Add data loading script and README.md

README.md

@@ -0,0 +1,334 @@
+---
+annotations_creators:
+- other
+language:
+- en
+language_creators:
+- found
+license:
+- other
+multilinguality:
+- monolingual
+pretty_name: KBP37 is an English Relation Classification dataset
+size_categories:
+- 10K<n<100K
+source_datasets:
+- extended|other
+tags:
+- relation extraction
+task_categories:
+- text-classification
+task_ids:
+- multi-class-classification
+dataset_info:
+- config_name: kbp37
+  features:
+  - name: id
+    dtype: string
+  - name: sentence
+    dtype: string
+  - name: relation
+    dtype:
+      class_label:
+        names:
+          '0': no_relation
+          '1': org:alternate_names(e1,e2)
+          '2': org:alternate_names(e2,e1)
+          '3': org:city_of_headquarters(e1,e2)
+          '4': org:city_of_headquarters(e2,e1)
+          '5': org:country_of_headquarters(e1,e2)
+          '6': org:country_of_headquarters(e2,e1)
+          '7': org:founded(e1,e2)
+          '8': org:founded(e2,e1)
+          '9': org:founded_by(e1,e2)
+          '10': org:founded_by(e2,e1)
+          '11': org:members(e1,e2)
+          '12': org:members(e2,e1)
+          '13': org:stateorprovince_of_headquarters(e1,e2)
+          '14': org:stateorprovince_of_headquarters(e2,e1)
+          '15': org:subsidiaries(e1,e2)
+          '16': org:subsidiaries(e2,e1)
+          '17': org:top_members/employees(e1,e2)
+          '18': org:top_members/employees(e2,e1)
+          '19': per:alternate_names(e1,e2)
+          '20': per:alternate_names(e2,e1)
+          '21': per:cities_of_residence(e1,e2)
+          '22': per:cities_of_residence(e2,e1)
+          '23': per:countries_of_residence(e1,e2)
+          '24': per:countries_of_residence(e2,e1)
+          '25': per:country_of_birth(e1,e2)
+          '26': per:country_of_birth(e2,e1)
+          '27': per:employee_of(e1,e2)
+          '28': per:employee_of(e2,e1)
+          '29': per:origin(e1,e2)
+          '30': per:origin(e2,e1)
+          '31': per:spouse(e1,e2)
+          '32': per:spouse(e2,e1)
+          '33': per:stateorprovinces_of_residence(e1,e2)
+          '34': per:stateorprovinces_of_residence(e2,e1)
+          '35': per:title(e1,e2)
+          '36': per:title(e2,e1)
+  splits:
+  - name: train
+    num_bytes: 3570626
+    num_examples: 15917
+  - name: validation
+    num_bytes: 388935
+    num_examples: 1724
+  - name: test
+    num_bytes: 762806
+    num_examples: 3405
+  download_size: 5106673
+  dataset_size: 4722367
+- config_name: kbp37_formatted
+  features:
+  - name: id
+    dtype: string
+  - name: token
+    sequence: string
+  - name: subj_start
+    dtype: int32
+  - name: subj_end
+    dtype: int32
+  - name: obj_start
+    dtype: int32
+  - name: obj_end
+    dtype: int32
+  - name: relation
+    dtype:
+      class_label:
+        names:
+          '0': no_relation
+          '1': org:alternate_names(e1,e2)
+          '2': org:alternate_names(e2,e1)
+          '3': org:city_of_headquarters(e1,e2)
+          '4': org:city_of_headquarters(e2,e1)
+          '5': org:country_of_headquarters(e1,e2)
+          '6': org:country_of_headquarters(e2,e1)
+          '7': org:founded(e1,e2)
+          '8': org:founded(e2,e1)
+          '9': org:founded_by(e1,e2)
+          '10': org:founded_by(e2,e1)
+          '11': org:members(e1,e2)
+          '12': org:members(e2,e1)
+          '13': org:stateorprovince_of_headquarters(e1,e2)
+          '14': org:stateorprovince_of_headquarters(e2,e1)
+          '15': org:subsidiaries(e1,e2)
+          '16': org:subsidiaries(e2,e1)
+          '17': org:top_members/employees(e1,e2)
+          '18': org:top_members/employees(e2,e1)
+          '19': per:alternate_names(e1,e2)
+          '20': per:alternate_names(e2,e1)
+          '21': per:cities_of_residence(e1,e2)
+          '22': per:cities_of_residence(e2,e1)
+          '23': per:countries_of_residence(e1,e2)
+          '24': per:countries_of_residence(e2,e1)
+          '25': per:country_of_birth(e1,e2)
