LLaMA in R with Keras and TensorFlow
Implementation and walk-through of LLaMA, a Large Language Model, in R, with TensorFlow and Keras.
Natural Language Processing
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What are Large Language Models? What are they not?
This is a high-level, introductory article about Large Language Models (LLMs), the core technology that enables the much-en-vogue chatbots as well as other Natural Language Processing (NLP) applications. It is directed at a general audience, possibly with some technical and/or scientific background, but no knowledge is assumed of either deep learning or NLP. Having looked
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GPT-2 from scratch with torch
Implementing a language model from scratch is, arguably, the best way to develop an accurate idea of how its engine works. Here, we use torch to code GPT-2, the immediate successor to the original GPT. In the end, you’ll dispose of an R-native model that can make direct use of Hugging Face’s pre-trained GPT-2 model
Introducing mall for R…and Python
We are proud to introduce the {mall} package. With {mall}, you can use a local LLM to run NLP operations across a data frame. (sentiment, summarization, translation, etc). {mall} has been simultaneously released to CRAN and PyPi (as an extension to Polars).
Introducing the text package
The text package attempts to provide user-friendly access and pipelines to HuggingFace’s transformer language models in R.
State-of-the-art NLP models from R
Nowadays, Microsoft, Google, Facebook, and OpenAI are sharing lots of state-of-the-art models in the field of Natural Language Processing. However, fewer materials exist how to use these models from R. In this post, we will show how R users can access and benefit from these models as well.
BERT from R
A deep learning model – BERT from Google AI Research – has yielded state-of-the-art results in a wide variety of Natural Language Processing (NLP) tasks. In this tutorial, we will show how to load and train the BERT model from R, using Keras.
Innocent unicorns considered harmful? How to experiment with GPT-2 from R
Is society ready to deal with challenges brought about by artificially-generated information – fake images, fake videos, fake text? While this post won’t answer that question, it should help form an opinion on the threat exerted by fake text as of this writing, autumn 2019. We introduce gpt2, an R package that wraps OpenAI’s public
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Attention-based Neural Machine Translation with Keras
As sequence to sequence prediction tasks get more involved, attention mechanisms have proven helpful. A prominent example is neural machine translation. Following a recent Google Colaboratory notebook, we show how to implement attention in R.
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Attention-based Image Captioning with Keras
Image captioning is a challenging task at intersection of vision and language. Here, we demonstrate using Keras and eager execution to incorporate an attention mechanism that allows the network to concentrate on image features relevant to the current state of text generation.