diff --git a/stanza/models/lemma_classifier/baseline_model.py b/stanza/models/lemma_classifier/baseline_model.py
new file mode 100644
index 0000000000..75c5f12539
--- /dev/null
+++ b/stanza/models/lemma_classifier/baseline_model.py
@@ -0,0 +1,8 @@
+"""
+Baseline model for the existing lemmatizer which always predicts "be" and never "have" on the "'s" token.
+"""
+
+
+
+
+
diff --git a/stanza/models/lemma_classifier/evaluate_models.py b/stanza/models/lemma_classifier/evaluate_models.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/stanza/models/lemma_classifier/model.py b/stanza/models/lemma_classifier/model.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/stanza/utils/visualization/dependency_visualization.py b/stanza/utils/visualization/dependency_visualization.py
deleted file mode 100644
index 814ae0fa1e..0000000000
--- a/stanza/utils/visualization/dependency_visualization.py
+++ /dev/null
@@ -1,104 +0,0 @@
-from stanza.models.common.constant import is_right_to_left
-import stanza
-import spacy
-from spacy import displacy
-from spacy.tokens import Doc
-
-
-def visualize_doc(doc, language):
-    """
-    Takes in a Document and visualizes it using displacy.
-
-    The document to visualize must be from the stanza pipeline.
-
-    right-to-left languages such as Arabic are displayed right-to-left based on the language code
-    """
-    visualization_options = {"compact": True, "bg": "#09a3d5", "color": "white", "distance": 90,
-                             "font": "Source Sans Pro", "arrow_spacing": 25}
-    # blank model - we don't use any of the model features, just the viz
-    nlp = spacy.blank("en")
-    sentences_to_visualize = []
-    for sentence in doc.sentences:
-        words, lemmas, heads, deps, tags = [], [], [], [], []
-        if is_right_to_left(language):  # order of words displayed is reversed, dependency arcs remain intact
-            sent_len = len(sentence.words)
-            for word in reversed(sentence.words):
-                words.append(word.text)
-                lemmas.append(word.lemma)
-                deps.append(word.deprel)
-                tags.append(word.upos)
-                if word.head == 0:  # spaCy head indexes are formatted differently than that of Stanza
-                    heads.append(sent_len - word.id)
-                else:
-                    heads.append(sent_len - word.head)
-        else:   # left to right rendering
-            for word in sentence.words:
-                words.append(word.text)
-                lemmas.append(word.lemma)
-                deps.append(word.deprel)
-                tags.append(word.upos)
-                if word.head == 0:
-                    heads.append(word.id - 1)
-                else:
-                    heads.append(word.head - 1)
-        document_result = Doc(nlp.vocab, words=words, lemmas=lemmas, heads=heads, deps=deps, pos=tags)
-        sentences_to_visualize.append(document_result)
-
-    for line in sentences_to_visualize:  # render all sentences through displaCy
-        # If this program is NOT being run in a Jupyter notebook, replace displacy.render with displacy.serve
-        # and the visualization will be hosted locally, link being provided in the program output.
-        displacy.render(line, style="dep", options=visualization_options)
-
-
-def visualize_str(text, pipeline_code, pipe):
-    """
-    Takes a string and visualizes it using displacy.
-
-    The string is processed using the stanza pipeline and its
-    dependencies are formatted into a spaCy doc object for easy
-    visualization. Accepts valid stanza (UD) pipelines as the pipeline
-    argument. Must supply the stanza pipeline code (the two-letter
-    abbreviation of the language, such as 'en' for English. Must also
-    supply the stanza pipeline object as the third argument.
-    """
-    doc = pipe(text)
-    visualize_doc(doc, pipeline_code)
-
-
-def visualize_docs(docs, lang_code):
-    """
-    Takes in a list of Stanza document objects and a language code (ex: 'en' for English) and visualizes the
-    dependency relationships within each document.
-
-    This function uses spaCy visualizations. See the visualize_doc function for more details.
-    """
-    for doc in docs:
-        visualize_doc(doc, lang_code)
-
-
-def visualize_strings(texts, lang_code):
-    """
-    Takes a language code (ex: 'en' for English) and a list of strings to process and visualizes the
-    dependency relationships in each text.
-
-    This function loads the Stanza pipeline for the given language and uses it to visualize all of the strings provided.
-    """
-    pipe = stanza.Pipeline(lang_code, processors="tokenize,pos,lemma,depparse")
-    for text in texts:
-        visualize_str(text, lang_code, pipe)
-
-
-def main():
-    ar_strings = ['برلين ترفض حصول شركة اميركية على رخصة تصنيع دبابة "ليوبارد" الالمانية', "هل بإمكاني مساعدتك؟",
-               "أراك في مابعد", "لحظة من فضلك"]
-    en_strings = ["This is a sentence.",
-                  "Barack Obama was born in Hawaii. He was elected President of the United States in 2008."]
-    zh_strings = ["中国是一个很有意思的国家。"]
-    # Testing with right to left language
-    visualize_strings(ar_strings, "ar")
-    # Testing with left to right languages
-    visualize_strings(en_strings, "en")
-    visualize_strings(zh_strings, "zh")
-
-if __name__ == '__main__':
-    main()