Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN) for character-level language models in Torch. Based on code of https://github.com/karpathy/char-rnn. Support Chinese and other things.
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Updated
Oct 19, 2016 - Lua
Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN) for character-level language models in Torch. Based on code of https://github.com/karpathy/char-rnn. Support Chinese and other things.
Multi lingual character based named entity recognizer
Code accompanying Incorporating Chinese Characters of Words for Lexical Sememe Prediction (ACL2018) https://arxiv.org/abs/1806.06349
Implementation of the Character-level Intra Attention Network (CIAN) for Natural Language Inference (NLI) upon SNLI and MultiNLI corpus
LittleLM: A tiny character-level n-gram language model for local corpus building and collaborative experimentation.
a 5M parameter solution to a problem you could solve by counting on your fingers
In this project, I worked with a small corpus consisting of simple sentences. I tokenized the words using n-grams from the NLTK library and performed word-level and character-level one-hot encoding. Additionally, I utilized the Keras Tokenizer to tokenize the sentences and implemented word embedding using the Embedding layer. For sentiment analysis
An implementation of character level text generation with LSTM.
On Anonymous Commenting: A Greedy Approach to Balance Utilization and Anonymity for Instagram Users - Accepted at SIGIR 2019
Explore AI-powered text generation with a character-level transformer model that mimics Shakespeare’s style.
Character-level fork of Fairseq for sequence-to-sequence learning
A simple implementation of a Markov chain text generator that can work at both character and word levels.
Optimized LSTM-based character-level text generator trained on Shakespeare, achieving 3.5x faster training with mixed precision.
Character-level language model focused on training, architecture, and optimization.
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