Update parquet files

README.md

@@ -1,10 +0,0 @@
----
-license: cc-by-nc-4.0
----
-
-## Required installation
-
-```bash
-pip3 install pypdf2 pdf2image
-sudo apt-get install poppler-utils
-```

data/data_dir.zip → default/unit-test_pd_ffolder-test.parquet

@@ -1,3 +1,3 @@
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+oid sha256:f7fde82f823fa9e527516fc991f6a49e6c2c8554fdae3c9c79e8657f1c6e318b
+size 17154588

default/unit-test_pd_ffolder-train.parquet

@@ -0,0 +1,3 @@
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+size 17154588

data/data.tar.gz → default/unit-test_pd_ffolder-validation.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0e7002d267f56b381eb8049312f389f87b9a8e525daf9e03b65c2fb30dfe9b7d
-size 572943
+oid sha256:1072eef50e6fc765fd8c96e3ceec3dc779ccc9d2e33d188706e849da1a879543
+size 1058

unit-test_PDFfolder.py

@@ -1,80 +0,0 @@
-import os
-from typing import List
-
-import datasets
-import pdf2image
-
-logger = datasets.logging.get_logger(__name__)
-
-_DESCRIPTION = "A generic pdf folder"
-
-_CLASSES = ["categoryA", "categoryB"]  # define in advance
-
-_URL = "https://huggingface.co/datasets/jordyvl/unit-test_PDFfolder/resolve/main/data/data.tar.gz"
-
-# folder
-#   train
-#       categoryA
-#           file1
-#   test
-# ...
-
-
-class PdfFolder(datasets.GeneratorBasedBuilder):
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "file": datasets.Sequence(datasets.Image()),
-                    "labels": datasets.features.ClassLabel(names=_CLASSES),
-                }
-            ),
-            task_templates=None,
-        )
-
-    def _split_generators(
-        self, dl_manager: datasets.DownloadManager
-    ) -> List[datasets.SplitGenerator]:
-
-        archive_path = dl_manager.download(_URL)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "archive_iterator": dl_manager.iter_archive(archive_path),
-                    "supposed_labelset": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "archive_iterator": dl_manager.iter_archive(archive_path),
-                    "supposed_labelset": "test",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "archive_iterator": dl_manager.iter_archive(archive_path),
-                    "supposed_labelset": "val",
-                },
-            ),
-        ]
-
-    def _generate_examples(self, archive_iterator, supposed_labelset):
-
-        extensions = {"pdf", "PDF"}
-        for file_path, file_obj in archive_iterator:
-
-            if file_path.split(".")[-1] not in extensions:  # metadata.jsonlines
-                continue
-
-            folder, labelset, label, filename = file_path.split("/")
-            if labelset != supposed_labelset:
-                continue
-
-            images = pdf2image.convert_from_bytes(file_obj.read())
-
-            yield file_path, {"file": images, "labels": label}