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#' In this example we use tfhub to obtain pre-trained word-embeddings from the
#' BERT model.
#'
#' This example is based on [this colab notebook](https://colab.research.google.com/github/google-research/bert/blob/master/predicting_movie_reviews_with_bert_on_tf_hub.ipynb#scrollTo=hhbGEfwgdEtw)
#'
#' The dataset comes from the Toxic Comment Classification Challenge in Kaggle and
#' can be downlaoded here: https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data
#'
library(keras)
library(tfhub)
library(readr)
# Read data ---------------------------------------------------------------
comments <- read_csv("train.csv.zip")
ind_train <- sample.int(nrow(comments), 0.8*nrow(comments))
train <- comments[ind_train,]
test <- comments[-ind_train,]
# Build the model ---------------------------------------------------------
# We the token based text embedding trained on English Google News 130GB corpus.
# https://tfhub.dev/google/tf2-preview/gnews-swivel-20dim/1
# The model is available at the above URL.
embeddings <- layer_hub(
handle = "https://tfhub.dev/google/tf2-preview/gnews-swivel-20dim/1",
trainable = FALSE
)
input <- layer_input(shape = shape(), dtype = "string")
output <- input %>%
embeddings() %>%
layer_dense(units = 32, activation = "relu") %>%
layer_dense(units = 6, activation = "sigmoid")
model <- keras_model(input, output)
model %>%
compile(
loss = "binary_crossentropy",
optimizer = "adam",
metrics = "accuracy"
)
# Fit the model -----------------------------------------------------------
model %>%
fit(
x = train$comment_text,
y = as.matrix(train[,-c(1:2)]),
validation_split = 0.2
)
model %>%
evaluate(x = test$comment_text, y = as.matrix(test[,-c(1,2)]))
# Calculating the AUC for each class
purrr::map2_dbl(
as.data.frame(actual),
as.data.frame(preds),
Metrics::auc
)
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