add dataset

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README.md

@@ -0,0 +1,163 @@
+---
+annotations_creators:
+- weak supervision
+- expert-generated
+languages:
+- '''en-US'''
+licenses:
+- unknown
+multilinguality:
+- monolingual
+pretty_name: products-2017
+size_categories:
+- 1K<n<10K
+- 10K<n<100K
+source_datasets:
+- original
+task_categories: 
+- text-classification
+- data-integration
+task_ids:
+- entity-matching
+- identity-resolution
+- product-matching
+paperswithcode_id: wdc-products
+---
+
+# Dataset Card for [products-2017]
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Annotations](#annotations)
+- [Additional Information](#additional-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** [LSPCv2 Homepage](http://webdatacommons.org/largescaleproductcorpus/v2/index.html)
+- **Point of Contact:** [Ralph Peeters](mailto:ralph.peeters@uni-mannheim.de)
+
+### Dataset Summary
+
+Many e-shops have started to mark-up product data within their HTML pages using the schema.org vocabulary. The Web Data Commons project regularly extracts such data from the Common Crawl, a large public web crawl. The Web Data Commons Training and Test Sets for Large-Scale Product Matching contain product offers from different e-shops in the form of binary product pairs (with corresponding label "match" or "no match")
+
+In order to support the evaluation of machine learning-based matching methods, the data is split into training, validation and test set. We provide training and validation sets in four different sizes for four product categories. The labels of the test sets were manually checked while those of the training sets were derived using shared product identifiers from the Web via weak supervision.
+
+The data stems from the WDC Product Data Corpus for Large-Scale Product Matching - Version 2.0 which consists of 26 million product offers originating from 79 thousand websites.
+
+
+### Supported Tasks and Leaderboards
+
+Entity Matching, Product Matching
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+The data is structured as pairs of product offers with the corresponding match/non-match label. This is an example instance from the computers category:
+
+```
+{"pair_id":"581109#16637861","label":0,"id_left":581109,"category_left":"Computers_and_Accessories","cluster_id_left":1324529,"brand_left":"\"Gigabyte\"@en","title_left":" \"Gigabyte Radeon RX 480 G1 Gaming 4096MB GDDR5 PCI-Express Graphics Card\"@en \"Gigabyte Gr| OcUK\"@en","description_left":"\"GV-RX480G1 GAMING-4GD, Core Clock: 1202MHz, Boost Clock: 1290MHz, Memory: 4096MB 7000MHz GDDR5, Stream Processors: 2304, Crossfire Ready, VR Ready, FreeSync Ready, 3 Years Warranty\"@en ","price_left":null,"specTableContent_left":null,"id_right":16637861,"category_right":"Computers_and_Accessories","cluster_id_right":107415,"brand_right":"\"Gigabyte\"@en","title_right":" \"Gigabyte Radeon RX 550 Gaming OC 2048MB GDDR5 PCI-Express Graphics Card\"@en \"Gigabyte Gr| OcUK\"@en","description_right":"\"GV-RX550GAMING OC-2GD, Boost: 1219MHz, Memory: 2048MB 7000MHz GDDR5, Stream Processors: 512, DirectX 12 Support, 3 Years Warranty\"@en ","price_right":null,"specTableContent_right":null}
+```
+
+### Data Fields
+
+- pair_id: unique identifier of a pair (string)
+- label: binary label, match or non-match (int)
+
+The following attributes are contained twice, once for the first and once for the second product offer
+
+- id: unique id of the product offer (int)
+- category: product category (string)
+- cluster_id: id of the product cluster from the original corpus this offer belongs to (int)
+- brand: brand of the product (string)
+- title: product title (string)
+- description: longer product description (string)
+- price: price of the product offer (string)
+- specTableContent: additional data found in specification tables on the webpage that contains the product offer (string)
+
+### Data Splits
+- Computers
+  - Test set - 1100 pairs
+  - Small Train set - 2267 pairs
+  - Small Validation set - 567 pairs
+  - Medium Train set - 6475 pairs
+  - Medium Validation set - 1619 pairs
+  - Large Train set - 26687 pairs
+  - Large Validation set - 6672 pairs
+  - XLarge Train set - 54768 pairs
+  - Xlarge Validation set - 13693 pairs
+
+- Cameras
+  - Test set - 1100 pairs
+  - Small Train set - 1508 pairs
+  - Small Validation set - 378 pairs
+  - Medium Train set - 4204 pairs
+  - Medium Validation set - 1051 pairs
+  - Large Train set - 16028 pairs
+  - Large Validation set - 4008 pairs
+  - XLarge Train set - 33821 pairs
+  - Xlarge Validation set - 8456 pairs
+
+- Watches
+  - Test set - 1100 pairs
+  - Small Train set - 1804 pairs
+  - Small Validation set - 451 pairs
+  - Medium Train set - 5130 pairs
+  - Medium Validation set - 1283 pairs
+  - Large Train set - 21621 pairs
+  - Large Validation set - 5406 pairs
+  - XLarge Train set - 49255 pairs
+  - Xlarge Validation set - 12314 pairs
+
+- Shoes
+  - Test set - 1100 pairs
+  - Small Train set - 1650 pairs
+  - Small Validation set - 413 pairs
+  - Medium Train set - 4644 pairs
+  - Medium Validation set - 1161 pairs
+  - Large Train set - 18391 pairs
+  - Large Validation set - 4598 pairs
+  - XLarge Train set - 33943 pairs
+  - Xlarge Validation set - 8486 pairs
+
+
+## Dataset Creation
+
+### Annotations
+
+#### Annotation process
+
+- Training and Validation sets: distant supervision via shared schema.org product IDs
+- Test sets: Single expert annotator
+
+#### Who are the annotators?
+
+[Ralph Peeters](https://www.uni-mannheim.de/dws/people/researchers/phd-students/ralph-peeters/)
+
+## Additional Information
+
+### Citation Information
+
+```
+@inproceedings{primpeli2019wdc,
+  title={The WDC training dataset and gold standard for large-scale product matching},
+  author={Primpeli, Anna and Peeters, Ralph and Bizer, Christian},
+  booktitle={Companion Proceedings of The 2019 World Wide Web Conference},
+  pages={381--386},
+  year={2019}
+}
+```

