use pie-modules instead of pytorch-ie

see https://github.com/ArneBinder/pie-datasets/pull/204 for further information

README.md

@@ -9,7 +9,7 @@ Therefore, the `sciarg` dataset as described here follows the data structure fro
 ```python
 from pie_datasets import load_dataset
 from pie_datasets.builders.brat import BratDocumentWithMergedSpans, BratDocument
-from pytorch_ie.documents import TextDocumentWithLabeledMultiSpansBinaryRelationsAndLabeledPartitions, TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions
+from pie_modules.documents import TextDocumentWithLabeledMultiSpansBinaryRelationsAndLabeledPartitions, TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions
 
 # load default version
 dataset = load_dataset("pie/sciarg")
@@ -74,20 +74,20 @@ See [PIE-Brat Data Schema](https://huggingface.co/datasets/pie/brat#data-schema)
 
 The dataset provides document converters for the following target document types:
 
-- `pytorch_ie.documents.TextDocumentWithLabeledSpansAndBinaryRelations`
+- `pie_modules.documents.TextDocumentWithLabeledSpansAndBinaryRelations`
   - `LabeledSpans`, converted from `BratDocument`'s `spans`
     - labels: `background_claim`, `own_claim`, `data`
     - if `spans` contain whitespace at the beginning and/or the end, the whitespace are trimmed out.
   - `BinraryRelations`, converted from `BratDocument`'s `relations`
     - labels: `supports`, `contradicts`, `semantically_same`, `parts_of_same`
     - if the `relations` label is `semantically_same` or `parts_of_same`, they are merged if they are the same arguments after sorting.
-- `pytorch_ie.documents.TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions`
+- `pie_modules.documents.TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions`
   - `LabeledSpans`, as above
   - `BinaryRelations`, as above
   - `LabeledPartitions`, partitioned `BratDocument`'s `text`, according to the paragraph, using regex.
     - labels: `title`, `abstract`, `H1`
 
-See [here](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py) for the document type
+See [here](https://github.com/ArneBinder/pie-modules/blob/main/src/pie_modules/documents.py) for the document type
 definitions.
 
 ### Data Splits
@@ -155,7 +155,7 @@ possibly since [Lauscher et al., 2018](https://aclanthology.org/W18-5206/) prese
 
 - `supports`:
   - if the assumed veracity of *b* increases with the veracity of *a*
-  - "Usually, this relationship exists from data to claim, but in many cases a claim might support another claim. Other combinations are still possible." -  (*Annotation Guidelines*, p. 3)
+  - "Usually, this relationship exists from data to claim, but in many cases a claim might support another claim. Other combinations are still possible." - (*Annotation Guidelines*, p. 3)
 - `contradicts`:
   - if the assumed veracity of *b* decreases with the veracity of *a*
   - It is a **bi-directional**, i.e., symmetric relationship.
@@ -183,7 +183,7 @@ Above: Diagram from *Annotation Guildelines* (p.6)
 
 Below: Subset of relations in `A01`
 
-![sample2](img/sciarg-sam.png)
+![sample2](img/sciarg_train_0.png)
 
 ### Collected Statistics after Document Conversion
 
@@ -335,7 +335,7 @@ python src/evaluate_documents.py dataset=sciarg_base metric=count_text_tokens
 
 ### Curation Rationale
 
-"\[C\]omputational methods for analyzing scientific writing are becoming paramount...there is no publicly available corpus of scientific publications (in English), annotated with fine-grained argumentative structures. ...\[A\]rgumentative structure of scientific publications should not be studied in isolation, but rather in relation to other rhetorical aspects, such as the
+"[C]omputational methods for analyzing scientific writing are becoming paramount...there is no publicly available corpus of scientific publications (in English), annotated with fine-grained argumentative structures. ...[A]rgumentative structure of scientific publications should not be studied in isolation, but rather in relation to other rhetorical aspects, such as the
 discourse structure.
 (Lauscher et al. 2018, p. 40)
 
@@ -343,7 +343,7 @@ discourse structure.
 
 #### Initial Data Collection and Normalization
 
-"\[W\]e randomly selected a set of 40 documents, available in PDF format, among a bigger collection provided by experts in the domain, who pre-selected a representative sample of articles in Computer Graphics. Articles were classified into four important subjects in this area: Skinning, Motion Capture, Fluid Simulation and Cloth Simulation. We included in the corpus 10 highly representative articles for each subject." (Fisas et al. 2015, p. 44)
+"[W]e randomly selected a set of 40 documents, available in PDF format, among a bigger collection provided by experts in the domain, who pre-selected a representative sample of articles in Computer Graphics. Articles were classified into four important subjects in this area: Skinning, Motion Capture, Fluid Simulation and Cloth Simulation. We included in the corpus 10 highly representative articles for each subject." (Fisas et al. 2015, p. 44)
 
 "The Corpus includes 10,789 sentences, with an average of 269.7 sentences per document." (p. 45)
 
@@ -367,7 +367,7 @@ The annotation were done using BRAT Rapid Annotation Tool ([Stenetorp et al., 20
 
 ### Personal and Sensitive Information
 
-\[More Information Needed\]
+[More Information Needed]
 
