Add loading script and README.md

README.md

@@ -0,0 +1,267 @@
+---
+annotations_creators:
+- other
+language:
+- en
+language_creators:
+- found
+license:
+- other
+multilinguality:
+- monolingual
+pretty_name: Google-IISc Distant Supervision (GIDS) dataset for distantly-supervised
+  relation extraction
+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: gids
+  features:
+  - name: sentence
+    dtype: string
+  - name: subj_id
+    dtype: string
+  - name: obj_id
+    dtype: string
+  - name: subj_text
+    dtype: string
+  - name: obj_text
+    dtype: string
+  - name: relation
+    dtype:
+      class_label:
+        names:
+          '0': NA
+          '1': /people/person/education./education/education/institution
+          '2': /people/person/education./education/education/degree
+          '3': /people/person/place_of_birth
+          '4': /people/deceased_person/place_of_death
+  splits:
+  - name: train
+    num_bytes: 5088421
+    num_examples: 11297
+  - name: validation
+    num_bytes: 844784
+    num_examples: 1864
+  - name: test
+    num_bytes: 2568673
+    num_examples: 5663
+  download_size: 8941490
+  dataset_size: 8501878
+- config_name: gids_formatted
+  features:
+  - 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': NA
+          '1': /people/person/education./education/education/institution
+          '2': /people/person/education./education/education/degree
+          '3': /people/person/place_of_birth
+          '4': /people/deceased_person/place_of_death
+  splits:
+  - name: train
+    num_bytes: 7075362
+    num_examples: 11297
+  - name: validation
+    num_bytes: 1173957
+    num_examples: 1864
+  - name: test
+    num_bytes: 3573706
+    num_examples: 5663
+  download_size: 8941490
+  dataset_size: 11823025
+---
+# Dataset Card for "gids"
+## 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:** [RE-DS-Word-Attention-Models](https://github.com/SharmisthaJat/RE-DS-Word-Attention-Models/tree/master/Data/GIDS)
+- **Paper:** [Improving Distantly Supervised Relation Extraction using Word and Entity Based Attention](https://arxiv.org/abs/1804.06987)
+- **Size of downloaded dataset files:** 8.94 MB
+- **Size of the generated dataset:** 11.82 MB
+
+### Dataset Summary
+The Google-IISc Distant Supervision (GIDS) is a new dataset for distantly-supervised relation extraction.
+GIDS is seeded from the human-judged Google relation extraction corpus. 
+See the paper for full details: [Improving Distantly Supervised Relation Extraction using Word and Entity Based Attention](https://arxiv.org/abs/1804.06987)
+
+Note: 
+- There is a formatted version that you can load with `datasets.load_dataset('gids', name='gids_formatted')`. This version is tokenized with spaCy, removes the underscores in the entities and provides entity offsets.
+
+### Supported Tasks and Leaderboards
+- **Tasks:** Relation Classification
+- **Leaderboards:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+### Languages
+The language in the dataset is English.
+## Dataset Structure
+### Data Instances
+- **Size of downloaded dataset files:** 8.94 MB
+- **Size of the generated dataset:** 11.82 MB
+
+#### gids
+An example of 'train' looks as follows:
+```json
+{
+  "relation": "org:founded_by",
+  "sentence": ["Tom", "Thabane", "resigned", "in", "October", "last", "year", "to", "form", "the", "All", "Basotho", "Convention", "-LRB-", "ABC", "-RRB-", ",", "crossing", "the", "floor", "with", "17", "members", "of", "parliament", ",", "causing", "constitutional", "monarch", "King", "Letsie", "III", "to", "dissolve", "parliament", "and", "call", "the", "snap", "election", "."],
+  "subj_text": 10,
+  "subj_id": 13,
+  "obj_text": 0,
+  "obj_id": 2
+}
+```
+
+#### gids_formatted
+An example of 'train' looks as follows:
+```json
+{
+  "relation": "org:founded_by",
+  "token": ["Tom", "Thabane", "resigned", "in", "October", "last", "year", "to", "form", "the", "All", "Basotho", "Convention", "-LRB-", "ABC", "-RRB-", ",", "crossing", "the", "floor", "with", "17", "members", "of", "parliament", ",", "causing", "constitutional", "monarch", "King", "Letsie", "III", "to", "dissolve", "parliament", "and", "call", "the", "snap", "election", "."],
+  "subj_start": 10,
+  "subj_end": 13,
+  "obj_start": 0,
+  "obj_end": 2
+}
+```
+
+### Data Fields
+The data fields are the same among all splits.
+
+#### gids
+- `sentence`: the sentence, a `string` feature.
+- `subj_id`: the id of the relation subject mention, a `string` feature.
+- `obj_id`: the id of the relation object mention, a `string` feature.
+- `subj_text`: the text of the relation subject mention, a `string` feature.
+- `obj_text`: the text of the relation object mention, a `string` feature.
+- `relation`: the relation label of this instance, a `string` classification label.
+
+#### gids_formatted
+- `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 |
+|------|-------|------|------|
+| GIDS | 11297 | 1864 | 5663 |
+
+## 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/abs-1804-06987,
+  author    = {Sharmistha Jat and
+               Siddhesh Khandelwal and
+               Partha P. Talukdar},
+  title     = {Improving Distantly Supervised Relation Extraction using Word and
+               Entity Based Attention},
+  journal   = {CoRR},
+  volume    = {abs/1804.06987},
+  year      = {2018},
+  url       = {http://arxiv.org/abs/1804.06987},
+  eprinttype = {arXiv},
+  eprint    = {1804.06987},
+  timestamp = {Fri, 15 Nov 2019 17:16:02 +0100},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-1804-06987.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```
+
+### Contributions
+Thanks to [@phucdev](https://github.com/phucdev) for adding this dataset.

