setup({
sc <- testthat_spark_connection()
text_tbl <- testthat_tbl("test_text")
# These lines should set a pipeline that will ultimately create the columns needed for testing the annotator
assembler <- nlp_document_assembler(sc, input_col = "text", output_col = "document")
sentdetect <- nlp_sentence_detector(sc, input_cols = c("document"), output_col = "sentence")
tokenizer <- nlp_tokenizer(sc, input_cols = c("sentence"), output_col = "token")
pipeline <- ml_pipeline(assembler, sentdetect, tokenizer)
test_data <- ml_fit_and_transform(pipeline, text_tbl)
assign("sc", sc, envir = parent.frame())
assign("pipeline", pipeline, envir = parent.frame())
assign("test_data", test_data, envir = parent.frame())
})
teardown({
rm(sc, envir = .GlobalEnv)
rm(pipeline, envir = .GlobalEnv)
rm(test_data, envir = .GlobalEnv)
})
test_that("nlp_xlm_roberta_token_classification pretrained", {
model <- nlp_xlm_roberta_token_classification_pretrained(sc, input_cols = c("sentence", "token"), output_col = "bert")
transformed_data <- ml_transform(model, test_data)
expect_true("bert" %in% colnames(transformed_data))
})
test_that("nlp_xlm_roberta_token_classification load", {
model_files <- list.files("~/cache_pretrained/")
bert_model_file <- max(Filter(function(s) startsWith(s, "xlm_roberta_base_token"), model_files))
model <- ml_load(sc, paste0("~/cache_pretrained/", bert_model_file))
model <- nlp_set_output_col(model, "label")
transformed_data <- ml_transform(model, test_data)
expect_true("label" %in% colnames(transformed_data))
})
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