From 18cc22695b8c596044f4d2e85f6373ba37012258 Mon Sep 17 00:00:00 2001 From: Bruce Date: Mon, 20 May 2024 22:45:09 +0800 Subject: [PATCH] [2024.5] --- CRAN-SUBMISSION | 6 +++--- README.md | 14 +++----------- 2 files changed, 6 insertions(+), 14 deletions(-) diff --git a/CRAN-SUBMISSION b/CRAN-SUBMISSION index e67005f..de94884 100644 --- a/CRAN-SUBMISSION +++ b/CRAN-SUBMISSION @@ -1,3 +1,3 @@ -Version: 2024.4 -Date: 2024-04-29 22:07:52 UTC -SHA: 29a49647c903b6fd553130639efb5cff7646b3ae +Version: 2024.5 +Date: 2024-05-19 05:24:12 UTC +SHA: e9adda42c47daffb54fbab63ec47cd2b185be9e8 diff --git a/README.md b/README.md index 02681fa..e44f5d2 100644 --- a/README.md +++ b/README.md @@ -65,21 +65,13 @@ pip install transformers torch See [Guidance for GPU Acceleration] for installation guidance if you have an NVIDIA GPU device on your PC and want to use GPU to accelerate the pipeline. -Alternative approach (NOT suggested): Besides the pip/conda installation in the *Conda Environment*, you might instead create and use a *Virtual Environment* (see R code below with the `reticulate` package), but then you need to specify the Python interpreter as **"\~/.virtualenvs/r-reticulate/Scripts/python.exe"** in RStudio. - -``` r -## DON'T RUN THIS UNLESS YOU PREFER VIRTUAL ENVIRONMENT -library(reticulate) -# install_python() -virtualenv_create() -virtualenv_install(packages=c("transformers", "torch")) -``` - ## Guidance for FMAT ### Step 1: Download BERT Models -Use `BERT_download()` to load [BERT models]. Model files are permanently saved to your local folder "%USERPROFILE%/.cache/huggingface". A full list of BERT-family models are available at [Hugging Face](https://huggingface.co/models?pipeline_tag=fill-mask&library=transformers). +Use `BERT_download()` to download [BERT models]. Model files are saved to your local folder "%USERPROFILE%/.cache/huggingface". A full list of BERT models are available at [Hugging Face](https://huggingface.co/models?pipeline_tag=fill-mask&library=transformers). + +Use `BERT_info()` and `BERT_vocab()` to find detailed information of BERT models. ### Step 2: Design FMAT Queries