Lingua Franca Translation

Getting Started Notebook for Lingua Franca Translation

Getting Started with Language Translation

Using Seq2Seq Model

In this puzzle, the problem statement is quite simple. We need to build a translator that should be able to translate to English from a fake language. There are many ways to build a machine translator. In this notebook, we'll use a seq2seq model.
This notebook is inspired by NLP from: Translation with a Seq2Seq and Attention
A generalized architecture of machine translator we are going to build in this notebook:

Download the files 💾¶ using AIcrowd CLI¶

We will first install aicrowd-cli which will help you download and later make submission directly via the notebook.

In [1]:

%%capture
!pip install aicrowd-cli
%load_ext aicrowd.magic

In [2]:

%aicrowd login

Please login here: https://api.aicrowd.com/auth/1qeOWxKEVVQ6hOD1AweWxJgv5tjeYws3G9bDzq1qz80
API Key valid
Saved API Key successfully!

In [3]:

!rm -rf data
!mkdir data
%aicrowd ds dl -c lingua-franca-translation -o data

Importing Libraries¶

In [4]:

%%capture
!pip install torchtext==0.6.0 --quiet

In [5]:

import os
import numpy as np
import pandas as pd
import spacy
import random

import torch
import torch.nn as nn
import torch.optim as optim
from spacy.lang.en import English
from spacy.tokenizer import Tokenizer
from torchtext.data import Field, BucketIterator, TabularDataset

from torchtext.data.metrics import bleu_score
from pprint import pprint
from torch.utils.tensorboard import SummaryWriter
from torchsummary import summary

from sklearn.model_selection import train_test_split

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

Understanding Dataset¶

In [6]:

train_df = pd.read_csv("data/train.csv")

In [7]:

test_df = pd.read_csv("data/test.csv")

In [8]:

sample = pd.read_csv("data/sample_submission.csv")

In [9]:

train_df.head()

Out[9]:

	id	crowdtalk	english
0	31989	wraov driourth wreury hyuirf schneiald chix lo...	upon this ladder one of them mounted
1	29884	treuns schleangly kriaors draotz pfiews schlio...	and solicited at the court of Augustus to be p...
2	26126	toirts choolt chiugy knusm squiend sriohl gheold	but how am I sunk!
3	44183	schlioncy yoik yahoos dynuewn maery schlioncy ...	the Yahoos draw home the sheaves in carriages
4	19108	treuns schleangly tsiens mcgaantz schmeecks tr...	and placed his hated hands before my eyes

In [57]:

train_df.crowdtalk[0]

Out[57]:

'wraov driourth wreury hyuirf schneiald chix loir schloors rhiuny pfaiacts'

In [10]:

test_df.head()

Out[10]:

	id	crowdtalk
0	27226	treuns schleangly throuys praests qeipp cyclui...
1	31034	feosch treuns schleangly gliath spluiey gheuck...
2	35270	scraocs knaedly squiend sriohl clield whaioght...
3	23380	sqaups schlioncy yoik gnoirk cziourk schnaunk ...
4	92117	schlioncy yoik psycheiancy mcountz pously mcna...

In [11]:

english = train_df.english.values
crowdtalk = train_df.crowdtalk.values

In [12]:

print("Length of english sentence:", len(english))
print("Length of fake sentence:", len(crowdtalk))

Length of english sentence: 11955
Length of fake sentence: 11955

In [13]:

train_df.isnull().sum()

Out[13]:

id           0
crowdtalk    0
english      0
dtype: int64

Findings:¶

Here, We've 19286 samples of fake and english sentences. We can see that we need to build a model that can translate the fake language to English. So, going ahead we need to prepare data to feed the translator model.

Data Preparation:¶

We'll focus on two things:

Tokenization: Tokenization is a way of separating a piece of text into smaller units called tokens.
Encoding: We'll encode the sentence with the help of tokenizer and pad sequence(It helps to keep the length constant).

Note:¶

We will use Pytorch Torchtext. From Torchtext, we will use Fields, TabularDataset, BucketIterator to do all the heavy preprocessing for NLP tasks, such as numericalizing, padding, building vocabulary, which saves us a lot of time to focus on actually training the models!
Since the crowdtalk language is derived from English we will use Spacy's English Tokenizer for both English & Crowdtalk sentences

In [14]:

nlp = English()
tokenizer = Tokenizer(nlp.vocab)

def tokens(text):
  return [token.text for token in tokenizer(text)]

In [15]:

sample_text = "I am in love with Machine Translation."
print(tokens(sample_text))

['I', 'am', 'in', 'love', 'with', 'Machine', 'Translation.']

In [16]:

english = Field(sequential=True, use_vocab=True, tokenize=tokens, lower=True)
crowdtalk = Field(sequential=True, use_vocab=True, tokenize=tokens, lower=True)

In [17]:

train, test = train_test_split(train_df, test_size=0.25)

In [18]:

# Get train, test data to json and csv format which can be read by torchtext
train.to_json("train.json", orient="records", lines=True)
test.to_json("test.json", orient="records", lines=True)

In [19]:

fields = {"english": ("eng", english), "crowdtalk": ("crowd", crowdtalk)}

In [20]:

train_data, test_data = TabularDataset.splits(
    path="", train="train.json", test="test.json", format="json", fields=fields
)

In [21]:

english.build_vocab(train_data, max_size=10000, min_freq=2)
crowdtalk.build_vocab(train_data, max_size=10000, min_freq=2)

In [22]:

e = list(english.vocab.__dict__.values())
for i in e:
  print(i)

