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fasttext-model

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Successfully developed a Named Entity Recognition (NER) model for German text using a Bidirectional LSTM with Attention on the Multilingual NER dataset, effectively identifying entities across multilingual corpora with contextual understanding.

  • Updated Jul 4, 2025
  • Jupyter Notebook

Successfully developed a Named Entity Recognition (NER) model using a Bidirectional GRU with Attention on the MIT Movies dataset to identify and classify movie-related entities like titles, actors, and genres.

  • Updated Jul 4, 2025
  • Jupyter Notebook

Successfully developed a Named Entity Recognition (NER) model on the CoNLL-2003 dataset using a Bidirectional LSTM with Attention mechanism to accurately identify entities such as persons, locations, organizations, and miscellaneous categories in English text.

  • Updated Jul 4, 2025
  • Jupyter Notebook

Successfully developed a Named Entity Recognition (NER) model on the BC5CDR dataset using Stacked Bidirectional GRUs with Attention mechanism, designed to accurately identify chemical and disease entities from biomedical texts.

  • Updated Jul 4, 2025
  • Jupyter Notebook

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