Update files from the datasets library (from 1.2.0)

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

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+*.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
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+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+annotations_creators:
+- machine-generated
+language_creators:
+- crowdsourced
+languages:
+- en
+licenses:
+- other-university-of-washington-academic
+multilinguality:
+- monolingual
+size_categories:
+- n>1M
+source_datasets:
+- original
+task_categories:
+- other
+task_ids:
+- other-stuctured-to-text
+- other-other-relation-extraction
+---
+
+# Dataset Card for Ollie
+
+## 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:** [Ollie](https://knowitall.github.io/ollie/)
+- **Repository:** [Github](https://github.com/knowitall/ollie)
+- **Paper:** [Aclweb](https://www.aclweb.org/anthology/D12-1048/)
+
+### Dataset Summary
+
+The Ollie dataset includes two configs for the data
+used to train the Ollie informatation extraction algorithm, for 18M
+sentences and 3M sentences respectively. 
+
+This data is for academic use only. From the authors:
+
+Ollie is a program that automatically identifies and extracts binary
+relationships from English sentences. Ollie is designed for Web-scale
+information extraction, where target relations are not specified in
+advance.
+
+Ollie is our second-generation information extraction system . Whereas
+ReVerb operates on flat sequences of tokens, Ollie works with the
+tree-like (graph with only small cycles) representation using
+Stanford's compression of the dependencies. This allows Ollie to
+capture expression that ReVerb misses, such as long-range relations.
+
+Ollie also captures context that modifies a binary relation. Presently
+Ollie handles attribution (He said/she believes) and enabling
+conditions (if X then).
+
+More information is available at the Ollie homepage:
+https://knowitall.github.io/ollie/
+
+### Languages
+en
+
+## Dataset Structure
+
+### Data Instances
+
+There are two configurations for the dataset: ollie_lemmagrep which
+are 18M sentences from web searches for a subset of the Reverb
+relationships (110,000 relationships), and the 3M sentences for
+ollie_patterned which is a subset of the ollie_lemmagrep dataset
+derived from patterns according to the Ollie paper.
+
+An example of an ollie_lemmagrep record:
+
+``
+{'arg1': 'adobe reader',
+ 'arg2': 'pdf',
+ 'chunk': 'B-NP I-NP I-NP I-NP B-PP B-NP I-NP B-VP B-PP B-NP I-NP O B-VP B-NP I-NP I-NP I-NP B-VP I-VP I-VP O',
+ 'pos': 'JJ NNS CC NNS IN PRP$ NN VBP IN NNP NN CC VB DT NNP NNP NNP TO VB VBN .',
+ 'rel': 'be require to view',
+ 'search_query': 'require reader pdf adobe view',
+ 'sentence': 'Many documents and reports on our site are in PDF format and require the Adobe Acrobat Reader to be viewed .',
+ 'sentence_cnt': '9',
+ 'words': 'many,document,and,report,on,our,site,be,in,pdf,format,and,require,the,adobe,acrobat,reader,to,be,view'}
+``
+
+An example of an ollie_patterned record:
+``
+{'arg1': 'english',
+ 'arg2': 'internet',
