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import gradio as gr
from transformers.utils import logging
logging.set_verbosity_error()
from sentence_transformers import SentenceTransformer
from sentence_transformers import util
def compare(sentences1, sentences2):
sentences1 = sentences1.splitlines()
sentences2 = sentences2.splitlines()
embeddings1 = model.encode(sentences1, convert_to_tensor=True)
embeddings2 = model.encode(sentences2, convert_to_tensor=True)
cosine_scores = util.cos_sim(embeddings1, embeddings2)
output = ""
for i in range(len(sentences1)):
output += "Score: {:.4f} \t\t {} \t\t {}\n".format(cosine_scores[i][i], sentences1[i], sentences2[i])
return output
model = SentenceTransformer("all-MiniLM-L6-v2")
demo = gr.Interface(
compare,
[
gr.Textbox(
label="Text",
info="Initial text",
lines=3,
value="I like cats\nTea puts me to sleep\nThe quick brown fox jumped over the lazy dogs.",
),
gr.Textbox(
label="Compare Text",
info="Text to compare",
lines=3,
value="I love kittens\nCoffee wakes me up\nThe fast brown fox jumps over lazy dogs.",
),
], outputs="text")
demo.launch()
gr.close_all() |