Kaggle competition: University of Liverpool - Ion Switching
neutral_text = neutral_train.selected_text[:100].values
generate_word_cloud(neutral_text )<import_modules>
df944 = pd.read_csv('/kaggle/input/uoflivoutputs/submission_wavenet.csv', dtype={'time': object})
 df942 = pd.read_csv('/kaggle/input/uoflivoutputs/gru_preds.csv', dtype={'time': object} )
import numpy as np
import pandas as pd
import os
import transformers
import tokenizers
import string
import torch
import torch.nn as nn
from torch.nn import functional as F
from tqdm import tqdm
import re
import json
import requests<define_variables>
df944.to_csv('df944.csv', index=False)
 df942.to_csv('df942.csv', index=False )
MAX_LEN = 192
TRAIN_BATCH_SIZE = 32
VALID_BATCH_SIZE = 8
EPOCHS = 5
ROBERTA_PATH = 'roberta-base'<define_variables>
ens1 = df944.copy()
for ii in([0.1,0.3,0.5,0.7,0.9]):
ens1['open_channels'] = ii * df944['open_channels'] +(1.0 - ii)* df942['open_channels']
ens1['open_channels'] = ens1['open_channels'].apply(lambda x: round(x))
ens1.to_csv('ens_'+str(ii)+'.csv', index=False )