WEBNLG challenge 2017 on Google Colab error

This page summarizes the projects mentioned and recommended in the original post on reddit.com/r/LanguageTechnology

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  • GitHub repo WebNLG-Challenge-2017-test

    https://github.com/dariodellamura/WebNLG-Challenge-2017-test/blob/main/nlg_pipeline.ipynb.

  • GitHub repo OpenNMT

    Open Source Neural Machine Translation in Torch (deprecated)

    It looks like this uses the version of OpenNMT implemented in torch, which has been deprecated. You will be much better off using the pytorch implementation of OpenNMT or the transformers library. In fact, I would recommend taking a look at the GEM benchmark, since it also uses the WebNLG dataset. Here is a tutorial to get started, you can change the dataset here to WebNLG instead of CommonGen.

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  • GitHub repo OpenNMT-py

    Open Source Neural Machine Translation in PyTorch

    It looks like this uses the version of OpenNMT implemented in torch, which has been deprecated. You will be much better off using the pytorch implementation of OpenNMT or the transformers library. In fact, I would recommend taking a look at the GEM benchmark, since it also uses the WebNLG dataset. Here is a tutorial to get started, you can change the dataset here to WebNLG instead of CommonGen.

  • GitHub repo transformers

    🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

    It looks like this uses the version of OpenNMT implemented in torch, which has been deprecated. You will be much better off using the pytorch implementation of OpenNMT or the transformers library. In fact, I would recommend taking a look at the GEM benchmark, since it also uses the WebNLG dataset. Here is a tutorial to get started, you can change the dataset here to WebNLG instead of CommonGen.

  • It looks like this uses the version of OpenNMT implemented in torch, which has been deprecated. You will be much better off using the pytorch implementation of OpenNMT or the transformers library. In fact, I would recommend taking a look at the GEM benchmark, since it also uses the WebNLG dataset. Here is a tutorial to get started, you can change the dataset here to WebNLG instead of CommonGen.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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