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Texar is a toolkit aiming to support a broad set of machine learning, especially natural language processing and text generation tasks. Texar provides a library of easy-to-use ML modules and functionalities for composing whatever models and algorithms. The tool is designed for both researchers and practitioners for fast prototyping and experimentation. Texar was originally developed and is actively contributed by Petuum and CMU in collaboration with other institutes. A mirror of this repository is maintained by Petuum Open Source. Two Versions, (Mostly) Same Interfaces. Texar-TensorFlow (this repo) and Texar-PyTorch have mostly the same interfaces. Both further combine the best design of TF and PyTorch. Rich Pre-trained Models, Rich Usage with Uniform Interfaces. BERT, GPT2, XLNet, etc, for encoding, classification, generation, and composing complex models with other Texar components!
Features
- Interfaces and variable sharing in PyTorch convention
- Excellent factorization and rich functionalities in TF convention
- Free to plug in whatever external modules, since Texar is fully compatible with the native TF/PyTorch APIs
- Encoder(s) to decoder(s), sequential- and self-attentions, memory, hierarchical models, classifiers
- Maximum likelihood learning, reinforcement learning, adversarial learning, probabilistic modeling
- Distributed model training with multiple GPUs
Project Samples
