<jupyter_start><jupyter_text>**Simply execute all cells to test the language model with different words**<jupyter_code>%%capture
!pip install ktrain
import os, ktrain
import pandas as pd
import numpy as np
from pydrive.auth import GoogleAuth
from pydrive.drive import GoogleDrive
from google.colab import auth
from oauth2client.client import GoogleCredentials
from tqdm import tqdm
print('\n successfully executed, please proceed with next step\n')
# You will need to provide a Google Account for authentification purposes

auth.authenticate_user()
gauth = GoogleAuth()
gauth.credentials = GoogleCredentials.get_application_default()
drive = GoogleDrive(gauth)
print('\n successfully executed, please proceed with next step\n')
# This will take a few minutes

os.makedirs('/content/downloads/', exist_ok=True)
os.chdir('/content/downloads/')

folder_id = '1kYgvTOAUPA11oE2plZEaDiYGyrtLfM8X'
file_list = drive.ListFile({'q': "'{}' in parents and trashed=false".format(folder_id)}).GetList()
for file in tqdm(file_list):
    file.GetContentFile(file['title'])

os.chdir('/content/')
print('\n successfully executed, please proceed with next step\n')
needs = ['Autonomy','Competence','Self-esteem','Money-luxury',
         'Physical-thriving','Pleasure','Popularity-influence',
         'Relatedness','Security','Self-actualization']
model = ktrain.load_predictor('/content/downloads/')
print('\n successfully executed, please proceed with next step\n')<jupyter_output><empty_output><jupyter_text>**You may add different texts below, to check language model performance**<jupyter_code>text = ['swimming','new gucci bag','netflix n chill','pray in church'] #change texts here

pd.DataFrame(model.predict(text,return_proba = True).T,index=needs,columns=text).round(3)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12053571428571429, 'fraction_numerical': 0.00390625, 'mean_word_length': 6.829694323144105, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 3, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '211836', 'license': 'MIT', 'n_tokens_mistral': 566, 'n_tokens_neox': 551, 'n_words': 147, 'path': 'Human_Needs_Language_Model.ipynb', 'repo_name': 'doubleblindreview2/Human_Needs_Dynamics', 'size': '1792'}