Natural language processing

Natural language processing (NLP) is a field of computer science that studies how computers and humans interact. In the 1950s, Alan Turing published an article that proposed a measure of intelligence, now called the Turing test. More modern techniques, such as deep learning, have produced results in the fields of language modeling, parsing, and natural-language tasks.

This issue is part of our Doc Test Sprint. If you're interested in helping out come join us on Discord and talk with other contributors!

Docstring examples are often the first point of contact when trying out a new library! So far we haven't done a very good job at ensuring that all docstring examples work correctly in 🤗 Transformers - but we're now very

Problem: It is pretty challenging to find resource material and valuable articles, videos and such, and we spend a lot of time searching and finding the appropriate resource for us.

Proposed solution: Faceted search can come a long way when looking for a quick way to find a solution designed for our needs. Ratings on the resource can help us select the best solution based on our search

In gensim/models/fasttext.py:

    model = FastText(
        vector_size=m.dim,
        vector_size=m.dim,
        window=m.ws,
        window=m.ws,
        epochs=m.epoch,
        epochs=m.epoch,
        negative=m.neg,
        negative=m.neg,
        # FIXME: these next 2 lines read in unsupported FB FT modes (loss=3 softmax or loss=4 onevsall,
        # or model=3 supervi

Is your feature request related to a problem? Please describe.
I typically used compressed datasets (e.g. gzipped) to save disk space. This works fine with AllenNLP during training because I can write my dataset reader to load the compressed data. However, the predict command opens the file and reads lines for the Predictor. This fails when it tries to load data from my compressed files.

Rather than simply caching nltk_data until the cache expires and it's forced to re-download the entire nltk_data, we should perform a check on the index.xml which refreshes the cache if it differs from some previous cache.

I would advise doing this in the same way that it's done for requirements.txt:
https://github.com/nltk/nltk/blob/59aa3fb88c04d6151f2409b31dcfe0f332b0c9ca/.github/wor

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Natural language processing

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huggingface / transformers

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apachecn / ailearning

google-research / bert

hankcs / HanLP

explosion / spaCy

oxford-cs-deepnlp-2017 / lectures

jina-ai / jina

RasaHQ / rasa

virgili0 / Virgilio

RaRe-Technologies / gensim

keon / awesome-nlp

bharathgs / Awesome-pytorch-list

huggingface / datasets

NLP-LOVE / ML-NLP

flairNLP / flair

allenai / allennlp

nltk / nltk

graykode / nlp-tutorial

spencermountain / compromise

chiphuyen / stanford-tensorflow-tutorials

hanxiao / bert-as-service

botpress / botpress

stanfordnlp / CoreNLP

ashishpatel26 / 500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code

sloria / TextBlob

PaddlePaddle / PaddleHub

brightmart / text_classification

brightmart / nlp_chinese_corpus

crownpku / Awesome-Chinese-NLP

ymcui / Chinese-BERT-wwm