Search Results for "select"

Retrieval-Based Conversational Model in Tensorflow is a project implementing a retrieval-based conversational model using a dual LSTM encoder architecture in TensorFlow, illustrating how neural networks can be trained to select appropriate responses from a fixed set of candidate replies rather than generate them from scratch. The core idea is to embed both the conversation context and potential replies into vector representations, then score how well each candidate fits the current dialogue, choosing the best match accordingly. Designed to work with datasets like the Ubuntu Dialogue Corpus, this codebase includes data preparation, model training, and evaluation components for building and assessing dialog models that can handle multi-turn conversations.

...It can integrate exogenous variables, static categorical features, and perform channel-mixing for richer multivariate forecasting. The get_model() utility makes it easy to auto-select the best TTM model for specific context and prediction lengths. These models significantly outperform benchmarks like Chronos, GPT4TS, and Moirai while demanding a fraction of the compute.