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PyTorch implementation of the Quasi-Recurrent Neural Network - up to 16 times faster than NVIDIA's cuDNN LSTM
This project is a PyTorch sentiment analysis tutorial and a deep learning implementation for analyzing text. It provides a natural language processing sequence classification pipeline designed to clean text data and train neural networks to categorize sequences of words. The implementation focuses on adapting pretrained language models for specific text classification tasks using custom datasets. It includes a process for fine-tuning large-scale language models and implementing recurrent networks and transformers for emotional tone detection. The project covers the broader surface of text se
Sequence to Sequence Models with PyTorch
The main features of maximumentropy/seq2seq-pytorch are: Model Implementations, Natural Language Processing, Recurrent Neural Networks (RNNs).
Open-source alternatives to maximumentropy/seq2seq-pytorch include: salesforce/pytorch-qrnn — PyTorch implementation of the Quasi-Recurrent Neural Network - up to 16 times faster than NVIDIA's cuDNN LSTM. dsksd/deepnlp-models-pytorch — Pytorch implementations of various Deep NLP models in cs-224n(Stanford Univ). barissayil/sentimentanalysis. bentrevett/pytorch-sentiment-analysis — This project is a PyTorch sentiment analysis tutorial and a deep learning implementation for analyzing text. It… deepsound-project/samplernn-pytorch. dasguptar/treelstm.pytorch — Tree LSTM implementation in PyTorch.
