Add SemEval 2016-2017 task3 subtasksABC. Fix #18 (#19)

* add config * add schema * upd field schema * add labeled dataset * add license info * update names + add descriptions + dummy fields * regenrate README.md
piskvorky · Feb 7, 2018 · a2cc165 · a2cc165
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diff --git a/README.md b/README.md
@@ -106,11 +106,15 @@ To load a model or corpus, use either the Python or command line interface of [G
 |------|-----------|-----------|-------------|---------|
 | 20-newsgroups | 13 MB | <ul><li>http://qwone.com/~jason/20Newsgroups/</li></ul> | The notorious collection of approximately 20,000 newsgroup posts, partitioned (nearly) evenly across 20 different newsgroups. | not found |
 | fake-news | 19 MB | <ul><li>https://www.kaggle.com/mrisdal/fake-news</li></ul> | News dataset, contains text and metadata from 244 websites and represents 12,999 posts in total from a specific window of 30 days. The data was pulled using the webhose.io API, and because it's coming from their crawler, not all websites identified by their BS Detector are present in this dataset. Data sources that were missing a label were simply assigned a label of 'bs'. There are (ostensibly) no genuine, reliable, or trustworthy news sources represented in this dataset (so far), so don't trust anything you read. | https://creativecommons.org/publicdomain/zero/1.0/ |
+| patent-2017 | 2944 MB | <ul><li>http://patents.reedtech.com/pgrbft.php</li></ul> | Patent Grant Full Text. Contains the full text including tables, sequence data and 'in-line' mathematical expressions of each patent grant issued in 2017. | not found |
 | quora-duplicate-questions | 20 MB | <ul><li>https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs</li></ul> | Over 400,000 lines of potential question duplicate pairs. Each line contains IDs for each question in the pair, the full text for each question, and a binary value that indicates whether the line contains a duplicate pair or not. | probably https://www.quora.com/about/tos |
+| semeval-2016-2017-task3-subtaskA-unannotated | 223 MB | <ul><li>http://alt.qcri.org/semeval2016/task3/</li> <li>http://alt.qcri.org/semeval2016/task3/data/uploads/semeval2016-task3-report.pdf</li> <li>https://github.com/RaRe-Technologies/gensim-data/issues/18</li> <li>https://github.com/Witiko/semeval-2016_2017-task3-subtaskA-unannotated-english</li></ul> | SemEval 2016 / 2017 Task 3 Subtask A unannotated dataset contains 189,941 questions and 1,894,456 comments in English collected from the Community Question Answering (CQA) web forum of Qatar Living. These can be used as a corpus for language modelling. | These datasets are free for general research use. |
+| semeval-2016-2017-task3-subtaskBC | 6 MB | <ul><li>http://alt.qcri.org/semeval2017/task3/</li> <li>http://alt.qcri.org/semeval2017/task3/data/uploads/semeval2017-task3.pdf</li> <li>https://github.com/RaRe-Technologies/gensim-data/issues/18</li> <li>https://github.com/Witiko/semeval-2016_2017-task3-subtaskB-english</li></ul> | SemEval 2016 / 2017 Task 3 Subtask B and C datasets contain train+development (317 original questions, 3,169 related questions, and 31,690 comments), and test datasets in English. The description of the tasks and the collected data is given in sections 3 and 4.1 of the task paper http://alt.qcri.org/semeval2016/task3/data/uploads/semeval2016-task3-report.pdf linked in section “Papers” of https://github.com/RaRe-Technologies/gensim-data/issues/18. | All files released for the task are free for general research use |
 | text8 | 31 MB | <ul><li>http://mattmahoney.net/dc/textdata.html</li></ul> | First 100,000,000 bytes of plain text from Wikipedia. Used for testing purposes; see wiki-english-* for proper full Wikipedia datasets. | not found |
 | wiki-english-20171001 | 6214 MB | <ul><li>https://dumps.wikimedia.org/enwiki/20171001/</li></ul> | Extracted Wikipedia dump from October 2017. Produced by `python -m gensim.scripts.segment_wiki -f enwiki-20171001-pages-articles.xml.bz2 -o wiki-en.gz` | https://dumps.wikimedia.org/legal.html |
 