Counter({'the': 4960, 'of': 3289, 'and': 3052, 'to': 2725, 'i': 2540, 'a': 1849, 'my': 1542, 'in': 1534, 'was': 1131, 'that': 1052, 'me': 837, 'with': 792, 'his': 684, 'had': 682, 'as': 677, 'he': 622, 'for': 621, 'it': 611, 'which': 605, 'by': 596, 'but': 576, 'not': 527, 'be': 499, 'they': 445, 'on': 443, 'at': 440, 'their': 429, 'from': 414, 'this': 399, 'were': 387, 'have': 346, 'so': 313, 'is': 306, 'or': 297, 'her': 288, 'all': 280, 'would': 278, 'could': 272, 'when': 270, 'an': 263, 'them': 258, 'you': 251, 'upon': 246, 'some': 232, 'one': 230, 'him': 228, 'are': 215, 'who': 214, 'into': 205, 'we': 197, 'great': 182, 'our': 174, 'been': 168, 'very': 167, 'should': 162, 'no': 162, 'any': 158, 'more': 156, 'she': 156, 'these': 152, 'those': 151, 'your': 150, 'made': 150, 'than': 148, 'only': 144, 'if': 143, 'other': 143, 'about': 140, 'might': 135, 'two': 133, 'out': 131, 'what': 125, 'up': 125, 'own': 124, 'most': 124, 'then': 124, 'much': 123, 'being': 122, 'every': 120, 'before': 119, 'several': 117, 'time': 117, 'first': 116, 'myself': 115, 'did': 111, 'will': 111, 'where': 110, 'such': 110, 'there': 108, 'same': 106, 'do': 104, 'found': 101, 'after': 101, 'now': 97, 'many': 96, 'saw': 94, 'country': 91, 'came': 87, 'three': 87, 'yet': 86, 'among': 86, 'shall': 84, 'little': 84, 'never': 84, 'how': 83, 'me.': 80, 'down': 80, 'well': 79, 'life': 78, 'eyes': 75, 'man': 75, 'its': 73, 'day': 72, 'can': 72, 'took': 71, 'people': 70, 'often': 70, 'must': 69, 'good': 69, 'us': 67, 'over': 67, 'ever': 67, 'see': 67, 'gave': 66, 'has': 65, 'master': 65, 'whom': 65, 'like': 65, 'may': 65, 'against': 65, 'although': 64, 'while': 64, 'thought': 64, 'towards': 63, 'heard': 63, 'am': 63, 'long': 62, 'having': 62, 'without': 62, 'take': 61, 'because': 60, 'part': 59, 'each': 59, 'soon': 58, 'again': 58, '“that': 57, 'put': 57, 'sometimes': 57, 'desired': 56, 'make': 56, 'least': 56, 'both': 56, 'observed': 55, 'left': 55, 'words': 54, 'mind': 54, 'hundred': 54, 'another': 53, 'world': 53, 'old': 53, 'manner': 53, 'said': 53, 'place': 53, 'few': 52, 'whole': 52, 'almost': 51, 'appeared': 51, 'nature': 51, 'still': 51, 'rest': 50, 'let': 49, 'house': 49, 'under': 49, 'days': 48, 'give': 48, 'honour': 48, 'able': 48, 'always': 48, 'death': 47, 'island': 47, 'even': 47, 'during': 46, 'last': 46, 'hand': 45, 'men': 45, 'through': 45, 'years': 45, 'return': 45, 'reason': 45, 'near': 45, 'father': 45, 'majesty': 45, 'whose': 45, 'account': 45, 'indeed': 44, 'feet': 44, 'kind': 43, 'far': 43, 'between': 43, 'night': 43, 'emperor': 43, 'himself': 42, 'sea': 42, 'went': 42, 'hands': 41, 'passed': 41, 'began': 41, 'king': 41, 'seemed': 41, 'large': 41, 'half': 41, 'side': 41, 'nor': 41, 'felt': 40, 'yahoos': 40, 'court': 40, 'sun': 40, 'thus': 40, 'person': 40, 'above': 39, 'till': 39, 'body': 39, 'thousand': 39, 'times': 39, 'whether': 39, 'within': 39, 'called': 39, 'language': 38, 'brought': 38, 'four': 38, 'set': 38, 'off': 38, 'ship': 38, 'young': 38, 'love': 38, '“i': 37, 'family': 37, 'ground': 37, 'head': 37, 'know': 37, 'small': 37, 'lay': 37, 'thing': 37, 'five': 37, 'think': 36, 'already': 36, 'since': 36, 'therefore': 36, 'taken': 36, 'given': 36, 'placed': 35, 'high': 35, 'neither': 35, 'feelings': 35, 'new': 35, 'carried': 35, 'human': 34, 'friends': 34, 'away': 34, 'greatest': 33, 'come': 33, 'fear': 33, 'persons': 33, 'heart': 33, 'here': 33, 'voice': 32, 'received': 32, 'understand': 32, 'got': 32, 'discovered': 32, 'face': 32, 'months': 32, 'became': 31, 'use': 31, 'once': 31, 'end': 31, 'state': 31, 'it.': 31, 'too': 31, 'length': 31, 'leave': 31, 'common': 30, 'miserable': 30, 'nothing': 30, 'them.': 30, 'also': 30, 'creature': 30, 'feel': 30, 'majesty’s': 29, 'told': 29, 'learned': 29, 'boat': 29, 'work': 29, 'home': 29, 'cannot': 29, 'air': 29, 'elizabeth': 29, 'journey': 29, 'cause': 29, 'animal': 28, 'whereof': 28, 'power': 28, 'together': 28, 'room': 28, 'go': 28, 'moment': 28, 'sight': 28, 'word': 27, 'knew': 27, 'seen': 27, 'looked': 27, 'friend': 27, 'round': 27, 'reader': 27, 'certain': 27, 'six': 26, 'done': 26, 'glumdalclitch': 26, 'fell': 26, 'poor': 26, 'way': 26, 'easily': 26, 'arrived': 26, 'idea': 26, 'forced': 26, 'find': 26, 'full': 26, 'observe': 26, 'back': 25, 'better': 25, 'hardly': 25, 'right': 25, '”': 25, 'different': 25, 'strange': 25, 'enough': 25, 'care': 25, 'sat': 25, 'greater': 25, 'countenance': 25, 'present': 24, 'hours': 24, 'until': 24, 'beheld': 24, 'captain': 24, 'ill': 24, 'allowed': 24, 'fixed': 24, 'prince': 24, 'affection': 24, 'knowledge': 24, 'opinion': 24, 'understood': 24, 'order': 24, 'morning': 24, 'sent': 24, 'joy': 24, 'royal': 23, 'pleased': 23, 'light': 23, 'general': 23, 'town': 23, 'discover': 23, 'spoke': 23, 'felix': 23, 'appear': 23, 'returned': 23, 'author': 23, 'water': 23, 'best': 23, 'others': 23, 'happened': 23, 'girl': 22, 'door': 22, 'walked': 22, 'believe': 22, 'misery': 22, 'making': 22, 'english': 22, 'queen': 22, 'longer': 22, 'box': 22, 'strength': 22, 'mine': 22, 'parts': 22, 'continued': 22, 'live': 22, 'employed': 21, 'strong': 21, 'distance': 21, 'dead': 21, 'less': 21, 'child': 21, 'yahoo': 21, 'cottage': 21, 'desire': 21, 'themselves': 21, 'trees': 21, 'held': 21, 'rather': 21, 'resolved': 21, 'kept': 21, 'yards': 21, 'short': 21, 'thoughts': 20, 'youth': 20, 'spent': 20, 'close': 20, 'children': 20, 'turned': 20, 'houyhnhnms': 20, 'things': 20, 'public': 20, 'except': 20, 'him.': 20, 'subject': 20, 'table': 20, 'next': 20, 'servants': 20, 'hope': 20, 'tell': 20, 'alone': 20, 'course': 20, 'country.': 20, 'natural': 20, 'point': 20, 'endeavoured': 20, 'proper': 20, 'occasion': 20, 'shore': 20, 'war': 20, 'fire': 19, 'bed': 19, 'hear': 19, 'england': 19, 'possessed': 19, 'ordered': 19, 'immediately': 19, 'lived': 19, 'answer': 19, 'sides': 19, 'filled': 19, 'scene': 19, 'entered': 19, 'means': 19, 'ready': 19, 'provided': 19, 'expressed': 19, 'attended': 19, 'cut': 19, 'blood': 19, 'kingdom': 19, 'ten': 19, 'palace': 19, 'horror': 19, 'turn': 19, 'twenty': 18, 'stood': 18, 'afterwards': 18, 'inhabitants': 18, 'ice': 18, 'justine': 18, 'food': 18, 'pass': 18, 'used': 18, 'say': 18, 'call': 18, 'evil': 18, 'gentle': 18, 'miles': 18, 'wind': 18, 'lost': 18, 'visit': 18, 'look': 18, 'suddenly': 18, 'spirit': 18, 'clerval': 18, 'justice': 18, 'letter': 18, 'native': 18, 'wife': 18, 'drew': 18, 'favour': 18, 'animals': 18, '“the': 18, 'horses': 18, 'wherein': 18, 'why': 18, 'hopes': 18, 'land': 18, 'change': 18, 'itself': 17, 'likewise': 17, 'none': 17, 'forward': 17, 'speak': 17, 'just': 17, 'wholly': 17, 'either': 17, 'quality': 17, 'show': 17, 'field': 17, 'law': 17, 'top': 17, 'necessary': 17, 'former': 17, 'something': 17, 'condition': 17, 'covered': 17, 'going': 17, 'europe': 17, 'myself.': 17, 'blefuscu': 16, 'ran': 16, 'company': 16, 'voyage': 16, 'lives': 16, 'bodies': 16, 'degrees': 16, 'hour': 16, 'health': 16, 'mountains': 16, 'view': 16, 'perceived': 16, 'died': 16, 'liberty': 16, 'perhaps': 16, 'wish': 16, 'name': 16, 'delight': 16, 'capable': 16, 'usual': 16, 'degree': 16, 'opened': 16, 'orders': 16, 'names': 16, 'destroy': 16, 'fortune': 16, 'wished': 16, 'accident': 16, 'form': 16, 'known': 16, 'get': 16, 'mother': 16, 'pleasure': 16, 'carry': 16, 'happiness': 16, 'imperial': 15, 'appearance': 15, 'houses': 15, 'utmost': 15, 'spirits': 15, 'vessel': 15, 'affairs': 15, 'sort': 15, 'servant': 15, 'story': 15, 'us.': 15, 'notice': 15, 'enemy': 15, 'difficulty': 15, 'city': 15, 'dear': 15, 'past': 15, 'quantity': 15, 'concerning': 15, 'various': 15, 'suffer': 15, 'whence': 15, 'rage': 15, 'conversation': 15, 'grief': 15, 'read': 15, 'race': 15, 'woman': 15, 'generally': 15, 'sufficient': 15, 'become': 15, 'wonder': 15, 'behind': 14, 'tears': 14, 'observing': 14, 'draw': 14, 'driven': 14, 'gently': 14, 'remained': 14, 'objects': 14, 'purpose': 14, 'stranger': 14, 'particular': 14, 'entirely': 14, 'around': 14, 'life.': 14, 'determined': 14, 'open': 14, 'signs': 14, 'obliged': 14, 'formed': 14, 'showed': 14, 'vast': 14, 'noble': 14, 'raise': 14, 'companion': 14, 'princes': 14, 'laws': 14, 'looking': 14, 'peace': 14, 'suffered': 14, 'nearly': 14, 'free': 14, 'despair': 14, 'middle': 14, 'clothes': 14, 'led': 14, 'horse': 14, 'black': 14, 'usually': 14, 'retired': 14, 'fifty': 14, 'living': 14, 'danger': 14, 'answered': 13, 'intended': 13, 'pieces': 13, 'remain': 13, 'cold': 13, 'nurse': 13, 'earth': 13, 'forth': 13, 'pressed': 13, 'highest': 13, 'cottagers': 13, 'bound': 13, 'loud': 13, 'king’s': 13, 'thirty': 13, 'asked': 13, '“he': 13, 'further': 13, 'produced': 13, 'keep': 13, 'storm': 13, 'species': 13, 'principal': 13, 'imagination': 13, 'countries': 13, 'practice': 13, 'help': 13, 'giving': 13, 'convenient': 13, 'unable': 13, 'history': 13, 'anguish': 13, 'quitted': 13, 'deeply': 13, 'impossible': 13, 'quickly': 13, 'motion': 13, 'remember': 13, '(for': 13, 'certainly': 13, 'whereupon': 13, 'sound': 13, 'pocket': 13, 'spot': 13, 'gone': 13, 'stone': 13, 'conceive': 13, 'manners': 13, 'hair': 13, 'approach': 13, 'continue': 13, 'tale': 13, 'sail': 12, 'wood': 12, 'equal': 12, 'presence': 12, 'events': 12, 'mentioned': 12, 'force': 12, 'vain': 12, 'instruments': 12, 'england.': 12, 'mention': 12, 'noise': 12, 'innocence': 12, 'rendered': 12, 'pursue': 12, 'commanded': 12, 'design': 12, 'move': 12, 'melancholy': 12, 'rock': 12, 'cast': 12, 'preserve': 12, 'apartment': 12, 'pains': 12, 'presented': 12, 'happy': 12, 'fastened': 12, 'wretched': 12, 'express': 12, 'enter': 12, 'extreme': 12, 'seek': 12, 'struck': 12, 'taking': 12, 'prodigious': 12, 'virtue': 12, 'dominions': 12, 'third': 12, 'mark': 12, 'distant': 12, 'money': 12, 'weight': 12, 'departure': 12, 'destruction': 12, 'sign': 12, 'prepared': 12, 'assured': 12, 'raised': 12, 'finding': 12, 'consent': 12, 'attention': 12, 'questions': 12, 'future': 12, 'curiosity': 12, 'qualities': 12, 'progress': 12, 'horrible': 12, 'real': 12, 'want': 12, 'across': 12, 'attend': 12, 'along': 12, '(which': 12, 'fiend': 12, '“it': 11, 'met': 11, 'loved': 11, 'labour': 11, 'mountain': 11, 'minutes': 11, 'arrival': 11, 'loss': 11, 'stones': 11, 'sleep': 11, 'leaves': 11, 'reflect': 11, 'subjects': 11, 'frequent': 11, 'white': 11, 'grew': 11, 'admiration': 11, 'virtues': 11, 'however': 11, 'ceremony': 11, 'creatures': 11, 'performed': 11, 'females': 11, 'learn': 11, 'daughter': 11, 'walking': 11, 'fresh': 11, 'moon': 11, 'offer': 11, 'hoped': 11, 'court.': 11, 'ignorant': 11, 'private': 11, 'companions': 11, 'receive': 11, 'feeling': 11, 'seven': 11, 'disposition': 11, 'true': 11, 'broken': 11, 'confirmed': 11, 'business': 11, 'equally': 11, 'memory': 11, 'mean': 11, 'shape': 11, 'larger': 11, 'offered': 11, 'charge': 11, '(as': 11, '“what': 11, 'number': 11, 'waited': 11, 'approached': 11, 'letters': 11, 'low': 11, 