-
This repository contains the source code of our recent publication Spatial Dependency Parsing for Semi-Structured Document Information Extraction. The paper is accepted at Findins of ACL 2021.
-
SPADE
♠️ (SPAtial DEpendency parsing) accepts 2D text (text segments and their xy-coordinates). -
SPADE♠ generates the graph that represents semi-structured documents (such as receipts, name cards, invoices).
- The code is tested on
NVIDIA-P40
,NAME="Ubuntu", VERSION="16.04.6 LTS (Xenial Xerus)"
conda create --name spade_open python==3.7.10
git clone [this-repo]
pip install -r requirements
- Download
data.tar.gz
from here. The file also include the small model trained on CORD dataset.
mv data.tar.gz [project-dir]
tar xvfz data.tar.gz
- Download pretrained multi-lingual bert
cd scripts
python download_pretrained_models.py
-
Test the code with the sample data (input:
./data/samples/cord_predict.json
)bash scripts/predict_cord.sh
-
(Optional) Download funsd dataset
bash scripts/preprocess_funsd.sh
- Example from CORD-dev (
data/sample/cord_dev.jsonl
){ "data_id": 0, "fields": ["menu.cnt", "menu.discountprice", "menu.itemsubtotal", "menu.nm", "menu.price", "menu.sub_cnt", "menu.sub_nm", "menu.sub_price", "menu.unitprice", "menu.sub_num", "menu.discountprice", "menu.num", "menu.sub_discountprice", "menu.sub_etc", "menu.etc", "menu.vatyn", "menu.itemsubtotal", "menu.sub_unitprice", "sub_total.discount_price", "sub_total.service_price", "sub_total.subtotal_price", "sub_total.tax_price", "sub_total.tax_and_service", "sub_total.etc", "sub_total.othersvc_price", "total.total_price", "total.menuqty_cnt", "total.total_etc", "total.emoneyprice", "total.menutype_cnt", "total.cashprice", "total.changeprice", "total.creditcardprice", "void_menu.nm", "void_menu.cnt", "void_menu.price", "void_menu.unitprice", "void_total.total_price", "void_total.subtotal_price", "void_total.tax_price", "void_total.etc"], "field_rs": ["menu.nm", "sub_total.subtotal_price", "total.total_price", "void_menu.nm", "void_total.total_price"], "text": ["1", "REAL", "GANACHE", "16,500", ...] "label": [[[1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]], "coord": [[[176, 556], [194, 556], [194, 586], [176, 586]], [[202, 554], [266, 554], [266, 586], [202, 586]], [[272, 554], [372, 554], [372, 586], [272, 586]], [[580, 552], [664, 552], [664, 584], [580, 584]], [[176, 590], [194, 590], [194, 620], [176, 620]], [[204, 588], [252, 588], [252, 620], [204, 620]], [[258, 588], [320, 588], [320, 618], [258, 618]], [[580, 586], [664, 586], [664, 618], [580, 618]], [[176, 624], [194, 624], [194, 654], [176, 654]], [[202, 622], [280, 622], [280, 654], [202, 654]], [[286, 620], [360, 620], [360, 652], [286, 652]], [[580, 620], [666, 620], [666, 650], [580, 650]], [[200, 686], [348, 686], [348, 748], [200, 748]], [[498, 684], [670, 684], [670, 746], [498, 746]], [[202, 746], [266, 746], [266, 778], [202, 778]], [[580, 740], [668, 740], [668, 770], [580, 770]], [[195, 779], [375, 770], [378, 833], [198, 841]], [[524, 772], [672, 772], [672, 834], [524, 834]]], "vertical": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], "img_sz": {"width": 864, "height": 1296}, "img_feature": null, "img_url": null }
fields
: a list of field types to be parsed.field_rs
: a list of representative field types that are used for inter-field grouping.text
: a list of text segments.label
: [label-s
,label-g
]label-s
:(n_field + n_text) x n_text
adjacency matrix expressingrel-s
(serialization).null
when predicting.label-g
:(n_field + n_text) x n_text
adjacency matrix expressingrel-g
(grouping).null
when predicting.coord
: a list ofxy-coord
of text-box.xy-coord
: [xy-top-left, xy-top-right, xy-bottom-right, xy-bottom-left]img_sz
: an image sizeimg_feature
: an image feature. Currently not used.img_url
: an image url.
- In the uploaded data.tar.gz, you can also find
type0
data where the data is organized reflecting their original format. In this case,raw_data_input_type
should be set totype0
andlabel
is generated while loading the data.
