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idl-wds

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---
license: other
license_name: idl-train
license_link: LICENSE
task_categories:
- image-to-text
size_categories:
- 10M<n<100M
---
# Dataset Card for Industry Documents Library (IDL)
## Dataset Description
- **Point of Contact from curators:** [Kate Tasker, UCSF](mailto:kate.tasker@ucsf.edu)
- **Point of Contact Hugging Face:** [Pablo Montalvo](mailto:pablo@huggingface.co)
### Dataset Summary
Industry Documents Library (IDL) is a document dataset filtered from [UCSF documents library](https://www.industrydocuments.ucsf.edu/) with 19 million pages kept as valid samples.
Each document exists as a collection of a pdf, a tiff image with the same contents rendered, a json file containing extensive Textract OCR annotations from the [idl_data](https://github.com/furkanbiten/idl_data) project, and a .ocr file with the original, older OCR annotation.
<center>
<img src="https://huggingface.co/datasets/pixparse/IDL-wds/resolve/main/doc_images/idl_page_example.png" alt="An addendum from an internal legal document" width="600" height="300">
<p><em>An example page of one pdf document from the Industry Documents Library. </em></p>
</center>
### Usage
For faster download, you can use directly the `huggingface_hub` library. Make sure `hf_transfer` is installed prior to downloading and mind that you have enough space locally.
```python
import os
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
from huggingface_hub import HfApi, logging
#logging.set_verbosity_debug()
hf = HfApi()
hf.snapshot_download("pixparse/IDL-wds", repo_type="dataset", local_dir_use_symlinks=False)
```
# TODO The following example uses fitz which is AGPL. We should also recommend the same with pypdf.
```python
from chug import create_wds_loader, create_doc_anno_pipe
# TODO import image transforms and text transforms from pixparse.data
decoder = create_decoder_pipe(
image_preprocess=image_preprocess, # callable of transforms to image tensors
anno_preprocess=anno_preprocess, # callable of transforms to text into tensors of labels and targets
image_key="pdf",
image_fmt="RGB",
)
loader = create_wds_loader(
"/my_data/idl-train-*.tar",
decoder,
is_train=True,
resampled=False,
start_interval=0,
num_samples=2159432,
workers=8,
batch_size=32, # adjust to your architecture capacity
seed=seed, # set a seed
world_size=world_size, # get world_size from your training environment
)
```
Further, a metadata file `_pdfa-english-train-info-minimal.json` contains the list of samples per shard, with same basename and `.json` or `.pdf` extension,
as well as the count of files per shard.
#### Words and lines document metadata
Initially, we obtained the raw data from the IDL API and combined it with the `idl_data` annotation. This information is then reshaped into lines organized in reading order, under the key lines. We keep non-reshaped word and bounding box information under the word key, should users want to use their own heuristic.
The way we obtain an approximate reading order is simply by looking at the frequency peaks of the leftmost word x-coordinate. A frequency peak means that a high number of lines are starting from the same point. Then, we keep track of the x-coordinate of each such identified column. If no peaks are found, the document is assumed to be readable in plain format.
The code to detect columns can be found here.
```python
def get_columnar_separators(page, min_prominence=0.3, num_bins=10, kernel_width=1):
"""
Identifies the x-coordinates that best separate columns by analyzing the derivative of a histogram
of the 'left' values (xmin) of bounding boxes.
Args:
page (dict): Page data with 'bbox' containing bounding boxes of words.
min_prominence (float): The required prominence of peaks in the histogram.
num_bins (int): Number of bins to use for the histogram.
kernel_width (int): The width of the Gaussian kernel used for smoothing the histogram.
Returns:
separators (list): The x-coordinates that separate the columns, if any.
"""
try:
left_values = [b[0] for b in page['bbox']]
hist, bin_edges = np.histogram(left_values, bins=num_bins)
hist = scipy.ndimage.gaussian_filter1d(hist, kernel_width)
min_val = min(hist)
hist = np.insert(hist, [0, len(hist)], min_val)
bin_width = bin_edges[1] - bin_edges[0]
bin_edges = np.insert(bin_edges, [0, len(bin_edges)], [bin_edges[0] - bin_width, bin_edges[-1] + bin_width])
peaks, _ = scipy.signal.find_peaks(hist, prominence=min_prominence * np.max(hist))
derivatives = np.diff(hist)
separators = []
if len(peaks) > 1:
# This finds the index of the maximum derivative value between peaks
# which indicates peaks after trough --> column
for i in range(len(peaks)-1):
peak_left = peaks[i]
peak_right = peaks[i+1]
max_deriv_index = np.argmax(derivatives[peak_left:peak_right]) + peak_left
separator_x = bin_edges[max_deriv_index + 1]
separators.append(separator_x)
except Exception as e:
separators = []
return separators
```
For each pdf document, we store statistics on number of pages per shard, number of valid samples per shard. A valid sample is a sample that can be encoded then decoded, which we did for each sample.
This instance of IDL is in [webdataset](https://github.com/webdataset/webdataset/commits/main) .tar format. It can be used with webdataset library or current releases of Hugging Face `datasets`.
Here is an example using the "streaming" parameter. We do recommend downloading the dataset to save bandwidth.
