Docstrings in open source Python

Dmitry Berdov gensim, Open Source, Student Incubator

Hi everyone, my name is Dmitry Berdov, I’m a graduate student at the Ural Federal University, now working in QA testing (automation) sphere. I had no experience with writing documentation before joining the RARE Incubator, where my task has been to refactor and improve the poor state of Gensim docs. Now, after several months of shooting myself hard in the ...

New download API for pretrained NLP models and datasets in Gensim

Chaitali Saini Datasets, gensim, Open Source, Student Incubator Leave a Comment

There’s no shortage of websites and repositories that aggregate various machine learning datasets and pre-trained models (Kaggle, UCI MLR, DeepDive, individual repos like gloVe, FastText, Quora, blogs, individual university pages…). The only problem is, they all use widely different formats, cover widely different use-cases and go out of service with worrying regularity. For this reason, we decided to include free …

Machine learning benchmarks: Hardware providers (part 1)

Shiva Manne Machine Learning, Open Source, Student Incubator Leave a Comment

The rise of machine learning as a discipline brings new demands for number crunching and computing power. With easily accessible and cheap hardware resources, one has to pick the right platform to run the experiments and model training on. Should you use Amazon’s AWS EC2 instances? Or go with IBM’s Softlayer, Google’s Compute Engine, Microsoft’s Azure? How about a real …

The Mummy Effect: Bridging the gap between academia and industry (PyData keynote)

Radim Řehůřek Machine Learning, Open Source, Student Incubator

Last month, I gave a keynote at PyData Warsaw about the existing (and growing) gap between academia and industry, specifically when it comes to machine learning / data science. This is a topic close to my heart, since we’ve operated in that no-man’s land where academia and industry collide for a living for 7 years now. Between running our Student …

Chinmaya’s GSoC 2017 Summary: Integration with sklearn & Keras and implementing fastText

Chinmaya Pancholi gensim, Google Summer of Code, Student Incubator

This blog summarizes the work that I did for Google Summer of Code 2017 with Gensim. My work during the summer was divided into two parts: integrating Gensim with scikit-learn & Keras and adding a Python implementation of fastText model to Gensim. Gensim integration with scikit-learn and Keras Gensim is a topic modelling and information extraction library which mainly serves unsupervised …

Chinmaya’s Google Summer of Code 2017 Live-Blog : a Chronicle of Integrating Gensim with scikit-learn and Keras

Chinmaya Pancholi gensim, Student Incubator

2nd September, 2017 The final blogpost in the GSoC 2017 series summarising all the work that I did this summer can be found here. 15st August, 2017 During the last two weeks, I had been working primarily on adding a Python implementation of Facebook Research’s Fasttext model to Gensim. I was also simultaneously working on completing the tasks left for adding scikit-learn API for …

Parul’s Google Summer of Code 2017 Live-Blog : a chronicle of adding training and topic visualizations in gensim

Parul Sethi gensim, Student Incubator

19th August 2017 For last phase of my project, i’ll be adding a visualization which is an attempt to overcome some of the limitations of already available topic model visualizations. Current visualizations focus more on topics or topic-term relations leaving out the scope to comprehensively explore the document entity. I’d work on an interface which would allow us to interactively …

Google Summer of Code 2017 – Performance improvement in Gensim and fastText

Prakhar Pratyush gensim, Student Incubator

July 20, 2017 This week, I’ve mostly worked on implementing native unsupervised fastText (PR #1482) in gensim. It’s quite challenging as I had to look into the fasttext C codes, and read the research paper to properly understand how this is working, and then had to figure out the similarity with word2vec code. After lots of discussion with mentors, we …