Update files from the datasets library (from 1.4.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.4.0

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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+languages:
+- en
+licenses:
+- cc-by-nc-sa-3-0
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- multi-class-classification
+- sentiment-classification
+---
+
+# Dataset Card for financial_phrasebank
+
+## 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)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** [Kaggle](https://www.kaggle.com/ankurzing/sentiment-analysis-for-financial-news) [ResearchGate](https://www.researchgate.net/publication/251231364_FinancialPhraseBank-v10)
+- **Repository:**
+- **Paper:** [Arxiv](https://arxiv.org/abs/1307.5336)
+- **Leaderboard:** [Kaggle](https://www.kaggle.com/ankurzing/sentiment-analysis-for-financial-news/code) [PapersWithCode](https://paperswithcode.com/sota/sentiment-analysis-on-financial-phrasebank) =
+- **Point of Contact:**
+
+### Dataset Summary
+
+Polar sentiment dataset of sentences from financial news. The dataset consists of 4840 sentences from English language financial news categorised by sentiment. The dataset is divided by agreement rate of 5-8 annotators.
+
+### Supported Tasks and Leaderboards
+
+Sentiment Classification
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+```
+{ "sentence": "Pharmaceuticals group Orion Corp reported a fall in its third-quarter earnings that were hit by larger expenditures on R&D and marketing .",
+  "label": "negative"
+}
+```
+
+### Data Fields
+
+- sentence: a tokenized line from the dataset
+- label: a label corresponding to the class as a string: 'positive', 'negative' or 'neutral'
+
+### Data Splits
+There's no train/validation/test split.
+
+However the dataset is available in four possible configurations depending on the percentage of agreement of annotators:
+
+`sentences_50agree`; Number of instances with >=50% annotator agreement: 4846 
+`sentences_66agree`: Number of instances with >=66% annotator agreement: 4217
+`sentences_75agree`: Number of instances with >=75% annotator agreement: 3453
+`sentences_allagree`: Number of instances with 100% annotator agreement: 2264
+
+## Dataset Creation
+
+### Curation Rationale
+
+The key arguments for the low utilization of statistical techniques in
+financial sentiment analysis have been the difficulty of implementation for
+practical applications and the lack of high quality training data for building
+such models. Especially in the case of finance and economic texts, annotated
+collections are a scarce resource and many are reserved for proprietary use
+only. To resolve the missing training data problem, we present a collection of
+∼ 5000 sentences to establish human-annotated standards for benchmarking
+alternative modeling techniques. 
+
+The objective of the phrase level annotation task was to classify each example
+sentence into a positive, negative or neutral category by considering only the
+information explicitly available in the given sentence. Since the study is
+focused only on financial and economic domains, the annotators were asked to
+consider the sentences from the view point of an investor only; i.e. whether
+the news may have positive, negative or neutral influence on the stock price.
+As a result, sentences which have a sentiment that is not relevant from an
+economic or financial perspective are considered neutral.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The corpus used in this paper is made out of English news on all listed
+companies in OMX Helsinki. The news has been downloaded from the LexisNexis
+database using an automated web scraper. Out of this news database, a random
+subset of 10,000 articles was selected to obtain good coverage across small and
+large companies, companies in different industries, as well as different news
+sources. Following the approach taken by Maks and Vossen (2010), we excluded
+all sentences which did not contain any of the lexicon entities. This reduced
+the overall sample to 53,400 sentences, where each has at least one or more
+recognized lexicon entity. The sentences were then classified according to the
+types of entity sequences detected. Finally, a random sample of ∼5000 sentences
+was chosen to represent the overall news database.
+
+#### Who are the source language producers?
+
+The source data was written by various financial journalists.
+
+### Annotations
+
+#### Annotation process
+
+This release of the financial phrase bank covers a collection of 4840
+sentences. The selected collection of phrases was annotated by 16 people with
+adequate background knowledge on financial markets.
+
+Given the large number of overlapping annotations (5 to 8 annotations per
+sentence), there are several ways to define a majority vote based gold
+standard. To provide an objective comparison, we have formed 4 alternative
+reference datasets based on the strength of majority agreement: 
+
+#### Who are the annotators?
