debate2vec

Word-vectors created from a large corpus of competitive debate evidence, and data extraction / processing scripts

#usage

import fasttext.util
ft = fasttext.load_model('debate2vec.bin')
ft.get_word_vector('dialectics')

Download Link

Github won't let me store large files in their repos.

• FastText Vectors Here (~260mb)

About

Created from all publically available Cross Examination Competitive debate evidence posted by the community on Open Evidence (From 2013-2020)

Search through the original evidence by going to debate.cards

Stats about this corpus:

• 222485 unique documents larger than 200 words (DebateSum plus some additional debate docs that weren't well-formed enough for inclusion into DebateSum)
• 107555 unique words (showing up more than 10 times in the corpus)
• 101 million total words

Stats about debate2vec vectors:

• 300 dimensions, minimum number of appearances of a word was 10, trained for 100 epochs with lr set to 0.10 using FastText
• lowercased (will release cased)
• No subword information

The corpus includes the following topics

• 2013-2014 Cuba/Mexico/Venezuela Economic Engagement
• 2014-2015 Oceans
• 2015-2016 Domestic Surveillance
• 2016-2017 China
• 2017-2018 Education
• 2018-2019 Immigration
• 2019-2020 Reducing Arms Sales

Other topics that this word vector model will handle extremely well

• Philosophy (Especially Left-Wing / Post-modernist)
• Law
• Government
• Politics

Initial release is of fasttext vectors without subword information. Future releases will include fine-tuned GPT-2 and other high end models as my GPU compute allows.

Screenshots