hydrate

Create a grafzahl S3 object from the output_dir

Duct tape the 'quanteda' ecosystem (Benoit et al., 2018) <doi:10.21105/joss.00774> to modern Transformer-based text classification models (Wolf et al., 2020) <doi:10.18653/v1/2020.emnlp-demos.6>, in order to facilitate supervised machine learning for textual data. This package mimics the behaviors of 'quanteda.textmodels' and provides a function to setup the 'Python' environment to use the pretrained models from 'Hugging Face' <https://huggingface.co/>. More information: <doi:10.5117/CCR2023.1.003.CHAN>.

Chung-hong Chan

grafzahl

Supervised Machine Learning for Textual Data Using Transformers
and 'Quanteda'

hydrate function

<dl><dt>output_dir</dt>
<dd>string, location of the output model. If missing, the model will be stored in a temporary directory. Important: Please note that if this directory exists, it will be overwritten.</dd>
<dt>model_type</dt>
<dd>a string indicating model_type of the input model. If <code>NULL</code>, it will be inferred from <code>model_name</code>. Supported model types are available in supported_model_types.</dd>
<dt>regression</dt>
<dd>logical, if <code>TRUE</code>, the task is regression, classification otherwise.</dd></dl>

Arguments

Create a grafzahl S3 object from the output_dir — hydrate

<dl>

<dt>output_dir</dt>
<dd>string, location of the output model. If missing, the model will be stored in a temporary directory. Important: Please note that if this directory exists, it will be overwritten.</dd>


<dt>model_type</dt>
<dd>a string indicating model_type of the input model. If <code>NULL</code>, it will be inferred from <code>model_name</code>. Supported model types are available in supported_model_types.</dd>


<dt>regression</dt>
<dd>logical, if <code>TRUE</code>, the task is regression, classification otherwise.</dd>

</dl>

hydrate: Create a grafzahl S3 object from the output_dir

Description

Usage

Value

Arguments