carpedm20/MemN2N-tensorflow
"End-To-End Memory Networks" in Tensorflow
This project helps evaluate and train models that can understand and generate sequences of text. You provide raw text data, and it outputs a trained language model that can predict the next word in a sequence, along with performance metrics like perplexity. This is useful for researchers and practitioners working on natural language processing tasks, particularly those focused on language modeling.
827 stars. No commits in the last 6 months.
Use this if you are a researcher or NLP engineer looking to experiment with or implement End-To-End Memory Networks for language modeling on your own text datasets.
Not ideal if you need a ready-to-use, pre-trained language model or a general-purpose NLP toolkit for tasks beyond basic language modeling.
Stars
827
Forks
248
Language
Python
License
MIT
Category
Last pushed
Mar 14, 2017
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/carpedm20/MemN2N-tensorflow"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Compare
Related tools
domluna/memn2n
End-To-End Memory Network using Tensorflow
HKUST-KnowComp/R-Net
Tensorflow Implementation of R-Net
localminimum/QANet
A Tensorflow implementation of QANet for machine reading comprehension
allenai/bi-att-flow
Bi-directional Attention Flow (BiDAF) network is a multi-stage hierarchical process that...
YerevaNN/Dynamic-memory-networks-in-Theano
Implementation of Dynamic memory networks by Kumar et al. http://arxiv.org/abs/1506.07285