+ 'parse': '(in_IN_6), advmod(important_JJ_4, most_RBS_3); nsubj(language_NN_5, English_NNP_0); cop(language_NN_5, being_VBG_1); det(language_NN_5, the_DT_2); amod(language_NN_5, important_JJ_4); prep_in(language_NN_5, era_NN_9); punct(language_NN_5, ,_,_10); conj(language_NN_5, education_NN_12); det(era_NN_9, the_DT_7); nn(era_NN_9, Internet_NNP_8); amod(education_NN_12, English_JJ_11); nsubjpass(enriched_VBN_15, language_NN_5); aux(enriched_VBN_15, should_MD_13); auxpass(enriched_VBN_15, be_VB_14); punct(enriched_VBN_15, ._._16)',
+ 'pattern': '{arg1} <nsubj< {rel:NN} >prep_in> {slot0:NN} >nn> {arg2}',
+ 'rel': 'be language of',
+ 'search_query': 'english language internet',
+ 'sentence': 'English being the most important language in the Internet era , English education should be enriched .',
+ 'slot0': 'era'}
+``
+
+
+### Data Fields
+
+For ollie_lemmagrep:
+* rel: the relationship phrase/verb phrase. This may be empty, which represents the "be" relationship.
+* arg1: the first argument in the relationship
+* arg2: the second argument in the relationship.
+* chunk: a tag of each token in the sentence, showing the pos chunks
+* pos: part of speech tagging of the sentence
+* sentence: the sentence
+* sentence_cnt: the number of copies of this sentence encountered
+* search_query: a combintion of rel, arg1, arg2
+* words: the lemma of the words of the sentence separated by commas
+
+For ollie_patterned:
+* rel: the relationship phrase/verb phrase.
+* arg1: the first argument in the relationship
+* arg2: the second argument in the relationship.
+* slot0: the third argument in the relationship, which might be empty.
+* pattern: a parse pattern for the relationship
+* parse: a dependency parse forthe sentence
+* search_query: a combintion of rel, arg1, arg2
+* sentence: the senence
+
+### Data Splits
+
+There are no splits. 
+
+## Dataset Creation
+
+### Curation Rationale
+
+This dataset was created as part of research on open information extraction.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+See the research paper on OLlie. The training data is extracted from web pages (Cluebweb09).  
+
+#### Who are the source language producers?
+
+The Ollie authors at the Univeristy of Washington and data from Cluebweb09 and the open web. 
+
+### Annotations
+
+#### Annotation process
+
+The various parsers and code from the Ollie alogrithm.
+
+#### Who are the annotators?
+
+Machine annotated.
+
+### Personal and Sensitive Information
+
+Unkown, but likely there are names of famous individuals.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+The goal for the work is to help machines learn to extract information form open domains.
+
+### Discussion of Biases
+
+Since the data is gathered from the web, there is likely to be biased text and relationships.
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+
+## Additional Information
+
+### Dataset Curators
+
+The authors of Ollie at The University of Washington
+
+### Licensing Information
+
+The University of Washington acamdemic license: https://raw.githubusercontent.com/knowitall/ollie/master/LICENSE
+
+
+### Citation Information
+@inproceedings{ollie-emnlp12,
+  author = {Mausam and Michael Schmitz and Robert Bart and Stephen Soderland and Oren Etzioni},
+  title = {Open Language Learning for Information Extraction},
+  booktitle = {Proceedings of Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CONLL)},
+  year = {2012}
+}