 ## Considerations for Using the Data
 
@@ -384,7 +384,7 @@ of the different rhetorical aspects of scientific language (which we dub *scitor
 
 "While the background claims and own claims are on average of similar length (85 and 87 characters, respectively), they are much longer than data components (average of 25 characters)."
 
-"\[A\]nnotators identified an average of 141 connected component per publication...This indicates that either authors write very short argumentative chains or that our annotators had difficulties noticing long-range argumentative dependencies."
+"[A]nnotators identified an average of 141 connected component per publication...This indicates that either authors write very short argumentative chains or that our annotators had difficulties noticing long-range argumentative dependencies."
 
 (Lauscher et al. 2018, p.43)
 

requirements.txt

@@ -1,3 +1,3 @@
-pie-datasets>=0.6.0,<0.11.0
-pie-modules>=0.10.8,<0.12.0
+pie-datasets>=0.10.11,<0.11.0
+pie-modules>=0.15.9,<0.16.0
 networkx>=3.0.0,<4.0.0

sciarg.py

@@ -1,14 +1,15 @@
+import dataclasses
 import logging
 from typing import Union
 
+from pie_core import AnnotationLayer, Document, annotation_field
 from pie_modules.document.processing import (
     RegexPartitioner,
     RelationArgumentSorter,
     SpansViaRelationMerger,
     TextSpanTrimmer,
 )
-from pytorch_ie.core import Document
-from pytorch_ie.documents import (
+from pie_modules.documents import (
     TextDocumentWithLabeledMultiSpansAndBinaryRelations,
     TextDocumentWithLabeledMultiSpansBinaryRelationsAndLabeledPartitions,
     TextDocumentWithLabeledSpansAndBinaryRelations,
@@ -16,7 +17,12 @@ from pytorch_ie.documents import (
 )
 
 from pie_datasets.builders import BratBuilder, BratConfig
-from pie_datasets.builders.brat import BratDocument, BratDocumentWithMergedSpans
+from pie_datasets.builders.brat import (
+    BratAttribute,
+    BratDocument,
+    BratDocumentWithMergedSpans,
+    BratNote,
+)
 from pie_datasets.core.dataset import DocumentConvertersType
 from pie_datasets.document.processing import Caster, Pipeline
 
@@ -26,6 +32,35 @@ URL = "http://data.dws.informatik.uni-mannheim.de/sci-arg/compiled_corpus.zip"
 SPLIT_PATHS = {"train": "compiled_corpus"}
 
 
+@dataclasses.dataclass
+class ConvertedBratDocument(TextDocumentWithLabeledMultiSpansAndBinaryRelations):
+    span_attributes: AnnotationLayer[BratAttribute] = annotation_field(
+        target="labeled_multi_spans"
+    )
+    relation_attributes: AnnotationLayer[BratAttribute] = annotation_field(
+        target="binary_relations"
+    )
+    notes: AnnotationLayer[BratNote] = annotation_field(
+        targets=[
+            "labeled_multi_spans",
+            "binary_relations",
+            "span_attributes",
+            "relation_attributes",
+        ]
+    )
+
+
+@dataclasses.dataclass
+class ConvertedBratDocumentWithMergedSpans(TextDocumentWithLabeledSpansAndBinaryRelations):
+    span_attributes: AnnotationLayer[BratAttribute] = annotation_field(target="labeled_spans")
+    relation_attributes: AnnotationLayer[BratAttribute] = annotation_field(
+        target="binary_relations"
+    )
+    notes: AnnotationLayer[BratNote] = annotation_field(
+        targets=["labeled_spans", "binary_relations", "span_attributes", "relation_attributes"]
+    )
+
+
 def get_common_converter_pipeline_steps(target_document_type: type[Document]) -> dict:
     return dict(
         cast=Caster(
@@ -106,13 +141,36 @@ class SciArg(BratBuilder):
     def _generate_document(self, example, **kwargs):
         document = super()._generate_document(example, **kwargs)
         if self.config.resolve_parts_of_same:
-            document = SpansViaRelationMerger(
-                relation_layer="relations",
+            # we need to convert the document to a different type to be able to merge the spans:
+            # SpansViaRelationMerger expects the spans to be of type LabeledSpan,
+            # but the document has spans of type BratSpan
+            converted_doc = document.as_type(
+                ConvertedBratDocumentWithMergedSpans,
+                field_mapping={
+                    "spans": "labeled_spans",
+                    "relations": "binary_relations",
+                },
+                keep_remaining=True,
+            )
+            merged_document = SpansViaRelationMerger(
+                relation_layer="binary_relations",
                 link_relation_label="parts_of_same",
                 create_multi_spans=True,
-                result_document_type=BratDocument,
-                result_field_mapping={"spans": "spans", "relations": "relations"},
-            )(document)
+                result_document_type=ConvertedBratDocument,
+                result_field_mapping={
+                    "labeled_spans": "labeled_multi_spans",
+                    "binary_relations": "binary_relations",
+                    "span_attributes": "span_attributes",
+                    "relation_attributes": "relation_attributes",
+                    "notes": "notes",
+                },
+            )(converted_doc)
+            # convert back to BratDocument
+            document = merged_document.as_type(
+                BratDocument,
+                field_mapping={"labeled_multi_spans": "spans", "binary_relations": "relations"},
+                keep_remaining=True,
+            )
         else:
             # some documents have duplicate relations, remove them
             remove_duplicate_relations(document)