] = annotation_field(target="labeled_spans")
+
+
 def example_to_document(
     example: Dict[str, Any],
-    unit_bio_int2str: Callable[[int], str],
-    unit_label_int2str: Callable[[int], str],
-    relation_int2str: Callable[[int], str],
+    unit_bio: datasets.ClassLabel,
+    unit_label: datasets.ClassLabel,
+    relation: datasets.ClassLabel,
 ):
-
     document = SciDTBArgminDocument(id=example["id"], tokens=tuple(example["data"]["token"]))
-    bio_tags = unit_bio_int2str(example["data"]["unit-bio"])
-    unit_labels = unit_label_int2str(example["data"]["unit-label"])
-    roles = relation_int2str(example["data"]["role"])
+    bio_tags = unit_bio.int2str(example["data"]["unit-bio"])
+    unit_labels = unit_label.int2str(example["data"]["unit-label"])
+    roles = relation.int2str(example["data"]["role"])
     tag_sequence = [
         f"{bio}-{label}|{role}|{parent_offset}"
         for bio, label, role, parent_offset in zip(
@@ -62,7 +70,7 @@ def example_to_document(
     ]
     document.units.extend(units)
 
-    # TODO: check if direction of the relation is correct (what is the head / tail of the relation?)
+    # The relation direction is as in "f{head} {relation_label} {tail}"
     relations = []
     for idx, parent_offset in enumerate(span_parent_offsets):
         if span_roles[idx] != "none":
@@ -79,11 +87,10 @@ def example_to_document(
 
 def document_to_example(
     document: SciDTBArgminDocument,
-    unit_bio_str2int: Callable[[str], int],
-    unit_label_str2int: Callable[[str], int],
-    relation_str2int: Callable[[str], int],
+    unit_bio: datasets.ClassLabel,
+    unit_label: datasets.ClassLabel,
+    relation: datasets.ClassLabel,
 ) -> Dict[str, Any]:
-
     unit2idx = {unit: idx for idx, unit in enumerate(document.units)}
     unit2parent_relation = {relation.head: relation for relation in document.relations}
 
@@ -112,19 +119,39 @@ def document_to_example(
 
     data = {
         "token": list(document.tokens),
-        "unit-bio": unit_bio_str2int(bio_tags),
-        "unit-label": unit_label_str2int(unit_labels),
-        "role": relation_str2int(roles),
+        "unit-bio": unit_bio.str2int(bio_tags),
+        "unit-label": unit_label.str2int(unit_labels),
+        "role": relation.str2int(roles),
         "parent-offset": [int(idx_str) for idx_str in parent_offsets],
     }
     result = {"id": document.id, "data": data}
     return result
 
 
-class SciDTBArgmin(pytorch_ie.data.builder.GeneratorBasedBuilder):
+def convert_to_text_document_with_labeled_spans_and_binary_relations(
+    document: SciDTBArgminDocument,
+) -> TextDocumentWithLabeledSpansAndBinaryRelations:
+    doc_simplified = document.as_type(
+        SimplifiedSciDTBArgminDocument,
+        field_mapping={"units": "labeled_spans", "relations": "binary_relations"},
+    )
+    result = token_based_document_to_text_based(
+        doc_simplified,
+        result_document_type=TextDocumentWithLabeledSpansAndBinaryRelations,
+        join_tokens_with=" ",
+    )
+    return result
+
+
+class SciDTBArgmin(GeneratorBasedBuilder):
     DOCUMENT_TYPE = SciDTBArgminDocument
 
+    DOCUMENT_CONVERTERS = {
+        TextDocumentWithLabeledSpansAndBinaryRelations: convert_to_text_document_with_labeled_spans_and_binary_relations
+    }
+
     BASE_DATASET_PATH = "DFKI-SLT/scidtb_argmin"
+    BASE_DATASET_REVISION = "8c02587edcb47ab5b102692bd10bfffd1844a09b"
 
     BUILDER_CONFIGS = [datasets.BuilderConfig(name="default")]
 
@@ -132,15 +159,15 @@ class SciDTBArgmin(pytorch_ie.data.builder.GeneratorBasedBuilder):
 
     def _generate_document_kwargs(self, dataset):
         return {
-            "unit_bio_int2str": dataset.features["data"].feature["unit-bio"].int2str,
-            "unit_label_int2str": dataset.features["data"].feature["unit-label"].int2str,
-            "relation_int2str": dataset.features["data"].feature["role"].int2str,
+            "unit_bio": dataset.features["data"].feature["unit-bio"],
+            "unit_label": dataset.features["data"].feature["unit-label"],
+            "relation": dataset.features["data"].feature["role"],
         }
 
-    def _generate_document(self, example, unit_bio_int2str, unit_label_int2str, relation_int2str):
+    def _generate_document(self, example, unit_bio, unit_label, relation):
         return example_to_document(
             example,
-            unit_bio_int2str=unit_bio_int2str,
-            unit_label_int2str=unit_label_int2str,
-            relation_int2str=relation_int2str,
+            unit_bio=unit_bio,
+            unit_label=unit_label,
+            relation=relation,
         )