TensorFlow NLP Classification Examples

  • IMDB files: Sentimental analysis with dataset mapping & Embedding,
  • IMDB files: TensorBoard & Sentimental analysis with Embedding & a data preprocessing layer,
  • IMDB TF Dataset: Sentimental analysis with pre-trained TF Hub Embedding,
  • IMDB TF Dataset: Sentimental analysis with Bi-directional LSTM,
  • Illiad files classification using data processing with tf.text & TextLineDataset,

IMDB files: Sentimental analysis with dataset mapping & Embedding

  • transforming data using dataset pipelining mapping,
  • a Sequential model composed of embedding and dense layers.

IMDB files: TensorBoard & Sentimental analysis with Embedding & a data preprocessing layer

  • data preprocessing is done as a layer in a model instead of using data pipeline mapping, and
  • logging information into TensorBoard.

IMDB TF Dataset: Sentimental analysis with pre-trained TF Hub Embedding

  • data comes from the TensorFlow datasets,
  • the model uses a pre-trained embedding layer from the TF hub, and
  • we add dense layers to a Sequential model as a classification head.

IMDB TF Dataset: Sentimental analysis with Bi-directional LSTM

  • data is loaded from TF datasets,
  • use a TextVectorization with builtin standardization,
  • include the TextVectorization (encoder) inside a model, and
  • use bidirectional LSTM layers.
Left diagram source

Illiad files classification using data processing with tf.text, TextLineDataset (optional)

Credits and References

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