adviceModel: Pre-trained Concreteness Detection Model for Advice

In doc2concrete: Measuring Concreteness in Natural Language

Pre-trained Concreteness Detection Model for Advice

Description

This model was pre-trained on 3289 examples of feedback on different tasks (e.g. writing a cover letter, boggle, workplace annual reviews). All of those documents were annotated by research assistants for concreteness, and this model simulates those annotations on new documents.

Model pre-trained on advice data.

Usage

adviceModel

adviceModel(texts, num.mc.cores = 1)

Arguments

texts	character A vector of texts, each of which will be tallied for concreteness.
num.mc.cores	numeric number of cores for parallel processing - see parallel::detectCores(). Default is 1.

Format

A pre-trained glmnet model

Value

numeric Vector of concreteness ratings.

Source

Yeomans (2020). A Concrete Application of Open Science for Natural Language Processing.

doc2concrete documentation built on June 29, 2022, 1:05 a.m.