{
"test__avg_loss": 0.08372728526592255,
"test__f1": 0.9101991060544494,
"test__precision_edge_avg": 0.932888658103816,
"test__recall_edge_avg": 0.9192414351541544,
"test__f1_edge_avg": 0.9259259429039002,
"test__precision_edge_of_type_0": 0.9672624647224836,
"test__recall_edge_of_type_0": 0.9710993577635059,
"test__f1_edge_of_type_0": 0.9691771137713262,
"test__precision_edge_of_type_1": 0.8985148514851485,
"test__recall_edge_of_type_1": 0.8673835125448028,
"test__f1_edge_of_type_1": 0.882674772036474
}
-
ave_loss
: Average cross entropy loss -
f1
:$F_1$ of parse. -
[precision|recall|f1]_edge_avg
: An average precision, recall, and$F_1$ of dependency parsing. -
[precision|recall|f1]_edge_of_type[0|1]
: Precision, recall, and$F_1$ of dependency parsing of individual types:type0
forrel-s
andtype
forrel-g
.
In addition to the scores shown in CORD example, it includes
{
"p_r_f1_entity": [
[
0.59375,
0.3114754098360656,
0.40860215053763443
],
[
0.8152524167561761,
0.7047353760445683,
0.7559760956175299
],
[
0.8589341692789969,
0.6674786845310596,
0.7511994516792323
],
[
0.6359447004608295,
0.4423076923076923,
0.5217391304347826
]
],
"p_r_f1_all_entity_ELB": [
0.8016216216216216,
0.635934819897084,
0.7092300334768054
],
"p_r_f1_link_ELK": [
0.6720977596741344,
0.3101503759398496,
0.42443729903536975
]
}
-
p_r_f1_entity
:[ [p_r_f1_question], [p_r_f1_answer], [p_r_f1_header], [p_r_f1_others]]
for the entity labeling task. -
p_r_f1_entity_ELB
: The FUNSD entity labeling task precision, recall, and$F_1$ scores for all fields. -
p_r_f1_entity_ELK
: The FUNSD entity linking task precision, recall, and$F_1$ scores for all fields.
{
"data_id": "00081",
"text_unit": ["1", "SU", "##RI", "##MI","29", ... ],
"pr_parse": [
[{"menu.nm": "SURIMI"}, {"menu.cnt": "1"}, {"menu.price": "29,091"}],
[{"menu.nm": "CREAMY CHK CLS FTC"}, {"menu.cnt": "1"}, {"menu.price": "42,727"}],
[{"menu.nm": "MIX 4FUN CHOCOLATE"}, {"menu.cnt": "1"}],
[{"menu.nm": "GREEN ITSODA PITCHER"}, {"menu.price": "19,091"}, {"menu.cnt": "1"}],
[{"menu.nm": "SC/R GRILLED STEAK"}, {"menu.cnt": "1"}, {"menu.price": "99,091"}],
[{"sub_total.subtotal_price": "250,909"}, {"sub_total.tax_price": "25,091"}],
[{"total.total_price": "276,000"}]],
"pr_label": [
[
[
1,
0,
0,
...
],
...
]
]
"pr_text_unit_field_label": ["menu.cnt","menu.nm","menu.nm","menu.nm", "menu.price",...]
}
-
data_id
: A data id. -
text_unit
: A list of tokens or text segments. -
pr_parse
: A predicted parse. -
pr_label
: A predicted adjacency matrices representing a dependency graph. Simliar tolabel
in the input but each columm and row represents a text unit which is either token or text segment. -
pr_text_unit_field_label
: A list of field-type label for each token intext_unit
.
- The preprocessed data and trained model is already included for CORD datset (type1).
- To generate them from from (almost) raw data (type0), do
bash scripts/preprocess_cord.sh
bash scripts/preprocess_funsd.sh
bash scripts/train_[task].sh
- Takes around 4 days on 6 P40 gpus with DDP.
- The best model is picked using dev set for
cord
. - For
FUNSD
task, use 'eary stopping' for the model validation. If the model is trained with uploaded config file with 6 P40, 2000-4000 epochs are recommended (some fluctuation in the final score is expected due to small size of the dataset depending on the random seed). Do not use the validation score for the model selection. It is dummy.
bash scripts/test_[task].sh
bash scripts/predict_cord.sh
@inproceedings{hwang2021spade,
title = "Spatial Dependency Parsing for Semi-Structured Document Information Extraction",
author = {Wonseok Hwang and
Jinyeung Yim and
Seunghyun Park and
Sohee Yang and
Minjoon Seo},
booktitle = "ACL",
year = {2021}
}
Copyright 2021-present NAVER Corp.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an " AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.