'bottom': 11, 'below': 11, 'matter': 11, 'learning': 11, 'fall': 11, 'weeks': 11, 'ears': 11, 'reach': 11, 'hands.': 11, 'foot': 11, 'prospect': 11, 'neck': 11, 'diseases': 11, 'ancient': 11, 'clear': 11, 'lying': 11, 'fallen': 11, 'described': 11, 'conceived': 11, 'wonderful': 11, 'largest': 11, 'advantage': 11, 'fast': 11, 'directed': 11, 'visited': 11, 'case': 11, 'parents': 11, 'minister': 11, 'ships': 11, 'silver': 11, 'accidents': 11, 'kindness': 11, 'innocent': 11, 'prove': 11, 'truth': 11, 'brother': 11, 'does': 11, 'fields': 11, 'hold': 11, 'avoid': 11, 'numbers': 11, 'cheerful': 10, 'search': 10, 'whatever': 10, 'returning': 10, 'occupied': 10, 'drawn': 10, 'please': 10, 'putting': 10, 'death.': 10, 'mighty': 10, 'fair': 10, 'ladies': 10, 'seeing': 10, 'terrible': 10, 'effects': 10, 'directly': 10, '“how': 10, 'speech': 10, 'safe': 10, 'lake': 10, 'inches': 10, 'calm': 10, 'labours': 10, 'recovered': 10, 'proceeded': 10, 'violent': 10, 'surrounded': 10, 'rain': 10, 'lovely': 10, 'lose': 10, 'early': 10, 'eye': 10, 'walk': 10, 'concealed': 10, 'sweet': 10, 'seized': 10, 'break': 10, 'lie': 10, 'art': 10, 'service': 10, 'mr.': 10, 'monster': 10, 'grand': 10, 'existence': 10, 'endeavouring': 10, 'rose': 10, 'arms': 10, 'study': 10, 'hard': 10, 'her.': 10, 'considerable': 10, 'satisfied': 10, 'support': 10, 'confess': 10, 'shut': 10, 'modern': 10, 'believed': 10, 'perfect': 10, 'suppose': 10, 'superior': 10, 'league': 10, 'according': 10, 'title': 10, 'window': 10, 'ought': 10, 'possible': 10, 'meat': 10, 'followed': 10, 'endeavour': 10, 'curious': 10, 'river': 10, 'entreated': 10, 'excellent': 10, 'circumstances': 10, 'gold': 10, 'evening': 10, 'beautiful': 10, 'peculiar': 10, 'informed': 10, 'sensations': 10, 'threw': 10, 'method': 10, 'piece': 10, 'safie': 10, 'bitter': 10, 'contrived': 10, 'clouds': 10, 'struldbrugs': 10, 'mortal': 10, 'steps': 10, 'beginning': 10, 'you.': 10, 'considered': 10, 'eat': 10, 'meant': 10, 'experienced': 10, 'drink': 10, 'period': 10, 'terms': 10, 'nation': 9, 'replied': 9, 'ministers': 9, 'vengeance': 9, 'somewhat': 9, ')': 9, 'army': 9, 'spend': 9, 'endure': 9, 'william': 9, 'advanced': 9, 'flesh': 9, 'discovery': 9, 'moved': 9, 'murder': 9, 'taught': 9, 'governor': 9, 'dozen': 9, 'friends.': 9, 'continual': 9, 'divine': 9, 'fellow': 9, 'otherwise': 9, 'master’s': 9, 'beside': 9, 'run': 9, 'dark': 9, 'waves': 9, 'world.': 9, 'forget': 9, 'assembly': 9, 'european': 9, 'causes': 9, 'lower': 9, 'nine': 9, 'officers': 9, 'pay': 9, 'violence': 9, 'unhappy': 9, 'task': 9, 'dare': 9, 'moments': 9, 'conceal': 9, 'forty': 9, 'following': 9, 'opportunity': 9, '“my': 9, 'applied': 9, 'especially': 9, 'ideas': 9, 'finished': 9, 'lady': 9, 'die': 9, 'iron': 9, 'strongly': 9, 'supposed': 9, 'single': 9, 'relate': 9, 'warm': 9, 'garden': 9, 'canoe': 9, 'coast': 9, 'trouble': 9, 'surprised': 9, 'passage': 9, 'object': 9, 'share': 9, 'behold': 9, 'figure': 9, 'standing': 9, 'desolate': 9, 'eight': 9, '“they': 9, 'luggnagg': 9, 'defence': 9, 'promise': 9, 'instant': 9, 'thou': 9, 'fancy': 9, 'murderer': 9, 'agony': 9, 'mistress': 9, 'council': 9, 'books': 9, 'herself': 9, 'probably': 9, 'square': 9, 'odious': 9, 'bring': 9, 'describe': 9, 'travels': 9, 'descended': 9, 'man’s': 9, 'closet': 9, 'deep': 9, 'presently': 8, 'step': 8, 'instead': 8, 'conduct': 8, 'fleet': 8, 'works': 8, 'younger': 8, 'bear': 8, 'worth': 8, 'accordingly': 8, 'ventured': 8, 'twelve': 8, 'cabin': 8, 'beyond': 8, 'accustomed': 8, 'sailed': 8, 'behaviour': 8, 'confined': 8, 'year': 8, 'heads': 8, 'necessity': 8, 'holding': 8, 'endured': 8, 'remorse': 8, 'beauty': 8, 'remote': 8, 'nations': 8, 'reflections': 8, 'rise': 8, 'dress': 8, 'gazed': 8, 'board': 8, 'advice': 8, 'scenes': 8, 'increased': 8, 'remembrance': 8, 'frightful': 8, 'restored': 8, 'flying': 8, 'gained': 8, 'burning': 8, 'despair.': 8, 'henry': 8, 'father’s': 8, 'train': 8, 'sank': 8, 'female': 8, 'second': 8, 'father.': 8, 'effect': 8, 'hurt': 8, 'follow': 8, 'crime': 8, 'gratitude': 8, 'perfectly': 8, 'destroyed': 8, 'neighbourhood': 8, 'boy': 8, 'concluded': 8, 'engine': 8, 'doing': 8, 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'sheep': 1852, 'shoot': 1853, 'sister': 1854, 'sky.': 1855, 'sledge': 1856, 'slight': 1857, 'smell': 1858, 'smile': 1859, 'solitary': 1860, 'solitude': 1861, 'stature': 1862, 'stick': 1863, 'stopped': 1864, 'strangers': 1865, 'streets': 1866, 'strictly': 1867, 'style': 1868, 'success': 1869, 'summer': 1870, 'summit': 1871, 'supplied': 1872, 'supposing': 1873, 'sure': 1874, 'swear': 1875, 'switzerland': 1876, 'tallest': 1877, 'tempted': 1878, 'theirs': 1879, 'threat': 1880, 'threatened': 1881, 'tolerable': 1882, 'tongue': 1883, 'tools': 1884, 'torture': 1885, 'town.': 1886, 'towns': 1887, 'travellers': 1888, 'treated': 1889, 'tree': 1890, 'trembled': 1891, 'tremendous': 1892, 'trial': 1893, 'truly': 1894, 'trust': 1895, 'underwent': 1896, 'universal': 1897, 'unknown': 1898, 'useful': 1899, 'useless': 1900, 'uttered': 1901, 'utterly': 1902, 'variety': 1903, 'vices': 1904, 'victuals': 1905, 'violently': 1906, 'voyages': 1907, 'watch': 1908, 'waters': 1909, 'weary': 1910, 'weather': 1911, 'weep': 1912, 'wept': 1913, 'whereby': 1914, 'wherewith': 1915, 'whoever': 1916, 'women': 1917, 'wondered': 1918, 'wonders': 1919, 'woods': 1920, 'worked': 1921, 'worthy': 1922, 'wound': 1923, 'write': 1924, 'yahoos.': 1925, '“if': 1926, '“in': 1927, '“whether': 1928, 'absent': 1929, 'accept': 1930, 'accuse': 1931, 'acquaintance': 1932, 'acquainted': 1933, 'added': 1934, 'address': 1935, 'admiration.': 1936, 'admire': 1937, 'advancing': 1938, 'adventures': 1939, 'advised': 1940, 'affair': 1941, 'affection.': 1942, 'ages': 1943, 'agitated': 1944, 'ago': 1945, 'agonies': 1946, 'agreed': 1947, 'aid': 1948, 'allow': 1949, 'alone.': 1950, 'alphabet': 1951, 'amusement': 1952, 'ancestors': 1953, 'angel': 1954, 'animated': 1955, 'answers': 1956, 'apartments': 1957, 'apparent': 1958, 'apparently': 1959, 'apparition': 1960, 'appetite': 1961, 'application': 1962, 'apprehend': 1963, 'apprehension': 1964, 'ardent': 1965, 'arise': 1966, 'arranging': 1967, 'arrow': 1968, 'articulate': 1969, 'artist': 1970, 'ashore': 1971, 'asleep': 1972, 'assisted': 1973, 'assuredly': 1974, 'attached': 1975, 'attendants': 1976, 'attracted': 1977, 'authority': 1978, 'avalanche': 1979, 'awaked': 1980, 'backs': 1981, 'banks': 1982, 'barbarous': 1983, 'beds': 1984, 'belonged': 1985, 'belonging': 1986, 'benefit': 1987, 'berries': 1988, 'bid': 1989, 'big': 1990, 'big-endian': 1991, 'bitterness': 1992, 'boat.': 1993, 'bore': 1994, 'bosom': 1995, 'brook': 1996, 'burnt': 1997, 'cables': 1998, 'capital': 1999, 'carefully': 2000, 'carriage': 2001, 'cat': 2002, 'cell': 2003, 'censure': 2004, 'chamber.': 2005, 'changes': 2006, 'channel': 2007, 'charged': 2008, 'childhood': 2009, 'children.': 2010, 'city.': 2011, 'civilities': 2012, 'clean': 2013, 'collected': 2014, 'collecting': 2015, 'colours': 2016, 'comfort': 2017, 'commence': 2018, 'commerce': 2019, 'communicated': 2020, 'complained': 2021, 'composure': 2022, 'concealing': 2023, 'concern': 2024, 'condemned': 2025, 'confide': 2026, 'confident': 2027, 'confine': 2028, 'consequence': 2029, 'constant': 2030, 'consulted': 2031, 'contain': 2032, 'contemptible': 2033, 'convenience': 2034, 'conversations': 2035, 'conviction': 2036, 'corruption': 2037, 'cousin': 2038, 'created': 2039, 'creator': 2040, 'creep': 2041, 'cried': 2042, 'cruel': 2043, 'damage': 2044, 'deadly': 2045, 'deal': 2046, 'dearest': 2047, 'debate': 2048, 'deceived': 2049, 'decline': 2050, 'deeper': 2051, 'deformity': 2052, 'delight.': 2053, 'delirium': 2054, 'deny': 2055, 'departed': 2056, 'deprived': 2057, 'deserts': 2058, 'desiring': 2059, 'desirous': 2060, 'destiny': 2061, 'destruction.': 2062, 'detail': 2063, 'detested': 2064, 'difficult': 2065, 'discharged': 2066, 'discourses': 2067, 'discoveries': 2068, 'discovering': 2069, 'disdain': 2070, 'disgrace': 2071, 'disposed': 2072, 'disturb': 2073, 'dogs': 2074, 'dread': 2075, 'dwelt': 2076, 'dæmon': 2077, 'eagerly': 2078, 'ears.': 2079, 'educated': 2080, 'effectual': 2081, 'eggs': 2082, 'elevated': 2083, 'embraced': 2084, 'emotions': 2085, 'employ': 2086, 'employment': 2087, 'emptied': 2088, 'enabled': 2089, 'end.': 2090, 'endowed': 2091, 'enjoyment': 2092, 'enlarged': 2093, 'enthusiasm': 2094, 'entreat': 2095, 'envy': 2096, 'europe.': 2097, 'exactness': 2098, 'examine': 2099, 'example': 2100, 'exiles': 2101, 'expectation': 2102, 'exposed': 2103, 'extinguish': 2104, 'extraordinary': 2105, 'families': 2106, 'family.': 2107, 'fate.': 2108, 'fatigue': 2109, 'favourites': 2110, 'favours': 2111, 'fearful': 2112, 'features': 2113, 'feed': 2114, 'fine': 2115, 'finish': 2116, 'firm': 2117, 'fitted': 2118, 'fix': 2119, 'flestrin': 2120, 'fly': 2121, 'folly': 2122, 'forefeet': 2123, 'foreign': 2124, 'fourth': 2125, 'france': 2126, 'friend.': 2127, 'fright': 2128, 'fully': 2129, 'fundamental': 2130, 'gestures': 2131, 'girls': 2132, 'glorious': 2133, 'glory': 2134, 'governing': 2135, 'grace': 2136, 'grant': 2137, 'guilt': 2138, 'hammock': 2139, 'happens': 2140, 'harbour': 2141, 'hastened': 2142, 'hatred.': 2143, 'heap': 2144, 'heart.': 2145, 'heat': 2146, 'herds': 2147, 'hired': 2148, 'honourable': 2149, 'hoof': 2150, 'horizon': 2151, 'horrid': 2152, 'hour.': 2153, 'houyhnhnms.': 2154, 'humankind': 2155, 'hundreds': 2156, 'immediate': 2157, 'impatience': 2158, 'impatient': 2159, 'impeachment': 2160, 'impressed': 2161, 'impulse': 2162, 'inclined': 2163, 'inferior': 2164, 'inflicted': 2165, 'ingolstadt.': 2166, 'injuries': 2167, 'inn': 2168, 'inquire': 2169, 'inquired': 2170, 'insisted': 2171, 'instance': 2172, 'instruct': 2173, 'intelligence': 2174, 'intention': 2175, 'irksome': 2176, 'island.': 2177, 'islands': 2178, 'it:': 2179, 'italy': 2180, 'japanese': 2181, 'kennel': 2182, 'keys': 2183, 'kill': 2184, 'killed': 2185, 'kissed': 2186, 'knocked': 2187, 'knowing': 2188, 'known.': 2189, 'laborious': 2190, 'lacey': 2191, 'ladders': 2192, 'landed': 2193, 'languages': 2194, 'lately': 2195, 'latitude': 2196, 'laughter': 2197, 'lawyers': 2198, 'lead': 2199, 'leagues': 2200, 'lecture': 2201, 'leg': 2202, 'letting': 2203, 'lifting': 2204, 'light.': 2205, 'lilliputians': 2206, 'limbs': 2207, 'lips': 2208, 'littleness': 2209, 'loadstone': 2210, 'long-boat': 2211, 'longed': 2212, 'loveliness': 2213, 'luxury': 2214, 'maintain': 2215, 'makes': 2216, 'mankind.': 2217, 'married': 2218, 'mathematical': 2219, 'meantime': 2220, 'mechanics': 2221, 'merits': 2222, 'metal': 2223, 'mingled': 2224, 'mischief': 2225, 'mixture': 2226, 'more.': 2227, 'moritz': 2228, 'mortals': 2229, 'mountain.': 2230, 'mournful': 2231, 'mouth': 2232, 'naked': 2233, 'natives': 2234, 'neighed': 2235, 'news': 2236, 'nobility': 2237, 'noon': 2238, 'north-west': 2239, 'obtain': 2240, 'obvious': 2241, 