https://github.com/ArneBinder/pie-datasets/pull/100

README.md

@@ -3,6 +3,27 @@
 This is a [PyTorch-IE](https://github.com/ChristophAlt/pytorch-ie) wrapper for the
 [ArgMicro Huggingface dataset loading script](https://huggingface.co/datasets/DFKI-SLT/argmicro).
 
+## Usage
+
+```python
+from pie_datasets import load_dataset
+from pytorch_ie.documents import TextDocumentWithLabeledSpansAndBinaryRelations
+
+# load English variant
+dataset = load_dataset("pie/argmicro", name="en")
+
+# if required, normalize the document type (see section Document Converters below)
+dataset_converted = dataset.to_document_type(TextDocumentWithLabeledSpansAndBinaryRelations)
+assert isinstance(dataset_converted["train"][0], TextDocumentWithLabeledSpansAndBinaryRelations)
+
+# get first relation in the first document
+doc = dataset_converted["train"][0]
+print(doc.binary_relations[0])
+# BinaryRelation(head=LabeledSpan(start=0, end=81, label='opp', score=1.0), tail=LabeledSpan(start=326, end=402, label='pro', score=1.0), label='reb', score=1.0)
+print(doc.binary_relations[0].resolve())
+# ('reb', (('opp', "Yes, it's annoying and cumbersome to separate your rubbish properly all the time."), ('pro', 'We Berliners should take the chance and become pioneers in waste separation!')))
+```
+
 ## Dataset Variants
 
 The dataset contains two `BuilderConfig`'s:
@@ -53,3 +74,122 @@ The dataset provides document converters for the following target document types
 
 See [here](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py) for the document type
 definitions.
+
+### Collected Statistics after Document Conversion
+
+We use the script `evaluate_documents.py` from [PyTorch-IE-Hydra-Template](https://github.com/ArneBinder/pytorch-ie-hydra-template-1) to generate these statistics.
+After checking out that code, the statistics and plots can be generated by the command:
+
+```commandline
+python src/evaluate_documents.py dataset=argmicro_base metric=METRIC
+```
+
+where a `METRIC` is called according to the available metric configs in `config/metric/METRIC` (see [metrics](https://github.com/ArneBinder/pytorch-ie-hydra-template-1/tree/main/configs/metric)).
+
+This also requires to have the following dataset config in `configs/dataset/argmicro_base.yaml` of this dataset within the repo directory:
+
+```commandline
+_target_: src.utils.execute_pipeline
+input:
+  _target_: pie_datasets.DatasetDict.load_dataset
+  path: pie/argmicro
+  revision: 28ef031d2a2c97be7e9ed360e1a5b20bd55b57b2
+  name: en
+```
+
+For token based metrics, this uses `bert-base-uncased` from `transformer.AutoTokenizer` (see [AutoTokenizer](https://huggingface.co/docs/transformers/v4.37.1/en/model_doc/auto#transformers.AutoTokenizer), and [bert-based-uncased](https://huggingface.co/bert-base-uncased) to tokenize `text` in `TextDocumentWithLabeledSpansAndBinaryRelations` (see [document type](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py)).
+
+#### Relation argument (outer) token distance per label
+
+The distance is measured from the first token of the first argumentative unit to the last token of the last unit, a.k.a. outer distance.
+
+We collect the following statistics: number of documents in the split (*no. doc*), no. of relations (*len*), mean of token distance (*mean*), standard deviation of the distance (*std*), minimum outer distance (*min*), and maximum outer distance (*max*).
+We also present histograms in the collapsible, showing the distribution of these relation distances (x-axis; and unit-counts in y-axis), accordingly.
+
+<details>
+<summary>Command</summary>
+
+```
+python src/evaluate_documents.py dataset=argmicro_base metric=relation_argument_token_distances
+```
+
+</details>
+
+|       |  len | max |   mean | min |    std |
+| :---- | ---: | --: | -----: | --: | -----: |
+| ALL   | 1018 | 127 | 44.434 |  14 | 21.501 |
+| exa   |   18 |  63 | 33.556 |  16 | 13.056 |
+| joint |   88 |  48 | 30.091 |  17 |  9.075 |
+| reb   |  220 | 127 | 49.327 |  16 | 24.653 |
+| sup   |  562 | 124 | 46.534 |  14 | 22.079 |
+| und   |  130 |  84 | 38.292 |  17 | 12.321 |
+
+<details>
+  <summary>Histogram (split: train, 112 documents)</summary>
+
+![rtd-label_argmicro.png](img%2Frtd-label_argmicro.png)
+
+</details>
+
+#### Span lengths (tokens)
+
+The span length is measured from the first token of the first argumentative unit to the last token of the particular unit.
+
+We collect the following statistics: number of documents in the split (*no. doc*), no. of spans (*len*), mean of number of tokens in a span (*mean*), standard deviation of the number of tokens (*std*), minimum tokens in a span (*min*), and maximum tokens in a span (*max*).
+We also present histograms in the collapsible, showing the distribution of these token-numbers (x-axis; and unit-counts in y-axis), accordingly.
+
+<details>
+<summary>Command</summary>
+
+```
+python src/evaluate_documents.py dataset=argmicro_base metric=span_lengths_tokens
+```
+
+</details>
+
+| statistics |  train |
+| :--------- | -----: |
+| no. doc    |    112 |
+| len        |    576 |
+| mean       | 16.365 |
+| std        |  6.545 |
+| min        |      4 |
+| max        |     41 |
+
+<details>
+  <summary>Histogram (split: train, 112 documents)</summary>
+
+![slt_argmicro.png](img%2Fslt_argmicro.png)
+
+</details>
+
+#### Token length (tokens)
+
+The token length is measured from the first token of the document to the last one.
+
+We collect the following statistics: number of documents in the split (*no. doc*), mean of document token-length (*mean*), standard deviation of the length (*std*), minimum number of tokens in a document (*min*), and maximum number of tokens in a document (*max*).
+We also present histograms in the collapsible, showing the distribution of these token lengths (x-axis; and unit-counts in y-axis), accordingly.
+
+<details>
+<summary>Command</summary>
+
+```
+python src/evaluate_documents.py dataset=argmicro_base metric=count_text_tokens
+```
+
+</details>
+
+| statistics |  train |
+| :--------- | -----: |
+| no. doc    |    112 |
+| mean       | 84.161 |
+| std        | 22.596 |
+| min        |     36 |
+| max        |    153 |
+
+<details>
+  <summary>Histogram (split: train, 112 documents)</summary>
+
+![tl_argmicro.png](img%2Ftl_argmicro.png)
+
+</details>

argmicro.py

@@ -1,283 +1,283 @@
-import copy
-import dataclasses
-import logging
-from collections import defaultdict
-from itertools import combinations
-from typing import Any, Dict, List, Optional, Set, Tuple
-
-import datasets
-from pytorch_ie.annotations import BinaryRelation, Label, LabeledSpan, Span
-from pytorch_ie.core import Annotation, AnnotationList, annotation_field
-from pytorch_ie.documents import (
-    TextBasedDocument,
-    TextDocumentWithLabeledSpansAndBinaryRelations,
-)
-
-from pie_datasets import GeneratorBasedBuilder
-
-log = logging.getLogger(__name__)
-
-
-def dl2ld(dict_of_lists):
-    return [dict(zip(dict_of_lists, t)) for t in zip(*dict_of_lists.values())]
-
-
-def ld2dl(list_of_dicts, keys: Optional[List[str]] = None):
-    return {k: [d[k] for d in list_of_dicts] for k in keys}
-
-
-@dataclasses.dataclass(frozen=True)
-class LabeledAnnotationCollection(Annotation):
-    annotations: Tuple[Annotation, ...]
-    label: str
-
-
-@dataclasses.dataclass(frozen=True)
-class MultiRelation(Annotation):
-    heads: Tuple[Annotation, ...]  # sources == heads
-    tails: Tuple[Annotation, ...]  # targets == tails
-    label: str
-
-
-@dataclasses.dataclass
-class ArgMicroDocument(TextBasedDocument):
-    topic_id: Optional[str] = None
-    stance: AnnotationList[Label] = annotation_field()
-    edus: AnnotationList[Span] = annotation_field(target="text")
-    adus: AnnotationList[LabeledAnnotationCollection] = annotation_field(target="edus")
-    relations: AnnotationList[MultiRelation] = annotation_field(target="adus")
-
-
-def example_to_document(
-    example: Dict[str, Any],
-    adu_type_label: datasets.ClassLabel,
-    edge_type_label: datasets.ClassLabel,
-    stance_label: datasets.ClassLabel,
-) -> ArgMicroDocument:
-    stance = stance_label.int2str(example["stance"])
-    document = ArgMicroDocument(
-        id=example["id"],
-        text=example["text"],
-        topic_id=example["topic_id"] if example["topic_id"] != "UNDEFINED" else None,
-    )
-    if stance != "UNDEFINED":
-        document.stance.append(Label(label=stance))
-
-    # build EDUs
-    edus_dict = {
-        edu["id"]: Span(start=edu["start"], end=edu["end"]) for edu in dl2ld(example["edus"])
-    }
-    # build ADUs
-    adu_id2edus = defaultdict(list)
-    edges_multi_source = defaultdict(dict)
-    for edge in dl2ld(example["edges"]):
-        edge_type = edge_type_label.int2str(edge["type"])
-        if edge_type == "seg":
-            adu_id2edus[edge["trg"]].append(edus_dict[edge["src"]])
-        elif edge_type == "add":
-            if "src" not in edges_multi_source[edge["trg"]]:
-                edges_multi_source[edge["trg"]]["src"] = []
-            edges_multi_source[edge["trg"]]["src"].append(edge["src"])
-        else:
-            edges_multi_source[edge["id"]]["type"] = edge_type
-            edges_multi_source[edge["id"]]["trg"] = edge["trg"]
-            if "src" not in edges_multi_source[edge["id"]]:
-                edges_multi_source[edge["id"]]["src"] = []
-            edges_multi_source[edge["id"]]["src"].append(edge["src"])
-    adus_dict = {}
-    for adu in dl2ld(example["adus"]):
-        adu_type = adu_type_label.int2str(adu["type"])
-        adu_edus = adu_id2edus[adu["id"]]
-        adus_dict[adu["id"]] = LabeledAnnotationCollection(
-            annotations=tuple(adu_edus), label=adu_type
-        )
-    # build relations
-    rels_dict = {}
-    for edge_id, edge in edges_multi_source.items():
-        edge_target = edge["trg"]
-        if edge_target in edges_multi_source:
-            targets = edges_multi_source[edge_target]["src"]
-        else:
-            targets = [edge_target]
-        if any(target in edges_multi_source for target in targets):
-            raise Exception("Multi-hop relations are not supported")
-        rel = MultiRelation(
-            heads=tuple(adus_dict[source] for source in edge["src"]),
-            tails=tuple(adus_dict[target] for target in targets),
-            label=edge["type"],
-        )
-        rels_dict[edge_id] = rel
-
-    document.edus.extend(edus_dict.values())
-    document.adus.extend(adus_dict.values())
-    document.relations.extend(rels_dict.values())
-    document.metadata["edu_ids"] = list(edus_dict.keys())
-    document.metadata["adu_ids"] = list(adus_dict.keys())
-    document.metadata["rel_ids"] = list(rels_dict.keys())
-
-    document.metadata["rel_seg_ids"] = {
-        edge["src"]: edge["id"]
-        for edge in dl2ld(example["edges"])
-        if edge_type_label.int2str(edge["type"]) == "seg"
-    }
-    document.metadata["rel_add_ids"] = {
-        edge["src"]: edge["id"]
-        for edge in dl2ld(example["edges"])
-        if edge_type_label.int2str(edge["type"]) == "add"
-    }
-    return document
-
-
-def document_to_example(
-    document: ArgMicroDocument,
-    adu_type_label: datasets.ClassLabel,
-    edge_type_label: datasets.ClassLabel,
-    stance_label: datasets.ClassLabel,
-) -> Dict[str, Any]:
-    stance = document.stance[0].label if len(document.stance) else "UNDEFINED"
-    result = {
-        "id": document.id,
-        "text": document.text,
-        "topic_id": document.topic_id or "UNDEFINED",
-        "stance": stance_label.str2int(stance),
-    }
-
-    # construct EDUs
-    edus = {
-        edu: {"id": edu_id, "start": edu.start, "end": edu.end}
-        for edu_id, edu in zip(document.metadata["edu_ids"], document.edus)
-    }
-    result["edus"] = ld2dl(
-        sorted(edus.values(), key=lambda x: x["id"]), keys=["id", "start", "end"]
-    )
-
-    # construct ADUs
-    adus = {
-        adu: {"id": adu_id, "type": adu_type_label.str2int(adu.label)}
-        for adu_id, adu in zip(document.metadata["adu_ids"], document.adus)
-    }
-    result["adus"] = ld2dl(sorted(adus.values(), key=lambda x: x["id"]), keys=["id", "type"])
-
-    # construct edges
-    rels_dict: Dict[str, MultiRelation] = {
-        rel_id: rel for rel_id, rel in zip(document.metadata["rel_ids"], document.relations)
-    }
-    heads2rel_id = {
-        rel.heads: red_id for red_id, rel in zip(document.metadata["rel_ids"], document.relations)
-    }
-    edges = []
-    for rel_id, rel in rels_dict.items():
-        # if it is an undercut attack, we need to change the target to the relation that connects the target
-        if rel.label == "und":
-            target_id = heads2rel_id[rel.tails]
-        else:
-            if len(rel.tails) > 1:
-                raise Exception("Multi-target relations are not supported")
-            target_id = adus[rel.tails[0]]["id"]
-        source_id = adus[rel.heads[0]]["id"]
-        edge = {
-            "id": rel_id,
-            "src": source_id,
-            "trg": target_id,
-            "type": edge_type_label.str2int(rel.label),
-        }
-        edges.append(edge)
-        # if it is an additional support, we need to change the source to the relation that connects the source
-        for head in rel.heads[1:]:
-            source_id = adus[head]["id"]
-            edge_id = document.metadata["rel_add_ids"][source_id]
-            edge = {
-                "id": edge_id,
-                "src": source_id,
-                "trg": rel_id,
-                "type": edge_type_label.str2int("add"),
-            }
-            edges.append(edge)
-
-    for adu_id, adu in zip(document.metadata["adu_ids"], document.adus):
-        for edu in adu.annotations:
-            source_id = edus[edu]["id"]
-            target_id = adus[adu]["id"]
-            edge_id = document.metadata["rel_seg_ids"][source_id]
-            edge = {
-                "id": edge_id,
-                "src": source_id,
-                "trg": target_id,
-                "type": edge_type_label.str2int("seg"),
-            }
-            edges.append(edge)
-
-    result["edges"] = ld2dl(
-        sorted(edges, key=lambda x: x["id"]), keys=["id", "src", "trg", "type"]
-    )
-    return result
-
-
-def convert_to_text_document_with_labeled_spans_and_binary_relations(
-    doc: ArgMicroDocument,
-) -> TextDocumentWithLabeledSpansAndBinaryRelations:
-    # convert adus to entities
-    entities = []
-    adu2entity: Dict[LabeledAnnotationCollection, Span] = {}
-    for adu in doc.adus:
-        edus: Set[Span] = set(adu.annotations)
-        start = min(edu.start for edu in edus)
-        end = max(edu.end for edu in edus)
-        # assert there are no edus overlapping with the adu, but not part of it
-        for edu in doc.edus:
-            if (start <= edu.start < end or start < edu.end <= end) and edu not in edus:
-                raise Exception(f"edu {edu} is overlapping with adu {adu}, but is not part of it")
-        entity = LabeledSpan(start=start, end=end, label=adu.label)
-        entities.append(entity)
-        adu2entity[adu] = entity
-    relations = []
-    for relation in doc.relations:
-        # add all possible combinations of heads and tails
-        for head in relation.heads:
-            for tail in relation.tails:
-                rel = BinaryRelation(
-                    label=relation.label, head=adu2entity[head], tail=adu2entity[tail]
-                )
-                relations.append(rel)
-        # also add the relations between the heads themselves
-        for head1, head2 in combinations(relation.heads, 2):
-            rel = BinaryRelation(label="joint", head=adu2entity[head1], tail=adu2entity[head2])
-            relations.append(rel)
-            # also add the reverse relation
-            rel = BinaryRelation(label="joint", head=adu2entity[head2], tail=adu2entity[head1])
-            relations.append(rel)
-
-    metadata = copy.deepcopy(doc.metadata)
-    if len(doc.stance) > 0:
-        metadata["stance"] = doc.stance[0].label
-    metadata["topic"] = doc.topic_id
-    result = TextDocumentWithLabeledSpansAndBinaryRelations(
-        text=doc.text, id=doc.id, metadata=doc.metadata
-    )
-    result.labeled_spans.extend(entities)
-    result.binary_relations.extend(relations)
-
-    return result
-
-
-class ArgMicro(GeneratorBasedBuilder):
-    DOCUMENT_TYPE = ArgMicroDocument
-
-    DOCUMENT_CONVERTERS = {
-        TextDocumentWithLabeledSpansAndBinaryRelations: convert_to_text_document_with_labeled_spans_and_binary_relations
-    }
-
-    BASE_DATASET_PATH = "DFKI-SLT/argmicro"
-    BASE_DATASET_REVISION = "282733d6d57243f2a202d81143c4e31bb250e663"
-
-    BUILDER_CONFIGS = [datasets.BuilderConfig(name="en"), datasets.BuilderConfig(name="de")]
-
-    def _generate_document_kwargs(self, dataset):
-        return {
-            "adu_type_label": dataset.features["adus"].feature["type"],
-            "edge_type_label": dataset.features["edges"].feature["type"],
-            "stance_label": dataset.features["stance"],
-        }
-
-    def _generate_document(self, example, **kwargs):
-        return example_to_document(example, **kwargs)
+import copy
+import dataclasses
+import logging
+from collections import defaultdict
+from itertools import combinations
+from typing import Any, Dict, List, Optional, Set, Tuple
+
+import datasets
+from pytorch_ie.annotations import BinaryRelation, Label, LabeledSpan, Span
+from pytorch_ie.core import Annotation, AnnotationList, annotation_field
+from pytorch_ie.documents import (
+    TextBasedDocument,
+    TextDocumentWithLabeledSpansAndBinaryRelations,
+)
+
+from pie_datasets import GeneratorBasedBuilder
+
+log = logging.getLogger(__name__)
+
+
+def dl2ld(dict_of_lists):
+    return [dict(zip(dict_of_lists, t)) for t in zip(*dict_of_lists.values())]
+
+
+def ld2dl(list_of_dicts, keys: Optional[List[str]] = None):
+    return {k: [d[k] for d in list_of_dicts] for k in keys}
+
+
+@dataclasses.dataclass(frozen=True)
+class LabeledAnnotationCollection(Annotation):
+    annotations: Tuple[Annotation, ...]
+    label: str
+
+
+@dataclasses.dataclass(frozen=True)
+class MultiRelation(Annotation):
+    heads: Tuple[Annotation, ...]  # sources == heads
+    tails: Tuple[Annotation, ...]  # targets == tails
+    label: str
+
+
+@dataclasses.dataclass
+class ArgMicroDocument(TextBasedDocument):
+    topic_id: Optional[str] = None
+    stance: AnnotationList[Label] = annotation_field()
+    edus: AnnotationList[Span] = annotation_field(target="text")
+    adus: AnnotationList[LabeledAnnotationCollection] = annotation_field(target="edus")
+    relations: AnnotationList[MultiRelation] = annotation_field(target="adus")
+
+
+def example_to_document(
+    example: Dict[str, Any],
+    adu_type_label: datasets.ClassLabel,
+    edge_type_label: datasets.ClassLabel,
+    stance_label: datasets.ClassLabel,
+) -> ArgMicroDocument:
+    stance = stance_label.int2str(example["stance"])
+    document = ArgMicroDocument(
+        id=example["id"],
+        text=example["text"],
+        topic_id=example["topic_id"] if example["topic_id"] != "UNDEFINED" else None,
+    )
+    if stance != "UNDEFINED":
+        document.stance.append(Label(label=stance))
+
+    # build EDUs
+    edus_dict = {
+        edu["id"]: Span(start=edu["start"], end=edu["end"]) for edu in dl2ld(example["edus"])
+    }
+    # build ADUs
+    adu_id2edus = defaultdict(list)
+    edges_multi_source = defaultdict(dict)
+    for edge in dl2ld(example["edges"]):
+        edge_type = edge_type_label.int2str(edge["type"])
+        if edge_type == "seg":
+            adu_id2edus[edge["trg"]].append(edus_dict[edge["src"]])
+        elif edge_type == "add":
+            if "src" not in edges_multi_source[edge["trg"]]:
+                edges_multi_source[edge["trg"]]["src"] = []
+            edges_multi_source[edge["trg"]]["src"].append(edge["src"])
+        else:
+            edges_multi_source[edge["id"]]["type"] = edge_type
+            edges_multi_source[edge["id"]]["trg"] = edge["trg"]
+            if "src" not in edges_multi_source[edge["id"]]:
+                edges_multi_source[edge["id"]]["src"] = []
+            edges_multi_source[edge["id"]]["src"].append(edge["src"])
+    adus_dict = {}
+    for adu in dl2ld(example["adus"]):
+        adu_type = adu_type_label.int2str(adu["type"])
+        adu_edus = adu_id2edus[adu["id"]]
+        adus_dict[adu["id"]] = LabeledAnnotationCollection(
+            annotations=tuple(adu_edus), label=adu_type
+        )
+    # build relations
+    rels_dict = {}
+    for edge_id, edge in edges_multi_source.items():
+        edge_target = edge["trg"]
+        if edge_target in edges_multi_source:
+            targets = edges_multi_source[edge_target]["src"]
+        else:
+            targets = [edge_target]
+        if any(target in edges_multi_source for target in targets):
+            raise Exception("Multi-hop relations are not supported")
+        rel = MultiRelation(
+            heads=tuple(adus_dict[source] for source in edge["src"]),
+            tails=tuple(adus_dict[target] for target in targets),
+            label=edge["type"],
+        )
+        rels_dict[edge_id] = rel
+
+    document.edus.extend(edus_dict.values())
+    document.adus.extend(adus_dict.values())
+    document.relations.extend(rels_dict.values())
+    document.metadata["edu_ids"] = list(edus_dict.keys())
+    document.metadata["adu_ids"] = list(adus_dict.keys())
+    document.metadata["rel_ids"] = list(rels_dict.keys())
+
+    document.metadata["rel_seg_ids"] = {
+        edge["src"]: edge["id"]
+        for edge in dl2ld(example["edges"])
+        if edge_type_label.int2str(edge["type"]) == "seg"
+    }
+    document.metadata["rel_add_ids"] = {
+        edge["src"]: edge["id"]
+        for edge in dl2ld(example["edges"])
+        if edge_type_label.int2str(edge["type"]) == "add"
+    }
+    return document
+
+
+def document_to_example(
+    document: ArgMicroDocument,
+    adu_type_label: datasets.ClassLabel,
+    edge_type_label: datasets.ClassLabel,
+    stance_label: datasets.ClassLabel,
+) -> Dict[str, Any]:
+    stance = document.stance[0].label if len(document.stance) else "UNDEFINED"
+    result = {
+        "id": document.id,
+        "text": document.text,
+        "topic_id": document.topic_id or "UNDEFINED",
+        "stance": stance_label.str2int(stance),
+    }
+
+    # construct EDUs
+    edus = {
+        edu: {"id": edu_id, "start": edu.start, "end": edu.end}
+        for edu_id, edu in zip(document.metadata["edu_ids"], document.edus)
+    }
+    result["edus"] = ld2dl(
+        sorted(edus.values(), key=lambda x: x["id"]), keys=["id", "start", "end"]
+    )
+
+    # construct ADUs
+    adus = {
+        adu: {"id": adu_id, "type": adu_type_label.str2int(adu.label)}
+        for adu_id, adu in zip(document.metadata["adu_ids"], document.adus)
+    }
+    result["adus"] = ld2dl(sorted(adus.values(), key=lambda x: x["id"]), keys=["id", "type"])
+
+    # construct edges
+    rels_dict: Dict[str, MultiRelation] = {
+        rel_id: rel for rel_id, rel in zip(document.metadata["rel_ids"], document.relations)
+    }
+    heads2rel_id = {
+        rel.heads: red_id for red_id, rel in zip(document.metadata["rel_ids"], document.relations)
+    }
+    edges = []
+    for rel_id, rel in rels_dict.items():
+        # if it is an undercut attack, we need to change the target to the relation that connects the target
+        if rel.label == "und":
+            target_id = heads2rel_id[rel.tails]
+        else:
+            if len(rel.tails) > 1:
+                raise Exception("Multi-target relations are not supported")
+            target_id = adus[rel.tails[0]]["id"]
+        source_id = adus[rel.heads[0]]["id"]
+        edge = {
+            "id": rel_id,
+            "src": source_id,
+            "trg": target_id,
+            "type": edge_type_label.str2int(rel.label),
