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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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin 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: 
+- en
+licenses: 
+- other-my-license
+multilinguality: 
+- monolingual
+size_categories: 
+- 10K<n<100K
+source_datasets: 
+- original
+task_categories: 
+- other
+task_ids: 
+- other-other-NCI-PID-PubMed Genomics Knowledge Base Completion Dataset
+---
+
+# 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:** [NCI-PID-PubMed Genomics Knowledge Base Completion Dataset](https://msropendata.com/datasets/80b4f6e8-5d7c-4abc-9c79-2e51dfedd791)
+- **Repository:** [NCI-PID-PubMed Genomics Knowledge Base Completion Dataset](NCI-PID-PubMed Genomics Knowledge Base Completion Dataset)
+- **Paper:** [Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text](https://www.aclweb.org/anthology/P16-1136/)
+- **Point of Contact:** [Kristina Toutanova](mailto:kristout@google.com)
+
+
+### Dataset Summary
+ 
+The database is derived from the NCI PID Pathway Interaction Database, and the textual mentions are extracted from cooccurring pairs of genes in PubMed abstracts, processed and annotated by Literome (Poon et al. 2014). This dataset was used in the paper “Compositional Learning of Embeddings for Relation Paths in Knowledge Bases and Text” (Toutanova, Lin, Yih, Poon, and Quirk, 2016). More details can be found in the included README.
+
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+English
+
+## Dataset Structure
+
+NCI-PID-PubMed Genomics Knowledge Base Completion Dataset
+
+This dataset includes a database of regulation relationships among genes and corresponding textual mentions of pairs of genes in PubMed article abstracts.
+The database is derived from the NCI PID Pathway Interaction Database, and the textual mentions are extracted from cooccurring pairs of genes in PubMed abstracts, processed and annotated by Literome. This dataset was used in the paper "Compositional Learning of Embeddings for Relation Paths in Knowledge Bases and Text". 
+
+FILE FORMAT DETAILS
+
+The files train.txt, valid.txt, and test.text contain the training, development, and test set knowledge base (database of regulation relationships) triples used in.
+The file text.txt contains the textual triples derived from PubMed via entity linking and processing with Literome. The textual mentions were used for knowledge base completion in.
+
+The separator is a tab character; the relations are Positive_regulation, Negative_regulation, and Family (Family relationships occur only in the training set).
+
+The format is:
+
+| GENE1    | relation   |    GENE2        |
+
+Example:
+ABL1    Positive_regulation     CDK2
+
+The separator is a tab character; the relations are Positive_regulation, Negative_regulation, and Family (Family relationships occur only in the training set).
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+The format is:
+
+| GENE1    | relation   |    GENE2        |
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+[More Information Needed]
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+[More Information Needed]
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+[More Information Needed]
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+[More Information Needed]
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+[More Information Needed]
+
+### Dataset Curators
+
+The dataset was initially created by Kristina Toutanova, Victoria Lin, Wen-tau Yih, Hoifung Poon and Chris Quirk, during work done at Microsoft Research.
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+```
+@inproceedings{toutanova-etal-2016-compositional,
+    title = "Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text",
+    author = "Toutanova, Kristina  and
+      Lin, Victoria  and
+      Yih, Wen-tau  and
+      Poon, Hoifung  and
+      Quirk, Chris",
+    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = aug,
+    year = "2016",
+    address = "Berlin, Germany",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/P16-1136",
+    doi = "10.18653/v1/P16-1136",
+    pages = "1434--1444",
+}
+```