dataset_infos.json

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+{"ollie_lemmagrep": {"description": "The Ollie dataset includes two configs for the data\nused to train the Ollie informatation extraction algorithm, for 18M\nsentences and 3M sentences respectively. \n\nThis data is for academic use only. From the authors:\n\nOllie is a program that automatically identifies and extracts binary\nrelationships from English sentences. Ollie is designed for Web-scale\ninformation extraction, where target relations are not specified in\nadvance.\n\nOllie is our second-generation information extraction system . Whereas\nReVerb operates on flat sequences of tokens, Ollie works with the\ntree-like (graph with only small cycles) representation using\nStanford's compression of the dependencies. This allows Ollie to\ncapture expression that ReVerb misses, such as long-range relations.\n\nOllie also captures context that modifies a binary relation. Presently\nOllie handles attribution (He said/she believes) and enabling\nconditions (if X then).\n\nMore information is available at the Ollie homepage:\nhttps://knowitall.github.io/ollie/\n", "citation": "@inproceedings{ollie-emnlp12,\n  author = {Mausam and Michael Schmitz and Robert Bart and Stephen Soderland and Oren Etzioni},\n  title = {Open Language Learning for Information Extraction},\n  booktitle = {Proceedings of Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CONLL)},\n  year = {2012}\n}", "homepage": "https://knowitall.github.io/ollie/", "license": "The University of Washington acamdemic license:\nhttps://raw.githubusercontent.com/knowitall/ollie/master/LICENSE\n", "features": {"arg1": {"dtype": "string", "id": null, "_type": "Value"}, "arg2": {"dtype": "string", "id": null, "_type": "Value"}, "rel": {"dtype": "string", "id": null, "_type": "Value"}, "search_query": {"dtype": "string", "id": null, "_type": "Value"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}, "words": {"dtype": "string", "id": null, "_type": "Value"}, "pos": {"dtype": "string", "id": null, "_type": "Value"}, "chunk": {"dtype": "string", "id": null, "_type": "Value"}, "sentence_cnt": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "ollie", "config_name": "ollie_lemmagrep", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 12324648919, "num_examples": 18674630, "dataset_name": "ollie"}}, "download_checksums": {"http://knowitall.cs.washington.edu/ollie/data/lemmagrep.txt.bz2": {"num_bytes": 1789363108, "checksum": "76ed3141fd95597c889eea1c05eb655914e76c72746893b856a00f2a422cbbab"}}, "download_size": 1789363108, "post_processing_size": null, "dataset_size": 12324648919, "size_in_bytes": 14114012027}, "ollie_patterned": {"description": "The Ollie dataset includes two configs for the data\nused to train the Ollie informatation extraction algorithm, for 18M\nsentences and 3M sentences respectively. \n\nThis data is for academic use only. From the authors:\n\nOllie is a program that automatically identifies and extracts binary\nrelationships from English sentences. Ollie is designed for Web-scale\ninformation extraction, where target relations are not specified in\nadvance.\n\nOllie is our second-generation information extraction system . Whereas\nReVerb operates on flat sequences of tokens, Ollie works with the\ntree-like (graph with only small cycles) representation using\nStanford's compression of the dependencies. This allows Ollie to\ncapture expression that ReVerb misses, such as long-range relations.\n\nOllie also captures context that modifies a binary relation. Presently\nOllie handles attribution (He said/she believes) and enabling\nconditions (if X then).\n\nMore information is available at the Ollie homepage:\nhttps://knowitall.github.io/ollie/\n", "citation": "@inproceedings{ollie-emnlp12,\n  author = {Mausam and Michael Schmitz and Robert Bart and Stephen Soderland and Oren Etzioni},\n  title = {Open Language Learning for Information Extraction},\n  booktitle = {Proceedings of Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CONLL)},\n  year = {2012}\n}", "homepage": "https://knowitall.github.io/ollie/", "license": "The University of Washington acamdemic license:\nhttps://raw.githubusercontent.com/knowitall/ollie/master/LICENSE\n", "features": {"rel": {"dtype": "string", "id": null, "_type": "Value"}, "arg1": {"dtype": "string", "id": null, "_type": "Value"}, "arg2": {"dtype": "string", "id": null, "_type": "Value"}, "slot0": {"dtype": "string", "id": null, "_type": "Value"}, "search_query": {"dtype": "string", "id": null, "_type": "Value"}, "pattern": {"dtype": "string", "id": null, "_type": "Value"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}, "parse": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "ollie", "config_name": "ollie_patterned", "version": "1.1.0", "splits": {"train": {"name": "train", "num_bytes": 2930309084, "num_examples": 3048961, "dataset_name": "ollie"}}, "download_checksums": {"http://knowitall.cs.washington.edu/ollie/data/patterned-all.txt.bz2": {"num_bytes": 387514061, "checksum": "a99e0907ff4c20f4a02a1a86453097affa73d6ab4160441c9b7203d756348f0d"}}, "download_size": 387514061, "post_processing_size": null, "dataset_size": 2930309084, "size_in_bytes": 3317823145}}

dummy/ollie_lemmagrep/1.1.0/dummy_data.zip

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dummy/ollie_patterned/1.1.0/dummy_data.zip