'occasion.': 2242, 'offices': 2243, 'ones': 2244, 'operation': 2245, 'ordinary': 2246, 'ourselves': 2247, 'overwhelming': 2248, 'owed': 2249, 'own.': 2250, 'pale': 2251, 'papers': 2252, 'park': 2253, 'parties': 2254, 'passion': 2255, 'passions': 2256, 'peaceful': 2257, 'perceive': 2258, 'perfection': 2259, 'perpetually': 2260, 'persuaded': 2261, 'philosopher': 2262, 'pile': 2263, 'pirates': 2264, 'plain': 2265, 'pleasant': 2266, 'pleased.': 2267, 'plenty': 2268, 'poison': 2269, 'poisoned': 2270, 'position': 2271, 'possession': 2272, 'posture': 2273, 'powder': 2274, 'practised': 2275, 'presents': 2276, 'preserved': 2277, 'pride': 2278, 'proficiency': 2279, 'pronounced': 2280, 'provisions': 2281, 'purpose.': 2282, 'purse': 2283, 'quarrel': 2284, 'quarrels': 2285, 'quarters': 2286, 'quietly': 2287, 'quinbus': 2288, 'rational': 2289, 'reasons': 2290, 'reception': 2291, 'recollection': 2292, 'recommended': 2293, 'red': 2294, 'reduced': 2295, 'refuse': 2296, 'regarded': 2297, 'religion': 2298, 'remove': 2299, 'requires': 2300, 'resembling': 2301, 'respects': 2302, 'rested': 2303, 'restore': 2304, 'returns': 2305, 'reverence': 2306, 'reward': 2307, 'rhine': 2308, 'ridge': 2309, 'ridiculous': 2310, 'rob': 2311, 'rooms': 2312, 'root': 2313, 'roused': 2314, 'rowing': 2315, 'rule': 2316, 'scattered': 2317, 'secured': 2318, 'security': 2319, 'seeking': 2320, 'sending': 2321, 'sentiment': 2322, 'sentiments': 2323, 'serene': 2324, 'series': 2325, 'setting': 2326, 'sex': 2327, 'shake': 2328, 'shaken': 2329, 'shed': 2330, 'shelter': 2331, 'ship.': 2332, 'shock': 2333, 'shoulders': 2334, 'shows': 2335, 'sick': 2336, 'sickness': 2337, 'sight.': 2338, 'silent': 2339, 'simple': 2340, 'sit': 2341, 'situated': 2342, 'sleep.': 2343, 'sleeping': 2344, 'smart': 2345, 'softly': 2346, 'sold': 2347, 'soothed': 2348, 'sorrow': 2349, 'sorrows': 2350, 'sought': 2351, 'source': 2352, 'south': 2353, 'south-east': 2354, 'space': 2355, 'spared': 2356, 'spectacles': 2357, 'speculations': 2358, 'speedily': 2359, 'staid': 2360, 'staples': 2361, 'stockings': 2362, 'stole': 2363, 'stop': 2364, 'story.': 2365, 'straight': 2366, 'strangely': 2367, 'street': 2368, 'stretched': 2369, 'strings': 2370, 'strip': 2371, 'stronger': 2372, 'structure': 2373, 'subjects.': 2374, 'succeeded': 2375, 'surely': 2376, 'sustenance': 2377, 'swallowed': 2378, 'sympathy': 2379, 'systems': 2380, 'tall': 2381, 'taller': 2382, 'teach': 2383, 'terrific': 2384, 'terror': 2385, 'throw': 2386, 'thunder': 2387, 'together.': 2388, 'toil': 2389, 'tore': 2390, 'toward': 2391, 'towers': 2392, 'trace': 2393, 'tranquil': 2394, 'tranquillity': 2395, 'triumph': 2396, 'urine': 2397, 'value': 2398, 'venture': 2399, 'vice': 2400, 'volume': 2401, 'volumes': 2402, 'waded': 2403, 'waist.': 2404, 'waldman': 2405, 'walks': 2406, 'wants': 2407, 'was.': 2408, 'washed': 2409, 'waste': 2410, 'whilst': 2411, 'windows': 2412, 'winter': 2413, 'wise': 2414, 'witnesses': 2415, 'wounds': 2416, 'wretch': 2417, 'wrote': 2418, 'yahoo.': 2419, 'yellow': 2420, 'yours': 2421, '“as': 2422, '“but': 2423, '“there': 2424, '“when': 2425, '(i': 2426, '(if': 2427, '(to': 2428, 'abhor': 2429, 'abhorrence': 2430, 'absence': 2431, 'academy.': 2432, 'accent': 2433, 'accompany': 2434, 'account.': 2435, 'action': 2436, 'acute': 2437, 'adamantine': 2438, 'additional': 2439, 'admiral': 2440, 'adored': 2441, 'advance': 2442, 'advanced.': 2443, 'affect': 2444, 'afford': 2445, 'afternoon': 2446, 'agility': 2447, 'airs': 2448, 'alarmed': 2449, 'alike': 2450, 'allowing': 2451, 'altered': 2452, 'ambition': 2453, 'america': 2454, 'ample': 2455, 'amsterdam': 2456, 'amuse': 2457, 'anchor': 2458, 'animation': 2459, 'anxious': 2460, 'anything': 2461, 'appearances': 2462, 'appetites': 2463, 'approve': 2464, 'april': 2465, 'aristotle': 2466, 'armed': 2467, 'arrives': 2468, 'ascend': 2469, 'ashamed': 2470, 'astonishing': 2471, 'attending': 2472, 'attracting': 2473, 'august': 2474, 'author.': 2475, 'bade': 2476, 'balls': 2477, 'balnibarbi': 2478, 'banished': 2479, 'bare': 2480, 'barrier': 2481, 'basket': 2482, 'battle': 2483, 'beast': 2484, 'beaten': 2485, 'beaufort': 2486, 'befitting': 2487, 'begin': 2488, 'beings.': 2489, 'belong': 2490, 'bestowing': 2491, 'better.': 2492, 'betwixt': 2493, 'bitterly': 2494, 'blanc': 2495, 'blasted': 2496, 'bless': 2497, 'blessed': 2498, 'blooming': 2499, 'blotted': 2500, 'bodily': 2501, 'body.': 2502, 'boldly': 2503, 'borne': 2504, 'bounded': 2505, 'brain': 2506, 'brains': 2507, 'breakfast': 2508, 'brutality': 2509, 'buildings': 2510, 'built': 2511, 'buried': 2512, 'bury': 2513, 'cabin.': 2514, 'cabinet': 2515, 'candour': 2516, 'captains': 2517, 'cases': 2518, 'castles': 2519, 'cattle': 2520, 'certainty': 2521, 'chained': 2522, 'character': 2523, 'cheerfulness': 2524, 'chemical': 2525, 'chemistry': 2526, 'childish': 2527, 'choice': 2528, 'choose': 2529, 'civilize': 2530, 'clings': 2531, 'coffin': 2532, 'collect': 2533, 'combined': 2534, 'comely': 2535, 'commander': 2536, 'commanding': 2537, 'commenced': 2538, 'composed': 2539, 'computation': 2540, 'computed': 2541, 'conditions': 2542, 'conductor': 2543, 'conjunction': 2544, 'connected': 2545, 'consequences': 2546, 'consideration': 2547, 'consisted': 2548, 'constantly': 2549, 'constructed': 2550, 'consulting': 2551, 'consummate': 2552, 'contained': 2553, 'contains': 2554, 'contemplate': 2555, 'contented': 2556, 'continent': 2557, 'continuing': 2558, 'cord': 2559, 'cornelius': 2560, 'corner': 2561, 'corruptions': 2562, 'cottage.': 2563, 'cottagers.': 2564, 'cottages': 2565, 'country.”': 2566, 'couple': 2567, 'cousin.': 2568, 'cows': 2569, 'create': 2570, 'creation.': 2571, 'cross': 2572, 'crowd': 2573, 'cultivated': 2574, 'curiosities': 2575, 'current': 2576, 'cursory': 2577, 'customs': 2578, 'cæsar': 2579, 'dangers': 2580, 'darling': 2581, 'dash': 2582, 'dashed': 2583, 'dashing': 2584, 'dawned': 2585, 'day.': 2586, 'day’s': 2587, 'declared': 2588, 'defended': 2589, 'dejected': 2590, 'delayed': 2591, 'delicious': 2592, 'delights': 2593, 'demand': 2594, 'demeanour': 2595, 'depth': 2596, 'descent': 2597, 'descriptions': 2598, 'deserve': 2599, 'deserved': 2600, 'designs': 2601, 'desolation': 2602, 'destined': 2603, 'destroyer': 2604, 'detestation': 2605, 'dews': 2606, 'dexterity': 2607, 'dexterous': 2608, 'diameter': 2609, 'diet': 2610, 'differed': 2611, 'difficulties': 2612, 'diffused': 2613, 'diminutive': 2614, 'directions': 2615, 'disagreeable': 2616, 'disappeared': 2617, 'discharge': 2618, 'discomposed': 2619, 'disconsolate': 2620, 'disease': 2621, 'disgust': 2622, 'disgusted': 2623, 'dish': 2624, 'dislike': 2625, 'displayed': 2626, 'disposal': 2627, 'distinct': 2628, 'distinctly': 2629, 'diversion': 2630, 'divided': 2631, 'do.': 2632, 'doctrine': 2633, 'dog': 2634, 'domestics': 2635, 'double': 2636, 'doubted': 2637, 'doubtless': 2638, 'dragged': 2639, 'draught': 2640, 'drawing': 2641, 'drops': 2642, 'drowned': 2643, 'due': 2644, 'dying': 2645, 'eager': 2646, 'ease': 2647, 'edge': 2648, 'edinburgh': 2649, 'educating': 2650, 'eldest': 2651, 'elizabeth.': 2652, 'elizabeth’s': 2653, 'emotion': 2654, 'empires': 2655, 'enable': 2656, 'encompassed': 2657, 'endued': 2658, 'endured.': 2659, 'englishmen': 2660, 'enjoy': 2661, 'enough.': 2662, 'entertained': 2663, 'entertainment': 2664, 'enveloped': 2665, 'erected': 2666, 'ernest': 2667, 'escape.': 2668, 'estate': 2669, 'everlasting': 2670, 'evidence': 2671, 'exalted': 2672, 'exceeded': 2673, 'excellency': 2674, 'exchanged': 2675, 'execution': 2676, 'exertion': 2677, 'experience': 2678, 'experiment': 2679, 'experiments': 2680, 'explanation': 2681, 'expressing': 2682, 'exquisite': 2683, 'extend': 2684, 'extent': 2685, 'extremities': 2686, 'extremity': 2687, 'faint': 2688, 'fairer': 2689, 'fall.': 2690, 'falsehood': 2691, 'famous': 2692, 'farmers': 2693, 'favour.': 2694, 'favourable': 2695, 'feeding': 2696, 'feet.': 2697, 'feet:': 2698, 'fifth': 2699, 'fight': 2700, 'figures': 2701, 'figures.': 2702, 'fingers': 2703, 'fish': 2704, 'fishing': 2705, 'fitter': 2706, 'flew': 2707, 'flight': 2708, 'flimnap': 2709, 'flowers': 2710, 'foolish': 2711, 'forbidden': 2712, 'forgot': 2713, 'forgotten': 2714, 'fortitude': 2715, 'fortunate': 2716, 'forwards': 2717, 'founded': 2718, 'fourscore': 2719, 'frame.': 2720, 'freedom': 2721, 'frog': 2722, 'frozen': 2723, 'fulfilment': 2724, 'fund': 2725, 'funeral': 2726, 'furniture': 2727, 'generosity': 2728, 'getting': 2729, 'glance': 2730, 'glass': 2731, 'gloomy': 2732, 'good.': 2733, 'gotten': 2734, 'grasp': 2735, 'grief.': 2736, 'groans': 2737, 'grow': 2738, 'grown': 2739, 'guided': 2740, 'guitar': 2741, 'hand.': 2742, 'hanger': 2743, 'harmless': 2744, 'harsh': 2745, 'hastily': 2746, 'hate': 2747, 'hated': 2748, 'hateful': 2749, 'hath': 2750, 'hay': 2751, 'hearing': 2752, 'hearts': 2753, 'hell': 2754, 'hellish': 2755, 'hence': 2756, 'herd': 2757, 'himself.': 2758, 'hinder': 2759, 'hitherto': 2760, 'hogsheads': 2761, 'holland': 2762, 'homer': 2763, 'horror.': 2764, 'horrors': 2765, 'humanity': 2766, 'hurried': 2767, 'husband': 2768, 'i.': 2769, 'ignominy': 2770, 'ignorance': 2771, 'illness': 2772, 'imagined': 2773, 'imbued': 2774, 'imperfect': 2775, 'improvements': 2776, 'imputed': 2777, 'in:': 2778, 'incidents': 2779, 'induced': 2780, 'indulged': 2781, 'information': 2782, 'ingredients': 2783, 'inquiring': 2784, 'insatiable': 2785, 'insects': 2786, 'inspire': 2787, 'integrity': 2788, 'intentions': 2789, 'interpret': 2790, 'interrupt': 2791, 'introduced': 2792, 'invasion': 2793, 'inventions': 2794, 'inventory': 2795, 'isle': 2796, 'issue': 2797, 'it.”': 2798, 'joined': 2799, 'joints': 2800, 'journal': 2801, 'june': 2802, 'kindly': 2803, 'kingdoms': 2804, 'kings': 2805, 'krempe': 2806, 'laid': 2807, 'lands': 2808, 'language.': 2809, 'largeness': 2810, 'lawyer': 2811, 'leaning': 2812, 'lenity': 2813, 'liberty.': 2814, 'lies': 2815, 'lifted': 2816, 'lighted': 2817, 'lightning': 2818, 'likely': 2819, 'lineaments': 2820, 'linen': 2821, 'liquor': 2822, 'listening': 2823, 'livelihood': 2824, 'loaded': 2825, 'loathsome': 2826, 'lodging': 2827, 'long.': 2828, 'lords': 2829, 'lover': 2830, 'lovers': 2831, 'machine': 2832, 'mad': 2833, 'madness': 2834, 'majesty.': 2835, 'male': 2836, 'malice': 2837, 'malice.': 2838, 'malignity': 2839, 'manage': 2840, 'managing': 2841, 'master.': 2842, 'matters': 2843, 'maxim': 2844, 'me!': 2845, 'me)': 2846, 'me.”': 2847, 'meanest': 2848, 'meeting': 2849, 'men.': 2850, 'merit': 2851, 'millions': 2852, 'minute': 2853, 'mischief.': 2854, 'miseries': 2855, 'misfortunes.': 2856, 'mock': 2857, 'mode': 2858, 'moderate': 2859, 'monarchs': 2860, 'morality': 2861, 'morning.': 2862, 'mother.': 2863, 'motions': 2864, 'mouth.': 2865, 'mouths': 2866, 'musical': 2867, 'myself)': 2868, 'mystery': 2869, 'nardac': 2870, 'narrow': 2871, 'necessaries': 2872, 'neighing': 2873, 'nobles': 2874, 'numerous': 2875, 