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R Language Analysis Suite
An R-package for analyzing natural language with transformers-based
large language models. The text package is part of the R Language
Analysis Suite, including:
talk- a package that transforms voice recordings into text, audio features, or embeddings.text- a package that provides tools for many language tasks such as converting digital text into word embeddings.talkandtextoffer access to Large Language Models from Hugging Face.topicsa package with tools for visualizing language patterns into topics.the L-BAM Librarya library that provides pre-trained models for different psychological assessments such as mental health issues, personality and related behaviours.
The R Language Analysis Suite is created through a collaboration between psychology and computer science to address research needs and ensure state-of-the-art techniques. The suite is continuously tested on Ubuntu, Mac OS and Windows using the latest stable R version.
The text-package has two main objectives: * First, to serve
R-users as a point solution for transforming text to state-of-the-art
word embeddings that are ready to be used for downstream tasks. The
package provides a user-friendly link to language models based on
transformers from Hugging Face. *
Second, to serve as an end-to-end solution that provides
state-of-the-art AI techniques tailored for social and behavioral
scientists. Please reference our tutorial article when using the
text package: The text-package: An R-package for Analyzing and
Visualizing Human Language Using Natural Language Processing and Deep
Learning.
Point solution for transforming text to embeddings
Recent significant advances in NLP research have resulted in improved representations of human language (i.e., language models). These language models have produced big performance gains in tasks related to understanding human language. Text are making these SOTA models easily accessible through an interface to HuggingFace in Python.
Text provides many of the contemporary state-of-the-art language models that are based on deep learning to model word order and context. Multilingual language models can also represent several languages; multilingual BERT comprises 104 different languages.
Table 1. Some of the available language models
| Models | References | Layers | Dimensions | Language |
|---|---|---|---|---|
| ‘bert-base-uncased’ | Devlin et al. 2019 | 12 | 768 | English |
| ‘roberta-base’ | Liu et al. 2019 | 12 | 768 | English |
| ‘distilbert-base-cased’ | Sahn et al., 2019 | 6 | 768 | English |
| ‘bert-base-multilingual-cased’ | Devlin et al. 2019 | 12 | 768 | 104 top languages at Wikipedia |
| ‘xlm-roberta-large’ | Liu et al | 24 | 1024 | 100 language |
See HuggingFace for a more comprehensive list of models.
An end-to-end package
Text also provides functions to analyse the word embeddings with
well-tested machine learning algorithms and statistics. The focus is to
analyze and visualize text, and their relation to other text or
numerical variables. For example, the textTrain() function is used to
examine how well the word embeddings from a text can predict a numeric
or categorical variable. Another example is functions plotting
statistically significant words in the word embedding space.