Spaces:
Sleeping
Sleeping
File size: 1,495 Bytes
5b68c01 4df9e3a 877d19f 693d1fb 4df9e3a 877d19f e227d6e 4df9e3a 877d19f 4df9e3a 711ffac b498339 d183fc7 fd398d9 d183fc7 da18950 d183fc7 693d1fb b2e1c8b 693d1fb d183fc7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 |
import streamlit as st # type: ignore
from PIL import Image
import os
import ast
import contextlib
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
from translate_app import tr
title = "Sentence Similarity"
sidebar_name = "Sentence Similarity"
dataPath = st.session_state.DataPath
def run():
st.write("")
st.write("")
st.title(tr(title))
sentences = ["This is an example sentence", "Each sentence is converted"]
sentences[0] = st.text_area(label=tr("Saisir un élément issu de la proposition de valeur (quelque soit la langue):"), value="This is an example sentence")
sentences[1] = st.text_area(label=tr("Saisir une phrase issue de l'acte de vente (quelque soit la langue):"), value="Each sentence is converted", height=200)
st.button(label=tr("Validez"), type="primary")
model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')
embeddings = model.encode(sentences)
st.write(tr("Transformation de chaque phrase en vecteur (dimension = 384 ):"))
st.write(embeddings)
st.write("")
# Calculate cosine similarity between the two sentences
similarity = cosine_similarity([embeddings[0]], [embeddings[1]])
st.write(tr("**Cosine similarity** comprise entre 0 et 1 :"), similarity[0][0])
st.write("")
st.write("")
st.write("") |