+          '26': per:country_of_birth(e2,e1)
+          '27': per:employee_of(e1,e2)
+          '28': per:employee_of(e2,e1)
+          '29': per:origin(e1,e2)
+          '30': per:origin(e2,e1)
+          '31': per:spouse(e1,e2)
+          '32': per:spouse(e2,e1)
+          '33': per:stateorprovinces_of_residence(e1,e2)
+          '34': per:stateorprovinces_of_residence(e2,e1)
+          '35': per:title(e1,e2)
+          '36': per:title(e2,e1)
+  splits:
+  - name: train
+    num_bytes: 4975792
+    num_examples: 15917
+  - name: validation
+    num_bytes: 542576
+    num_examples: 1724
+  - name: test
+    num_bytes: 1062977
+    num_examples: 3405
+  download_size: 5106673
+  dataset_size: 6581345
+---
+# Dataset Card for "kbp37"
+## 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)
+  - [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)
+  - [Contributions](#contributions)
+
+## Dataset Description
+- **Homepage:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Repository:** [kbp37](https://github.com/zhangdongxu/kbp37)
+- **Paper:** [Relation Classification via Recurrent Neural Network](https://arxiv.org/abs/1508.01006)
+- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Size of downloaded dataset files:** 5.11 MB
+- **Size of the generated dataset:** 6.58 MB
+
+### Dataset Summary
+KBP37 is a revision of MIML-RE annotation dataset, provided by Gabor Angeli et al. (2014). They use both the 2010 and 
+2013 KBP official document collections, as well as a July 2013 dump of Wikipedia as the text corpus for annotation. 
+There are 33811 sentences been annotated. Zhang and Wang made several refinements:
+1. They add direction to the relation names, e.g. '`per:employee_of`' is split into  '`per:employee of(e1,e2)`'
+and '`per:employee of(e2,e1)`'. They also replace '`org:parents`' with '`org:subsidiaries`' and replace
+'`org:member of’ with '`org:member`' (by their reverse directions).
+2. They discard low frequency relations such that both directions of each relation occur more than 100 times in the 
+dataset.
+
+KBP37 contains 18 directional relations and an additional '`no_relation`' relation, resulting in 37 relation classes.
+
+Note: 
+- There is a formatted version that you can load with `datasets.load_dataset('kbp37', name='kbp37_formatted')`. This version is tokenized with spaCy and provides entity offsets.
+
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Languages
+
+The language data in KBP37 is in English (BCP-47 en)
+
+## Dataset Structure
+
+### Data Instances
+
+#### kbp37
+An example of 'train' looks as follows:
+```json
+{
+  "id": "0",
+  "sentence": "<e1> Thom Yorke </e1> of <e2> Radiohead </e2> has included the + for many of his signature distortion sounds using a variety of guitars to achieve various tonal options .",
+  "relation": 27
+}
+```
+
+#### kbp37_formatted
+An example of 'train' looks as follows:
+```json
+{
+  "id": "1",
+  "token": ["Leland", "High", "School", "is", "a", "public", "high", "school", "located", "in", "the", "Almaden", "Valley", "in", "San", "Jose", "California", "USA", "in", "the", "San", "Jose", "Unified", "School", "District", "."],
+  "subj_start": 0,
+  "subj_end": 3,
+  "obj_start": 14,
+  "obj_end": 16,
+  "relation": 3
+}
+```
+
+### Data Fields
+
+#### kbp37
+- `id`: the instance id of this sentence, a `string` feature.
+- `sentence`: the sentence, a `string` features.
+- `relation`: the relation label of this instance, a `string` classification label.
+
+#### kbp37_formatted
+- `id`: the instance id of this sentence, a `string` feature.
+- `token`: the list of tokens of this sentence, obtained with spaCy, a `list` of `string` features.
+- `subj_start`: the 0-based index of the start token of the relation subject mention, an `ìnt` feature.
+- `subj_end`: the 0-based index of the end token of the relation subject mention, exclusive, an `ìnt` feature.
+- `obj_start`: the 0-based index of the start token of the relation object mention, an `ìnt` feature.
