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Word2Vec-Finetuning-Service-Base-Models

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  1. .gitattributes +34 -0
  2. DE_AT/DE_AT.model +3 -0
  3. DE_AT/DE_AT.model.syn1neg.npy +3 -0
  4. DE_AT/DE_AT.model.wv.vectors.npy +3 -0
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  26. EN_CA/EN_CA.model.wv.vectors.npy +3 -0
  27. EN_GB/EN_GB.model +3 -0
  28. EN_GB/EN_GB.model.syn1neg.npy +3 -0
  29. EN_GB/EN_GB.model.wv.vectors.npy +3 -0
  30. EN_Wiki/.DS_Store +0 -0
  31. EN_Wiki/EN_Wiki.model +3 -0
  32. EN_Wiki/EN_Wiki.model.syn1neg.npy +3 -0
  33. EN_Wiki/EN_Wiki.model.wv.vectors.npy +3 -0
  34. EN_ZA/EN_ZA.model +3 -0
  35. EN_ZA/EN_ZA.model.syn1neg.npy +3 -0
  36. EN_ZA/EN_ZA.model.wv.vectors.npy +3 -0
  37. README.md +19 -0
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+ # Word2Vec-Finetuning-Service-Base-Models
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+
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+ This directory contains base models for Word2Vec Finetuning Service. The Github repo of the main service is ...
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+
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+ ## Model Description
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+
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+ |Model Name|Description|Source|
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+ |:-|:-|:-|
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+ |DE_Wiki|A selftrained German Word2Vec model based on German Wikipedia articles.||
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+ |EN_Wiki|An English Word2Vec model based on English Wikipedia articles.|https://zenodo.org/record/6542975|
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+ |DE_AT|A German Word2Vec model based on DE_Wiki, trained on the basis of Austrian public web content in 2019.|https://wortschatz.uni-leipzig.de/en/download/German|
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+ |DE_CH|A German Word2Vec model based on DE_Wiki, trained on the basis of Swiss public web content in 2019.|https://wortschatz.uni-leipzig.de/en/download/German|
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+ |DE_Positive|A German Word2Vec model based on DE_Wiki, trained on the basis of a list with positive words with regards to sentimental aspect of human beings.|https://wortschatz.uni-leipzig.de/de/download|
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+ |DE_Negative|A German Word2Vec model based on DE_Wiki, trained on the basis of a list with negative words with regards to sentimental aspect of human beings.|https://wortschatz.uni-leipzig.de/de/download|
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+ |DE_News|A German Word2Vec model based on DE_Wiki, trained on the basis of German newspaper content in 2022.|https://wortschatz.uni-leipzig.de/en/download/German#deu_news_2022|
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+ |EN_AU|An English Word2Vec model based on EN_Wiki, trained on the basis of Australian web content in 2020.|https://wortschatz.uni-leipzig.de/en/download/English|
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+ |EN_CA|An English Word2Vec model based on EN_Wiki, trained on the basis of Canadian web content in 2020.|https://wortschatz.uni-leipzig.de/en/download/English|
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+ |EN_GB|An English Word2Vec model based on EN_Wiki, trained on the basis of web content in 2002 from the Great Britain.|https://wortschatz.uni-leipzig.de/en/download/English|
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+ |EN_ZA|An English Word2Vec model based on EN_Wiki, trained on the basis of South African web content in 2020.|https://wortschatz.uni-leipzig.de/en/download/English|