diff --git a/README.md b/README.md
index 50f456c..cad6773 100644
--- a/README.md
+++ b/README.md
@@ -2,4 +2,3 @@
 Experimental repository aimed at using transformers (such as CovidBERT) and Deep Learning techniques to retrieve and visualize semantic clusters underlying the CORD-19 database.
 
 ![Alt Text](/docs/cord19_semantic_clusters.gif)
-
diff --git a/asreviewcontrib/semantic_clustering/semantic_clustering.py b/asreviewcontrib/semantic_clustering/semantic_clustering.py
deleted file mode 100644
index 97d49af..0000000
--- a/asreviewcontrib/semantic_clustering/semantic_clustering.py
+++ /dev/null
@@ -1,83 +0,0 @@
-# Copyright 2021 The ASReview Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# import ASReview
-from tqdm import tqdm
-from asreview.data import ASReviewData
-
-# import numpy
-import numpy as np
-
-# import transformer autotokenizer and automodel
-from transformers import AutoTokenizer, AutoModel
-
-# disable transformer warning
-from transformers import logging
-logging.set_verbosity_error()
-
-# import tqdm
-
-
-def SemanticClustering(asreview_data_object):
-
-    # load data
-    print("Loading data...")
-    data = load_data(asreview_data_object)
-
-    # cut data for testing
-    data = data.iloc[:10, :]
-
-    # load scibert transformer
-    print("Loading scibert transformer...")
-    transformer = 'allenai/scibert_scivocab_uncased'
-
-    # load transformer and tokenizer
-    print("Loading tokenizer and model...")
-    tokenizer = AutoTokenizer.from_pretrained(transformer)
-    model = AutoModel.from_pretrained(transformer)
-
-    # tokenize abstracts and add to data
-    print("Tokenizing abstracts...")
-    data['tokenized'] = data['abstract'].apply(lambda x: tokenizer.encode_plus(
-        x,
-        padding='longest',
-        add_special_tokens=False,
-        return_tensors="pt"))
-
-    # generate embeddings
-    print("Generating embeddings...")
-    data['embeddings'] = data['tokenized'].apply(
-        lambda x: model(**x, output_hidden_states=False)[-1])
-
-    from dim_reduct import run_pca
-    n_components = .98
-    #pca = run_pca(data['embeddings'], n_components)
-
-    print(data['embeddings'][0].detach().numpy())
-
-
-def load_data(asreview_data_object):
-
-    # extract title and abstract, drop empty abstracts and reset index
-    data = asreview_data_object.df[['title', 'abstract']].copy()
-    data['abstract'] = data['abstract'].replace('', np.nan, inplace=False)
-    data.dropna(subset=['abstract'], inplace=True)
-    data = data.reset_index(drop=True)
-
-    return data
-
-
-if __name__ == "__main__":
-    filepath = "https://raw.githubusercontent.com/asreview/systematic-review-datasets/master/datasets/van_de_Schoot_2017/output/van_de_Schoot_2017.csv"
-    SemanticClustering(ASReviewData.from_file(filepath))
diff --git a/docs/semantic_clusters.gif b/docs/semantic_clusters.gif
deleted file mode 100644
index b28701b..0000000
Binary files a/docs/semantic_clusters.gif and /dev/null differ