'nurse.': 2876, 'obedience': 2877, 'obstinate': 2878, 'odd': 2879, 'offering': 2880, 'omitted': 2881, 'ours': 2882, 'overcame': 2883, 'owing': 2884, 'pace': 2885, 'packthreads': 2886, 'pages': 2887, 'painful': 2888, 'pardon': 2889, 'parliament': 2890, 'parted': 2891, 'passion.': 2892, 'patron': 2893, 'paused': 2894, 'penetrate': 2895, 'performance': 2896, 'perish': 2897, 'permission': 2898, 'petition': 2899, 'pick': 2900, 'pin': 2901, 'pines': 2902, 'pittance': 2903, 'places': 2904, 'places.': 2905, 'placid': 2906, 'pockets': 2907, 'politics': 2908, 'portion': 2909, 'possess': 2910, 'post': 2911, 'posterity': 2912, 'powerful': 2913, 'practical': 2914, 'praise': 2915, 'praises': 2916, 'precedents': 2917, 'precincts': 2918, 'preservation': 2919, 'presume': 2920, 'pretence': 2921, 'prevented': 2922, 'previously': 2923, 'prey': 2924, 'preyed': 2925, 'prince’s': 2926, 'prison': 2927, 'prisoner': 2928, 'probability': 2929, 'probable': 2930, 'productions': 2931, 'pronouncing': 2932, 'proposal': 2933, 'proposing': 2934, 'protector': 2935, 'protectors': 2936, 'provide': 2937, 'providence': 2938, 'pulling': 2939, 'pupils': 2940, 'qualified': 2941, 'quick': 2942, 'raising': 2943, 'rang': 2944, 'rapid': 2945, 'rats': 2946, 'reaching': 2947, 'reader.': 2948, 'reading': 2949, 'reality': 2950, 'realm': 2951, 'reason.': 2952, 'receiving': 2953, 'recent': 2954, 'reckoning': 2955, 'reduce': 2956, 'reflection.': 2957, 'refuge': 2958, 'refused': 2959, 'reign': 2960, 'relating': 2961, 'remaining': 2962, 'remarked': 2963, 'renowned': 2964, 'repeating': 2965, 'repose': 2966, 'represented': 2967, 'reproach': 2968, 'resemblance': 2969, 'resemble': 2970, 'resembled': 2971, 'reserved': 2972, 'respected': 2973, 'respite': 2974, 'restless': 2975, 'retire': 2976, 'return.': 2977, 'rewarded': 2978, 'riding': 2979, 'ring': 2980, 'roads': 2981, 'rocks': 2982, 'rough': 2983, 'row': 2984, 'rude': 2985, 'rushed': 2986, 'rustic': 2987, 'sad': 2988, 'safety': 2989, 'sake': 2990, 'satiated': 2991, 'saying': 2992, 'scene.': 2993, 'school': 2994, 'science.': 2995, 'sciences': 2996, 'scimitar': 2997, 'score': 2998, 'sea-weed': 2999, 'sealed': 3000, 'searched': 3001, 'seat': 3002, 'seem': 3003, 'seen.': 3004, 'sensations.': 3005, 'sentence': 3006, 'serviceable': 3007, 'severity': 3008, 'shadow': 3009, 'shame': 3010, 'shapes': 3011, 'shining': 3012, 'shoes': 3013, 'shore.': 3014, 'shudder': 3015, 'sickened': 3016, 'situation': 3017, 'six-and-thirty': 3018, 'sixteen': 3019, 'skilful': 3020, 'slip': 3021, 'snow': 3022, 'softness': 3023, 'solemn': 3024, 'son': 3025, 'soothe': 3026, 'sorrowful': 3027, 'south.': 3028, 'special': 3029, 'species.': 3030, 'speed.': 3031, 'sprang': 3032, 'squeeze': 3033, 'stage': 3034, 'stairs': 3035, 'stand': 3036, 'steal': 3037, 'steeple': 3038, 'steering': 3039, 'stepped': 3040, 'sticking': 3041, 'stomach': 3042, 'stony': 3043, 'stored': 3044, 'stores': 3045, 'stout': 3046, 'strike': 3047, 'stroke': 3048, 'strove': 3049, 'struggle': 3050, 'struggled': 3051, 'stuck': 3052, 'studies.': 3053, 'sublime': 3054, 'submit': 3055, 'substance': 3056, 'success.': 3057, 'sufferer': 3058, 'suffice': 3059, 'suit': 3060, 'suitable': 3061, 'sun.': 3062, 'sunshine': 3063, 'supernatural': 3064, 'support.': 3065, 'surface': 3066, 'surround': 3067, 'suspected': 3068, 'swelled': 3069, 'swore': 3070, 'sympathised': 3071, 'takes': 3072, 'talking': 3073, 'tax': 3074, 'tearing': 3075, 'temper': 3076, 'temperature': 3077, 'terminate': 3078, 'terribly': 3079, 'thanks': 3080, 'thicker': 3081, 'thin': 3082, 'thine': 3083, 'throat': 3084, 'throne': 3085, 'thumb': 3086, 'tip': 3087, 'topics': 3088, 'tops': 3089, 'tormented': 3090, 'torn': 3091, 'touched': 3092, 'tour': 3093, 'tower': 3094, 'toys': 3095, 'traitor': 3096, 'trample': 3097, 'travel': 3098, 'traveller': 3099, 'treasurer': 3100, 'treatise': 3101, 'treatment': 3102, 'tribute': 3103, 'truth.': 3104, 'try': 3105, 'turns': 3106, 'unacquainted': 3107, 'uncle': 3108, 'uneasiness': 3109, 'unexpected': 3110, 'unlike': 3111, 'unperceived': 3112, 'unusual': 3113, 'up.': 3114, 'upper': 3115, 'upwards': 3116, 'valley': 3117, 'valley.': 3118, 'valour': 3119, 'valuable': 3120, 'valued': 3121, 'vanity': 3122, 'vegetables': 3123, 'views': 3124, 'village': 3125, 'villages': 3126, 'virtue.': 3127, 'virtuous': 3128, 'visions': 3129, 'voyage.': 3130, 'vulgar': 3131, 'waist': 3132, 'wall': 3133, 'walls': 3134, 'warmed': 3135, 'wars': 3136, 'wasted': 3137, 'waved': 3138, 'weakness': 3139, 'wealth': 3140, 'wet': 3141, 'whereas': 3142, 'whispered': 3143, 'wife.': 3144, 'window.': 3145, 'winds': 3146, 'wings': 3147, 'wisest': 3148, 'wishes': 3149, 'withdrew': 3150, 'witness': 3151, 'wives': 3152, 'wondrous': 3153, 'wood.': 3154, 'words.': 3155, 'words:': 3156, 'wore': 3157, 'worse': 3158, 'worst': 3159, 'wrapped': 3160, 'writing': 3161, 'yahoos’': 3162, 'ye': 3163, 'years.': 3164, 'yielded': 3165, 'young.': 3166, 'zeal': 3167, '“for': 3168, '“was': 3169, '(a': 3170, '(although': 3171, '(an': 3172, '(so': 3173, '(the': 3174, '(these': 3175, '(this': 3176, '(who': 3177, '16th': 3178, '30': 3179, '9th': 3180, 'a-laughing': 3181, 'abhorred': 3182, 'aboard': 3183, 'abominable': 3184, 'abortive': 3185, 'about.': 3186, 'absolute': 3187, 'abundance': 3188, 'accepted': 3189, 'access': 3190, 'acknowledged': 3191, 'acquire': 3192, 'acquiring': 3193, 'action.': 3194, 'active': 3195, 'activity': 3196, 'adamant': 3197, 'adapted': 3198, 'add': 3199, 'adhere': 3200, 'admit': 3201, 'admittance': 3202, 'admittance.': 3203, 'adrift': 3204, 'advantages': 3205, 'adversary': 3206, 'advising': 3207, 'affirm': 3208, 'afterward': 3209, 'ages.': 3210, 'aggravation': 3211, 'agitates': 3212, 'agitation.': 3213, 'agony.': 3214, 'agrippa': 3215, 'aided': 3216, 'aim': 3217, 'airy': 3218, 'alive': 3219, 'all.”': 3220, 'alliance': 3221, 'allude': 3222, 'ally': 3223, 'alps': 3224, 'alter': 3225, 'amazement': 3226, 'amity': 3227, 'amusement.': 3228, 'anchors': 3229, 'angelic': 3230, 'anger': 3231, 'another.': 3232, 'answer.': 3233, 'answerable': 3234, 'answers.': 3235, 'antic': 3236, 'antipathy': 3237, 'antiquity': 3238, 'anxiety': 3239, 'anyone': 3240, 'apart': 3241, 'appalling': 3242, 'applying': 3243, 'appointed': 3244, 'apprehensions': 3245, 'appropriated': 3246, 'arch': 3247, 'arched': 3248, 'ardently': 3249, 'are.': 3250, 'argue': 3251, 'argued': 3252, 'arises': 3253, 'arranged': 3254, 'arrived.': 3255, 'arteries': 3256, 'article': 3257, 'artificial': 3258, 'ascribed': 3259, 'aside': 3260, 'asleep.': 3261, 'assassin': 3262, 'assemblage': 3263, 'assertion': 3264, 'assigned': 3265, 'associate': 3266, 'assumed': 3267, 'assure': 3268, 'ass’s': 3269, 'astonishment.': 3270, 'astronomy.': 3271, 'attack': 3272, 'attempt': 3273, 'attempting': 3274, 'attempts': 3275, 'attendance': 3276, 'attendant': 3277, 'auspicious': 3278, 'author’s': 3279, 'autumn': 3280, 'avarice': 3281, 'avoided': 3282, 'awake': 3283, 'awhile': 3284, 'balanced': 3285, 'bangs': 3286, 'barrel': 3287, 'bars': 3288, 'bay': 3289, 'be.': 3290, 'beach': 3291, 'beards': 3292, 'bearing': 3293, 'bears': 3294, 'beauties': 3295, 'beauty.': 3296, 'bedchamber': 3297, 'beef': 3298, 'beggar.': 3299, 'begging': 3300, 'behaved': 3301, 'behind.': 3302, 'beholding': 3303, 'believes': 3304, 'believing': 3305, 'bellows': 3306, 'belrive': 3307, 'bend': 3308, 'benignity': 3309, 'betray': 3310, 'bill': 3311, 'bird': 3312, 'birth.': 3313, 'bit': 3314, 'biting': 3315, 'bitterest': 3316, 'bitterness.': 3317, 'blast': 3318, 'blessing': 3319, 'bliss': 3320, 'blood.': 3321, 'bloody': 3322, 'bloom': 3323, 'blot': 3324, 'board.': 3325, 'boldness': 3326, 'bolgolam': 3327, 'borrowed': 3328, 'bottom.': 3329, 'boundary': 3330, 'boys': 3331, 'brave': 3332, 'breach': 3333, 'breadth': 3334, 'breakfast.': 3335, 'breath': 3336, 'bred': 3337, 'bright': 3338, 'bristol': 3339, 'brother?': 3340, 'bruised': 3341, 'brutus': 3342, 'buckets': 3343, 'buckle': 3344, 'buildings.': 3345, 'bundle': 3346, 'burned': 3347, 'burton': 3348, 'busied': 3349, 'business.': 3350, 'cadence': 3351, 'cake': 3352, 'calculations': 3353, 'california': 3354, 'calmed': 3355, 'calmer': 3356, 'caprices': 3357, 'captain’s': 3358, 'carcass': 3359, 'careful': 3360, 'careless': 3361, 'carelessness': 3362, 'carrying': 3363, 'catch': 3364, 'catching': 3365, 'caught': 3366, 'caution': 3367, 'cave': 3368, 'caves': 3369, 'celestial': 3370, 'censured': 3371, 'century': 3372, 'ceremony.': 3373, 'chain': 3374, 'chains': 3375, 'chanced': 3376, 'character.': 3377, 'characters': 3378, 'charles': 3379, 'charm': 3380, 'cheat': 3381, 'cheek': 3382, 'cheeks': 3383, 'cheerfully': 3384, 'chin': 3385, 'circular': 3386, 'circumference': 3387, 'circumstances.': 3388, 'citizens': 3389, 'civility': 3390, 'clad': 3391, 'clearly': 3392, 'clemency': 3393, 'climate': 3394, 'climate.': 3395, 'climb': 3396, 'climbed': 3397, 'climbing': 3398, 'cling': 3399, 'cloak': 3400, 'closely': 3401, 'closer': 3402, 'closet-window': 3403, 'cloud': 3404, 'clue': 3405, 'coaches': 3406, 'coarser': 3407, 'coasts': 3408, 'coat-pocket': 3409, 'collar': 3410, 'college': 3411, 'college.': 3412, 'colonies': 3413, 'colour.': 3414, 'comb': 3415, 'command.”': 3416, 'commands': 3417, 'comment': 3418, 'companions.': 3419, 'compelled': 3420, 'complain': 3421, 'complaisant': 3422, 'complete': 3423, 'completely': 3424, 'comprehend.': 3425, 'comprehended': 3426, 'conceited': 3427, 'conceived.': 3428, 'conceptions': 3429, 'concerned': 3430, 'condemn': 3431, 'conferred': 3432, 'confided': 3433, 'confidence': 3434, 'confines': 3435, 'confirm': 3436, 'conflict.': 3437, 'confounded': 3438, 'confusion': 3439, 'conquered': 3440, 'conquering': 3441, 'conscious': 3442, 'consists': 3443, 'consolation.': 3444, 'conspiracies': 3445, 'constitution': 3446, 'consume': 3447, 'consummation': 3448, 'continuance': 3449, 'continuation': 3450, 'contrast': 3451, 'contrive': 3452, 'control': 3453, 'conveniences': 3454, 'conversation.': 3455, 'conversed': 3456, 'cooks': 3457, 'cords': 3458, 'corners': 3459, 'corrupted': 3460, 'could:': 3461, 'counsellors': 3462, 'countenances.': 3463, 'country?”': 3464, 'court:': 3465, 'courteous': 3466, 'cracked': 3467, 'created.': 3468, 'creating': 3469, 'creature.': 3470, 'creatures.': 3471, 'crimes.': 3472, 'crossed': 3473, 'crowded': 3474, 'crown': 3475, 'crush': 3476, 'cub': 3477, 'cultivate': 3478, 'cunning': 3479, 'curtains': 3480, 'custom-house': 3481, 'd': 3482, 'dancing': 3483, 'daniel': 3484, 'darkened': 3485, 'darts': 3486, 'date': 3487, 'daubed': 3488, 'dearer': 3489, 'death.”': 3490, 'deaths': 3491, 'december': 3492, 'decide.': 3493, 'declaration': 3494, 'declare': 3495, 'declining': 3496, 'deed.': 3497, 'deemed': 3498, 'deepest': 3499, 'defect': 3500, 'deficient': 3501, 'deformities': 3502, 'deformity.': 3503, 'degenerated': 3504, 'delay': 3505, 'delighting': 3506, 'delineate': 3507, 'demoniacal': 3508, 'demonstrate': 3509, 