KnutJaegersberg
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topic-classification-IPTC-subject-labels

Text Classification

sentence-transformers

feature-extraction

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KnutJaegersberg commited on Feb 12, 2023

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 # IPTC topic classifier (multilingual)
-A SetFit model fit on downlsampled multilingual IPTC Subject labels (concatenated for the lowest hierarchy level into artificial sentences of keywords) to predict the mid level news categories.
 The purpose of this classifier is to support exploring corpora as weak labeler, since the representations of these descriptions are only approximations of real documents from those topics.
 Accuracy on highest level labels in eval:
 0.9779412

 # IPTC topic classifier (multilingual)
+A SetFit model fit on 166 downlsampled multilingual IPTC Subject labels (concatenated for the lowest hierarchy level into artificial sentences of keywords) to predict the mid level news categories.
 The purpose of this classifier is to support exploring corpora as weak labeler, since the representations of these descriptions are only approximations of real documents from those topics.
 Accuracy on highest level labels in eval:
 0.9779412