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    <identifier>10.11922/sciencedb.o00114.00045</identifier>
    <datestamp>2021-12-09T13:33:55Z</datestamp>
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  <dc:date>2021-12-09</dc:date>
  <dc:title>Pre-trained Language Model Based Tibetan Text Classification Method</dc:title>
  <dc:identifier>doi:10.11922/sciencedb.o00114.00045</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>Tibetan text classification is a basic task in Tibetan natural language processing. Based on large-scale pre-trained language model and fine-tuning is the current mainstream text classification model. However, Tibetan lacks open source large-scale text and pre-trained language model. To solve the above problems, this paper crawls a large-scale Tibetan text dataset, and trains a Tibetan pre-trained language model (bert-base-tibetan) based on the corpus. On the basis of this model, the experimental results on a variety of text classification models based on neural network show that the pre-trained language model can significantly improve the performance of Tibetan text classification (F1 value is increased by 9.3% on average), which verifies the value of the Tibetan pre-trained language model in Tibetan text classification task and other related Tibetan natural language processing tasks.</dc:description>
  <dc:subject>Deep Learning; Tibetan Text Classification; Pre-trained Language Model</dc:subject>
  <dc:creator>Bo An</dc:creator>
  <dc:creator>Congjun Long</dc:creator>
  <dc:rights>PUBLIC</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/</dc:rights>
  <dc:type>dataset</dc:type>
  <dc:publisher>Science Data Bank</dc:publisher>
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