Showing 4 open source projects for "processing"

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  • 1
    amrlib

    amrlib

    A python library that makes AMR parsing, generation and visualization

    A python library that makes AMR parsing, generation and visualization simple. amrlib is a python module designed to make processing for Abstract Meaning Representation (AMR) simple by providing the following functions. Sentence to Graph (StoG) parsing to create AMR graphs from English sentences. Graph to Sentence (GtoS) generation for turning AMR graphs into English sentences. A QT-based GUI to facilitate the conversion of sentences to graphs and back to sentences.
    Downloads: 0 This Week
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  • 2

    Companionem Linguae

    Ultra Large Language Model

    Companionem Linguae is an ultra large language model in early stages of development. Companionem Linguae is being reworked. I have uploaded a part of the new training data (Latein.json). Although English is the standard for international communication, I decided to train the model with Latin and German first, because these languages have many grammatical features the English language doesn't have, but they could be useful for translations into French, Portuguese, Spanish, or other...
    Downloads: 0 This Week
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  • 3
    Texar-PyTorch

    Texar-PyTorch

    Integrating the Best of TF into PyTorch, for Machine Learning

    ...Texar-PyTorch integrates many of the best features of TensorFlow into PyTorch, delivering highly usable and customizable modules superior to PyTorch native ones. Texar-PyTorch (this repo) and Texar-TF have mostly the same interfaces. Both further combine the best design of TF and PyTorch. Data processing, model architectures, loss functions, training and inference algorithms, evaluation, etc.
    Downloads: 0 This Week
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  • 4
    gpt2-client

    gpt2-client

    Easy-to-use TensorFlow Wrapper for GPT-2 117M, 345M, 774M, etc.

    GPT-2 is a Natural Language Processing model developed by OpenAI for text generation. It is the successor to the GPT (Generative Pre-trained Transformer) model trained on 40GB of text from the internet. It features a Transformer model that was brought to light by the Attention Is All You Need paper in 2017. The model has 4 versions - 124M, 345M, 774M, and 1558M - that differ in terms of the amount of training data fed to it and the number of parameters they contain.
    Downloads: 1 This Week
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