```python
dataset = load_dataset('pixparse/IDL-wds', streaming=True)
print(next(iter(dataset['train'])).keys())
>> dict_keys(['__key__', '__url__', 'json', 'ocr', 'pdf', 'tif'])
```
For faster download, you can use directly the `huggingface_hub` library. Make sure `hf_transfer` is install prior to downloading and mind that you have enough space locally.
```python
import os
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
from huggingface_hub import HfApi, logging
#logging.set_verbosity_debug()
hf = HfApi()
hf.snapshot_download("pixparse/pdfa-english-train", repo_type="dataset", local_dir_use_symlinks=False)
```
### Data, metadata and statistics.
Add example image here
The metadata for each document has been formatted in this way. Each `pdf` is paired with a `json` file with the following structure. Entries have been shortened for readability.
```json
{
"pages": [
{
"text": [
"COVIDIEN",
"Mallinckrodt",
"Addendum",
"This Addendum to the Consulting Agreement (the \"Agreement\") of July 28, 2010 (\"Effective Date\") by",
"and between David Brushwod, R.Ph., J.D., with an address at P.O. Box 100496, Gainesville, FL 32610-",
],
"bbox": [
[0.185964, 0.058857, 0.092199, 0.011457],
[0.186465, 0.079529, 0.087209, 0.009247],
[0.459241, 0.117854, 0.080015, 0.011332],
[0.117109, 0.13346, 0.751004, 0.014365],
[0.117527, 0.150306, 0.750509, 0.012954]
],
"poly": [
[
{"X": 0.185964, "Y": 0.058857}, {"X": 0.278163, "Y": 0.058857}, {"X": 0.278163, "Y": 0.070315}, {"X": 0.185964, "Y": 0.070315}
],
[
{"X": 0.186465, "Y": 0.079529}, {"X": 0.273673, "Y": 0.079529}, {"X": 0.273673, "Y": 0.088777}, {"X": 0.186465, "Y": 0.088777}
],
[
{"X": 0.459241, "Y": 0.117854}, {"X": 0.539256, "Y": 0.117854}, {"X": 0.539256, "Y": 0.129186}, {"X": 0.459241, "Y": 0.129186}
],
[
{"X": 0.117109, "Y": 0.13346}, {"X": 0.868113, "Y": 0.13346}, {"X": 0.868113, "Y": 0.147825}, {"X": 0.117109, "Y": 0.147825}
],
[
{"X": 0.117527, "Y": 0.150306}, {"X": 0.868036, "Y": 0.150306}, {"X": 0.868036, "Y": 0.163261}, {"X": 0.117527, "Y": 0.163261}
]
],
"score": [
0.9939, 0.5704, 0.9961, 0.9898, 0.9935
]
}
]
}
```
The top-level key, `pages`, is a list of every page in the document. The above example shows only one page. `text` is a list of lines in the document, with their individual associated bounding box in the next entry. `bbox` contains the bounding box coordinates in `left, top, width, height` format, with coordinates relative to the page size. `poly` is the corresponding polygon.
`score` is the confidence score for each line obtained with Textract.
### Data Splits
#### Train
* `idl-train-*.tar`
* Downloaded on 2023/12/16
* 3000 shards, 3144726 samples, 19174595 pages
## Additional Information
### Dataset Curators
Pablo Montalvo, Ross Wightman
### Licensing Information
While the Industry Documents Library is a public archive of documents and audiovisual materials, companies or individuals hold the rights to the information they created, meaning material cannot be “substantially” reproduced in books or other media without the copyright holder’s permission.
The use of copyrighted material, including reproduction, is governed by United States copyright law (Title 17, United States Code). The law may permit the “fair use” of a copyrighted work, including the making of a photocopy, “for purposes such as criticism, comment, news reporting, teaching (including multiple copies for classroom use), scholarship or research.” 17 U.S.C. § 107.
The Industry Documents Library makes its collections available under court-approved agreements with the rightsholders or under the fair use doctrine, depending on the collection.
According to the US Copyright Office, when determining whether a particular use comes under “fair use” you must consider the following:
the purpose and character of the use, including whether it is of commercial nature or for nonprofit educational purposes;
the nature of the copyrighted work itself;
how much of the work you are using in relation to the copyrighted work as a whole (1 page of a 1000 page work or 1 print advertisement vs. an entire 30 second advertisement);
the effect of the use upon the potential market for or value of the copyrighted work. (For additional information see the US Copyright Office Fair Use Index).
Each user of this website is responsible for ensuring compliance with applicable copyright laws. Persons obtaining, or later using, a copy of copyrighted material in excess of “fair use” may become liable for copyright infringement. By accessing this website, the user agrees to hold harmless the University of California, its affiliates and their directors, officers, employees and agents from all claims and expenses, including attorneys’ fees, arising out of the use of this website by the user.
For more in-depth information on copyright and fair use, visit the [Stanford University Libraries’ Copyright and Fair Use website.](https://fairuse.stanford.edu/)
If you hold copyright to a document or documents in our collections and have concerns about our inclusion of this material, please see the IDL Take-Down Policy or contact us with any questions.
In the dataset, the API from the Industry Documents Library holds the following permissions counts per file, showing all are now public (none are "confidential" or "privileged", only formerly.)
```json
{'public/no restrictions': 3005133,
'public/formerly confidential': 264978,
'public/formerly privileged': 30063,
'public/formerly privileged/formerly confidential': 669,
'public/formerly confidential/formerly privileged': 397,
}
```
### Citation Information