Train LSTM RNN

[josh-segal]

• [x] Preprocess the text entailment dataset and convert sentences into numerical representations (e.g., word embeddings).
• [x] Train an LSTM RNN model on the preprocessed dataset to predict the entailment labels.
• [ ] Tune hyperparameters such as the number of LSTM layers, hidden units, and dropout rate using cross-validation.

[josh-segal]

Initially training with hypothesis and premise sentences concatenated. Experimenting with separate embeddings currently

[josh-segal]

Splitting sequences to separate embeddings is a model architecture design choice, not hyperparameter tuning