def visualize_similarity(
nlp: spacy.language.Language,
default_texts: Tuple[str, str] = ("apple", "orange"),
*,
threshold: float = 0.5,
title: Optional[str] = "Vectors & Similarity",
key: Optional[str] = None,
) -> None:
"""Visualizer for semantic similarity using word vectors."""
meta = nlp.meta.get("vectors", {})
if title:
st.header(title)
if not meta.get("width", 0):
st.warning("No vectors available in the model.")
else:
cols = st.beta_columns(2)
text1 = cols[0].text_input("Text or word 1",
default_texts[0],
key=f"{key}_similarity_text1")
text2 = cols[1].text_input("Text or word 2",
default_texts[1],
key=f"{key}_similarity_text2")
doc1 = nlp.make_doc(text1)
doc2 = nlp.make_doc(text2)
similarity = doc1.similarity(doc2)
similarity_text = f"**Score:** `{similarity}`"
if similarity > threshold:
st.success(similarity_text)
else:
st.error(similarity_text)
exp = st.beta_expander("Vector information")
exp.code(meta)
def visualize_similarity(
nlp: spacy.language.Language,
default_texts: Tuple[str, str] = ("apple", "orange"),
*,
threshold: float = 0.5,
title: Optional[str] = "Vectors & Similarity",
) -> None:
"""Visualizer for semantic similarity using word vectors."""
meta = nlp.meta.get("vectors", {})
if title:
st.header(title)
if not meta.get("width", 0):
st.warning("No vectors available in the model.")
st.code(meta)
text1 = st.text_input("Text or word 1", default_texts[0])
text2 = st.text_input("Text or word 2", default_texts[1])
doc1 = nlp.make_doc(text1)
doc2 = nlp.make_doc(text2)
similarity = doc1.similarity(doc2)
if similarity > threshold:
st.success(similarity)
else:
st.error(similarity)