+- `obj_end`: the 0-based index of the end token of the relation object mention, exclusive, an `ìnt` feature.
+- `relation`: the relation label of this instance, a `string` classification label.
+
+### Data Splits
+
+|       | Train | Dev  | Test |
+|-------|-------|------|------|
+| KBP37 | 15917 | 1724 | 3405 |
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the source language producers?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the annotators?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Personal and Sensitive Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Discussion of Biases
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Other Known Limitations
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Licensing Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Citation Information
+
+```
+@article{DBLP:journals/corr/ZhangW15a,
+  author    = {Dongxu Zhang and
+               Dong Wang},
+  title     = {Relation Classification via Recurrent Neural Network},
+  journal   = {CoRR},
+  volume    = {abs/1508.01006},
+  year      = {2015},
+  url       = {http://arxiv.org/abs/1508.01006},
+  eprinttype = {arXiv},
+  eprint    = {1508.01006},
+  timestamp = {Fri, 04 Nov 2022 18:37:50 +0100},
+  biburl    = {https://dblp.org/rec/journals/corr/ZhangW15a.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```
+
+### Contributions
+
+Thanks to [@phucdev](https://github.com/phucdev) for adding this dataset.

kbp37.py

@@ -0,0 +1,234 @@
+# coding=utf-8
+# Copyright 2022 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 KBP37 dataset for English Relation Classification"""
+
+import datasets
+
+_CITATION = """\
+@article{DBLP:journals/corr/ZhangW15a,
+  author    = {Dongxu Zhang and
+               Dong Wang},
+  title     = {Relation Classification via Recurrent Neural Network},
+  journal   = {CoRR},
+  volume    = {abs/1508.01006},
+  year      = {2015},
+  url       = {http://arxiv.org/abs/1508.01006},
+  eprinttype = {arXiv},
+  eprint    = {1508.01006},
+  timestamp = {Fri, 04 Nov 2022 18:37:50 +0100},
+  biburl    = {https://dblp.org/rec/journals/corr/ZhangW15a.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+"""
+
+_DESCRIPTION = """\
+KBP37 is a revision of MIML-RE annotation dataset, provided by Gabor Angeli et al. (2014). They use both the 2010 and 
+2013 KBP official document collections, as well as a July 2013 dump of Wikipedia as the text corpus for annotation. 
+There are 33811 sentences been annotated. Zhang and Wang made several refinements:
+1. They add direction to the relation names, e.g. '`per:employee_of`' is split into  '`per:employee of(e1,e2)`'
+and '`per:employee of(e2,e1)`'. They also replace '`org:parents`' with '`org:subsidiaries`' and replace
+'`org:member of’ with '`org:member`' (by their reverse directions).
+2. They discard low frequency relations such that both directions of each relation occur more than 100 times in the 
+dataset.
+
+KBP37 contains 18 directional relations and an additional '`no_relation`' relation, resulting in 37 relation classes.
+"""
+
+_HOMEPAGE = ""
+
+_LICENSE = ""
+
+
+# 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 = {
+    "train": "https://raw.githubusercontent.com/zhangdongxu/kbp37/master/train.txt",
+    "validation": "https://raw.githubusercontent.com/zhangdongxu/kbp37/master/dev.txt",
+    "test": "https://raw.githubusercontent.com/zhangdongxu/kbp37/master/test.txt"
+}
+
+_VERSION = datasets.Version("1.0.0")
+
+_CLASS_LABELS = [
+    "no_relation",
+    "org:alternate_names(e1,e2)",
+    "org:alternate_names(e2,e1)",
+    "org:city_of_headquarters(e1,e2)",
+    "org:city_of_headquarters(e2,e1)",
+    "org:country_of_headquarters(e1,e2)",
+    "org:country_of_headquarters(e2,e1)",
+    "org:founded(e1,e2)",
+    "org:founded(e2,e1)",
+    "org:founded_by(e1,e2)",
+    "org:founded_by(e2,e1)",
+    "org:members(e1,e2)",
+    "org:members(e2,e1)",
+    "org:stateorprovince_of_headquarters(e1,e2)",
+    "org:stateorprovince_of_headquarters(e2,e1)",
+    "org:subsidiaries(e1,e2)",
+    "org:subsidiaries(e2,e1)",
+    "org:top_members/employees(e1,e2)",
+    "org:top_members/employees(e2,e1)",
+    "per:alternate_names(e1,e2)",
+    "per:alternate_names(e2,e1)",
+    "per:cities_of_residence(e1,e2)",
+    "per:cities_of_residence(e2,e1)",
+    "per:countries_of_residence(e1,e2)",
+    "per:countries_of_residence(e2,e1)",
+    "per:country_of_birth(e1,e2)",
+    "per:country_of_birth(e2,e1)",
+    "per:employee_of(e1,e2)",
+    "per:employee_of(e2,e1)",
+    "per:origin(e1,e2)",
+    "per:origin(e2,e1)",
+    "per:spouse(e1,e2)",
+    "per:spouse(e2,e1)",
+    "per:stateorprovinces_of_residence(e1,e2)",
+    "per:stateorprovinces_of_residence(e2,e1)",
+    "per:title(e1,e2)",
+    "per:title(e2,e1)"
+]
+
+
+class KBP37(datasets.GeneratorBasedBuilder):
+    """KBP37 is a relation extraction dataset"""
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="kbp37", version=_VERSION, description="The KBP37 dataset."
