diff --git a/.circleci/config.yml b/.circleci/config.yml
index aca57512..1e56e542 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -48,6 +48,11 @@ jobs:
     docker:
       - image: cimg/python:3.10
 
+  py311:
+    <<: *shared
+    docker:
+      - image: cimg/python:3.11
+
 
 workflows:
   version: 2
@@ -57,3 +62,4 @@ workflows:
       - py38
       - py39
       - py310
+      - py311
diff --git a/bin/WordEmbedAPI b/bin/WordEmbedAPI
deleted file mode 100644
index b9f3af8b..00000000
--- a/bin/WordEmbedAPI
+++ /dev/null
@@ -1,101 +0,0 @@
-#!/usr/bin/env python
-
-# argument parsing
-import argparse
-
-argparser = argparse.ArgumentParser(description='Load word-embedding models into memory.')
-argparser.add_argument('filepath', help='file path of the word-embedding model')
-argparser.add_argument('--port', type=int, default=5000, help='port number')
-argparser.add_argument('--embedtype', default='word2vec', help='type of word-embedding algorithm (default: "word2vec), allowing "word2vec", "fasttext", and "poincare"')
-argparser.add_argument('--debug', default=False, action='store_true', help='Debug mode (Default: False)')
-args = argparser.parse_args()
-
-
-from flask import Flask, request, jsonify
-import shorttext
-
-app = Flask(__name__)
-if args.embedtype == 'word2vec':
-    w2v_model = shorttext.utils.load_word2vec_model(args.filepath, binary=True)
-elif args.embedtype == 'fasttext':
-    w2v_model = shorttext.utils.load_fasttext_model(args.filepath)
-elif args.embedtype == 'poincare':
-    w2v_model = shorttext.utils.load_poincare_model(args.filepath, binary=True)
-else:
-    raise KeyError("Argument 'embedtype' {} unknown.".format(args.embedtype))
-
-
-@app.route('/closerthan',methods=['POST'])
-def closer_than():
-    data = request.get_json(force=True)
-    entity1 = data['entity1']
-    entity2 = data['entity2']
-    close_entities = w2v_model.closer_than(entity1, entity2)
-    return jsonify(close_entities)
-
-
-@app.route('/distance',methods=['POST'])
-def distance():
-    data = request.get_json(force=True)
-    entity1 = data['entity1']
-    entity2 = data['entity2']
-    distance = w2v_model.distance(entity1, entity2)
-    return jsonify({'distance': distance})
-
-
-@app.route('/distances',methods=['POST'])
-def distances():
-    data = request.get_json(force=True)
-    entity1 = data['entity1']
-    other_entities = tuple(data['other_entities'])
-    distances = w2v_model.distances(entity1, other_entities)
-    return jsonify({'distances': list([float(distance) for distance in distances])})
-
-
-@app.route('/get_vector',methods=['POST'])
-def get_vector():
-    data = request.get_json(force=True)
-    token = data['token']
-    try:
-        vector = w2v_model.get_vector(token)
-        return jsonify({'vector': vector.tolist()})
-    except KeyError:
-        return jsonify({})
-
-
-@app.route('/most_similar',methods=['POST'])
-def most_similar():
-    keyword_args = request.get_json(force=True)
-    returned_results = w2v_model.most_similar(**keyword_args)
-    return jsonify(returned_results)
-
-
-@app.route('/most_similar_to_given',methods=['POST'])
-def most_similar_to_given():
-    data = request.get_json(force=True)
-    entity1 = data['entity1']
-    entities_list = data['entities_list']
-    entity = w2v_model.most_similar_to_given(entity1, entities_list)
-    return jsonify({'token': entity})
-
-
-@app.route('/rank',methods=['POST'])
-def rank():
-    data = request.get_json(force=True)
-    entity1 = data['entity1']
-    entity2 = data['entity2']
-    rank = w2v_model.rank(entity1, entity2)
-    return jsonify({'rank': rank})
-
-
-@app.route('/similarity',methods=['POST'])
-def similarity():
-    data = request.get_json(force=True)
-    entity1 = data['entity1']
-    entity2 = data['entity2']
-    similarity = w2v_model.similarity(entity1, entity2)
-    return jsonify({'similarity': float(similarity)})
-
-
-if __name__ == "__main__":
-    app.run(debug=args.debug, port=args.port)
\ No newline at end of file
diff --git a/docs/install.rst b/docs/install.rst
index 4482978d..95aa502a 100644
--- a/docs/install.rst
+++ b/docs/install.rst
@@ -72,7 +72,6 @@ Required Packages
 - Pandas_ (Python Data Analysis Library, version >= 1.0.0)
 - snowballstemmer_ (Snowball Stemmer, version >= 2.0.0)
 - TensorFlow_ (TensorFlow, version >= 2.0.0)
-- Flask_ (Flask, version >= 1.1.0)
 - Joblib_ (Joblib: lightweight Python pipelining, version >= 0.14)
 
 Home: :doc:`index`
@@ -88,6 +87,4 @@ Home: :doc:`index`
 .. _gensim: https://radimrehurek.com/gensim/
 .. _Pandas: http://pandas.pydata.org/
 .. _snowballstemmer: https://github.com/snowballstem/snowball
-.. _TensorFlow: https://www.tensorflow.org/
-.. _Flask: https://flask.palletsprojects.com/
 .. _Joblib: https://joblib.readthedocs.io/en/latest/
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index ec076999..e4ea69bb 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,7 +2,7 @@ Cython>=0.29.0
 numpy>=1.16.0
 scipy>=1.6.0
 joblib>=0.14
-scikit-learn>=0.23.0
+scikit-learn>=0.22.0
 tensorflow>=2.5.0
 keras>=2.4.0
 gensim>=4.0.0
diff --git a/setup.py b/setup.py
index 766ce5ea..39fd75f8 100644
--- a/setup.py
+++ b/setup.py
@@ -28,7 +28,7 @@ def test_requirements():
 
 
 setup(name='shorttext',
-      version='1.5.8',
+      version='1.5.9a1',
       description="Short Text Mining",
       long_description=package_description(),
       long_description_content_type='text/markdown',
@@ -86,8 +86,7 @@ def test_requirements():
       setup_requires=setup_requirements(),
       install_requires=install_requirements(),
       scripts=['bin/ShortTextCategorizerConsole',
-               'bin/ShortTextWordEmbedSimilarity',
-               'bin/WordEmbedAPI'],
+               'bin/ShortTextWordEmbedSimilarity'],
       test_suite="test",
       tests_requires=test_requirements(),
       zip_safe=False)