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seq2seq-model

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The main aim of the project is to construct a hybrid model of machine translation and sentiment analysis, and express it through five kinds of emotions. This project addresses the challenge of bilingual comments, where people tweet in two languages, in this case Chinese and English.

  • Updated Jun 18, 2024
  • Jupyter Notebook

Successfully established a text summarization model using Seq2Seq modeling with Luong Attention, which can give a short and concise summary of the global news headlines.

  • Updated May 6, 2024
  • Jupyter Notebook

Thesis scope: Train and Develop a Table-to-Text Transformer-based model for contextual summarization of tabular data. To achieve this T5-small , T5-base, Bart-base and Llama2 7B chat were finetuned on ToTTo and QTSumm. Regarding ToTTo, the models outperformed the benchmark.

  • Updated Apr 24, 2024
  • Jupyter Notebook

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