+        ),
+        datasets.BuilderConfig(
+            name="kbp37_formatted", version=_VERSION, description="The formatted KBP37 dataset."
+        )
+    ]
+
+    DEFAULT_CONFIG_NAME = "kbp37"  # type: ignore
+
+    def _info(self):
+        if self.config.name == "kbp37_formatted":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "token": datasets.Sequence(datasets.Value("string")),
+                    "subj_start": datasets.Value("int32"),
+                    "subj_end": datasets.Value("int32"),
+                    "obj_start": datasets.Value("int32"),
+                    "obj_end": datasets.Value("int32"),
+                    "relation": datasets.ClassLabel(names=_CLASS_LABELS),
+                }
+            )
+        else:
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "sentence": datasets.Value("string"),
+                    "relation": datasets.ClassLabel(names=_CLASS_LABELS),
+                }
+            )
+
+        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."""
+        # 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
+
+        downloaded_files = dl_manager.download_and_extract(_URLs)
+
+        return [datasets.SplitGenerator(name=i, gen_kwargs={"filepath": downloaded_files[str(i)]})
+                for i in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]]
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        # 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)
+
+        with open(filepath, encoding="utf-8") as f:
+            data = []
+            example_line = None
+            for idx, line in enumerate(f.readlines()):
+                line_no = idx % 4  # first line contains example, second line relation, third and fourth lines are \n
+                if line_no == 0:
+                    example_line = line.strip().split("\t")
+                elif line_no == 1:
+                    data.append({"example": example_line, "relation": line.strip()})
+            for example in data:
+                id_ = example["example"][0]
+                text = example["example"][1]
+                assert text[:2] == "\" " and text[-2:] == " \""
+                text = text[2:-2]
+                relation = example["relation"]
+
+                if self.config.name == "kbp37_formatted":
+                    text = text.replace("<e1>", " <e1> ")
+                    text = text.replace("<e2>", " <e2> ")
+                    text = text.replace("</e1>", " </e1> ")
+                    text = text.replace("</e2>", " </e2> ")
+                    text = text.strip().replace(r"\s\s+", r"\s")
+                    tokens = text.split()
+                    subj_start = tokens.index("<e1>")
+                    obj_start = tokens.index("<e2>")
+                    if subj_start < obj_start:
+                        tokens.pop(subj_start)
+                        subj_end = tokens.index("</e1>")
+                        tokens.pop(subj_end)
+                        obj_start = tokens.index("<e2>")
+                        tokens.pop(obj_start)
+                        obj_end = tokens.index("</e2>")
+                        tokens.pop(obj_end)
+                    else:
+                        tokens.pop(obj_start)
+                        obj_end = tokens.index("</e2>")
+                        tokens.pop(obj_end)
+                        subj_start = tokens.index("<e1>")
+                        tokens.pop(subj_start)
+                        subj_end = tokens.index("</e1>")
+                        tokens.pop(subj_end)
+
+                    yield int(id_), {
+                        "id": id_,
+                        "token": tokens,
+                        "subj_start": subj_start,
+                        "subj_end": subj_end,
+                        "obj_start": obj_start,
+                        "obj_end": obj_end,
+                        "relation": relation,
+                    }
+                else:
+                    yield int(id_), {
+                        "id": id_,
+                        "sentence": text,
+                        "relation": relation,
+                    }