Update dataset files

README.md

@@ -0,0 +1,30 @@
+# PIE Dataset Card for "imdb"
+
+This is a [PyTorch-IE](https://github.com/ChristophAlt/pytorch-ie) wrapper for the
+[imdb Huggingface dataset loading script](https://huggingface.co/datasets/imdb).
+
+## Data Schema
+
+The document type for this dataset is `ImdbDocument` which defines the following data fields:
+
+- `text` (str)
+- `id` (str, optional)
+- `metadata` (dictionary, optional)
+
+and the following annotation layers:
+
+- `label` (annotation type: `Label`, target: `None`)
+
+See [here](https://github.com/ArneBinder/pie-modules/blob/main/src/pie_modules/annotations.py) and
+[here](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/annotations.py) for the annotation
+type definitions.
+
+## Document Converters
+
+The dataset provides predefined document converters for the following target document types:
+
+- `pie_modules.documents.ExtractiveQADocument` (simple cast without any conversion)
+
+See [here](https://github.com/ArneBinder/pie-modules/blob/main/src/pie_modules/documents.py) and
+[here](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py) for the document type
+definitions.

imdb.py

@@ -1,55 +1,68 @@
 from dataclasses import dataclass
+from typing import Any, Dict
 
-import pytorch_ie.data.builder
+import datasets
 from pytorch_ie.annotations import Label
-from pytorch_ie.core import AnnotationList, annotation_field
-from pytorch_ie.documents import TextDocument
+from pytorch_ie.documents import TextDocumentWithLabel
 
-import datasets
+from pie_datasets import GeneratorBasedBuilder
 
 
-class ImdbConfig(datasets.BuilderConfig):
-    """BuilderConfig for IMDB"""
+@dataclass
+class ImdbDocument(TextDocumentWithLabel):
+    pass
 
-    def __init__(self, **kwargs):
-        """BuilderConfig for IMDB.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super().__init__(**kwargs)
 
+def example_to_document(example: Dict[str, Any], labels: datasets.ClassLabel) -> ImdbDocument:
+    text = example["text"]
+    document = ImdbDocument(text=text)
+    label_id = example["label"]
+    if label_id < 0:
+        return document
 
-@dataclass
-class ImdbDocument(TextDocument):
-    label: AnnotationList[Label] = annotation_field()
+    label = labels.int2str(label_id)
+    label_annotation = Label(label=label)
+    document.label.append(label_annotation)
 
+    return document
 
-class Imdb(pytorch_ie.data.builder.GeneratorBasedBuilder):
+
+def document_to_example(document: ImdbDocument, labels: datasets.ClassLabel) -> Dict[str, Any]:
+    if len(document.label) > 0:
+        label_id = labels.str2int(document.label[0].label)
+    else:
+        label_id = -1
+
+    return {
+        "text": document.text,
+        "label": label_id,
+    }
+
+
+class Imdb(GeneratorBasedBuilder):
     DOCUMENT_TYPE = ImdbDocument
 
     BASE_DATASET_PATH = "imdb"
+    BASE_DATASET_REVISION = "9c6ede893febf99215a29cc7b72992bb1138b06b"
 
     BUILDER_CONFIGS = [
-        ImdbConfig(
+        datasets.BuilderConfig(
             name="plain_text",
             version=datasets.Version("1.0.0"),
             description="IMDB sentiment classification dataset",
         ),
     ]
 
-    def _generate_document_kwargs(self, dataset):
-        return {"int2str": dataset.features["label"].int2str}
+    DOCUMENT_CONVERTERS = {TextDocumentWithLabel: {}}
 
-    def _generate_document(self, example, int2str):
+    def _generate_document_kwargs(self, dataset) -> Dict[str, Any]:
+        return {"labels": dataset.features["label"]}
 
-        text = example["text"]
-        document = ImdbDocument(text=text)
-        label_id = example["label"]
-        if label_id < 0:
-            return document
+    def _generate_document(self, example, **kwargs) -> ImdbDocument:
+        return example_to_document(example, **kwargs)
 
-        label = int2str(label_id)
-        label_annotation = Label(label=label)
-        document.label.append(label_annotation)
+    def _generate_example_kwargs(self, dataset) -> Dict[str, Any]:
+        return {"labels": dataset.features["label"]}
 
-        return document
+    def _generate_example(self, document: ImdbDocument, **kwargs) -> Dict[str, Any]:
+        return document_to_example(document, **kwargs)

requirements.txt

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+pie-datasets>=0.8.1,<0.9.0