gids.py

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+# 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 Google-IISc Distant Supervision (GIDS) dataset for distantly-supervised relation extraction"""
+
+import csv
+import datasets
+
+_CITATION = """\
+@inproceedings{bassignana-plank-2022-crossre,
+    title = "Cross{RE}: A {C}ross-{D}omain {D}ataset for {R}elation {E}xtraction",
+    author = "Bassignana, Elisa and Plank, Barbara",
+    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
+    year = "2022",
+    publisher = "Association for Computational Linguistics"
+}
+"""
+
+_DESCRIPTION = """\
+Google-IISc Distant Supervision (GIDS) is a new dataset for distantly-supervised relation extraction.
+GIDS is seeded from the human-judged Google relation extraction corpus.
+"""
+
+_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/SharmisthaJat/RE-DS-Word-Attention-Models/master/Data/GIDS/train.tsv",
+    "validation": "https://raw.githubusercontent.com/SharmisthaJat/RE-DS-Word-Attention-Models/master/Data/GIDS/dev.tsv",
+    "test": "https://raw.githubusercontent.com/SharmisthaJat/RE-DS-Word-Attention-Models/master/Data/GIDS/test.tsv",
+}
+_VERSION = datasets.Version("1.0.0")
+
+_CLASS_LABELS = [
+    "NA",
+    "/people/person/education./education/education/institution",
+    "/people/person/education./education/education/degree",
+    "/people/person/place_of_birth",
+    "/people/deceased_person/place_of_death"
+]
+
+
+def replace_underscore_in_span(text, start, end):
+    cleaned_text = text[:start] + text[start:end].replace("_", " ") + text[end:]
+    return cleaned_text
+
+
+class GIDS(datasets.GeneratorBasedBuilder):
+    """Google-IISc Distant Supervision (GIDS) is a new dataset for distantly-supervised relation extraction."""
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="gids", version=_VERSION, description="GIDS dataset."
+        ),
+        datasets.BuilderConfig(
+            name="gids_formatted", version=_VERSION, description="Formatted GIDS dataset."
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "gids"  # type: ignore
+
+    def _info(self):
+        if self.config.name == "gids_formatted":
+            features = datasets.Features(
+                {
+                    "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(
+                {
+                    "sentence": datasets.Value("string"),
+                    "subj_id": datasets.Value("string"),
+                    "obj_id": datasets.Value("string"),
+                    "subj_text": datasets.Value("string"),
+                    "obj_text": 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)
+        if self.config.name == "gids_formatted":
+            from spacy.lang.en import English
+            word_splitter = English()
+        else:
+            word_splitter = None
+        with open(filepath, encoding="utf-8") as f:
+            data = csv.reader(f, delimiter="\t")
+            for id_, example in enumerate(data):
+                text = example[5].strip()[:-9].strip()  # remove '###END###' from text,
+                subj_text = example[2]
+                obj_text = example[3]
+                rel_type = example[4]
+
+                if self.config.name == "gids_formatted":
+                    subj_char_start = text.find(subj_text)
+                    assert subj_char_start != -1, f"Did not find <{subj_text}> in the text"
+                    subj_char_end = subj_char_start + len(subj_text)
+                    obj_char_start = text.find(obj_text)
+                    assert obj_char_start != -1, f"Did not find <{obj_text}> in the text"
+                    obj_char_end = obj_char_start + len(obj_text)
+                    text = replace_underscore_in_span(text, subj_char_start, subj_char_end)
+                    text = replace_underscore_in_span(text, obj_char_start, obj_char_end)
+                    doc = word_splitter(text)
+                    word_tokens = [t.text for t in doc]
+                    subj_span = doc.char_span(subj_char_start, subj_char_end, alignment_mode="expand")
+                    obj_span = doc.char_span(obj_char_start, obj_char_end, alignment_mode="expand")
+
+                    yield id_, {
+                        "token": word_tokens,
+                        "subj_start": subj_span.start,
+                        "subj_end": subj_span.end,
+                        "obj_start": obj_span.start,
+                        "obj_end": obj_span.end,
+                        "relation": rel_type,
+                    }
+                else:
+                    yield id_, {
+                        "sentence": text,
+                        "subj_id": example[0],
+                        "obj_id": example[1],
+                        "subj_text": subj_text,
+                        "obj_text": obj_text,
+                        "relation": rel_type,
+                    }
+