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12th_sept_semantic_text_similarity.py
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12th_sept_semantic_text_similarity.py
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# -*- coding: utf-8 -*-
"""14th Sept Semantic_text_Similarity.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1bR3DR5DJnq9WrxEVFlZ-6gHyfJBS2ppv
"""
# Commented out IPython magic to ensure Python compatibility.
# %%writefile requirements.txt
# pip ==22.2.2
# pip3
# datasets
# sentence_transformers
# pandas
# streamlit
# sklearn
# pickle
# tqdm
# numpy==1.13.3
"""# Installations
# Libraries
"""
#loading training set
# writefile.write("anaconda3")
# writefile.write("pyngrok")
# writefile.write("stqdm")
import pandas as pd
import numpy as np
# from tqdm import tqdm
# tqdm.pandas()
from datasets import load_dataset
# Load the English STSB dataset
stsb_dataset = load_dataset('stsb_multi_mt', 'en')
stsb_train = pd.DataFrame(stsb_dataset['train'])
stsb_test = pd.DataFrame(stsb_dataset['test'])
# Check loaded data
print(stsb_train.shape, stsb_test.shape)
stsb_test.head()
"""## Creating helper functions
* The first function is to pre-process texts by lemmatizing, lowercasing, and removing numbers and stop words.
* The second function takes in two columns of text embeddings and returns the row-wise cosine similarity between the two columns.
"""
from sklearn.metrics.pairwise import cosine_similarity
import spacy
nlp = spacy.load("en_core_web_sm")
def text_processing(sentence):
"""
Lemmatize, lowercase, remove numbers and stop words
Args:
sentence: The sentence we want to process.
Returns:
A list of processed words
"""
sentence = [token.lemma_.lower()
for token in nlp(sentence)
if token.is_alpha and not token.is_stop]
return sentence
def cos_sim(sentence1_emb, sentence2_emb):
"""
Cosine similarity between two columns of sentence embeddings
Args:
sentence1_emb: sentence1 embedding column
sentence2_emb: sentence2 embedding column
Returns:
The row-wise cosine similarity between the two columns.
For instance is sentence1_emb=[a,b,c] and sentence2_emb=[x,y,z]
Then the result is [cosine_similarity(a,x), cosine_similarity(b,y), cosine_similarity(c,z)]
"""
cos_sim = cosine_similarity(sentence1_emb, sentence2_emb)
return np.diag(cos_sim)
"""# Data Setup"""
data = (pd.read_csv("/content/SBERT_data.csv")).drop(['Unnamed: 0'], axis = 1)
prompt = input("Enter prompt: ")
data['prompt']= prompt
data.rename(columns = {'target_text':'sentence2', 'prompt':'sentence1'}, inplace = True)
data['sentence2'] = data['sentence2'].astype('str')
data['sentence1'] = data['sentence1'].astype('str')
data.head()
"""# Loop"""
from sentence_transformers import CrossEncoder
XpathFinder = CrossEncoder("cross-encoder/stsb-roberta-base")
sentence_pairs = []
for sentence1, sentence2 in zip(data['sentence1'],data['sentence2']):
sentence_pairs.append([sentence1, sentence2])
data['SBERT CrossEncoder_Score'] = XpathFinder.predict(sentence_pairs, show_progress_bar = True)
#@title Sort
data.sort_values(by=['SBERT CrossEncoder_Score'], ascending=False)
"""### Download"""
import pickle
filename = 'XpathFinder1.sav'
pickle.dump(XpathFinder, open(filename, 'wb'))
"""# App"""
# Commented out IPython magic to ensure Python compatibility.
# %%writefile app.py
# import io
# import netrc
# import pickle
# import sys
# import pandas as pd
# import numpy as np
# import streamlit as st
# # let's import sentence transformer
# import sentence_transformers
# import torch
# #######################################
#
# st.markdown(
# f"""
# <style>
# .reportview-container .main .block-container{{
# max-width: 90%;
# padding-top: 5rem;
# padding-right: 5rem;
# padding-left: 5rem;
# padding-bottom: 5rem;
# }}
# img{{
# max-width:40%;
# margin-bottom:40px;
# }}
# </style>
# """,
# unsafe_allow_html=True,
# )
#
# # # let's load the saved model
# loaded_model = pickle.load(open('XpathFinder1.sav', 'rb'))
# #loaded_model = pickle.load('XpathFinder1.sav', map_location='cpu')
#
#
# #class CPU_Unpickler(pickle.Unpickler):
# # def find_class(self, module, name):
# # if module == 'torch.storage' and name == '_load_from_bytes':
# # return lambda b: torch.load(io.BytesIO(b), map_location='cpu')
# # else:
# # return super().find_class(module, name)
# #
#
# #loaded_model = CPU_Unpickler(open('XpathFinder1.sav', 'rb')).load()
#
#
# # Containers
# header_container = st.container()
# mod_container = st.container()
#
# # Header
# with header_container:
#
# # different levels of text you can include in your app
# st.title("Xpath Finder App")
#
#
# # model container
# with mod_container:
# # collecting input from user
# prompt = st.text_input("Enter your description below ...")
#
# # Loading e data
# data = (pd.read_csv("/content/SBERT_data.csv")).drop(['Unnamed: 0'], axis = 1)
#
# data['prompt']= prompt
# data.rename(columns = {'target_text':'sentence2', 'prompt':'sentence1'}, inplace = True)
# data['sentence2'] = data['sentence2'].astype('str')
# data['sentence1'] = data['sentence1'].astype('str')
#
# # let's pass the input to the loaded_model with torch compiled with cuda
# if prompt:
# # let's get the result
# simscore = XpathFinder.predict([prompt])
# from sentence_transformers import CrossEncoder
# XpathFinder = CrossEncoder("cross-encoder/stsb-roberta-base")
# sentence_pairs = []
# for sentence1, sentence2 in zip(data['sentence1'],data['sentence2']):
# sentence_pairs.append([sentence1, sentence2])
#
# # sorting the df to get highest scoring xpath_container
# data['SBERT CrossEncoder_Score'] = XpathFinder.predict(sentence_pairs)
# most_acc = data.head(5)
# # predictions
# st.write("Highest Similarity score: ", simscore)
# st.text("Is this one of these the Xpath you're looking for?")
# st.write(st.write(most_acc["input_text"]))
#
# from pyngrok import ngrok
# ngrok.set_auth_token("29Mzs7BHkeeRGNZM41x0Rn4Xilq_7TYKeCLdR34nSS2qBCTzo")
# !nohup streamlit run app.py --server.port 80 &
# url = ngrok.connect(port = '80')
# print(url)