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products-2017.py

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+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# 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.
+"""The WDC Product Data Corpus and Gold Standard for Large-Scale Product Matching - Version 2.0."""
+
+
+import csv
+import json
+import os
+
+import datasets
+
+from pdb import set_trace
+
+
+_CITATION = """\
+@inproceedings{primpeli2019wdc,
+  title={The WDC training dataset and gold standard for large-scale product matching},
+  author={Primpeli, Anna and Peeters, Ralph and Bizer, Christian},
+  booktitle={Companion Proceedings of The 2019 World Wide Web Conference},
+  pages={381--386},
+  year={2019}
+}
+"""
+
+_DESCRIPTION = """\
+Many e-shops have started to mark-up product data within their HTML pages using the schema.org vocabulary. The Web Data Commons project regularly extracts such data from the Common Crawl, a large public web crawl. The Web Data Commons Training and Test Sets for Large-Scale Product Matching contain product offers from different e-shops in the form of binary product pairs (with corresponding label "match" or "no match")
+
+In order to support the evaluation of machine learning-based matching methods, the data is split into training, validation and test set. We provide training and validation sets in four different sizes for four product categories. The labels of the test sets were manually checked while those of the training sets were derived using shared product identifiers from the Web via weak supervision.
+
+The data stems from the WDC Product Data Corpus for Large-Scale Product Matching - Version 2.0 which consists of 26 million product offers originating from 79 thousand websites.
+"""
+
+_HOMEPAGE = "http://webdatacommons.org/largescaleproductcorpus/v2/index.html"
+
+_LICENSE = ""
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URLS = {
+    "computers": "https://huggingface.co/datasets/wdc/products-2017/computers/",
+    "cameras": "https://huggingface.co/datasets/wdc/products-2017/cameras/",
+    "watches": "https://huggingface.co/datasets/wdc/products-2017/watches/",
+    "shoes": "https://huggingface.co/datasets/wdc/products-2017/shoes/"
+}
+
+_BASE_DATA_PAT_FORMAT_STR = "{category}/"
+
+class Products2017Config(datasets.BuilderConfig):
+    """The WDC Product Data Corpus and Gold Standard for Large-Scale Product Matching - Version 2.0."""
+
+    def __init__(self, name, category: str, **kwargs):
+        """BuilderConfig for WDC Products 2017.
+        Args:
+            category (str): The product category and training set size.
+        """
+
+        size = name.split('_')[1]
+        # Initialize the base class.
+        description = (
+            f"Dataset for category {name}"
+        )
+        super(Products2017Config, self).__init__(
+            name=name, **kwargs
+        )
+
+        # Additional attributes
+        self.name = name
+        self.category = category
+        self.size = size
+        self.base_data_path = _BASE_DATA_PAT_FORMAT_STR.format(
+            category=category
+        )
+
+class Products2017(datasets.GeneratorBasedBuilder):
+    """The WDC Product Data Corpus and Gold Standard for Large-Scale Product Matching - Version 2.0."""
+
+    VERSION = datasets.Version("2.1.0")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    BUILDER_CONFIGS = [
+        Products2017Config(
+            name='computers_xlarge',
+            category='computers',
+            version=VERSION,
+            description="The computers xlarge dataset part of Products-2017"),
+        Products2017Config(
+            name='computers_large',
+            category='computers',
+            version=VERSION,
+            description="The computers large dataset part of Products-2017"),
+        Products2017Config(
+            name='computers_medium',
+            category='computers',
+            version=VERSION,
+            description="The computers medium dataset part of Products-2017"),
+        Products2017Config(
+            name='computers_small',
+            category='computers',
+            version=VERSION,
+            description="The computers small dataset part of Products-2017"),
+        Products2017Config(
+            name='cameras_xlarge',
+            category='cameras',
+            version=VERSION,
+            description="The cameras xlarge dataset part of Products-2017"),
+        Products2017Config(
+            name='cameras_large',
+            category='cameras',
+            version=VERSION,
+            description="The cameras large dataset part of Products-2017"),
+        Products2017Config(
+            name='cameras_medium',
+            category='cameras',
+            version=VERSION,
+            description="The cameras medium dataset part of Products-2017"),
+        Products2017Config(
+            name='cameras_small',
