model compare

app.py

@@ -4,19 +4,28 @@ from sentence_transformers import SentenceTransformer
 from sklearn.metrics.pairwise import cosine_similarity
 import spacy
 
+left_text = st.text_input('First', 'This is a test')
+right_text = st.text_input('Second', 'This is another test')
+
+st.toast("Loading spacy...")
 nlp = spacy.load("en_core_web_sm")
+
+st.toast("Loading rufimelo/Legal-BERTimbau-sts-base...")
 model = SentenceTransformer("rufimelo/Legal-BERTimbau-sts-base")
 
-def compute_similarity(left_text: str, right_text: str) -> np.ndarray:
-    print("computing embeddings...")
-    embeddings = model.encode([left_text, right_text])
-    print("computing similarity...")
-    similarity = cosine_similarity(embeddings[: 1], embeddings[1 :])
+st.toast("Legal-BERTimbau-sts-base: computing embeddings...")
+embeddings = model.encode([left_text, right_text])
+
+st.toast("Legal-BERTimbau-sts-base: computing similarity...")
+similarity = cosine_similarity(embeddings[: 1], embeddings[1 :])
+st.dataframe(s)
 
-    return similarity
+st.toast("Loading nlpaueb/legal-bert-base-uncased...")
+model = SentenceTransformer("nlpaueb/legal-bert-base-uncased")
 
-first = st.text_input('First', 'This is a test')
-second = st.text_input('Second', 'This is another test')
+st.toast("legal-bert-base-uncased: computing embeddings...")
+embeddings = model.encode([left_text, right_text])
 
-s = compute_similarity(left_text=first, right_text=second)
+st.toast("legal-bert-base-uncased: computing similarity...")
+similarity = cosine_similarity(embeddings[: 1], embeddings[1 :])
 st.dataframe(s)