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ollie.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.
+"""Ollie"""
+
+from __future__ import absolute_import, division, print_function
+
+import bz2
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{ollie-emnlp12,
+  author = {Mausam and Michael Schmitz and Robert Bart and Stephen Soderland and Oren Etzioni},
+  title = {Open Language Learning for Information Extraction},
+  booktitle = {Proceedings of Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CONLL)},
+  year = {2012}
+}"""
+
+
+_DESCRIPTION = """The Ollie dataset includes two configs for the data
+used to train the Ollie informatation extraction algorithm, for 18M
+sentences and 3M sentences respectively.
+
+This data is for academic use only. From the authors:
+
+Ollie is a program that automatically identifies and extracts binary
+relationships from English sentences. Ollie is designed for Web-scale
+information extraction, where target relations are not specified in
+advance.
+
+Ollie is our second-generation information extraction system . Whereas
+ReVerb operates on flat sequences of tokens, Ollie works with the
+tree-like (graph with only small cycles) representation using
+Stanford's compression of the dependencies. This allows Ollie to
+capture expression that ReVerb misses, such as long-range relations.
+
+Ollie also captures context that modifies a binary relation. Presently
+Ollie handles attribution (He said/she believes) and enabling
+conditions (if X then).
+
+More information is available at the Ollie homepage:
+https://knowitall.github.io/ollie/
+"""
+
+
+_LICENSE = """The University of Washington acamdemic license:
+https://raw.githubusercontent.com/knowitall/ollie/master/LICENSE
+"""
+
+_URLs = {
+    "ollie_lemmagrep": "http://knowitall.cs.washington.edu/ollie/data/lemmagrep.txt.bz2",
+    "ollie_patterned": "http://knowitall.cs.washington.edu/ollie/data/patterned-all.txt.bz2",
+}
+
+
+class Ollie(datasets.GeneratorBasedBuilder):
+    """Ollie dataset for knowledge bases and knowledge graphs and underlying sentences."""
+
+    VERSION = datasets.Version("0.1.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="ollie_lemmagrep", description="The Ollie training data", version="1.1.0"),
+        datasets.BuilderConfig(
+            name="ollie_patterned", description="The Ollie data used in the Ollie paper.", version="1.1.0"
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "ollie_lemmagrep"
+
+    def _info(self):
+        if self.config.name == "ollie_lemmagrep":
+            features = datasets.Features(
+                {
+                    "arg1": datasets.Value("string"),
+                    "arg2": datasets.Value("string"),
+                    "rel": datasets.Value("string"),
+                    "search_query": datasets.Value("string"),
+                    "sentence": datasets.Value("string"),
+                    "words": datasets.Value("string"),
+                    "pos": datasets.Value("string"),
+                    "chunk": datasets.Value("string"),
+                    "sentence_cnt": datasets.Value("string"),
+                }
+            )
+        else:
+            features = datasets.Features(
+                {
+                    "rel": datasets.Value("string"),
+                    "arg1": datasets.Value("string"),
+                    "arg2": datasets.Value("string"),
+                    "slot0": datasets.Value("string"),
+                    "search_query": datasets.Value("string"),
+                    "pattern": datasets.Value("string"),
+                    "sentence": datasets.Value("string"),
+                    "parse": datasets.Value("string"),
+                }
+            )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage="https://knowitall.github.io/ollie/",
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        my_urls = _URLs[self.config.name]
+        data_dir = dl_manager.download_and_extract(my_urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_dir,
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """ Yields examples from the Ollie predicates and sentences. """
+
+        with bz2.open(filepath, "rt") as f:
+            id_ = -1
+            if self.config.name == "ollie_lemmagrep":
+                for row in f:
+                    row = row.strip().split("\t")
+                    id_ += 1
+                    if len(row) == 8:
+                        yield id_, {
+                            "arg1": row[0].strip(),
+                            "arg2": row[1].strip(),
+                            "rel": "",
+                            "search_query": row[2].strip(),
+                            "sentence": row[3].strip(),
+                            "words": row[4].strip(),
+                            "pos": row[5].strip(),
+                            "chunk": row[6].strip(),
+                            "sentence_cnt": row[7].strip(),
+                        }
+                    else:
+                        yield id_, {
+                            "arg1": row[1].strip(),
+                            "arg2": row[2].strip(),
+                            "rel": row[0].strip(),
+                            "search_query": row[3].strip(),
+                            "sentence": row[4].strip(),
+                            "words": row[5].strip(),
+                            "pos": row[6].strip(),
+                            "chunk": row[7].strip(),
+                            "sentence_cnt": row[8].strip(),
+                        }
+            else:
+                for row in f:
+                    row = row.strip().split("\t")
+                    id_ += 1
+                    if len(row) == 7:
+                        yield id_, {
+                            "rel": row[0].strip(),
+                            "arg1": row[1].strip(),
+                            "arg2": row[2].strip(),
+                            "slot0": "",
+                            "search_query": row[3].strip(),
+                            "pattern": row[4].strip(),
+                            "sentence": row[5].strip(),
+                            "parse": row[6].strip(),
+                        }
+                    else:
+                        yield id_, {
+                            "rel": row[0].strip(),
+                            "arg1": row[1].strip(),
+                            "arg2": row[2].strip(),
+                            "slot0": row[7].strip(),
+                            "search_query": row[3].strip(),
+                            "pattern": row[4].strip(),
+                            "sentence": row[5].strip(),
+                            "parse": row[6].strip(),
+                        }