diff --git a/mediapipe/calculators/tensor/BUILD b/mediapipe/calculators/tensor/BUILD
index e953342da..99b5b3e91 100644
--- a/mediapipe/calculators/tensor/BUILD
+++ b/mediapipe/calculators/tensor/BUILD
@@ -253,6 +253,26 @@ cc_library(
     alwayslink = 1,
 )
 
+cc_test(
+    name = "regex_preprocessor_calculator_test",
+    srcs = ["regex_preprocessor_calculator_test.cc"],
+    data = ["//mediapipe/tasks/testdata/text:text_classifier_models"],
+    linkopts = ["-ldl"],
+    deps = [
+        ":regex_preprocessor_calculator",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework/formats:tensor",
+        "//mediapipe/framework/port:gtest_main",
+        "//mediapipe/framework/port:parse_text_proto",
+        "//mediapipe/framework/tool:sink",
+        "//mediapipe/tasks/cc/core:utils",
+        "//mediapipe/tasks/cc/metadata:metadata_extractor",
+        "@com_google_absl//absl/status",
+        "@com_google_absl//absl/status:statusor",
+        "@com_google_absl//absl/strings",
+    ],
+)
+
 cc_library(
     name = "text_to_tensor_calculator",
     srcs = ["text_to_tensor_calculator.cc"],
diff --git a/mediapipe/tasks/cc/text/text_classifier/BUILD b/mediapipe/tasks/cc/text/text_classifier/BUILD
index a85538631..336b1bb45 100644
--- a/mediapipe/tasks/cc/text/text_classifier/BUILD
+++ b/mediapipe/tasks/cc/text/text_classifier/BUILD
@@ -63,6 +63,29 @@ cc_library(
     ],
 )
 
+cc_test(
+    name = "text_classifier_test",
+    srcs = ["text_classifier_test.cc"],
+    data = [
+        "//mediapipe/tasks/testdata/text:bert_text_classifier_models",
+        "//mediapipe/tasks/testdata/text:text_classifier_models",
+    ],
+    deps = [
+        ":text_classifier",
+        ":text_classifier_test_utils",
+        "//mediapipe/framework/deps:file_path",
+        "//mediapipe/framework/port:gtest_main",
+        "//mediapipe/tasks/cc:common",
+        "//mediapipe/tasks/cc/components/containers/proto:classifications_cc_proto",
+        "@com_google_absl//absl/flags:flag",
+        "@com_google_absl//absl/status",
+        "@com_google_absl//absl/status:statusor",
+        "@com_google_absl//absl/strings",
+        "@com_google_absl//absl/strings:cord",
+        "@org_tensorflow//tensorflow/lite/core/shims:cc_shims_test_util",
+    ],
+)
+
 cc_library(
     name = "text_classifier_test_utils",
     srcs = ["text_classifier_test_utils.cc"],
diff --git a/mediapipe/tasks/cc/text/text_classifier/text_classifier_test.cc b/mediapipe/tasks/cc/text/text_classifier/text_classifier_test.cc
index 5b33f6606..62837be8c 100644
--- a/mediapipe/tasks/cc/text/text_classifier/text_classifier_test.cc
+++ b/mediapipe/tasks/cc/text/text_classifier/text_classifier_test.cc
@@ -49,9 +49,6 @@ using ::mediapipe::tasks::kMediaPipeTasksPayload;
 using ::mediapipe::tasks::components::containers::proto::ClassificationResult;
 using ::testing::HasSubstr;
 using ::testing::Optional;
-using ::testing::proto::Approximately;
-using ::testing::proto::IgnoringRepeatedFieldOrdering;
-using ::testing::proto::Partially;
 
 constexpr float kEpsilon = 0.001;
 constexpr int kMaxSeqLen = 128;
@@ -110,127 +107,6 @@ TEST_F(TextClassifierTest, CreateSucceedsWithRegexModel) {
   MP_ASSERT_OK(TextClassifier::Create(std::move(options)));
 }
 