+            category='cameras',
+            version=VERSION,
+            description="The cameras small dataset part of Products-2017"),
+        Products2017Config(
+            name='watches_xlarge',
+            category='watches',
+            version=VERSION,
+            description="The watches xlarge dataset part of Products-2017"),
+        Products2017Config(
+            name='watches_large',
+            category='watches',
+            version=VERSION,
+            description="The watches large dataset part of Products-2017"),
+        Products2017Config(
+            name='watches_medium',
+            category='watches',
+            version=VERSION,
+            description="The watches medium dataset part of Products-2017"),
+        Products2017Config(
+            name='watches_small',
+            category='watches',
+            version=VERSION,
+            description="The watches small dataset part of Products-2017"),
+        Products2017Config(
+            name='shoes_xlarge',
+            category='shoes',
+            version=VERSION,
+            description="The shoes xlarge dataset part of Products-2017"),
+        Products2017Config(
+            name='shoes_large',
+            category='shoes',
+            version=VERSION,
+            description="The shoes large dataset part of Products-2017"),
+        Products2017Config(
+            name='shoes_medium',
+            category='shoes',
+            version=VERSION,
+            description="The shoes medium dataset part of Products-2017"),
+        Products2017Config(
+            name='shoes_small',
+            category='shoes',
+            version=VERSION,
+            description="The shoes small dataset part of Products-2017"),
+        
+    ]
+
+    DEFAULT_CONFIG_NAME = "computers_medium"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+
+        features = datasets.Features(
+            {
+                "pair_id": datasets.Value("string"),
+                "label": datasets.Value("int32"),
+                "id_left": datasets.Value("int32"),
+                "category_left": datasets.Value("string"),
+                "cluster_id_left": datasets.Value("int32"),
+                "brand_left": datasets.Value("string"),
+                "title_left": datasets.Value("string"),
+                "description_left": datasets.Value("string"),
+                "price_left": datasets.Value("string"),
+                "specTableContent_left": datasets.Value("string"),
+                "id_right": datasets.Value("int32"),
+                "category_right": datasets.Value("string"),
+                "cluster_id_right": datasets.Value("int32"),
+                "brand_right": datasets.Value("string"),
+                "title_right": datasets.Value("string"),
+                "description_right": datasets.Value("string"),
+                "price_right": datasets.Value("string"),
+                "specTableContent_right": datasets.Value("string"),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        main_path = self.config.base_data_path
+        size = self.config.size
+        relevant_files = [f'{main_path}train_{size}.json.gz', f'{main_path}valid_{size}.json.gz', f'{main_path}test.json.gz']
+        
+        data_dir = dl_manager.download_and_extract(relevant_files)
+        
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir[0],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir[2],
+                    "split": "test"
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir[1],
+                    "split": "validation",
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        with open(filepath, encoding="utf-8") as f:
+            for key, row in enumerate(f):
+                data = json.loads(row)
+                yield key, {
+                    "pair_id": data["pair_id"],
+                    "label": data["label"],
+                    "id_left": data["id_left"],
+                    "category_left": data["category_left"],
+                    "cluster_id_left": data["cluster_id_left"],
+                    "brand_left": data["brand_left"],
+                    "title_left": data["title_left"],
+                    "description_left": data["description_left"],
+                    "price_left": data["price_left"],
+                    "specTableContent_left": data["specTableContent_left"],
+                    "id_right": data["id_right"],
+                    "category_right": data["category_right"],
+                    "cluster_id_right": data["cluster_id_right"],
+                    "brand_right": data["brand_right"],
+                    "title_right": data["title_right"],
+                    "description_right": data["description_right"],
+                    "price_right": data["price_right"],
+                    "specTableContent_right": data["specTableContent_right"]
+                }

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