iamjanvijay

import time
from langchain.llms import VertexAI, OpenAI
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
import threading
from queue import Queue
from threading import Thread, Semaphore, Condition

class LLMWorker:

iamjanvijay

concepts,fraction_section_concepts,concept_mentions,fraction_section_concept_mentions,words,fraction_section_words,paragraphs,fraction_section_paragraphs,position,subject,num_images
5,12.8205,12,3.6474,75,1.9873,1,1.0989,0.0,M,0
5,12.8205,21,6.383,224,5.9353,2,2.1978,6.6667,M,0
8,20.5128,31,9.4225,240,6.3593,5,5.4945,13.3333,M,3
13,33.3333,46,13.9818,775,20.5352,24,26.3736,20.0,M,1
3,7.6923,5,1.5198,56,1.4838,4,4.3956,26.6667,M,0
7,17.9487,10,3.0395,126,3.3386,3,3.2967,33.3333,M,0
2,5.1282,4,1.2158,153,4.0541,3,3.2967,40.0,M,2
10,25.641,35,10.6383,491,13.0101,6,6.5934,46.6667,M,0
5,12.8205,16,4.8632,232,6.1473,4,4.3956,53.3333,M,1

iamjanvijay

import smtplib
from email.message import EmailMessage

# set your email and password
# please use App Password
email_address = "janvijay.singh.cse14@gmail.com"
email_password = "xxxxxxxx" # generate the "app password" using the instructions mentioned here: https://support.google.com/accounts/answer/185833

# create email
msg = EmailMessage()

iamjanvijay

# sample command                                                                                                                                                                                            
# python run_experiment.py train --config_args ./train_config/config_1.json                                                                                                                                 
                                                                                                                                                                                                            
import torch                                                                                                                                                                                                
import numpy as np                                                                                                                                                                                          

iamjanvijay

3iL École d'ingénieurs | France
A.T. Still University | United States
Aalborg Universitet | Denmark
Aalto-yliopisto | Finland
Aarhus Universitet | Denmark
Aba Teachers University | China
Abasyn University | Pakistan
Abbottabad University of Science and Technology | Pakistan
Abdul Wali Khan University Mardan | Pakistan
Abdullah Gül Üniversitesi | Turkey

iamjanvijay

# This file is an adapted version of modelling_clip.py taken from transformers==4.21.3

# coding=utf-8
# Copyright 2021 The OpenAI Team Authors and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0

iamjanvijay

// sample launch.json file for python debugging in vscode

{
    // Use IntelliSense to learn about possible attributes.
    // Hover to view descriptions of existing attributes.
    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
    "version": "0.2.0",
    "configurations": [
        {
            "name": "meta-train-samp-1",

iamjanvijay

# sample command
# CUDA_VISIBLE_DEVICES=0; python run_experiment.py train --config_args ./train_config/config_1.json;

import torch
import numpy as np
import argparse

parser = argparse.ArgumentParser()
parser.add_argument(
    "mode",

iamjanvijay

import shutil
from concurrent.futures import ThreadPoolExecutor
import os
import sys
from copy import deepcopy

f = open(sys.argv[1])

if not os.path.exists(os.path.dirname(sys.argv[2])):
    os.makedirs(os.path.dirname(sys.argv[2]))

iamjanvijay

	for f in fnames:
		# Read text from json and tokenize
		fpath = os.path.join(data_folder, f)
		text, sentences, lists, items, items1, items2, items3, items4 = read_json(fpath)
		tokenised_text = word_tokenize(text)

		# Additional splits to clean the dataset - mannual
		add_splits = {"Classe6":["Classe", "6"], "Class.The":["Class", ".", "The"], ".The":[".", "The"], ".Such":[".", "Such"], ".These":[".", "These"]}
		temp_tokenised_text = []
		for token in tokenised_text: