use pie-modules instead of pytorch-ie

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

README.md

@@ -1,8 +1,29 @@
-# PIE Dataset Card for "CDCP"
+# PIE Dataset Card for "cdcp"
 
 This is a [PyTorch-IE](https://github.com/ChristophAlt/pytorch-ie) wrapper for the
 [CDCP Huggingface dataset loading script](https://huggingface.co/datasets/DFKI-SLT/cdcp).
 
+## Usage
+
+```python
+from pie_datasets import load_dataset
+from pie_modules.documents import TextDocumentWithLabeledSpansAndBinaryRelations
+
+# load English variant
+dataset = load_dataset("pie/cdcp")
+
+# 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=78, label='value', score=1.0), tail=LabeledSpan(start=79, end=242, label='value', score=1.0), label='reason', score=1.0)
+print(doc.binary_relations[0].resolve())
+# ('reason', (('value', 'State and local court rules sometimes make default judgments much more likely.'), ('value', 'For example, when a person who allegedly owes a debt is told to come to court on a work day, they may be forced to choose between a default judgment and their job.')))
+```
+
 ## Data Schema
 
 The document type for this dataset is `CDCPDocument` which defines the following data fields:
@@ -17,13 +38,162 @@ and the following annotation layers:
 - `relations` (annotation type: `BinaryRelation`, target: `propositions`)
 - `urls` (annotation type: `Attribute`, target: `propositions`)
 
-See [here](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/annotations.py) for the annotation type definitions.
+See [here](https://github.com/ArneBinder/pie-modules/blob/main/src/pie_modules/annotations.py) for the annotation type definitions.
 
 ## Document Converters
 
 The dataset provides document converters for the following target document types:
 
-- `pytorch_ie.documents.TextDocumentWithLabeledSpansAndBinaryRelations`
+- `pie_modules.documents.TextDocumentWithLabeledSpansAndBinaryRelations`
+  - `labeled_spans`: `LabeledSpan` annotations, converted from `CDCPDocument`'s `propositions`
+    - labels: `fact`, `policy`, `reference`, `testimony`, `value`
+    - if `propositions` contain whitespace at the beginning and/or the end, the whitespace are trimmed out.
+  - `binary_relations`: `BinaryRelation` annotations, converted from `CDCPDocument`'s `relations`
+    - labels: `reason`, `evidence`
 
-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.
+
+### 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=cdcp_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/cdcp_base.yaml` of this dataset within the repo directory:
+
+```commandline
+_target_: src.utils.execute_pipeline
+input:
+  _target_: pie_datasets.DatasetDict.load_dataset
+  path: pie/cdcp
+  revision: 001722894bdca6df6a472d0d186a3af103e392c5
+```
+
+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/ArneBinder/pie-modules/blob/main/src/pie_modules/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=cdcp_base metric=relation_argument_token_distances
+```
+
+</details>
+
+##### train (580 documents)
+
+|          |  len | max |   mean | min |    std |
+| :------- | ---: | --: | -----: | --: | -----: |
+| ALL      | 2204 | 240 | 48.839 |   8 | 31.462 |
+| evidence |   94 | 196 | 66.723 |  14 | 42.444 |
+| reason   | 2110 | 240 | 48.043 |   8 |  30.64 |
+
+<details>
+  <summary>Histogram (split: train, 580 documents)</summary>
+
+![rtd-label_cdcp_train.png](img%2Frtd-label_cdcp_train.png)
+
+</details>
+
+##### test (150 documents)
+
+|          | len | max |   mean | min |    std |
+| :------- | --: | --: | -----: | --: | -----: |
+| ALL      | 648 | 212 | 51.299 |   8 | 31.159 |
+| evidence |  52 | 170 | 73.923 |  20 | 39.855 |
+| reason   | 596 | 212 | 49.326 |   8 |  29.47 |
+
+<details>
+  <summary>Histogram (split: test, 150 documents)</summary>
+
+![rtd-label_cdcp_test.png](img%2Frtd-label_cdcp_test.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=cdcp_base metric=span_lengths_tokens
+```
+
+</details>
+
+| statistics |  train |   test |
+| :--------- | -----: | -----: |
+| no. doc    |    580 |    150 |
+| len        |   3901 |   1026 |
+| mean       | 19.441 | 18.758 |
+| std        |  11.71 | 10.388 |
+| min        |      2 |      3 |
+| max        |    142 |     83 |
+
+<details>
+  <summary>Histogram (split: train, 580 documents)</summary>
+
+![slt_cdcp_train.png](img%2Fslt_cdcp_train.png)
+
+</details>
+  <details>
+  <summary>Histogram (split: test, 150 documents)</summary>
+
+![slt_cdcp_test.png](img%2Fslt_cdcp_test.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=cdcp_base metric=count_text_tokens
+```
+
+</details>
+
+| statistics |   train |    test |
+| :--------- | ------: | ------: |
+| no. doc    |     580 |     150 |
+| mean       | 130.781 | 128.673 |
+| std        | 101.121 |  98.708 |
+| min        |      13 |      15 |
+| max        |     562 |     571 |
+
+<details>
+  <summary>Histogram (split: train, 580 documents)</summary>
+
+![tl_cdcp_train.png](img%2Ftl_cdcp_train.png)
+
+</details>
+  <details>
+  <summary>Histogram (split: test, 150 documents)</summary>
+
+![tl_cdcp_test.png](img%2Ftl_cdcp_test.png)
+
+</details>

cdcp.py

@@ -1,143 +1,143 @@
-import dataclasses
-import logging
-from typing import Any, Dict, List, Optional
-
-import datasets
-from pie_modules.document.processing.text_span_trimmer import trim_text_spans
-from pytorch_ie.annotations import BinaryRelation, LabeledSpan
-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 Attribute(Annotation):
-    value: str
-    annotation: Annotation
-
-
-@dataclasses.dataclass
-class CDCPDocument(TextBasedDocument):
-    propositions: AnnotationList[LabeledSpan] = annotation_field(target="text")
-    relations: AnnotationList[BinaryRelation] = annotation_field(target="propositions")
-    urls: AnnotationList[Attribute] = annotation_field(target="propositions")
-
-
-def example_to_document(
-    example: Dict[str, Any],
-    relation_label: datasets.ClassLabel,
-    proposition_label: datasets.ClassLabel,
-):
-    document = CDCPDocument(id=example["id"], text=example["text"])
-    for proposition_dict in dl2ld(example["propositions"]):
-        proposition = LabeledSpan(
-            start=proposition_dict["start"],
-            end=proposition_dict["end"],
-            label=proposition_label.int2str(proposition_dict["label"]),
-        )
-        document.propositions.append(proposition)
-        if proposition_dict.get("url", "") != "":
-            url = Attribute(annotation=proposition, value=proposition_dict["url"])
-            document.urls.append(url)
-
-    for relation_dict in dl2ld(example["relations"]):
-        relation = BinaryRelation(
-            head=document.propositions[relation_dict["head"]],
-            tail=document.propositions[relation_dict["tail"]],
-            label=relation_label.int2str(relation_dict["label"]),
-        )
-        document.relations.append(relation)
-
-    return document
-
-
-def document_to_example(
-    document: CDCPDocument,
-    relation_label: datasets.ClassLabel,
-    proposition_label: datasets.ClassLabel,
-) -> Dict[str, Any]:
-    result = {"id": document.id, "text": document.text}
-    proposition2dict = {}
-    proposition2idx = {}
-    for idx, proposition in enumerate(document.propositions):
-        proposition2dict[proposition] = {
-            "start": proposition.start,
-            "end": proposition.end,
-            "label": proposition_label.str2int(proposition.label),
-            "url": "",
-        }
-        proposition2idx[proposition] = idx
-    for url in document.urls:
-        proposition2dict[url.annotation]["url"] = url.value
-
-    result["propositions"] = ld2dl(
-        proposition2dict.values(), keys=["start", "end", "label", "url"]
-    )
-
-    relations = [
-        {
-            "head": proposition2idx[relation.head],
-            "tail": proposition2idx[relation.tail],
-            "label": relation_label.str2int(relation.label),
-        }
-        for relation in document.relations
-    ]
-    result["relations"] = ld2dl(relations, keys=["head", "tail", "label"])
-
-    return result
-
-
-def convert_to_text_document_with_labeled_spans_and_binary_relations(
-    document: CDCPDocument,
-    verbose: bool = True,
-) -> TextDocumentWithLabeledSpansAndBinaryRelations:
-    doc_simplified = document.as_type(
-        TextDocumentWithLabeledSpansAndBinaryRelations,
-        field_mapping={"propositions": "labeled_spans", "relations": "binary_relations"},
-    )
-    result = trim_text_spans(
-        doc_simplified,
-        layer="labeled_spans",
-        verbose=verbose,
-    )
-    return result
-
-
-class CDCP(GeneratorBasedBuilder):
-    DOCUMENT_TYPE = CDCPDocument
-
-    DOCUMENT_CONVERTERS = {
-        TextDocumentWithLabeledSpansAndBinaryRelations: convert_to_text_document_with_labeled_spans_and_binary_relations
-    }
-
-    BASE_DATASET_PATH = "DFKI-SLT/cdcp"
-    BASE_DATASET_REVISION = "3cf79257900b3f97e4b8f9faae2484b1a534f484"
-
-    BUILDER_CONFIGS = [datasets.BuilderConfig(name="default")]
-
-    DEFAULT_CONFIG_NAME = "default"  # type: ignore
-
-    def _generate_document_kwargs(self, dataset):
-        return {
-            "relation_label": dataset.features["relations"].feature["label"],
-            "proposition_label": dataset.features["propositions"].feature["label"],
-        }
-
-    def _generate_document(self, example, relation_label, proposition_label):
-        return example_to_document(
-            example, relation_label=relation_label, proposition_label=proposition_label
-        )
+import dataclasses
+import logging
+from typing import Any, Dict, List, Optional
+
+import datasets
