BERT, or Bidirectional Encoder Representations from Transformers, is a new method of pre-training language representations which obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks.
To make it easier to build and deploy natural language processing (NLP) systems, we are open-sourcing PyText, a modeling framework that blurs the boundaries between experimentation and large-scale deployment. PyText is a library built on PyTorch, our unified, open source deep learning framework.
In a paper presented this week at the Conference on Empirical Methods in Natural Language Processing in Brussels, Belgium, Google researchers described offline, on-device AI systems — Self-Governing Neural Networks (SGNNs) — that achieve state-of-the-air results in specific dialog-related tasks.
“The main challenges with developing and deploying deep neural network models on-device are (1) the tiny memory footprint, (2) inference latency and (3) significantly low computational capacity compared to high-performance computing systems, such as CPUs, GPUs, and TPUs on the cloud,” the team wrote.
How is GloVe different from word2vec? – Quora
Both models learn geometrical encodings (vectors) of words from their co-occurrence information (how frequently they appear together in large text corpora). They differ in that word2vec is a « predictive » model, whereas GloVe is a « count-based » mod…