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tango
Organize your experiments into discrete steps that can be cached and reused throughout the lifetime of your research project. (by allenai)
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catwalk
This project studies the performance and robustness of language models and task-adaptation methods.
It depends on what you use AllenNLP for. AllenNLP has a ton of functionality for vectorizing text. Most of the tokenizer/indexer/embedder stuff is about that. But these days we all use transformers for that, so there isn't much of a need to experiment with ways to vectorize.
If you like the trainer, or the configuration language, or some of the other components you should check out Tango (https://github.com/allenai/tango). One of Tango's origins is the question "What if AllenNLP supported workflow steps other than read -> train -> evaluate?". We noticed that a lot of work in NLP no longer fit that simple pattern, so we needed a new tool that can support more complex experiments.
If you like the metrics, try torchmetrics. Torchmetrics has almost exactly the same API as AllenNLP metrics.
If you like any of the nn components, please get in touch with the Tango team (on GitHub). We recently had some discussion around rescuing a few of those, since there seems to be some excitement.
I think spaCy (https://spacy.io/) is a great library for NLP
Maybe we need to re-work the docs if the DAG aspects stick out to you so much. The main functionality is the cache. If you have a complex experiment, you can still write the code as if all the steps were fast, and let them be slow only the first time you run it. The DAG stuff is also nice, but less important.
That said, you could execute sklearn. If that's what your experiment needs, it's the right thing to do. This is why it gives us the flexibility to also support Jax: https://github.com/allenai/tango/pull/313
The DL-specific stuff is in the components we supply. Like the trainer, dataset handling stuff, file formats, and increasingly, https://github.com/allenai/catwalk.