These alternatives generally cannot beat n-grams on the classical language modelling problem of predicting the next word in a sequence, but they are used to model other processes in which their extra structure is more explicitly required. Given an unlimited amount of data and computing resources, ...
To alleviate the problem mentioned, in this paper, we propose a bidirectional language models based multi-task learning method for text classification. More specifically, we add language modelling as an auxiliary task to the private part, aiming to enhance its ability to extract task-specific ...
we save the activations that are expensive to compute, such as the outputs of linear layers. This is achieved by manually implementing the backward function for the transformer layers, instead of relying on the PyTorch autograd.
A critical step in this process is converting domain-specific data into a sequence of tokens for language modelling. In chemistry, molecules are often represented by molecular linear notations, and chemical reactions are depicted as sequence pairs of reactants and products. However, this approach ...
Riboformer: a deep learning framework for predicting context-dependent translation dynamics Article Open access 05 March 2024 Multi-purpose RNA language modelling with motif-aware pretraining and type-guided fine-tuning Article Open access 13 May 2024 Deep learning prediction of ribosome profiling...
word-embeddingstopic-modelingsemantic-searchberttext-searchtopic-searchdocument-embeddingtopic-modellingtext-semantic-similaritysentence-encoderpre-trained-language-modelstopic-vectorsentence-transformerstop2vec UpdatedNov 14, 2024 Python brightmart/roberta_zh ...
These alternatives generally cannot beat n-grams on the classical language modelling problem of predicting the next word in a sequence, but they are used to model other processes in which their extra structure is more explicitly required. Given an unlimited amount of data and computing resources, ...
However, previous approaches to this problem require large amounts of expert demonstrations and task-specific reward functions, both of which are impractical for new tasks. In this work, we show that a pre-trained large language model (LLM) agent can execute computer tasks guided by natural ...
Unifying Molecular and Textual Representations via Multi-task Language Modelling ICML 2023 Dimitrios Christofidellis, Giorgio Giannone, Jannis Born, Ole Winther, Teodoro Laino, Matteo Manica [PDF] [Code], 2023.05, What can Large Language Models do in chemistry? A comprehensive benchmark on eight ...
Agent-based modeling and simulation have evolved as a powerful tool for modeling complex systems, offering insights into emergent behaviors and interactions among diverse agents. Recently, integrating large language models into agent-based modeling and s