A very simple framework for state-of-the-art Natural Language Processing (NLP)
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📐 Compute distance between sequences. 30+ algorithms, pure python implementation, common interface, optional external libs usage.
- distance
- algorithm
- python
- textdistance
Signal forecasting with a Sequence-to-Sequence (seq2seq) Recurrent Neural Network (RNN) model in TensorFlow - Guillaume Chevalier
- seq2seq
- tensorflow
- tensorflow-tutorials
- python
RNNLIB is a recurrent neural network library for sequence learning problems. Forked from Alex Graves work http://sourceforge.net/projects/rnnl/
DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome
- kmer
- dnabert-model
- kmer-format
- gpu
Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.
- deep-learning
- protein-sequences
- protein-structure
- semi-supervised-learning
A list of Neural MT implementations
- nmt
- mt
- neural-machine-translation
- machine-translation
TransformerCPI: Improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments(BIOINFORMATICS 2020) https://doi.org/10.1093/bioinformatics/btaa524
- self-attention
- compound-protein-interaction
- transformercpi
- structure-free-virtual-sreening
Graph to sequence implemented in Pytorch combining Graph convolutional networks and opennmt-py