allennlp

NLTK might not be the best example of this--it was originally written for pedagogical purposes[1]. One that was sorely needed, too, as in 2006 it was very difficult for a student in an intro to NLP class to easily track down and use existing implementations of various algorithms. NLTK continues to be useful for similar reasons today, as it provides the only relatively usable interface to some valuable but poorly engineered (by modern standards) academic resources such as FrameNet. Anyway, original commenter seems to presuppose that there's no value in collating and polishing existing resources. Isn't that all, say, a Linux distro is? Is it true that "[Canonical] just wrap all the open source [code] into a [distro] and just want to sell the hype"? If it seems like curating and polishing has value on the market, maybe we ought to at least e

For those interested in related/alternative approaches, one or more of the following established open-source libraries might appeal to you: - Snorkel (training data curation, weak supervision, heuristic labeling functions, uncertainty sampling, relation extraction): https://github.com/snorkel-team/snorkel - AllenNLP (many pretrained NLP research models for tasks beyond text classification, model training and serving, visualization/interpretability utilities): https://github.com/allenai/allennlp - Spacy (tokenization, NER/POS + tagging visualizer, pretrained word vectors, integration with DL models): https://github.com/allenai/allennlp -huggingface Transformers (latest and greatest pretrained models, e.g. BERT): https://github.com/huggingface/transformers - ...or a barebones “from scratch” solution in less than an hour with a Colab noteboo

I really like AllenNLP. https://github.com/allenai/allennlp

Here's what they're doing: https://github.com/allenai/allennlp/blob/master/tutorials/ta...

I'm not sure how that allennlp site is doing it, but source is here: https://github.com/allenai/allennlp/blob/b0ea7ab6be2787495fa...

Allennlp is one such project for NLP. Uses dependency injection to separate out the components driving for maximum re-usability. https://github.com/allenai/allennlp

AllenNLP uses Jsonnet for config files, for anyone ondering what one potential use case is. For example, here's a config for NER with ELMo: https://github.com/allenai/allennlp/blob/master/training_con... I guess the only really jsonnet-specific thing here is std.extVar() call, but generally speaking it's just a more flexible JSON file that doesn't cost much more to use (brainpower or physical resources). I use it to mix and match configs while debugging sometimes as you can do things like if statements and adding dicts together. It's also nice for modularization, you can do things like having a function that returns a dict, so if your config needs two slightly different datareaders you can just pull out all the common parts and call the function twice instead of having two copies of the entire datareader config (with only minor difference

Text prediction is usually called "language modeling" in NLP. Because it's useful as a weak supervision signal to improve performance on other tasks, most of the mentioned libraries support it. However, they might not always provide complete examples, instead assuming that you know how to express the model and train it using the primitives provided by the library. Flair: https://github.com/zalandoresearch/flair/blob/master/flair/m... Allen NLP: https://github.com/allenai/allennlp/blob/master/allennlp/dat... PyText: https://github.com/facebookresearch/pytext/blob/master/pytex... spaCy seems to focus on language analysis and I couldn't find an API that'd be directly usable for text generation.

The closest alternatives in this space would be allennlp [1], the recently released pytext [2] and spacy [3]. pytext's authors wrote some comparison on the accompanying paper [4] and this GitHub issue [5]. [1] https://github.com/allenai/allennlp [2] https://github.com/facebookresearch/pytext [3] https://spacy.io [4] https://arxiv.org/pdf/1812.08729.pdf [5] https://github.com/facebookresearch/pytext/issues/110

You can find ELMo here: https://github.com/allenai/allennlp/blob/master/tutorials/ho... And ULMFiT here: http://nlp.fast.ai/category/classification.html