'departed.': 3510, 'depend': 3511, 'depended': 3512, 'deposed': 3513, 'deposition': 3514, 'deprive': 3515, 'descending': 3516, 'descried': 3517, 'deservedly': 3518, 'deserves': 3519, 'deserving': 3520, 'despatch': 3521, 'desperate': 3522, 'despicable': 3523, 'despondency': 3524, 'desponding': 3525, 'destiny.': 3526, 'destroyed.': 3527, 'destructive': 3528, 'determinate': 3529, 'determining': 3530, 'detestable': 3531, 'developed': 3532, 'devil': 3533, 'devotion.': 3534, 'devour': 3535, 'devoured.': 3536, 'diameter.': 3537, 'die.”': 3538, 'diemen’s': 3539, 'digestion': 3540, 'dignity': 3541, 'digression.': 3542, 'diligently': 3543, 'dim': 3544, 'dimmed': 3545, 'dined': 3546, 'directing': 3547, 'dirt': 3548, 'disadvantage': 3549, 'disagreeable.': 3550, 'disappointed': 3551, 'discern': 3552, 'discompose': 3553, 'discontent': 3554, 'discoursed': 3555, 'diseased': 3556, 'disguise': 3557, 'disorder': 3558, 'dispose': 3559, 'disquiets': 3560, 'dissipate': 3561, 'distance.': 3562, 'distant.': 3563, 'distinction.': 3564, 'distorted': 3565, 'districts': 3566, 'diverts': 3567, 'divide': 3568, 'dizzy': 3569, 'docile': 3570, 'doctor': 3571, 'don': 3572, 'doors': 3573, 'doth': 3574, 'down.': 3575, 'downs': 3576, 'downwards': 3577, 'drag': 3578, 'draught.': 3579, 'dreaded': 3580, 'dreamt': 3581, 'dreary': 3582, 'dried': 3583, 'dropped': 3584, 'dug': 3585, 'dungeon': 3586, 'dust': 3587, 'dwindled': 3588, 'e': 3589, 'eagle': 3590, 'ear.': 3591, 'earnest': 3592, 'earnestly': 3593, 'earnestness': 3594, 'earthen': 3595, 'eastern': 3596, 'eastward': 3597, 'eating': 3598, 'economy': 3599, 'ecstasy': 3600, 'edifice': 3601, 'either.': 3602, 'elements': 3603, 'elizabeth:': 3604, 'eloquence': 3605, 'eloquent': 3606, 'embers': 3607, 'embrace': 3608, 'employed.': 3609, 'employing': 3610, 'empress’s': 3611, 'encomiums': 3612, 'endeavours': 3613, 'endless': 3614, 'ends': 3615, 'enjoined': 3616, 'enlarge': 3617, 'enslaved': 3618, 'entertaining': 3619, 'enthusiastic': 3620, 'entrance': 3621, 'epoch': 3622, 'erecting': 3623, 'errors': 3624, 'especial': 3625, 'estates': 3626, 'esteem': 3627, 'esteemed': 3628, 'europeans': 3629, 'evaded': 3630, 'everything': 3631, 'evils': 3632, 'examining': 3633, 'excelled': 3634, 'excellence': 3635, 'excellent.': 3636, 'excite': 3637, 'execute': 3638, 'executed': 3639, 'execution.': 3640, 'exempt': 3641, 'exercises.': 3642, 'exercising': 3643, 'exert': 3644, 'exerted': 3645, 'exertions': 3646, 'exhaustion': 3647, 'exhortations': 3648, 'exist': 3649, 'expected.': 3650, 'expedient': 3651, 'expedient.': 3652, 'expense': 3653, 'expense.': 3654, 'exploded': 3655, 'exploit': 3656, 'expressed.': 3657, 'extinction': 3658, 'extinguished': 3659, 'extinguished.': 3660, 'extract': 3661, 'extracting': 3662, 'exultation': 3663, 'fact': 3664, 'faith': 3665, 'faithful': 3666, 'falls': 3667, 'falsely': 3668, 'famine': 3669, 'fanned': 3670, 'fashioned': 3671, 'fastening': 3672, 'fatality': 3673, 'father:': 3674, 'fault': 3675, 'fear.': 3676, 'fearfully': 3677, 'fearing': 3678, 'feats': 3679, 'feature': 3680, 'february': 3681, 'feel.': 3682, 'feel.”': 3683, 'feelings.': 3684, 'felix.': 3685, 'felt.': 3686, 'fervour': 3687, 'fetter': 3688, 'fiddles': 3689, 'fields.': 3690, 'fierce': 3691, 'fifteen': 3692, 'film': 3693, 'final': 3694, 'finds': 3695, 'finest': 3696, 'fingers.': 3697, 'firmly': 3698, 'firmness': 3699, 'fishermen': 3700, 'fist': 3701, 'fits': 3702, 'fixing': 3703, 'flesh.': 3704, 'flint': 3705, 'flit': 3706, 'flourished': 3707, 'flowed': 3708, 'folds': 3709, 'follies': 3710, 'followers': 3711, 'follows': 3712, 'foot-path': 3713, 'footmen': 3714, 'fore-foot': 3715, 'fore-sail': 3716, 'foreigners': 3717, 'forgetfulness': 3718, 'forgetfulness.': 3719, 'form?': 3720, 'formal': 3721, 'formerly': 3722, 'forsaken': 3723, 'fort': 3724, 'fought': 3725, 'fours': 3726, 'framed': 3727, 'frankenstein': 3728, 'freed': 3729, 'french': 3730, 'fright.': 3731, 'fro': 3732, 'frontiers.': 3733, 'fulfilled': 3734, 'furious': 3735, 'furnished': 3736, 'fury': 3737, 'gait': 3738, 'gale': 3739, 'gassendi': 3740, 'gay': 3741, 'gaze': 3742, 'geese': 3743, 'generation': 3744, 'genial': 3745, 'gentry': 3746, 'germany': 3747, 'gesture': 3748, 'ghastly': 3749, 'ghosts': 3750, 'gift': 3751, 'glaciers': 3752, 'gladness': 3753, 'glasses': 3754, 'glimmer': 3755, 'gloom': 3756, 'glubbdubdrib.': 3757, 'glumdalclitch’s': 3758, 'godlike': 3759, 'governor’s': 3760, 'grandeur': 3761, 'grandfather': 3762, 'gratify': 3763, 'grave.': 3764, 'graves': 3765, 'greatness': 3766, 'grey': 3767, 'grievous': 3768, 'grievously': 3769, 'grildrig': 3770, 'groans.': 3771, 'ground:': 3772, 'group': 3773, 'guard': 3774, 'guardians': 3775, 'guest': 3776, 'guest.': 3777, 'guide.': 3778, 'gush': 3779, 'had.': 3780, 'hailed': 3781, 'hair.': 3782, 'hairiness': 3783, 'handle': 3784, 'hands.”': 3785, 'hang': 3786, 'happen.': 3787, 'happier': 3788, 'happily': 3789, 'happy.': 3790, 'harden': 3791, 'harmony': 3792, 'hat': 3793, 'haunches': 3794, 'heard.': 3795, 'heavens.': 3796, 'hedge': 3797, 'helped': 3798, 'helping': 3799, 'helpless': 3800, 'herbage': 3801, 'herbs': 3802, 'hereafter': 3803, 'heroism': 3804, 'hers': 3805, 'hiding-place': 3806, 'hiding-places.': 3807, 'high-treason': 3808, 'hill': 3809, 'hindered': 3810, 'hinges': 3811, 'historical': 3812, 'history.': 3813, 'holds': 3814, 'holland.': 3815, 'honour’s': 3816, 'hoofs': 3817, 'hook': 3818, 'hooks': 3819, 'horse.': 3820, 'howling': 3821, 'hue': 3822, 'humblest': 3823, 'humming': 3824, 'hurgo': 3825, 'hurts': 3826, 'husbands': 3827, 'hut': 3828, 'ice.': 3829, 'ices': 3830, 'ideas.': 3831, 'idleness': 3832, 'ill-will': 3833, 'illuminate': 3834, 'images': 3835, 'imitation': 3836, 'imminent': 3837, 'immortal': 3838, 'impaired': 3839, 'impending': 3840, 'importance': 3841, 'impotent': 3842, 'impress': 3843, 'impression': 3844, 'improbable': 3845, 'improve': 3846, 'impudence': 3847, 'in.”': 3848, 'inanimate': 3849, 'incident': 3850, 'inclemency': 3851, 'inconceivable': 3852, 'inconstant': 3853, 'incredible': 3854, 'indefatigable': 3855, 'indies': 3856, 'indifference': 3857, 'induce': 3858, 'indulge': 3859, 'indulgent': 3860, 'indulging': 3861, 'ineffectual': 3862, 'inexhaustible': 3863, 'infallible': 3864, 'infamous': 3865, 'infancy': 3866, 'infant': 3867, 'infinite': 3868, 'infirmities.': 3869, 'influence': 3870, 'ingratitude': 3871, 'inhabitants.': 3872, 'inhabited': 3873, 'injured': 3874, 'injury': 3875, 'inmost': 3876, 'innumerable': 3877, 'inquirers': 3878, 'inquisitive': 3879, 'inroads': 3880, 'insanity': 3881, 'inside': 3882, 'inspired': 3883, 'instances': 3884, 'instructed': 3885, 'instruction': 3886, 'insurrection': 3887, 'intensity': 3888, 'intent': 3889, 'intercept': 3890, 'interchange': 3891, 'interested': 3892, 'interesting': 3893, 'interfere': 3894, 'intermingled': 3895, 'interrupted': 3896, 'interruption': 3897, 'interwoven': 3898, 'intimate': 3899, 'intolerable': 3900, 'intrigue': 3901, 'invade': 3902, 'invader': 3903, 'invisible': 3904, 'inward': 3905, 'issued': 3906, 'itself.': 3907, 'jealous': 3908, 'jewry': 3909, 'joint': 3910, 'jolt': 3911, 'joys.': 3912, 'judicature': 3913, 'juice': 3914, 'juncture': 3915, 'justiciary': 3916, 'justified': 3917, 'keen': 3918, 'key': 3919, 'kind.': 3920, 'knees.': 3921, 'knelt': 3922, 'knocking': 3923, 'knows': 3924, 'labouring': 3925, 'lad': 3926, 'ladder': 3927, 'laden': 3928, 'lakes': 3929, 'landscape.': 3930, 'lap': 3931, 'lappet': 3932, 'laputa': 3933, 'lark': 3934, 'lark.': 3935, 'lastly': 3936, 'later': 3937, 'laugh': 3938, 'laughing': 3939, 'lawfully': 3940, 'lay.': 3941, 'leader': 3942, 'leading': 3943, 'lean': 3944, 'learning.': 3945, 'learnt': 3946, 'lectures': 3947, 'left.': 3948, 'leghorn': 3949, 'lent': 3950, 'lessened': 3951, 'levee': 3952, 'leyden': 3953, 'liable': 3954, 'life.”': 3955, 'life?': 3956, 'lifeless': 3957, 'lift': 3958, 'limb': 3959, 'limits': 3960, 'lineament': 3961, 'link': 3962, 'lips.': 3963, 'lisbon': 3964, 'list': 3965, 'live.': 3966, 'living.': 3967, 'load': 3968, 'loaf': 3969, 'locked': 3970, 'longer.': 3971, 'longitude': 3972, 'lord.': 3973, 'loss.': 3974, 'lot': 3975, 'louder': 3976, 'lowest': 3977, 'lucerne': 3978, 'luckiest': 3979, 'luggnagg.': 3980, 'machinations.': 3981, 'machines': 3982, 'madman': 3983, 'magnanimous': 3984, 'magnificence': 3985, 'magnificence.': 3986, 'maintaining': 3987, 'majestic': 3988, 'majesties': 3989, 'maladies': 3990, 'maldonada': 3991, 'maldonada.': 3992, 'malignant': 3993, 'manifest': 3994, 'manner:': 3995, 'manufactures': 3996, 'map': 3997, 'march': 3998, 'mare': 3999, 'mares': 4000, 'marking': 4001, 'marrow': 4002, 'marrying': 4003, 'mathematicians': 4004, 'maturely': 4005, 'maxims': 4006, 'me.’': 4007, 'meals': 4008, 'meant.': 4009, 'meanwhile': 4010, 'measures': 4011, 'mechanical': 4012, 'meditate': 4013, 'memorials': 4014, 'memories': 4015, 'menial': 4016, 'mentioning': 4017, 'merchantman': 4018, 'mere': 4019, 'messenger': 4020, 'metropolis.': 4021, 'military': 4022, 'milk': 4023, 'milk.': 4024, 'miniature': 4025, 'minister.': 4026, 'minuteness': 4027, 'miserable.': 4028, 'miserably': 4029, 'mist': 4030, 'mistaken': 4031, 'mistakes': 4032, 'mizen': 4033, 'modest': 4034, 'monarch’s': 4035, 'monkey': 4036, 'monotonous': 4037, 'moral': 4038, 'mortals.': 4039, 'mortification': 4040, 'mortified': 4041, 'motive': 4042, 'mount': 4043, 'mountainous': 4044, 'mountains.': 4045, 'mouse’s': 4046, 'multiplying': 4047, 'multitude': 4048, 'mummy': 4049, 'murdering': 4050, 'muscle': 4051, 'muscles': 4052, 'music.': 4053, 'mutiny': 4054, 'mutton': 4055, 'n.': 4056, 'nail': 4057, 'named': 4058, 'narration': 4059, 'narrative': 4060, 'nastiness': 4061, 'nation.': 4062, 'natives.': 4063, 'nauseous': 4064, 'navigation': 4065, 'necessities': 4066, 'necessity.': 4067, 'needle': 4068, 'neglect': 4069, 'neighbour': 4070, 'neighbours': 4071, 'nerves': 4072, 'night.': 4073, 'nights': 4074, 'ninety': 4075, 'north.': 4076, 'northern': 4077, 'northward': 4078, 'notions': 4079, 'nought': 4080, 'november': 4081, 'nursed': 4082, 'oar': 4083, 'oars': 4084, 'oath': 4085, 'obey': 4086, 'objection': 4087, 'obliging': 4088, 'oblique': 4089, 'obliterated': 4090, 'occasioned': 4091, 'occupation': 4092, 'occur': 4093, 'occurred': 4094, 'occurred.': 4095, 'ocean.': 4096, 'offence': 4097, 'offensive': 4098, 'offers': 4099, 'offspring': 4100, 'ointment': 4101, 'old.': 4102, 'older': 4103, 'oldest': 4104, 'ooze': 4105, 'operate': 4106, 'operations.': 4107, 'opinion.': 4108, 'opportunities': 4109, 'opportunity.': 4110, 'opposed': 4111, 'orb': 4112, 'original': 4113, 'orphan': 4114, 'other’s': 4115, 'ours.': 4116, 'out.': 4117, 'outcast': 4118, 'outer': 4119, 'outward': 4120, 'overheard': 4121, 'overlooked': 4122, 'overtake': 4123, 'overwhelmed': 4124, 'owned': 4125, 'owner': 4126, 'oxford': 4127, 'o’clock': 4128, 'paces': 4129, 'packthread': 4130, 'paddling': 4131, 'paid': 