+from pie_core import Annotation, AnnotationLayer, annotation_field
+from pie_modules.annotations import BinaryRelation, LabeledSpan
+from pie_modules.document.processing.text_span_trimmer import trim_text_spans
+from pie_modules.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 Attribute(Annotation):
+    value: str
+    annotation: Annotation
+
+
+@dataclasses.dataclass
+class CDCPDocument(TextBasedDocument):
+    propositions: AnnotationLayer[LabeledSpan] = annotation_field(target="text")
+    relations: AnnotationLayer[BinaryRelation] = annotation_field(target="propositions")
+    urls: AnnotationLayer[Attribute] = annotation_field(target="propositions")
+
+
+def example_to_document(
+    example: Dict[str, Any],
+    relation_label: datasets.ClassLabel,
+    proposition_label: datasets.ClassLabel,
+):
+    document = CDCPDocument(id=example["id"], text=example["text"])
+    for proposition_dict in dl2ld(example["propositions"]):
+        proposition = LabeledSpan(
+            start=proposition_dict["start"],
+            end=proposition_dict["end"],
+            label=proposition_label.int2str(proposition_dict["label"]),
+        )
+        document.propositions.append(proposition)
+        if proposition_dict.get("url", "") != "":
+            url = Attribute(annotation=proposition, value=proposition_dict["url"])
+            document.urls.append(url)
+
+    for relation_dict in dl2ld(example["relations"]):
+        relation = BinaryRelation(
+            head=document.propositions[relation_dict["head"]],
+            tail=document.propositions[relation_dict["tail"]],
+            label=relation_label.int2str(relation_dict["label"]),
+        )
+        document.relations.append(relation)
+
+    return document
+
+
+def document_to_example(
+    document: CDCPDocument,
+    relation_label: datasets.ClassLabel,
+    proposition_label: datasets.ClassLabel,
+) -> Dict[str, Any]:
+    result = {"id": document.id, "text": document.text}
+    proposition2dict = {}
+    proposition2idx = {}
+    for idx, proposition in enumerate(document.propositions):
+        proposition2dict[proposition] = {
+            "start": proposition.start,
+            "end": proposition.end,
+            "label": proposition_label.str2int(proposition.label),
+            "url": "",
+        }
+        proposition2idx[proposition] = idx
+    for url in document.urls:
+        proposition2dict[url.annotation]["url"] = url.value
+
+    result["propositions"] = ld2dl(
+        proposition2dict.values(), keys=["start", "end", "label", "url"]
+    )
+
+    relations = [
+        {
+            "head": proposition2idx[relation.head],
+            "tail": proposition2idx[relation.tail],
+            "label": relation_label.str2int(relation.label),
+        }
+        for relation in document.relations
+    ]
+    result["relations"] = ld2dl(relations, keys=["head", "tail", "label"])
+
+    return result
+
+
+def convert_to_text_document_with_labeled_spans_and_binary_relations(
+    document: CDCPDocument,
+    verbose: bool = True,
+) -> TextDocumentWithLabeledSpansAndBinaryRelations:
+    doc_simplified = document.as_type(
+        TextDocumentWithLabeledSpansAndBinaryRelations,
+        field_mapping={"propositions": "labeled_spans", "relations": "binary_relations"},
+    )
+    result = trim_text_spans(
+        doc_simplified,
+        layer="labeled_spans",
+        verbose=verbose,
+    )
+    return result
+
+
+class CDCP(GeneratorBasedBuilder):
+    DOCUMENT_TYPE = CDCPDocument
+
+    DOCUMENT_CONVERTERS = {
+        TextDocumentWithLabeledSpansAndBinaryRelations: convert_to_text_document_with_labeled_spans_and_binary_relations
+    }
+
+    BASE_DATASET_PATH = "DFKI-SLT/cdcp"
+    BASE_DATASET_REVISION = "3cf79257900b3f97e4b8f9faae2484b1a534f484"
+
+    BUILDER_CONFIGS = [datasets.BuilderConfig(name="default")]
+
+    DEFAULT_CONFIG_NAME = "default"  # type: ignore
+
+    def _generate_document_kwargs(self, dataset):
+        return {
+            "relation_label": dataset.features["relations"].feature["label"],
+            "proposition_label": dataset.features["propositions"].feature["label"],
+        }
+
+    def _generate_document(self, example, relation_label, proposition_label):
+        return example_to_document(
+            example, relation_label=relation_label, proposition_label=proposition_label
+        )

requirements.txt

@@ -1,2 +1,2 @@
-pie-datasets>=0.6.0,<0.9.0
-pie-modules>=0.8.0,<0.9.0
+pie-datasets>=0.10.11,<0.11.0
+pie-modules>=0.15.9,<0.16.0