+
+Three of the annotators were researchers and the remaining 13 annotators were
+master's students at Aalto University School of Business with majors primarily
+in finance, accounting, and economics.
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+All annotators were from the same institution and so interannotator agreement
+should be understood with this taken into account.
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+License: Creative Commons Attribution 4.0 International License (CC-BY)
+
+### Citation Information
+
+```
+@article{Malo2014GoodDO,
+  title={Good debt or bad debt: Detecting semantic orientations in economic texts},
+  author={P. Malo and A. Sinha and P. Korhonen and J. Wallenius and P. Takala},
+  journal={Journal of the Association for Information Science and Technology},
+  year={2014},
+  volume={65}
+}
+```
+
+### Contributions
+
+Thanks to [@frankier](https://github.com/frankier) for adding this dataset.

dataset_infos.json

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+{"sentences_allagree": {"description": "The key arguments for the low utilization of statistical techniques in\nfinancial sentiment analysis have been the difficulty of implementation for\npractical applications and the lack of high quality training data for building\nsuch models. Especially in the case of finance and economic texts, annotated\ncollections are a scarce resource and many are reserved for proprietary use\nonly. To resolve the missing training data problem, we present a collection of\n\u223c 5000 sentences to establish human-annotated standards for benchmarking\nalternative modeling techniques.\n\nThe objective of the phrase level annotation task was to classify each example\nsentence into a positive, negative or neutral category by considering only the\ninformation explicitly available in the given sentence. Since the study is\nfocused only on financial and economic domains, the annotators were asked to\nconsider the sentences from the view point of an investor only; i.e. whether\nthe news may have positive, negative or neutral influence on the stock price.\nAs a result, sentences which have a sentiment that is not relevant from an\neconomic or financial perspective are considered neutral.\n\nThis release of the financial phrase bank covers a collection of 4840\nsentences. The selected collection of phrases was annotated by 16 people with\nadequate background knowledge on financial markets. Three of the annotators\nwere researchers and the remaining 13 annotators were master\u2019s students at\nAalto University School of Business with majors primarily in finance,\naccounting, and economics.\n\nGiven the large number of overlapping annotations (5 to 8 annotations per\nsentence), there are several ways to define a majority vote based gold\nstandard. To provide an objective comparison, we have formed 4 alternative\nreference datasets based on the strength of majority agreement: all annotators\nagree, >=75% of annotators agree, >=66% of annotators agree and >=50% of\nannotators agree.\n", "citation": "@article{Malo2014GoodDO,\n  title={Good debt or bad debt: Detecting semantic orientations in economic texts},\n  author={P. Malo and A. Sinha and P. Korhonen and J. Wallenius and P. 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Especially in the case of finance and economic texts, annotated\ncollections are a scarce resource and many are reserved for proprietary use\nonly. To resolve the missing training data problem, we present a collection of\n\u223c 5000 sentences to establish human-annotated standards for benchmarking\nalternative modeling techniques.\n\nThe objective of the phrase level annotation task was to classify each example\nsentence into a positive, negative or neutral category by considering only the\ninformation explicitly available in the given sentence. Since the study is\nfocused only on financial and economic domains, the annotators were asked to\nconsider the sentences from the view point of an investor only; i.e. whether\nthe news may have positive, negative or neutral influence on the stock price.\nAs a result, sentences which have a sentiment that is not relevant from an\neconomic or financial perspective are considered neutral.\n\nThis release of the financial phrase bank covers a collection of 4840\nsentences. The selected collection of phrases was annotated by 16 people with\nadequate background knowledge on financial markets. Three of the annotators\nwere researchers and the remaining 13 annotators were master\u2019s students at\nAalto University School of Business with majors primarily in finance,\naccounting, and economics.\n\nGiven the large number of overlapping annotations (5 to 8 annotations per\nsentence), there are several ways to define a majority vote based gold\nstandard. To provide an objective comparison, we have formed 4 alternative\nreference datasets based on the strength of majority agreement: all annotators\nagree, >=75% of annotators agree, >=66% of annotators agree and >=50% of\nannotators agree.