4132, 'palm': 4133, 'palms': 4134, 'panegyric': 4135, 'panes': 4136, 'pangs': 4137, 'papa': 4138, 'paradise': 4139, 'parched': 4140, 'pardoned': 4141, 'paris': 4142, 'paris.': 4143, 'park.': 4144, 'part.': 4145, 'partial': 4146, 'partiality': 4147, 'partly': 4148, 'party.': 4149, 'passages': 4150, 'passes': 4151, 'past.': 4152, 'pastern': 4153, 'path.': 4154, 'patient': 4155, 'paw': 4156, 'payment': 4157, 'peasant': 4158, 'peculiarly': 4159, 'pedro': 4160, 'peeping': 4161, 'pelted': 4162, 'people!': 4163, 'perceiving': 4164, 'performing': 4165, 'peril': 4166, 'permit': 4167, 'perplexed': 4168, 'persecuted': 4169, 'person.': 4170, 'personal': 4171, 'person’s': 4172, 'persuading': 4173, 'persuasions': 4174, 'perverting': 4175, 'petitions': 4176, 'phenomenon': 4177, 'philosophers.': 4178, 'physiognomy': 4179, 'picking': 4180, 'pieces.': 4181, 'pigmies': 4182, 'pillar': 4183, 'pillars': 4184, 'pinched': 4185, 'piny': 4186, 'pirate': 4187, 'pistols': 4188, 'place.': 4189, 'placing': 4190, 'plain.': 4191, 'plaited': 4192, 'plank': 4193, 'plans': 4194, 'planting': 4195, 'plates': 4196, 'playfully': 4197, 'playing': 4198, 'plaything': 4199, 'pleading': 4200, 'pleasing': 4201, 'pleasure.': 4202, 'plentiful': 4203, 'plunge': 4204, 'pointing': 4205, 'points': 4206, 'poisonous': 4207, 'poles': 4208, 'polite': 4209, 'political': 4210, 'pond': 4211, 'possessions': 4212, 'posterity.': 4213, 'pouch': 4214, 'pounds': 4215, 'pounds.': 4216, 'precaution': 4217, 'preceded': 4218, 'precious': 4219, 'precipitate': 4220, 'predictions': 4221, 'prejudice': 4222, 'prejudiced': 4223, 'prejudices': 4224, 'preparation': 4225, 'preparations': 4226, 'prescribed': 4227, 'presentiment': 4228, 'preserving': 4229, 'pretended': 4230, 'prey.': 4231, 'price': 4232, 'priests': 4233, 'prime': 4234, 'principally': 4235, 'principle': 4236, 'proceed.': 4237, 'proceeds': 4238, 'process': 4239, 'proclamation': 4240, 'prodigy': 4241, 'professed': 4242, 'profit': 4243, 'progresses': 4244, 'projector': 4245, 'projectors': 4246, 'prolonged': 4247, 'promontory': 4248, 'proof': 4249, 'propagate': 4250, 'properly': 4251, 'properties': 4252, 'proportion.': 4253, 'proposes': 4254, 'protection': 4255, 'protection.': 4256, 'protectors.': 4257, 'protested': 4258, 'proved': 4259, 'provided.': 4260, 'province': 4261, 'prow': 4262, 'prudence': 4263, 'public.': 4264, 'pulleys': 4265, 'pulleys.': 4266, 'pulse': 4267, 'punish': 4268, 'purchase': 4269, 'purposes': 4270, 'purses': 4271, 'pursuit': 4272, 'pursuits': 4273, 'pushed': 4274, 'quarter': 4275, 'question.': 4276, 'quilt': 4277, 'rabble': 4278, 'races': 4279, 'raft': 4280, 'rags': 4281, 'rallied': 4282, 'range': 4283, 'ranked': 4284, 'rapture.': 4285, 'rare': 4286, 'rarely': 4287, 'rarities': 4288, 'rays': 4289, 'reap': 4290, 'rebellion': 4291, 'recesses': 4292, 'reckoned': 4293, 'recognised': 4294, 'recollect': 4295, 'recollected': 4296, 'reconcile': 4297, 'recorded': 4298, 'redeem': 4299, 'redriff': 4300, 'refined': 4301, 'reflecting': 4302, 'reflections.': 4303, 'refrain': 4304, 'refreshed': 4305, 'regret': 4306, 'regular': 4307, 'regularity': 4308, 'regulation': 4309, 'reigned': 4310, 'rejected': 4311, 'rejoiced': 4312, 'related.': 4313, 'relates': 4314, 'relations': 4315, 'relations.': 4316, 'relative': 4317, 'relief': 4318, 'relieved': 4319, 'rely': 4320, 'remember.': 4321, 'remind': 4322, 'reminds': 4323, 'rendering': 4324, 'renders': 4325, 'renewed': 4326, 'repaired': 4327, 'repast': 4328, 'repeat': 4329, 'repeat.': 4330, 'repelling': 4331, 'repent': 4332, 'repetition': 4333, 'replaced': 4334, 'reply.': 4335, 'reported': 4336, 'represent': 4337, 'representation': 4338, 'representations': 4339, 'repugnance': 4340, 'repulsive': 4341, 'requested': 4342, 'resentment': 4343, 'reserve': 4344, 'resolution.': 4345, 'resolving': 4346, 'respectful': 4347, 'result.': 4348, 'retinue': 4349, 'retreat': 4350, 'revenge.': 4351, 'revenue': 4352, 'revisit': 4353, 'revolution': 4354, 'revolutions': 4355, 'richer': 4356, 'ride': 4357, 'rider': 4358, 'risen': 4359, 'rising': 4360, 'roared': 4361, 'rolled': 4362, 'ropes': 4363, 'rotterdam': 4364, 'rouse': 4365, 'rows': 4366, 'rubbed': 4367, 'rubbish': 4368, 'rudder': 4369, 'rudiments': 4370, 'rue': 4371, 'ruin.': 4372, 'ruins': 4373, 'rules': 4374, 'rush': 4375, 'sad.': 4376, 'sails': 4377, 'salutations': 4378, 'saluted': 4379, 'sanctity': 4380, 'sang': 4381, 'sanguinary': 4382, 'satisfy': 4383, 'save': 4384, 'saving': 4385, 'scenery': 4386, 'scent': 4387, 'scents': 4388, 'scholars': 4389, 'school-mistress': 4390, 'schoolboys': 4391, 'scope': 4392, 'scotland': 4393, 'scotland.': 4394, 'scrofulous': 4395, 'seaport': 4396, 'searching': 4397, 'seasons': 4398, 'seats': 4399, 'secluded': 4400, 'secondary': 4401, 'secretaries': 4402, 'seeming': 4403, 'seemingly': 4404, 'seizure': 4405, 'selfish': 4406, 'sell': 4407, 'senates': 4408, 'senator': 4409, 'sensible': 4410, 'sensibly': 4411, 'sensitive': 4412, 'separated': 4413, 'serious': 4414, 'sets': 4415, 'shade': 4416, 'shaded': 4417, 'shaped': 4418, 'shared': 4419, 'sharp': 4420, 'sheets': 4421, 'sheltered': 4422, 'shine': 4423, 'shipping': 4424, 'shirt': 4425, 'shirts': 4426, 'shook': 4427, 'shooting': 4428, 'short.': 4429, 'shoulder': 4430, 'shout': 4431, 'shrieked': 4432, 'shrill': 4433, 'shuddering': 4434, 'shunned': 4435, 'sickening': 4436, 'sights': 4437, 'signal': 4438, 'signed': 4439, 'signification': 4440, 'silken': 4441, 'sincere': 4442, 'sincerely': 4443, 'singular': 4444, 'singularly': 4445, 'sitting': 4446, 'situations': 4447, 'sixth': 4448, 'sixty': 4449, 'skeleton': 4450, 'slavery': 4451, 'sloop': 4452, 'slowly': 4453, 'smallest': 4454, 'smiled': 4455, 'smiles.': 4456, 'smiling': 4457, 'snows': 4458, 'snowy': 4459, 'soaring': 4460, 'society.': 4461, 'softened': 4462, 'soil': 4463, 'soldier': 4464, 'soldiers': 4465, 'solve': 4466, 'somebody': 4467, 'someone': 4468, 'sons': 4469, 'soothing': 4470, 'sorry': 4471, 'sorts': 4472, 'soul.': 4473, 'sound.': 4474, 'sour': 4475, 'south-west': 4476, 'southwards': 4477, 'spare': 4478, 'sparrow': 4479, 'speak.': 4480, 'spears': 4481, 'spectacle': 4482, 'spectre': 4483, 'speculation': 4484, 'speculative': 4485, 'spider.': 4486, 'spire': 4487, 'spite': 4488, 'split': 4489, 'spoken': 4490, 'spot.': 4491, 'spring.': 4492, 'springs': 4493, 'sprugs': 4494, 'squeezed': 4495, 'st.': 4496, 'stalks': 4497, 'standard': 4498, 'starboard': 4499, 'started': 4500, 'starving': 4501, 'states': 4502, 'station': 4503, 'statute': 4504, 'stay': 4505, 'stayed': 4506, 'steep': 4507, 'steer': 4508, 'steered': 4509, 'stick.': 4510, 'stile': 4511, 'stock': 4512, 'stool': 4513, 'stopping': 4514, 'store': 4515, 'stories': 4516, 'strangled': 4517, 'strasburgh': 4518, 'straw.': 4519, 'stream.': 4520, 'streams': 4521, 'strength.': 4522, 'strewed': 4523, 'strictest': 4524, 'strikes': 4525, 'striving': 4526, 'stroking': 4527, 'struldbrug': 4528, 'studying': 4529, 'stumps': 4530, 'stupendous': 4531, 'stupid': 4532, 'subservient': 4533, 'subsisted': 4534, 'succeed': 4535, 'sufferings.': 4536, 'sufficiently': 4537, 'summits': 4538, 'sunbeams': 4539, 'supper': 4540, 'supplicating': 4541, 'supposition': 4542, 'suspect': 4543, 'suspense': 4544, 'suspicion': 4545, 'sustain': 4546, 'sustained.': 4547, 'swept': 4548, 'swiftness': 4549, 'swiss': 4550, 'syllable.': 4551, 'sympathies': 4552, 'table.': 4553, 'talent': 4554, 'talents': 4555, 'talked': 4556, 'talons.': 4557, 'tame': 4558, 'tapped': 4559, 'tartary': 4560, 'taste.': 4561, 'tasted': 4562, 'taxed': 4563, 'teaching': 4564, 'tempest': 4565, 'temptation': 4566, 'tended': 4567, 'tender': 4568, 'terminated': 4569, 'terms.': 4570, 'terrified': 4571, 'thank': 4572, 'thanked': 4573, 'theirs.': 4574, 'them)': 4575, 'them:': 4576, 'therein': 4577, 'this:': 4578, 'thoroughly': 4579, 'thousands': 4580, 'thread': 4581, 'threads': 4582, 'threats': 4583, 'thrice': 4584, 'thrill': 4585, 'thrust': 4586, 'thunder.': 4587, 'thyself': 4588, 'time.': 4589, 'tincture': 4590, 'toe': 4591, 'toils.': 4592, 'tomb': 4593, 'tones': 4594, 'tongue.': 4595, 'tongues': 4596, 'tonquin': 4597, 'top-mast': 4598, 'topmast': 4599, 'tormenting': 4600, 'torture.': 4601, 'tortured': 4602, 'tortures': 4603, 'touch': 4604, 'touching': 4605, 'tract': 4606, 'trades': 4607, 'trained': 4608, 'trampling': 4609, 'tranquillity.': 4610, 'translate': 4611, 'translation': 4612, 'traverse': 4613, 'traversed': 4614, 'tread': 4615, 'treasure': 4616, 'treble': 4617, 'trees.': 4618, 'tremble': 4619, 'trencher': 4620, 'trifling': 4621, 'trod': 4622, 'troublesome': 4623, 'truest': 4624, 'trusty': 4625, 'tubes': 4626, 'tumbling': 4627, 'turk': 4628, 'turkey': 4629, 'tutor': 4630, 'twenty.': 4631, 'unbound': 4632, 'uncertain': 4633, 'uncommon': 4634, 'unconscious': 4635, 'uncontrollable': 4636, 'understand.': 4637, 'understanding.': 4638, 'undertaking': 4639, 'undone': 4640, 'unearthly': 4641, 'uneasy': 4642, 'unfair': 4643, 'unfolded': 4644, 'unfortunately': 4645, 'unhappy?': 4646, 'united': 4647, 'university.': 4648, 'unjustly': 4649, 'unluckily': 4650, 'unmingled': 4651, 'untimely': 4652, 'unwilling': 4653, 'upright': 4654, 'upside': 4655, 'urchin': 4656, 'urgency': 4657, 'use:': 4658, 'uses': 4659, 'using': 4660, 'utility': 4661, 'v.': 4662, 'vainly': 4663, 'van': 4664, 'vanished': 4665, 'vary': 4666, 'vehicle': 4667, 'veins': 4668, 'venerable': 4669, 'veneration': 4670, 'vent': 4671, 'veracity.': 4672, 'verdant': 4673, 'vessel.': 4674, 'vicious': 4675, 'victims': 4676, 'victory': 4677, 'view.': 4678, 'viewing': 4679, 'villagers': 4680, 'vindication': 4681, 'visible': 4682, 'vision': 4683, 'voices': 4684, 'vote': 4685, 'vulgar.': 4686, 'wait': 4687, 'waking': 4688, 'wanderings': 4689, 'wanting': 4690, 'watched': 4691, 'watery': 4692, 'ways': 4693, 'wear': 4694, 'weariness': 4695, 'weasel': 4696, 'week': 4697, 'weekly': 4698, 'weeks.': 4699, 'weigh': 4700, 'weighed': 4701, 'welcomed': 4702, 'well.': 4703, 'west': 4704, 'west.': 4705, 'western': 4706, 'westward': 4707, 'whenever': 4708, 'whereat': 4709, 'wherefore': 4710, 'whipped': 4711, 'wickedness': 4712, 'wickedness.': 4713, 'widow': 4714, 'wildest': 4715, 'wildly': 4716, 'wilds': 4717, 'willing': 4718, 'willingly': 4719, 'wind.': 4720, 'windows.': 4721, 'wipe': 4722, 'wiping': 4723, 'wishing': 4724, 'woe': 4725, 'won': 4726, 'wonder.': 4727, 'wondering': 4728, 'word.': 4729, 'working': 4730, 'workmen': 4731, 'world.”': 4732, 'wounded': 4733, 'wreck': 4734, 'wretch.': 4735, 'wretchedness.': 4736, 'wrinkled': 4737, 'writers': 4738, 'writhed': 4739, 'writing.': 4740, 'wrong': 4741, 'wrung': 4742, 'yahoos.”': 4743, 'yard': 4744, 'yard.': 4745, 'yards.': 4746, 'yield': 4747, 'youngest': 4748, 'youthful': 4749, '’': 4750, '“and': 4751, '“are': 4752, '“at': 4753, '“do': 4754, '“felix': 4755, '“no': 4756, '“safie': 4757, '“since': 4758, '“these': 4759, '“we': 4760, '“why': 4761, '“‘that': 4762})
None