-TEST_F(TextClassifierTest, TextClassifierWithBert) {
-  auto options = std::make_unique<TextClassifierOptions>();
-  options->base_options.model_asset_path = GetFullPath(kTestBertModelPath);
-  MP_ASSERT_OK_AND_ASSIGN(std::unique_ptr<TextClassifier> classifier,
-                          TextClassifier::Create(std::move(options)));
-  MP_ASSERT_OK_AND_ASSIGN(
-      ClassificationResult negative_result,
-      classifier->Classify("unflinchingly bleak and desperate"));
-  ASSERT_THAT(negative_result,
-              Partially(IgnoringRepeatedFieldOrdering(Approximately(
-                  EqualsProto(R"pb(
-                    classifications {
-                      entries {
-                        categories { category_name: "negative" score: 0.956 }
-                        categories { category_name: "positive" score: 0.044 }
-                      }
-                    }
-                  )pb"),
-                  kEpsilon))));
-
-  MP_ASSERT_OK_AND_ASSIGN(
-      ClassificationResult positive_result,
-      classifier->Classify("it's a charming and often affecting journey"));
-  ASSERT_THAT(positive_result,
-              Partially(IgnoringRepeatedFieldOrdering(Approximately(
-                  EqualsProto(R"pb(
-                    classifications {
-                      entries {
-                        categories { category_name: "negative" score: 0.0 }
-                        categories { category_name: "positive" score: 1.0 }
-                      }
-                    }
-                  )pb"),
-                  kEpsilon))));
-  MP_ASSERT_OK(classifier->Close());
-}
-
-TEST_F(TextClassifierTest, TextClassifierWithIntInputs) {
-  auto options = std::make_unique<TextClassifierOptions>();
-  options->base_options.model_asset_path = GetFullPath(kTestRegexModelPath);
-  MP_ASSERT_OK_AND_ASSIGN(std::unique_ptr<TextClassifier> classifier,
-                          TextClassifier::Create(std::move(options)));
-  MP_ASSERT_OK_AND_ASSIGN(ClassificationResult negative_result,
-                          classifier->Classify("What a waste of my time."));
-  ASSERT_THAT(negative_result,
-              Partially(IgnoringRepeatedFieldOrdering(Approximately(
-                  EqualsProto(R"pb(
-                    classifications {
-                      entries {
-                        categories { category_name: "Negative" score: 0.813 }
-                        categories { category_name: "Positive" score: 0.187 }
-                      }
-                    }
-                  )pb"),
-                  kEpsilon))));
-
-  MP_ASSERT_OK_AND_ASSIGN(
-      ClassificationResult positive_result,
-      classifier->Classify("This is the best movie I’ve seen in recent years. "
-                           "Strongly recommend it!"));
-  ASSERT_THAT(positive_result,
-              Partially(IgnoringRepeatedFieldOrdering(Approximately(
-                  EqualsProto(R"pb(
-                    classifications {
-                      entries {
-                        categories { category_name: "Negative" score: 0.487 }
-                        categories { category_name: "Positive" score: 0.513 }
-                      }
-                    }
-                  )pb"),
-                  kEpsilon))));
-  MP_ASSERT_OK(classifier->Close());
-}
-
-TEST_F(TextClassifierTest, TextClassifierWithStringToBool) {
-  auto options = std::make_unique<TextClassifierOptions>();
-  options->base_options.model_asset_path = GetFullPath(kStringToBoolModelPath);
-  options->base_options.op_resolver = CreateCustomResolver();
-  MP_ASSERT_OK_AND_ASSIGN(std::unique_ptr<TextClassifier> classifier,
-                          TextClassifier::Create(std::move(options)));
-  MP_ASSERT_OK_AND_ASSIGN(ClassificationResult result,
-                          classifier->Classify("hello"));
-  ASSERT_THAT(result, Partially(IgnoringRepeatedFieldOrdering(EqualsProto(R"pb(
-                classifications {
-                  entries {
-                    categories { index: 1 score: 1 }
-                    categories { index: 0 score: 1 }
-                    categories { index: 2 score: 0 }
-                  }
-                }
-              )pb"))));
-}
-
-TEST_F(TextClassifierTest, BertLongPositive) {
-  std::stringstream ss_for_positive_review;
-  ss_for_positive_review
-      << "it's a charming and often affecting journey and this is a long";
-  for (int i = 0; i < kMaxSeqLen; ++i) {
-    ss_for_positive_review << " long";
-  }
-  ss_for_positive_review << " movie review";
-  auto options = std::make_unique<TextClassifierOptions>();
-  options->base_options.model_asset_path = GetFullPath(kTestBertModelPath);
-  MP_ASSERT_OK_AND_ASSIGN(std::unique_ptr<TextClassifier> classifier,
-                          TextClassifier::Create(std::move(options)));
-  MP_ASSERT_OK_AND_ASSIGN(ClassificationResult result,
-                          classifier->Classify(ss_for_positive_review.str()));
-  ASSERT_THAT(result,
-              Partially(IgnoringRepeatedFieldOrdering(Approximately(
-                  EqualsProto(R"pb(
-                    classifications {
-                      entries {
-                        categories { category_name: "negative" score: 0.014 }
-                        categories { category_name: "positive" score: 0.986 }
-                      }
-                    }
-                  )pb"),
-                  kEpsilon))));
-  MP_ASSERT_OK(classifier->Close());
-}
-
 }  // namespace
 }  // namespace text_classifier
 }  // namespace text
diff --git a/mediapipe/tasks/cc/text/tokenizers/BUILD b/mediapipe/tasks/cc/text/tokenizers/BUILD
index 048c7021d..e76d943c5 100644
--- a/mediapipe/tasks/cc/text/tokenizers/BUILD
+++ b/mediapipe/tasks/cc/text/tokenizers/BUILD
@@ -73,8 +73,6 @@ cc_library(
     ],
 )
 
-# TODO: This test fails in OSS
-
 cc_library(
     name = "tokenizer_utils",
     srcs = ["tokenizer_utils.cc"],
@@ -97,8 +95,6 @@ cc_library(
     ],
 )
 
-# TODO: This test fails in OSS
-
 cc_library(
     name = "regex_tokenizer",
     srcs = [
diff --git a/mediapipe/tasks/testdata/text/BUILD b/mediapipe/tasks/testdata/text/BUILD
index 6cce5ae41..14999a03e 100644
--- a/mediapipe/tasks/testdata/text/BUILD
+++ b/mediapipe/tasks/testdata/text/BUILD
@@ -76,9 +76,10 @@ filegroup(
 
 filegroup(
     name = "text_classifier_models",
-    srcs = glob([
-        "test_model_text_classifier*.tflite",
-    ]),
+    srcs = [
+        "test_model_text_classifier_bool_output.tflite",
+        "test_model_text_classifier_with_regex_tokenizer.tflite",
+    ],
 )
 
 filegroup(