QANet
Machine reading model
An implementation of Google's QANet for machine reading comprehension using TensorFlow.
A Tensorflow implementation of QANet for machine reading comprehension
983 stars
55 watching
305 forks
Language: Python
last commit: over 6 years ago
Linked from 1 awesome list
cnnmachine-comprehensionnlpsquadtensorflow
Related projects:
Repository | Description | Stars |
---|---|---|
uclnlp/jack | A framework for building machine reading comprehension models using natural language processing techniques | 257 |
qmlcode/qml | A toolkit for representing and learning properties of molecules and solids using quantum machine learning concepts | 199 |
jnhwkim/nips-mrn-vqa | This project presents a neural network model designed to answer visual questions by combining question and image features in a residual learning framework. | 39 |
mg2033/shufflenet | An implementation of a computationally efficient deep neural network architecture designed for mobile devices with limited computing power. | 383 |
leopiney/tensor-safe | A Haskell framework for defining and compiling valid deep learning models to external frameworks like TensorFlow JS or Keras. | 101 |
elbayadm/attn2d | A PyTorch implementation of 2D convolutional neural networks for sequence-to-sequence prediction in machine translation | 502 |
qmuntal/stateless | A Go library for creating finite state machines directly in code | 981 |
ai-hypercomputer/maxtext | A high-performance LLM written in Python/Jax for training and inference on Google Cloud TPUs and GPUs. | 1,557 |
intel/neural-compressor | Tools and techniques for optimizing large language models on various frameworks and hardware platforms. | 2,257 |
harvardnlp/seq2seq-attn | An implementation of a sequence-to-sequence model with attention mechanism using LSTMs and character embeddings for neural machine translation | 1,263 |
ekmett/bound | Combinators for manipulating locally-nameless generalized de Bruijn terms | 121 |
hkust-knowcomp/r-net | An implementation of R-NET, a machine reading comprehension model using scaled multiplicative attention and variational dropout. | 578 |
giuse/machine_learning_workbench | A comprehensive framework for practical machine learning in Ruby. | 20 |
priba/nmp_qc | An implementation of neural networks on graph structures for learning molecular properties | 340 |
kwotsin/tensorflow-enet | A deep neural network implementation for real-time semantic segmentation in computer vision | 257 |