text-to-text-transfer-transformer | Transfer Learning with a Unified Text | Machine Learning library

from time import perf_counter as now
import gzip
from collections import Counter

PATH = r".\googlebooks-eng-all-1gram-20120701-a.gz"

def chunked_read(f, byte_limit=10**9):
    while True:
        lines = f.readlines(byte_limit)
        i

class AllTeams:
  def __init__(self, TeamNum, TeamName, TeamScore):
    self.TeamNum = TeamNum
    self.TeamName = TeamName
    self.TeamScore = TeamScore

  def __repr__(self):
    return f'Team Number: {self.TeamNum} |-| Team Name: {self

#rename the comlumns
df1.rename(columns={'col2':'d1'},inplace=True)
df2.rename(columns={'col2':'d2'},inplace=True)
df3.rename(columns={'col2':'d3'},inplace=True)

#merge all the dfs; outer merge
from functools import reduce
dfs = [df1, df2

def set_color(letter, color):
    COLORS_DICT  = {"W" : "ffffff", "Y" : "ffff00", "G" : "00ff00"}
    c = COLORS_DICT.get(color, "W")
    return f"[color={c}]{letter}[/color]"

    def ShowPreviousWord(self):
      

how many halts = 

var _index = LOOKUPVALUE('Table'[Index], 'Table'[Index], [Index])
var _train = LOOKUPVALUE('Table'[Train nr],'Table'[Train nr], [Train nr])
var _haltafter = FILTER('Table', [Halt]=1 && [Index] > value(_index) 

def print_dataset(ds):

    list_sets = []

    for input, targets in ds:

        input = np.transpose(np.array(inputs)[0])
        label = np.transpose(np.array(targets)[0])

        for input_set, label_set in zip(input, label):

      

features = np.concatenate(list(ds.map(lambda x, y: tf.transpose(tf.squeeze(x, axis=0)))))
targets = np.concatenate(list(ds.map(lambda x, y: tf.transpose(tf.squeeze(y, axis=0)))))

values = list(map(lambda x: x[0]+ "\t" + x[1], zip([" ".joi

my_data_parent = ['', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B', 'C', 'C', 'C', 'C', 'C', 'C', 'D', 'D', 'D', 'D', 'E', 'E', 'E', 'F', 'F', 'F', 'F', 'G', 'G', 'G', 'H', 'AV', 'CF', 'CF', 'EL', 'EL', 'EV', 'EV', 'MT', 'DI'

from itertools import combinations

kinds      = "dhsc"
faces      = "A23456789TJQK"
cardValues = {kind+face:min(10,value) for kind in kinds
              for value,face in enumerate(faces,1)}

def count15s(cards):
    points = 0
    combo