Spaces:
Runtime error
Runtime error
Update app.py
Browse files
app.py
CHANGED
@@ -26,11 +26,10 @@ def thefuzz(text1, text2):
|
|
26 |
|
27 |
|
28 |
def tfidf(text1, text2):
|
29 |
-
print('hello')
|
30 |
t1_tfidf = vectorizer.fit_transform([text1])
|
31 |
t2_tfidf = vectorizer.transform([text2])
|
32 |
cosine_sim = cosine_similarity(t1_tfidf, t2_tfidf).flatten()[0]
|
33 |
-
return {'cosine similarity of tf-idf vectors':cosine_sim}
|
34 |
|
35 |
|
36 |
def simcse(text1, text2):
|
@@ -39,7 +38,7 @@ def simcse(text1, text2):
|
|
39 |
with torch.no_grad():
|
40 |
embeddings = model_simcse(**inputs, output_hidden_states=True, return_dict=True).pooler_output
|
41 |
cosine_sim = 1 - cosine(embeddings[0], embeddings[1])
|
42 |
-
return {"cosine similarity of simcse embeddings":cosine_sim}
|
43 |
|
44 |
|
45 |
def mpnet(text1, text2):
|
@@ -48,7 +47,7 @@ def mpnet(text1, text2):
|
|
48 |
model_output = model_mpnet(**encoded_input)
|
49 |
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
|
50 |
cosine_sim = 1 - cosine(sentence_embeddings[0], sentence_embeddings[1])
|
51 |
-
return {"cosine similarity of stsb-mpnet embeddings":cosine_sim}
|
52 |
|
53 |
|
54 |
def get_scores(text1, text2):
|
|
|
26 |
|
27 |
|
28 |
def tfidf(text1, text2):
|
|
|
29 |
t1_tfidf = vectorizer.fit_transform([text1])
|
30 |
t2_tfidf = vectorizer.transform([text2])
|
31 |
cosine_sim = cosine_similarity(t1_tfidf, t2_tfidf).flatten()[0]
|
32 |
+
return {'cosine similarity of tf-idf vectors':str(round(cosine_sim,2))}
|
33 |
|
34 |
|
35 |
def simcse(text1, text2):
|
|
|
38 |
with torch.no_grad():
|
39 |
embeddings = model_simcse(**inputs, output_hidden_states=True, return_dict=True).pooler_output
|
40 |
cosine_sim = 1 - cosine(embeddings[0], embeddings[1])
|
41 |
+
return {"cosine similarity of simcse embeddings":str(round(cosine_sim,2))}
|
42 |
|
43 |
|
44 |
def mpnet(text1, text2):
|
|
|
47 |
model_output = model_mpnet(**encoded_input)
|
48 |
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
|
49 |
cosine_sim = 1 - cosine(sentence_embeddings[0], sentence_embeddings[1])
|
50 |
+
return {"cosine similarity of stsb-mpnet embeddings":str(round(cosine_sim,2))}
|
51 |
|
52 |
|
53 |
def get_scores(text1, text2):
|