Load pdf data

Pdf support is experimental and is subject to change.

Pdf datasets have Pdf type columns, which contain pdfplumber objects.

To work with pdf datasets, you need to have the pdfplumber package installed. Check out the installation guide to learn how to install it.

When you load an pdf dataset and call the pdf column, the pdfs are decoded as pdfplumber Pdfs:

>>> from datasets import load_dataset, Pdf

>>> dataset = load_dataset("path/to/pdf/folder", split="train")
>>> dataset[0]["pdf"]
<pdfplumber.pdf.PDF at 0x1075bc320>

Index into an pdf dataset using the row index first and then the pdf column - dataset[0]["pdf"] - to avoid creating all the pdf objects in the dataset. Otherwise, this can be a slow and time-consuming process if you have a large dataset.

For a guide on how to load any type of dataset, take a look at the general loading guide.

Read pages

Access pages directly from a pdf using the PDF using .pages.

Then you can use the pdfplumber functions to read texts, tables and images, e.g.:

>>> pdf = dataset[0]["pdf"]
>>> first_page = pdf.pages[0]
>>> first_page
<Page:1>
>>> first_page.extract_text()
Docling Technical Report
Version1.0
ChristophAuer MaksymLysak AhmedNassar MicheleDolfi NikolaosLivathinos
PanosVagenas CesarBerrospiRamis MatteoOmenetti FabianLindlbauer
KasperDinkla LokeshMishra YusikKim ShubhamGupta RafaelTeixeiradeLima
ValeryWeber LucasMorin IngmarMeijer ViktorKuropiatnyk PeterW.J.Staar
AI4KGroup,IBMResearch
Ru¨schlikon,Switzerland
Abstract
This technical report introduces Docling, an easy to use, self-contained, MIT-
licensed open-source package for PDF document conversion.
...
>>> first_page.images
In [24]: first_page.images
Out[24]: 
[{'x0': 256.5,
  'y0': 621.0,
  'x1': 355.49519999999995,
  'y1': 719.9952,
  'width': 98.99519999999995,
  'height': 98.99519999999995,
  'name': 'Im1',
  'stream': <PDFStream(44): raw=88980, {'Type': /'XObject', 'Subtype': /'Image', 'BitsPerComponent': 8, 'ColorSpace': /'DeviceRGB', 'Filter': /'DCTDecode', 'Height': 1024, 'Length': 88980, 'Width': 1024}>,
  'srcsize': (1024, 1024),
  'imagemask': None,
  'bits': 8,
  'colorspace': [/'DeviceRGB'],
  'mcid': None,
  'tag': None,
  'object_type': 'image',
  'page_number': 1,
  'top': 72.00480000000005,
  'bottom': 171.0,
  'doctop': 72.00480000000005}]
>>> first_page.extract_tables()
[]

You can also load each page as a PIL.Image:

>>> import PIL.Image
>>> import io
>>> first_page.to_image()
<pdfplumber.display.PageImage at 0x107d68dd0>
>>> buffer = io.BytesIO()
>>> first_page.to_image().save(buffer)
>>> img = PIL.Image.open(buffer)
>>> img
<PIL.PngImagePlugin.PngImageFile image mode=P size=612x792>

Note that you can pass resolution= to .to_image() to render the image in higher resolution that the default (72 ppi).

Local files

You can load a dataset from the pdf path. Use the cast_column() function to accept a column of pdf file paths, and decode it into a pdfplumber pdf with the Pdf feature:

>>> from datasets import Dataset, Pdf

>>> dataset = Dataset.from_dict({"pdf": ["path/to/pdf_1", "path/to/pdf_2", ..., "path/to/pdf_n"]}).cast_column("pdf", Pdf())
>>> dataset[0]["pdf"]
<pdfplumber.pdf.PDF at 0x1657d0280>

If you only want to load the underlying path to the pdf dataset without decoding the pdf object, set decode=False in the Pdf feature:

>>> dataset = dataset.cast_column("pdf", Pdf(decode=False))
>>> dataset[0]["pdf"]
{'bytes': None,
 'path': 'path/to/pdf/folder/pdf0.pdf'}

PdfFolder

You can also load a dataset with an PdfFolder dataset builder which does not require writing a custom dataloader. This makes PdfFolder ideal for quickly creating and loading pdf datasets with several thousand pdfs for different vision tasks. Your pdf dataset structure should look like this:

folder/train/resume/0001.pdf
folder/train/resume/0002.pdf
folder/train/resume/0003.pdf

folder/train/invoice/0001.pdf
folder/train/invoice/0002.pdf
folder/train/invoice/0003.pdf

If the dataset follows the PdfFolder structure, then you can load it directly with load_dataset():

>>> from datasets import load_dataset

>>> dataset = load_dataset("username/dataset_name")
>>> # OR locally:
>>> dataset = load_dataset("/path/to/folder")

For local datasets, this is equivalent to passing pdffolder manually in load_dataset() and the directory in data_dir:

>>> dataset = load_dataset("pdffolder", data_dir="/path/to/folder")

Then you can access the pdfs as pdfplumber.pdf.PDF objects:

>>> dataset["train"][0]
{"pdf": <pdfplumber.pdf.PDF at 0x161715e50>, "label": 0}

>>> dataset["train"][-1]
{"pdf": <pdfplumber.pdf.PDF at 0x16170bd90>, "label": 1}

To ignore the information in the metadata file, set drop_metadata=True in load_dataset():

>>> from datasets import load_dataset

>>> dataset = load_dataset("username/dataset_with_metadata", drop_metadata=True)

If you don’t have a metadata file, PdfFolder automatically infers the label name from the directory name. If you want to drop automatically created labels, set drop_labels=True. In this case, your dataset will only contain an pdf column:

>>> from datasets import load_dataset

>>> dataset = load_dataset("username/dataset_without_metadata", drop_labels=True)

Finally the filters argument lets you load only a subset of the dataset, based on a condition on the label or the metadata. This is especially useful if the metadata is in Parquet format, since this format enables fast filtering. It is also recommended to use this argument with streaming=True, because by default the dataset is fully downloaded before filtering.

>>> filters = [("label", "=", 0)]
>>> dataset = load_dataset("username/dataset_name", streaming=True, filters=filters)

For more information about creating your own PdfFolder dataset, take a look at the Create a pdf dataset guide.

Pdf decoding

By default, pdfs are decoded sequentially as pdfplumber PDFs when you iterate on a dataset. It sequentially decodes the metadata of the pdfs, and doesn’t read the pdf pages until you access them.

However it is possible to speed up the dataset significantly using multithreaded decoding:

>>> import os
>>> num_threads = num_threads = min(32, (os.cpu_count() or 1) + 4)
>>> dataset = dataset.decode(num_threads=num_threads)
>>> for example in dataset:  # up to 20 times faster !
...     ...

You can enable multithreading using num_threads. This is especially useful to speed up remote data streaming. However it can be slower than num_threads=0 for local data on fast disks.

If you are not interested in the documents decoded as pdfplumber PDFs and would like to access the path/bytes instead, you can disable decoding:

>>> dataset = dataset.decode(False)

Note: IterableDataset.decode() is only available for streaming datasets at the moment.

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Datasets

Load pdf data

Read pages

Local files

PdfFolder

Pdf decoding