dataset_infos.json

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+{"default": {"description": "The database is derived from the NCI PID Pathway Interaction Database, and the textual mentions are extracted from cooccurring pairs of genes in PubMed abstracts, processed and annotated by Literome (Poon et al. 2014). This dataset was used in the paper \u201cCompositional Learning of Embeddings for Relation Paths in Knowledge Bases and Text\u201d (Toutanova, Lin, Yih, Poon, and Quirk, 2016). \n", "citation": "@inproceedings{toutanova-etal-2016-compositional,\n    title = \"Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text\",\n    author = \"Toutanova, Kristina  and\n      Lin, Victoria  and\n      Yih, Wen-tau  and\n      Poon, Hoifung  and\n      Quirk, Chris\",\n    booktitle = \"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",\n    month = aug,\n    year = \"2016\",\n    address = \"Berlin, Germany\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/P16-1136\",\n    doi = \"10.18653/v1/P16-1136\",\n    pages = \"1434--1444\",\n}\n", "homepage": "_HOMEPAGE", "license": "", "features": {"GENE1": {"dtype": "string", "id": null, "_type": "Value"}, "relation": {"num_classes": 3, "names": ["Positive_regulation", "Negative_regulation", "Family"], "names_file": null, "id": null, "_type": "ClassLabel"}, "GENE2": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "msr_genomics_kbcomp", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 256789, "num_examples": 12160, "dataset_name": "msr_genomics_kbcomp"}, "test": {"name": "test", "num_bytes": 58116, "num_examples": 2784, "dataset_name": "msr_genomics_kbcomp"}, "validation": {"name": "validation", "num_bytes": 27457, "num_examples": 1315, "dataset_name": "msr_genomics_kbcomp"}}, "download_checksums": {}, "download_size": 0, "post_processing_size": null, "dataset_size": 342362, "size_in_bytes": 342362}}

dummy/1.1.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4124e85ca1b81823d5f13edf7b7e03ff0c5fdd7a21b205fce30be064879f868f
+size 18809

msr_genomics_kbcomp.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.
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+import os
+
+import datasets
+
+
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@inproceedings{toutanova-etal-2016-compositional,
+    title = "Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text",
+    author = "Toutanova, Kristina  and
+      Lin, Victoria  and
+      Yih, Wen-tau  and
+      Poon, Hoifung  and
+      Quirk, Chris",
+    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = aug,
+    year = "2016",
+    address = "Berlin, Germany",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/P16-1136",
+    doi = "10.18653/v1/P16-1136",
+    pages = "1434--1444",
+}
+"""
+
+_DESCRIPTION = """\
+The database is derived from the NCI PID Pathway Interaction Database, and the textual mentions are extracted from cooccurring pairs of genes in PubMed abstracts, processed and annotated by Literome (Poon et al. 2014). This dataset was used in the paper “Compositional Learning of Embeddings for Relation Paths in Knowledge Bases and Text” (Toutanova, Lin, Yih, Poon, and Quirk, 2016).
+"""
+
+_HOMEPAGE = "https://msropendata.com/datasets/80b4f6e8-5d7c-4abc-9c79-2e51dfedd791"
+
+
+class MsrGenomicsKbcomp(datasets.GeneratorBasedBuilder):
+
+    VERSION = datasets.Version("1.1.0")
+
+    @property
+    def manual_download_instructions(self):
+        return """\
+  To use msr_genomics_kbcomp you need to download it manually. Please go to its homepage (https://msropendata.com/datasets/80b4f6e8-5d7c-4abc-9c79-2e51dfedd791)and login. Extract all files in one folder and use the path folder in datasets.load_dataset('msr_genomics_kbcomp', data_dir='path/to/folder/folder_name')
+  """
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # datasets.features.FeatureConnectors
+            features=datasets.Features(
+                {
+                    # These are the features of your dataset like images, labels ...
+                    "GENE1": datasets.Value("string"),
+                    "relation": datasets.features.ClassLabel(
+                        names=["Positive_regulation", "Negative_regulation", "Family"]
+                    ),
+                    "GENE2": datasets.Value("string"),
+                }
+            ),
+            # 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",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # dl_manager is a datasets.download.DownloadManager that can be used to
+        # download and extract URLs
+        data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
+
+        if not os.path.exists(data_dir):
+            raise FileNotFoundError(
+                "{} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('msr_genomics_kbcomp', data_dir=...)` that includes files unzipped from the reclor zip. Manual download instructions: {}".format(
+                    data_dir, self.manual_download_instructions
+                )
+            )
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": os.path.join(data_dir, "train.txt")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": os.path.join(data_dir, "test.txt")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": os.path.join(data_dir, "valid.txt")},
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        with open(filepath, encoding="utf-8") as f:
+            data = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
+            for id_, row in enumerate(data):
+                yield id_, {
+                    "GENE1": row["GENE1"],
+                    "relation": row["relation"],
+                    "GENE2": row["GENE2"],
+                }