\n", "citation": "@article{Malo2014GoodDO,\n  title={Good debt or bad debt: Detecting semantic orientations in economic texts},\n  author={P. Malo and A. Sinha and P. Korhonen and J. Wallenius and P. Takala},\n  journal={Journal of the Association for Information Science and Technology},\n  year={2014},\n  volume={65}\n}\n", "homepage": "https://www.kaggle.com/ankurzing/sentiment-analysis-for-financial-news", "license": "Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["negative", "neutral", "positive"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "builder_name": "financial_phrasebank", "config_name": "sentences_75agree", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 472707, "num_examples": 3453, "dataset_name": "financial_phrasebank"}}, "download_checksums": {"https://www.researchgate.net/profile/Pekka_Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip": {"num_bytes": 681890, "checksum": "0e1a06c4900fdae46091d031068601e3773ba067c7cecb5b0da1dcba5ce989a6"}}, "download_size": 681890, "post_processing_size": null, "dataset_size": 472707, "size_in_bytes": 1154597}, "sentences_66agree": {"description": "The key arguments for the low utilization of statistical techniques in\nfinancial sentiment analysis have been the difficulty of implementation for\npractical applications and the lack of high quality training data for building\nsuch models. Especially in the case of finance and economic texts, annotated\ncollections are a scarce resource and many are reserved for proprietary use\nonly. To resolve the missing training data problem, we present a collection of\n\u223c 5000 sentences to establish human-annotated standards for benchmarking\nalternative modeling techniques.\n\nThe objective of the phrase level annotation task was to classify each example\nsentence into a positive, negative or neutral category by considering only the\ninformation explicitly available in the given sentence. Since the study is\nfocused only on financial and economic domains, the annotators were asked to\nconsider the sentences from the view point of an investor only; i.e. whether\nthe news may have positive, negative or neutral influence on the stock price.\nAs a result, sentences which have a sentiment that is not relevant from an\neconomic or financial perspective are considered neutral.\n\nThis release of the financial phrase bank covers a collection of 4840\nsentences. The selected collection of phrases was annotated by 16 people with\nadequate background knowledge on financial markets. Three of the annotators\nwere researchers and the remaining 13 annotators were master\u2019s students at\nAalto University School of Business with majors primarily in finance,\naccounting, and economics.\n\nGiven the large number of overlapping annotations (5 to 8 annotations per\nsentence), there are several ways to define a majority vote based gold\nstandard. 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Especially in the case of finance and economic texts, annotated\ncollections are a scarce resource and many are reserved for proprietary use\nonly. To resolve the missing training data problem, we present a collection of\n\u223c 5000 sentences to establish human-annotated standards for benchmarking\nalternative modeling techniques.\n\nThe objective of the phrase level annotation task was to classify each example\nsentence into a positive, negative or neutral category by considering only the\ninformation explicitly available in the given sentence. Since the study is\nfocused only on financial and economic domains, the annotators were asked to\nconsider the sentences from the view point of an investor only; i.e. whether\nthe news may have positive, negative or neutral influence on the stock price.\nAs a result, sentences which have a sentiment that is not relevant from an\neconomic or financial perspective are considered neutral.\n\nThis release of the financial phrase bank covers a collection of 4840\nsentences. The selected collection of phrases was annotated by 16 people with\nadequate background knowledge on financial markets. Three of the annotators\nwere researchers and the remaining 13 annotators were master\u2019s students at\nAalto University School of Business with majors primarily in finance,\naccounting, and economics.\n\nGiven the large number of overlapping annotations (5 to 8 annotations per\nsentence), there are several ways to define a majority vote based gold\nstandard. To provide an objective comparison, we have formed 4 alternative\nreference datasets based on the strength of majority agreement: all annotators\nagree, >=75% of annotators agree, >=66% of annotators agree and >=50% of\nannotators agree.\n", "citation": "@article{Malo2014GoodDO,\n  title={Good debt or bad debt: Detecting semantic orientations in economic texts},\n  author={P. Malo and A. Sinha and P. Korhonen and J. Wallenius and P. 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financial_phrasebank.py

@@ -0,0 +1,149 @@
+# 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.