 ### Models
+
 | name | num vectors | file size | base dataset | read_more  | description | parameters | preprocessing | license |
 |------|-------------|-----------|--------------|------------|-------------|------------|---------------|---------|
 | conceptnet-numberbatch-17-06-300 | 1917247 | 1168 MB | ConceptNet, word2vec, GloVe, and OpenSubtitles 2016 | <ul><li>http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14972</li> <li>https://github.com/commonsense/conceptnet-numberbatch</li> <li>http://conceptnet.io/</li></ul> | ConceptNet Numberbatch consists of state-of-the-art semantic vectors (also known as word embeddings) that can be used directly as a representation of word meanings or as a starting point for further machine learning. ConceptNet Numberbatch is part of the ConceptNet open data project. ConceptNet provides lots of ways to compute with word meanings, one of which is word embeddings. ConceptNet Numberbatch is a snapshot of just the word embeddings. It is built using an ensemble that combines data from ConceptNet, word2vec, GloVe, and OpenSubtitles 2016, using a variation on retrofitting. | <ul><li>dimension - 300</li></ul> | - | https://github.com/commonsense/conceptnet-numberbatch/blob/master/LICENSE.txt |
@@ -123,10 +127,9 @@ To load a model or corpus, use either the Python or command line interface of [G
 | glove-wiki-gigaword-300 | 400000 | 376 MB | Wikipedia 2014 + Gigaword 5 (6B tokens, uncased) | <ul><li>https://nlp.stanford.edu/projects/glove/</li> <li>https://nlp.stanford.edu/pubs/glove.pdf</li></ul> | Pre-trained vectors based on Wikipedia 2014 + Gigaword, 5.6B tokens, 400K vocab, uncased (https://nlp.stanford.edu/projects/glove/). | <ul><li>dimension - 300</li></ul> | Converted to w2v format with `python -m gensim.scripts.glove2word2vec -i <fname> -o glove-wiki-gigaword-300.txt`. | http://opendatacommons.org/licenses/pddl/ |
 | glove-wiki-gigaword-50 | 400000 | 65 MB | Wikipedia 2014 + Gigaword 5 (6B tokens, uncased) | <ul><li>https://nlp.stanford.edu/projects/glove/</li> <li>https://nlp.stanford.edu/pubs/glove.pdf</li></ul> | Pre-trained vectors based on Wikipedia 2014 + Gigaword, 5.6B tokens, 400K vocab, uncased (https://nlp.stanford.edu/projects/glove/). | <ul><li>dimension - 50</li></ul> | Converted to w2v format with `python -m gensim.scripts.glove2word2vec -i <fname> -o glove-wiki-gigaword-50.txt`. | http://opendatacommons.org/licenses/pddl/ |
 | word2vec-google-news-300 | 3000000 | 1662 MB | Google News (about 100 billion words) | <ul><li>https://code.google.com/archive/p/word2vec/</li> <li>https://arxiv.org/abs/1301.3781</li> <li>https://arxiv.org/abs/1310.4546</li> <li>https://www.microsoft.com/en-us/research/publication/linguistic-regularities-in-continuous-space-word-representations/?from=http%3A%2F%2Fresearch.microsoft.com%2Fpubs%2F189726%2Frvecs.pdf</li></ul> | Pre-trained vectors trained on a part of the Google News dataset (about 100 billion words). The model contains 300-dimensional vectors for 3 million words and phrases. The phrases were obtained using a simple data-driven approach described in 'Distributed Representations of Words and Phrases and their Compositionality' (https://code.google.com/archive/p/word2vec/). | <ul><li>dimension - 300</li></ul> | - | not found |
-| word2vec-ruscorpora-300 | 184973 | 198 MB | Russian National Corpus (about 250M words) | <ul><li>https://www.academia.edu/24306935/WebVectors_a_Toolkit_for_Building_Web_Interfaces_for_Vector_Semantic_Models</li> <li>http://rusvectores.org/en/</li> <li>https://github.com/RaRe-Technologies/gensim-data/issues/3</li></ul> | Word2vec Continuous Skipgram vectors trained on full Russian National Corpus (about 250M words). The model contains 185K words. | <ul><li>window_size - 10</li> <li>dimension - 300</li></ul> | The corpus was lemmatized and tagged with Universal PoS | https://creativecommons.org/licenses/by/4.0/deed.en |
-
+| word2vec-ruscorpora-300 | 184973 | 198 MB | Russian National Corpus (about 250M words) | <ul><li>https://www.academia.edu/24306935/WebVectors_a_Toolkit_for_Building_Web_Interfaces_for_Vector_Semantic_Models</li> <li>http://rusvectores.org/en/</li> <li>https://github.com/RaRe-Technologies/gensim-data/issues/3</li></ul> | Word2vec Continuous Skipgram vectors trained on full Russian National Corpus (about 250M words). The model contains 185K words. | <ul><li>dimension - 300</li> <li>window_size - 10</li></ul> | The corpus was lemmatized and tagged with Universal PoS | https://creativecommons.org/licenses/by/4.0/deed.en |
 