+        }
+        edges.append(edge)
+        # if it is an additional support, we need to change the source to the relation that connects the source
+        for head in rel.heads[1:]:
+            source_id = adus[head]["id"]
+            edge_id = document.metadata["rel_add_ids"][source_id]
+            edge = {
+                "id": edge_id,
+                "src": source_id,
+                "trg": rel_id,
+                "type": edge_type_label.str2int("add"),
+            }
+            edges.append(edge)
+
+    for adu_id, adu in zip(document.metadata["adu_ids"], document.adus):
+        for edu in adu.annotations:
+            source_id = edus[edu]["id"]
+            target_id = adus[adu]["id"]
+            edge_id = document.metadata["rel_seg_ids"][source_id]
+            edge = {
+                "id": edge_id,
+                "src": source_id,
+                "trg": target_id,
+                "type": edge_type_label.str2int("seg"),
+            }
+            edges.append(edge)
+
+    result["edges"] = ld2dl(
+        sorted(edges, key=lambda x: x["id"]), keys=["id", "src", "trg", "type"]
+    )
+    return result
+
+
+def convert_to_text_document_with_labeled_spans_and_binary_relations(
+    doc: ArgMicroDocument,
+) -> TextDocumentWithLabeledSpansAndBinaryRelations:
+    # convert adus to entities
+    entities = []
+    adu2entity: Dict[LabeledAnnotationCollection, Span] = {}
+    for adu in doc.adus:
+        edus: Set[Span] = set(adu.annotations)
+        start = min(edu.start for edu in edus)
+        end = max(edu.end for edu in edus)
+        # assert there are no edus overlapping with the adu, but not part of it
+        for edu in doc.edus:
+            if (start <= edu.start < end or start < edu.end <= end) and edu not in edus:
+                raise Exception(f"edu {edu} is overlapping with adu {adu}, but is not part of it")
+        entity = LabeledSpan(start=start, end=end, label=adu.label)
+        entities.append(entity)
+        adu2entity[adu] = entity
+    relations = []
+    for relation in doc.relations:
+        # add all possible combinations of heads and tails
+        for head in relation.heads:
+            for tail in relation.tails:
+                rel = BinaryRelation(
+                    label=relation.label, head=adu2entity[head], tail=adu2entity[tail]
+                )
+                relations.append(rel)
+        # also add the relations between the heads themselves
+        for head1, head2 in combinations(relation.heads, 2):
+            rel = BinaryRelation(label="joint", head=adu2entity[head1], tail=adu2entity[head2])
+            relations.append(rel)
+            # also add the reverse relation
+            rel = BinaryRelation(label="joint", head=adu2entity[head2], tail=adu2entity[head1])
+            relations.append(rel)
+
+    metadata = copy.deepcopy(doc.metadata)
+    if len(doc.stance) > 0:
+        metadata["stance"] = doc.stance[0].label
+    metadata["topic"] = doc.topic_id
+    result = TextDocumentWithLabeledSpansAndBinaryRelations(
+        text=doc.text, id=doc.id, metadata=doc.metadata
+    )
+    result.labeled_spans.extend(entities)
+    result.binary_relations.extend(relations)
+
+    return result
+
+
+class ArgMicro(GeneratorBasedBuilder):
+    DOCUMENT_TYPE = ArgMicroDocument
+
+    DOCUMENT_CONVERTERS = {
+        TextDocumentWithLabeledSpansAndBinaryRelations: convert_to_text_document_with_labeled_spans_and_binary_relations
+    }
+
+    BASE_DATASET_PATH = "DFKI-SLT/argmicro"
+    BASE_DATASET_REVISION = "282733d6d57243f2a202d81143c4e31bb250e663"
+
+    BUILDER_CONFIGS = [datasets.BuilderConfig(name="en"), datasets.BuilderConfig(name="de")]
+
+    def _generate_document_kwargs(self, dataset):
+        return {
+            "adu_type_label": dataset.features["adus"].feature["type"],
+            "edge_type_label": dataset.features["edges"].feature["type"],
+            "stance_label": dataset.features["stance"],
+        }
+
+    def _generate_document(self, example, **kwargs):
+        return example_to_document(example, **kwargs)

requirements.txt

@@ -1 +1 @@
-pie-datasets>=0.3.3,<0.9.0
+pie-datasets>=0.3.3,<0.11.0