Host financial_phrasebank data on the Hub (#4598)

* Host financial_phrasebank data on the Hub

* Update documentation card

* Update metadata JSON

* Update dummy data

Commit from https://github.com/huggingface/datasets/commit/237b5d5de528dd0081d5ed829268fbbe0050e304

README.md

@@ -54,7 +54,7 @@ pretty_name: FinancialPhrasebank
 - **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:**
+- **Point of Contact:** [Pekka Malo](mailto:pekka.malo@aalto.fi) [Ankur Sinha](mailto:ankur.sinha@aalto.fi)
 
 ### Dataset Summary
 
@@ -181,7 +181,11 @@ should be understood with this taken into account.
 
 ### Licensing Information
 
-License: Creative Commons Attribution 4.0 International License (CC-BY)
+This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/.
+
+If you are interested in commercial use of the data, please contact the following authors for an appropriate license:
+- [Pekka Malo](mailto:pekka.malo@aalto.fi)
+- [Ankur Sinha](mailto:ankur.sinha@aalto.fi)
 
 ### Citation Information
 

dataset_infos.json

@@ -1 +1 @@
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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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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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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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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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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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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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+oid sha256:03f9eaa7b7b1ece14ae8aa3b0a466f074777367d6211cffa20fada965bb4fa31
+size 1048

dummy/sentences_allagree/1.0.0/dummy_data.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:2adcc1e0b3cc42c5fc0ec4c74ecbc285e610ae62e7c2624047692ced180c6227
-size 1044
+oid sha256:a5013f0fd3bb0f7c172674ad35641f4bfa2d3b6682efdb9061abcad98c40b2da
+size 1050

financial_phrasebank.py

@@ -72,7 +72,8 @@ _HOMEPAGE = "https://www.kaggle.com/ankurzing/sentiment-analysis-for-financial-n
 
 _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"
+_REPO = "https://huggingface.co/datasets/financial_phrasebank/resolve/main/data"
+_URL = f"{_REPO}/FinancialPhraseBank-v1.0.zip"
 
 
 _VERSION = datasets.Version("1.0.0")