diff --git a/tests/integration_test/__init__.py b/tests/integration_test/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/tests/integration_test/test_classification.py b/tests/integration_test/test_classification.py
deleted file mode 100644
index d3a2bae7..00000000
--- a/tests/integration_test/test_classification.py
+++ /dev/null
@@ -1,147 +0,0 @@
-import pandas as pd
-import copy
-import os
-import ast
-from sklearn.pipeline import Pipeline
-from sklearn.preprocessing import LabelEncoder
-
-from melusine.models.neural_architectures import cnn_model
-from melusine.models.train import NeuralModel
-from melusine.utils.transformer_scheduler import TransformerScheduler
-
-from melusine.prepare_email.manage_transfer_reply import check_mail_begin_by_transfer
-from melusine.prepare_email.manage_transfer_reply import update_info_for_transfer_mail
-from melusine.prepare_email.manage_transfer_reply import add_boolean_transfer
-from melusine.prepare_email.manage_transfer_reply import add_boolean_answer
-
-from melusine.prepare_email.build_historic import build_historic
-from melusine.prepare_email.mail_segmenting import structure_email
-from melusine.prepare_email.body_header_extraction import extract_last_body
-from melusine.prepare_email.cleaning import clean_body
-from melusine.prepare_email.cleaning import clean_header
-
-from melusine.nlp_tools.phraser import Phraser
-from melusine.nlp_tools.tokenizer import Tokenizer
-from melusine.nlp_tools.embedding import Embedding
-from melusine.summarizer.keywords_generator import KeywordsGenerator
-
-from melusine.prepare_email.metadata_engineering import MetaExtension
-from melusine.prepare_email.metadata_engineering import MetaDate
-from melusine.prepare_email.metadata_engineering import MetaAttachmentType
-from melusine.prepare_email.metadata_engineering import Dummifier
-
-from melusine.data.data_loader import load_email_data
-
-# Prevent GPU usage
-os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
-os.environ["CUDA_VISIBLE_DEVICES"] = ""
-
-
-def test_classification():
-
-    input_df = load_email_data()
-
-    # ============== Define Transformer Schedulers ==============
-    manageTransferReply = TransformerScheduler(
-        functions_scheduler=[
-            (check_mail_begin_by_transfer, None, ["is_begin_by_transfer"]),
-            (update_info_for_transfer_mail, None, None),
-            (add_boolean_answer, None, ["is_answer"]),
-            (add_boolean_transfer, None, ["is_transfer"]),
-        ]
-    )
-
-    segmenting = TransformerScheduler(
-        functions_scheduler=[
-            (build_historic, None, ["structured_historic"]),
-            (structure_email, None, ["structured_body"]),
-        ]
-    )
-    lastBodyHeaderCleaning = TransformerScheduler(
-        functions_scheduler=[
-            (extract_last_body, None, ["last_body"]),
-            (clean_body, None, ["clean_body"]),
-            (clean_header, None, ["clean_header"]),
-        ]
-    )
-
-    # ============== Tokenizer ==============
-    tokenizer = Tokenizer(input_column="clean_body")
-
-    # ============== Phraser ==============
-    phraser = Phraser(threshold=10, min_count=10)
-
-    # ============== Full Pipeline ==============
-    preprocessingPipeline = Pipeline(
-        [
-            ("ManageTransferReply", manageTransferReply),
-            ("Segmenting", segmenting),
-            ("LastBodyHeaderCleaning", lastBodyHeaderCleaning),
-            ("tokenizer", tokenizer),
-            ("phraser", phraser),
-        ]
-    )
-
-    # ============== Transform input DataFrame ==============
-    input_df = preprocessingPipeline.fit_transform(input_df)
-
-    # ============== MetaData Pipeline ==============
-    metadataPipeline = Pipeline(
-        [
-            ("MetaExtension", MetaExtension()),
-            ("MetaDate", MetaDate()),
-            ("MetaAttachmentType", MetaAttachmentType()),
-            ("Dummifier", Dummifier()),
-        ]
-    )
-
-    # ============== Transform MetaData ==============
-    input_df["attachment"] = input_df["attachment"].apply(ast.literal_eval)
-    df_meta = metadataPipeline.fit_transform(input_df)
-
-    # ============== Keywords Generator ==============
-    keywords_generator = KeywordsGenerator(n_max_keywords=4)
-    input_df = keywords_generator.fit_transform(input_df)
-
-    # ============== Embeddings ==============
-    pretrained_embedding = Embedding(tokens_column="tokens", workers=1, min_count=5)
-    pretrained_embedding.train(input_df)
-
-    # ============== CNN Classifier ==============
-    X = pd.concat([input_df["clean_body"], df_meta], axis=1)
-    y = input_df["label"]
-    le = LabelEncoder()
-    y = le.fit_transform(y)
-
-    nn_model = NeuralModel(
-        architecture_function=cnn_model,
-        pretrained_embedding=pretrained_embedding,
-        text_input_column="clean_body",
-        meta_input_list=[
-            "extension",
-            "dayofweek",
-            "hour",
-            "min",
-            "attachment_type",
-        ],
-        n_epochs=2,
-        seq_size=8,
-        batch_size=8,
-    )
-
-    nn_model.fit(X, y)
-
-    y_res = nn_model.predict(X)
-    le.inverse_transform(y_res)
-
-    # ============== Test dict compatibility ==============
-    dict_emails = input_df.to_dict(orient="records")[0]
-    dict_meta = metadataPipeline.transform(dict_emails)
-    keywords_generator.transform(dict_emails)
-
-    dict_input = copy.deepcopy(dict_meta)
-    dict_input["clean_body"] = dict_emails["clean_body"]
-
-    nn_model.predict(dict_input)
-
-    assert True
diff --git a/tests/integration_test/test_text_preprocessing.py b/tests/integration_test/test_text_preprocessing.py
deleted file mode 100644
index c67ceacc..00000000
--- a/tests/integration_test/test_text_preprocessing.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import os
-from tempfile import TemporaryDirectory
-
-import joblib
-from sklearn.pipeline import Pipeline
-
-from melusine.data.data_loader import load_email_data
-from melusine.nlp_tools.phraser import Phraser
-from melusine.nlp_tools.tokenizer import Tokenizer
-
-
-def test_text_preprocessing():
-    input_df = load_email_data()
-    input_col = "body"
-
-    pipeline = Pipeline(
-        steps=[
-            ("tokenizer", Tokenizer(input_column=input_col)),
-            ("phraser", Phraser()),
-        ]
-    )
-
-    pipeline.fit_transform(input_df)
-    _ = pipeline.transform(input_df)
-
-    with TemporaryDirectory() as tmpdir:
-        filepath = os.path.join(tmpdir, "pipe")
-        joblib.dump(pipeline, filepath)
-        pipeline_reload = joblib.load(filepath)
-
-    _ = pipeline_reload.transform(input_df)
-    assert True