+
+"""Financial Phrase Bank v1.0: Polar sentiment dataset of sentences from
+financial news. The dataset consists of 4840 sentences from English language
+financial news categorised by sentiment. The dataset is divided by agreement
+rate of 5-8 annotators."""
+
+from __future__ import absolute_import, division, print_function
+
+import os
+
+import datasets
+
+
+_CITATION = """\
+@article{Malo2014GoodDO,
+  title={Good debt or bad debt: Detecting semantic orientations in economic texts},
+  author={P. Malo and A. Sinha and P. Korhonen and J. Wallenius and P. Takala},
+  journal={Journal of the Association for Information Science and Technology},
+  year={2014},
+  volume={65}
+}
+"""
+
+_DESCRIPTION = """\
+The key arguments for the low utilization of statistical techniques in
+financial sentiment analysis have been the difficulty of implementation for
+practical applications and the lack of high quality training data for building
+such models. Especially in the case of finance and economic texts, annotated
+collections are a scarce resource and many are reserved for proprietary use
+only. To resolve the missing training data problem, we present a collection of
+∼ 5000 sentences to establish human-annotated standards for benchmarking
+alternative modeling techniques.
+
+The objective of the phrase level annotation task was to classify each example
+sentence into a positive, negative or neutral category by considering only the
+information explicitly available in the given sentence. Since the study is
+focused only on financial and economic domains, the annotators were asked to
+consider the sentences from the view point of an investor only; i.e. whether
+the news may have positive, negative or neutral influence on the stock price.
+As a result, sentences which have a sentiment that is not relevant from an
+economic or financial perspective are considered neutral.
+
+This release of the financial phrase bank covers a collection of 4840
+sentences. The selected collection of phrases was annotated by 16 people with
+adequate background knowledge on financial markets. Three of the annotators
+were researchers and the remaining 13 annotators were master’s students at
+Aalto University School of Business with majors primarily in finance,
+accounting, and economics.
+
+Given the large number of overlapping annotations (5 to 8 annotations per
+sentence), there are several ways to define a majority vote based gold
+standard. To provide an objective comparison, we have formed 4 alternative
+reference datasets based on the strength of majority agreement: all annotators
+agree, >=75% of annotators agree, >=66% of annotators agree and >=50% of
+annotators agree.
+"""
+
+_HOMEPAGE = "https://www.kaggle.com/ankurzing/sentiment-analysis-for-financial-news"
+
+_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License"
+
+_URL = "https://www.researchgate.net/profile/Pekka_Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip"
+
+
+_VERSION = datasets.Version("1.0.0")
+
+
+class FinancialPhraseBankConfig(datasets.BuilderConfig):
+    """BuilderConfig for FinancialPhraseBank."""
+
+    def __init__(
+        self,
+        split,
+        **kwargs,
+    ):
+        """BuilderConfig for Discovery.
+        Args:
+          filename_bit: `string`, the changing part of the filename.
+        """
+
+        super(FinancialPhraseBankConfig, self).__init__(name=f"sentences_{split}agree", version=_VERSION, **kwargs)
+
+        self.path = os.path.join("FinancialPhraseBank-v1.0", f"Sentences_{split.title()}Agree.txt")
+
+
+class FinancialPhrasebank(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIGS = [
+        FinancialPhraseBankConfig(
+            split="all",
+            description="Sentences where all annotators agreed",
+        ),
+        FinancialPhraseBankConfig(split="75", description="Sentences where at least 75% of annotators agreed"),
+        FinancialPhraseBankConfig(split="66", description="Sentences where at least 66% of annotators agreed"),
+        FinancialPhraseBankConfig(split="50", description="Sentences where at least 50% of annotators agreed"),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "sentence": datasets.Value("string"),
+                    "label": datasets.features.ClassLabel(
+                        names=[
+                            "negative",
+                            "neutral",
+                            "positive",
+                        ]
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        data_dir = dl_manager.download_and_extract(_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": os.path.join(data_dir, self.config.path)},
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """ Yields examples. """
+        with open(filepath, encoding="iso-8859-1") as f:
+            for id_, line in enumerate(f):
+                sentence, label = line.rsplit("@", 1)
+                yield id_, {"sentence": sentence, "label": label}