Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- expert-generated
+languages:
+- pl
+licenses:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- text-retrieval
+task_ids:
+- entity-linking-retrieval
+---
+
+# Dataset Card for [Dataset Name]
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** [bprec homepage](https://clarin-pl.eu/dspace/handle/11321/736)
+- **Repository:** [bprec repository](https://gitlab.clarin-pl.eu/team-semantics/semrel-extraction)
+- **Paper:** [bprec paper](https://www.aclweb.org/anthology/2020.lrec-1.233.pdf)
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+Brand-Product Relation Extraction Corpora in Polish
+
+### Supported Tasks and Leaderboards
+
+NER, Entity linking
+
+### Languages
+
+Polish
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+- id: int identifier of a text
+- text: string text, for example a consumer comment on the social media
+- ner: extracted entities and their relationship
+    - source and target: a pair of entities identified in the text
+        - from: int value representing starting character of the entity
+        - text: string value with the entity text
+        - to: int value representing end character of the entity
+        - type: one of pre-identified entity types:
+            - PRODUCT_NAME
+            - PRODUCT_NAME_IMP
+            - PRODUCT_NO_BRAND
+            - BRAND_NAME
+            - BRAND_NAME_IMP
+            - VERSION
+            - PRODUCT_ADJ
+            - BRAND_ADJ
+            - LOCATION
+            - LOCATION_IMP
+
+
+### Data Splits
+
+No train/validation/test split provided. Current dataset configurations point to 4 domain categories for the texts:
+- tele
+- electro
+- cosmetics
+- banking
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+```
+@inproceedings{inproceedings,
+author = {Janz, Arkadiusz and Kopociński, Łukasz and Piasecki, Maciej and Pluwak, Agnieszka},
+year = {2020},
+month = {05},
+pages = {},
+title = {Brand-Product Relation Extraction Using Heterogeneous Vector Space Representations}
+}
+```

bprec.py

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+# coding=utf-8
+# 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.
+"""Brand-Product Relation Extraction Corpora"""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+
+import datasets
+
+
+# DONE: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@inproceedings{inproceedings,
+author = {Janz, Arkadiusz and Kopociński, Łukasz and Piasecki, Maciej and Pluwak, Agnieszka},
+year = {2020},
+month = {05},
+pages = {},
+title = {Brand-Product Relation Extraction Using Heterogeneous Vector Space Representations}
+}
+"""
+
+# DONE: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+Dataset consisting of Polish language texts annotated to recognize brand-product relations.
+"""
+
+# DONE: Add a link to an official homepage for the dataset here
+_HOMEPAGE = "https://clarin-pl.eu/dspace/handle/11321/736"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace dataset library don't host the datasets but only point to the original files
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URLs = {
+    "tele": "https://minio.clarin-pl.eu/semrel/corpora/ner_export_json/ner_tele_export.json",
+    "electro": "https://minio.clarin-pl.eu/semrel/corpora/ner_export_json/ner_electro_export.json",
+    "cosmetics": "https://minio.clarin-pl.eu/semrel/corpora/ner_export_json/ner_cosmetics_export.json",
+    "banking": "https://minio.clarin-pl.eu/semrel/corpora/ner_export_json/ner_banking_export.json",
+}
+
+_CATEGORIES = {
+    "tele": "telecommunications",
+    "electro": "electronics",
+    "cosmetics": "cosmetics",
+    "banking": "banking",
+}
+_ALL_CATEGORIES = "all"
+_VERSION = "1.1.0"
+
+
+class BprecConfig(datasets.BuilderConfig):
+    """BuilderConfig for BprecConfig."""
+
+    def __init__(self, categories=None, **kwargs):
+        super(BprecConfig, self).__init__(version=datasets.Version(_VERSION, ""), **kwargs),
+        self.categories = categories
+
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class Bprec(datasets.GeneratorBasedBuilder):
+    """Brand-Product Relation Extraction Corpora in Polish"""
+
+    BUILDER_CONFIGS = [
+        BprecConfig(
+            name=_ALL_CATEGORIES,
+            categories=_CATEGORIES,
+            description="A collection of Polish language texts annotated to recognize brand-product relations",
+        )
+    ] + [
+        BprecConfig(
+            name=cat,
+            categories=[cat],
+            description=f"{_CATEGORIES[cat]} examples from a collection of Polish language texts annotated to recognize brand-product relations",
+        )
+        for cat in _CATEGORIES
+    ]
+    BUILDER_CONFIG_CLASS = BprecConfig
+    DEFAULT_CONFIG_NAME = _ALL_CATEGORIES
+
+    def _info(self):
+        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        features = datasets.Features(
+            {
+                "id": datasets.Value("int32"),
+                "category": datasets.Value("string"),
+                "text": datasets.Value("string"),
+                "ner": datasets.features.Sequence(
+                    {
+                        "source": {
+                            "from": datasets.Value("int32"),
+                            "text": datasets.Value("string"),
+                            "to": datasets.Value("int32"),
+                            "type": datasets.features.ClassLabel(
+                                names=[
+                                    "PRODUCT_NAME",
+                                    "PRODUCT_NAME_IMP",
+                                    "PRODUCT_NO_BRAND",
+                                    "BRAND_NAME",
+                                    "BRAND_NAME_IMP",
+                                    "VERSION",
+                                    "PRODUCT_ADJ",
+                                    "BRAND_ADJ",
+                                    "LOCATION",
+                                    "LOCATION_IMP",
+                                ]
+                            ),
+                        },
+                        "target": {
+                            "from": datasets.Value("int32"),
+                            "text": datasets.Value("string"),
+                            "to": datasets.Value("int32"),
+                            "type": datasets.features.ClassLabel(
+                                names=[
+                                    "PRODUCT_NAME",
+                                    "PRODUCT_NAME_IMP",
+                                    "PRODUCT_NO_BRAND",
+                                    "BRAND_NAME",
+                                    "BRAND_NAME_IMP",
+                                    "VERSION",
+                                    "PRODUCT_ADJ",
+                                    "BRAND_ADJ",
+                                    "LOCATION",
+                                    "LOCATION_IMP",
+                                ]
+                            ),
+                        },
+                    }
+                ),
+            }
+        )
+        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,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=None,
+            # 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):
+        """Returns SplitGenerators."""
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # 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
+        _my_urls = [_URLs[cat] for cat in self.config.categories]
+
+        downloaded_files = dl_manager.download_and_extract(_my_urls)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filedirs": downloaded_files}),
+        ]
+
+    def _generate_examples(self, filedirs, split="tele"):
+        """ Yields examples. """
+        # TODO: This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method.
+        # It is in charge of opening the given file and yielding (key, example) tuples from the dataset
+        # The key is not important, it's more here for legacy reason (legacy from tfds)
+        cats = [cat for cat in self.config.categories]
+        for cat, filepath in zip(cats, filedirs):
+            # print(cat, filepath)
+            with open(filepath, "r", encoding="utf-8") as f:
+                data = json.load(f)
+                for key in data.keys():
+                    example = data[key]
+                    id_ = example.get("id")
+                    text = example.get("text")
+                    ner = example.get("ner")
+                    yield id_, {
+                        "id": id_,
+                        "category": cat,
+                        "text": text,
+                        "ner": ner,
+                    }

dataset_infos.json

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