-(this table is generated automatically by [generate_table.py](https://github.com/RaRe-Technologies/gensim-data/blob/master/generate_table.py) based on [list.json](https://github.com/RaRe-Technologies/gensim-data/blob/master/list.json))
+(generated by generate_table.py based on list.json)
 
 ----
 

diff --git a/list.json b/list.json
@@ -1,5 +1,57 @@
 {
 	"corpora": {
+		"semeval-2016-2017-task3-subtaskBC": {
+			"num_records": -1,
+			"record_format": "dict",
+			"file_size": 6344358,
+			"reader_code": "https://github.com/RaRe-Technologies/gensim-data/releases/download/semeval-2016-2017-task3-subtaskB-eng/__init__.py",
+			"license": "All files released for the task are free for general research use",
+			"fields": {
+				"2016-train": ["..."],
+				"2016-dev": ["..."],
+				"2017-test": ["..."],
+				"2016-test": ["..."]
+			},
+			"description": "SemEval 2016 / 2017 Task 3 Subtask B and C datasets contain train+development (317 original questions, 3,169 related questions, and 31,690 comments), and test datasets in English. The description of the tasks and the collected data is given in sections 3 and 4.1 of the task paper http://alt.qcri.org/semeval2016/task3/data/uploads/semeval2016-task3-report.pdf linked in section “Papers” of https://github.com/RaRe-Technologies/gensim-data/issues/18.",
+			"checksum": "701ea67acd82e75f95e1d8e62fb0ad29",
+			"file_name": "semeval-2016-2017-task3-subtaskBC.gz",
+			"read_more": ["http://alt.qcri.org/semeval2017/task3/", "http://alt.qcri.org/semeval2017/task3/data/uploads/semeval2017-task3.pdf", "https://github.com/RaRe-Technologies/gensim-data/issues/18", "https://github.com/Witiko/semeval-2016_2017-task3-subtaskB-english"],
+			"parts": 1
+		},
+		"semeval-2016-2017-task3-subtaskA-unannotated": {
+			"num_records": 189941,
+			"record_format": "dict",
+			"file_size": 234373151,
+			"reader_code": "https://github.com/RaRe-Technologies/gensim-data/releases/download/semeval-2016-2017-task3-subtaskA-unannotated-eng/__init__.py",
+			"license": "These datasets are free for general research use.",
+			"fields": {
+				"THREAD_SEQUENCE": "",
+				"RelQuestion": {
+					"RELQ_CATEGORY": "question category, according to the Qatar Living taxonomy",
+					"RELQ_DATE": "date of posting",
+					"RELQ_ID": "question indentifier",
+					"RELQ_USERID": "identifier of the user asking the question",
+					"RELQ_USERNAME": "name of the user asking the question",
+					"RelQBody": "body of question",
+					"RelQSubject": "subject of question"
+				},
+				"RelComments": [
+					{
+						"RelCText": "text of answer",
+						"RELC_USERID": "identifier of the user posting the comment",
+						"RELC_ID": "comment identifier",
+						"RELC_USERNAME": "name of the user posting the comment",
+						"RELC_DATE": "date of posting"
+					}
+
+				]
+			},
+			"description": "SemEval 2016 / 2017 Task 3 Subtask A unannotated dataset contains 189,941 questions and 1,894,456 comments in English collected from the Community Question Answering (CQA) web forum of Qatar Living. These can be used as a corpus for language modelling.",
+			"checksum": "2de0e2f2c4f91c66ae4fcf58d50ba816",
+			"file_name": "semeval-2016-2017-task3-subtaskA-unannotated.gz",
+			"read_more": ["http://alt.qcri.org/semeval2016/task3/", "http://alt.qcri.org/semeval2016/task3/data/uploads/semeval2016-task3-report.pdf", "https://github.com/RaRe-Technologies/gensim-data/issues/18", "https://github.com/Witiko/semeval-2016_2017-task3-subtaskA-unannotated-english"],
+			"parts": 1
+		},
 		"patent-2017": {
 			"num_records": 353197,
 			"record_format": "dict",