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erickrf/multiffn-nli: Implementation of the multi feed-forward network architecture by Parikh et al. (2016) for Natural Language Inference.
This code is a Tensorflow implementation of the models described in A Decomposable Attention Model for Natural Language Inference and Enhancing and Combining Sequential and Tree LSTM for Natural Language Inference (for the latter, only the sequential model is implemented).

github  entailment  snli  deep-learning  nlp 
yesterday by dineru
harvardnlp/decomp-attn: Decomposable Attention Model for Sentence Pair Classification (from
Implementation of the paper A Decomposable Attention Model for Natural Language Inference. Parikh et al. EMNLP 2016.

github  entailment  snli  deep-learning  nlp 
yesterday by dineru
spaCy/examples/keras_parikh_entailment at master · explosion/spaCy
This directory contains an implementation of the entailment prediction model described by Parikh et al. (2016). The model is notable for its competitive performance with very few parameters.

keras, spacy
github  entailment  snli  deep-learning  nlp 
yesterday by dineru
Open source license usage on
March 9, 2015 @benbalter benbalter Watercooler

Open source simply isn't open source without a proper license.

Breakdown of license usage

We also wanted to look at the relative breakdown of the most popular open source licenses. You can see their popularity expressed below as a percentage of licensed projects on

Rank License % of projects
1 MIT 44.69%
2 Other 15.68%
3 GPLv2 12.96%
4 Apache 11.19%
5 GPLv3 8.88%
6 BSD 3-clause 4.53%
7 Unlicense 1.87%
8 BSD 2-clause 1.70%
9 LGPLv3 1.30%
10 AGPLv3 1.05%

Unsurprisingly, MIT, Apache, and GPL are the clear front runners, with some 15% of licensed projects opting for a non-standard license or standard license not among those listed on
github  license  opensource  statistics  Blog 
yesterday by ndf

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