Frequently Asked Questions (FAQ) about UBOS Asset Marketplace TensorFlow Text Classification

Q: What is the purpose of this TensorFlow text classification asset?

A: This asset provides a TensorFlow implementation of various text classification models, including CNN, RNN, and pre-trained NLP models, designed for sentiment analysis and other text-related tasks.

Q: What datasets are supported by this asset?

A: The asset is primarily designed for use with the IMDB movie review dataset, but it can be adapted to work with other text datasets as well.

Q: What are the key components of this asset?

A: The asset includes modules for data pre-processing, word embedding (Word2Vec), model definition (CNN, RNN, etc.), and training.

Q: Which models are implemented in this asset?

A: This asset implements textCNN, charCNN, Bi-LSTM, Bi-LSTM + Attention, RCNN, adversarialLSTM, Transformer, ELMo and BERT models.

Q: What is the minimum Python and TensorFlow version required to run this asset?

A: The asset requires Python 3.5.6 and TensorFlow-GPU 1.10.0.

Q: How can I train the Word2Vec embeddings?

A: You can train the Word2Vec embeddings using the provided script in the /word2vec/genWord2Vec.ipynb notebook.

Q: Can I use pre-trained word embeddings with this asset?

A: Yes, the asset supports the use of pre-trained word embeddings.

Q: How do I preprocess the data for use with the models?

A: The data pre-processing steps are detailed in the /dataHelper/processData.ipynb notebook.

Q: Can I use this asset for sentiment analysis tasks?

A: Yes, this asset is well-suited for sentiment analysis tasks, especially with the included IMDB dataset.

Q: How do I integrate this asset with the UBOS platform?

A: After downloading the asset from the UBOS Asset Marketplace, follow the included documentation to set up the environment and begin training and deploying your models. Ensure you meet the version requirements for Python and TensorFlow.