In [23]:

word_2_idx = dict(e[3])
idx_2_word = {}
for k,v in word_2_idx.items():
  idx_2_word[v] = k

In [24]:

train_iterator, test_iterator = BucketIterator.splits(
    (train_data, test_data), batch_size=32, device=device
)

In [25]:

print(f"Number of training examples: {len(train_data.examples)}")
print(f"Number of testing examples: {len(test_data.examples)}")

print(train_data[5].__dict__.keys())
print(train_data[5].__dict__.values())

Number of training examples: 8966
Number of testing examples: 2989
dict_keys(['eng', 'crowd'])
dict_values([['from', 'the', 'back', 'of', 'which', 'were', 'extended', 'twenty', 'long', 'poles'], ['schlioncy', 'yoik', 'mccruems', 'zally', 'koirry', 'schneiald', 'chix', 'wheians', 'veurg', 'spaash', 'blountz', 'draests', 'mcnaens', 'truids', 'thionk']])

In [26]:

count = 0
max_len_eng = []
max_len_crowd = []
for data in train_data:
  max_len_crowd.append(len(data.crowd))
  max_len_eng.append(len(data.eng))
  if count < 10 :
    print("CrowdTalk - ",*data.crowd, " Length - ", len(data.crowd))
    print("English - ",*data.eng, " Length - ", len(data.eng))
    print()
  count += 1

print("Maximum Length of English sentence {} and CrowdTalk sentence {} in the dataset".format(max(max_len_eng),max(max_len_crowd)))
print("Minimum Length of English sentence {} and CrowdTalk sentence {} in the dataset".format(min(max_len_eng),min(max_len_crowd)))

CrowdTalk -  sooc kniousts tsiens schlioncy yoik spleorry mcmiaors stiird  Length -  8
English -  was placed on the floor  Length -  5

CrowdTalk -  squiend sriohl wheians veurg sauew cycluiedly flueh hydriouz sprauedy  Length -  9
English -  which i answered in great humility  Length -  6

CrowdTalk -  mcnaiaks traff synohl mcgaantz schmeecks schneiald chix synoags cyclat fraipp scraiosh luag rhairk spreerts gnoirk cziourk sheiaty schwauh szaiabs cycluiedly flueh psychioud syneens hrobs  Length -  24
English -  that six of his majesty’s greatest scholars should be employed to instruct me in their language  Length -  16

CrowdTalk -  vux reork schlioncy yoik czogy stoorty dynoact  Length -  7
English -  seemingly incommoded by the burden  Length -  5

CrowdTalk -  toirts choolt wraiorn tiontly dieung spieth xauenn treuns schleangly pfuots squiend sriohl swuasp gnoirk cziourk jaests  Length -  16
English -  but presently he calmed himself and proceeded— “i intended to reason.  Length -  11

CrowdTalk -  schlioncy yoik mccruems zally koirry schneiald chix wheians veurg spaash blountz draests mcnaens truids thionk  Length -  15
English -  from the back of which were extended twenty long poles  Length -  10

CrowdTalk -  treuns schleangly phaiodly czogy stoorty gnoirk cziourk gniedy schlioncy yoik ghuags  Length -  11
English -  and stood by to hand the fore-sail  Length -  7

CrowdTalk -  squiend sriohl mcfoirly phost floff naan gnoably treiahl typeauty squiend sriohl  Length -  11
English -  i waded with what haste i could  Length -  7

CrowdTalk -  zan qeems schlioncy yoik pheauds schneiald chix mcmoirst  Length -  8
English -  taller than the sons of men  Length -  6

CrowdTalk -  mcgeeld xeech schniery schlungly qiobly  Length -  5
English -  where we saw mont salêve  Length -  5

Maximum Length of English sentence 25 and CrowdTalk sentence 39 in the dataset
Minimum Length of English sentence 5 and CrowdTalk sentence 5 in the dataset

In [27]:

count = 0
for data in train_iterator:
  if count < 1 :
    print("Shapes", data.crowd.shape, data.eng.shape)
    print()
    print("CrowdTalk - ",*data.crowd, " Length - ", len(data.crowd))
    print()
    print("English - ",*data.eng, " Length - ", len(data.eng))
    temp_crowd = data.crowd
    temp_eng = data.eng
    count += 1

Shapes torch.Size([29, 32]) torch.Size([22, 32])

CrowdTalk -  tensor([ 522,  193,    2,   13,   43,   10,   14,    7, 2663,   69,    7,  341,
          10,    7,    7,   27,   20,   16,  125,   43,    0,   10,    7,   27,
          80,    2,   27,  165,   27,   37,   39,   58], device='cuda:0') tensor([  16, 2458,    3,   12,   42,   11,   15,    6,   40,   68,    6,   19,
          11,    6,    6,   26,   21,   17,  698,   42,    2,   11,    6,   26,
          81,    3,   26,  297,   26,   36,   38,   59], device='cuda:0') tensor([  17,   13,    7,  776,   32,  819,  579,   58,   41,  267,  110,   18,
         114,  149, 2908,   89,   19,  177,    0,    2,    3,   76,  592,    2,
         179,   25,    2,   13,   62,   45,   49,  200], device='cuda:0') tensor([ 140,   12,    6,  264,   33, 1367,   19,   59,   82,  592,  113,    2,
         658,  152,   35,   27,   18, 1141,  190,    3,  537,   77,   95,    3,
        2729,   24,    3,   12,   63,   44,   48,  196], device='cuda:0') tensor([3394,   16, 1082,    5,   19, 2606,   18,   19, 1744,    9,   61,    3,
          10,   61,   34,   26,   10,   80,  613,  826, 2090,   45,  177, 3729,
          22,    0, 1054, 1049,   10,   76,  907,   22], device='cuda:0') tensor([3602,   17,    5,    4,   18,    5,    2,   18,  783,    8,   60, 1639,
          11,   60,   14,  149,   11,   81,  368,    7,   25,   44,    0, 2221,
          23,    5,  454,    2,   11,   77,    2,   23], device='cuda:0') tensor([   1, 1142,    4, 2690,  315,    4,    3,    9,    7,  236,    2,   10,
          19,   14,   15,   10,  420,  383,    2,    6,   24, 1634,   10,    5,
         162,    4,   35,    3,  336,  744,    3,   64], device='cuda:0') tensor([   1,   79, 1149,    1,  201,    2,  108,    8,    6,   13,    3,   11,
          18,   15,  579,   11,    9,   27,    3, 1152,   14,  208,   11,    4,
          10,   69,   34,    9,  248,   28,   52,   65], device='cuda:0') tensor([   1,   78,    5,    1,   85,    3, 1213,   22, 3080,   12,   16,  228,
         891,  752,    7,   30,    8,   26,    0,    2,   15,    2,  124,   99,
          11,   68,  125,    8,    1,   29,   53,   89], device='cuda:0') tensor([   1,  113,    4,    1,    2, 4252, 1442,   23, 1323, 3693,   17, 2782,
           2,    1,    6,   31,    0,  292,    1,    3,  238,    3,   30,  385,
        1388, 1642, 1424, 1938,    1,  713,   88, 2288], device='cuda:0') tensor([   1, 4313,  367,    1,    3, 2458,    7,  193,    1,    5, 2536,   51,
           3,    1,  492, 3388,    1,   27,    1,    5, 1417,   25,   31,    5,
         197,  904,  582,    1,    1,    1,  787,    1], device='cuda:0') tensor([   1,   51,    1,    1,   40,   30,    6,    2,    1,    4,    5,   50,
         112,    1,    1,    2,    1,   26,    1,    4,    1,   24, 1014,    4,
        3378,    1, 1185,    1,    1,    1,  386,    1], device='cuda:0') tensor([   1,   50,    1,    1,   41,   31, 2892,    3,    1, 1017,    4,   73,
         273,    1,    1,    3,    1,  438,    1,  150,    1, 2816,    1,   39,
          16,    1,    1,    1,    1,    1,    1,    1], device='cuda:0') tensor([   1,   13,    1,    1,  253,   10,   10, 1435,    1,   93,    0,   72,
           1,    1,    1,   16,    1,   62,    1,    1,    1,    5,    1,   38,
          17,    1,    1,    1,    1,    1,    1,    1], device='cuda:0') tensor([   1,   12,    1,    1, 1028,   11,   11,    5,    1,   91, 4328,    5,
           1,    1,    1,   17,    1,   63,    1,    1,    1,    4,    1,   19,
         145,    1,    1,    1,    1,    1,    1,    1], device='cuda:0') tensor([   1, 1286,    1,    1,    1,  669,   39,    4,    1,   14,    7,    4,
           1,    1,    1,  969,    1,  720,    1,    1,    1,  384,    1,   18,
           0,    1,    1,    1,    1,    1,    1,    1], device='cuda:0') tensor([   1,    1,    1,    1,    1,    1,   38, 1164,    1,   15,    6,  108,
           1,    1,    1,    1,    1,  222,    1,    1,    1, 2694,    1,   10,
          27,    1,    1,    1,    1,    1,    1,    1], device='cuda:0') tensor([   1,    1,    1,    1,    1,    1,  786, 2699,    1,    5, 4442,    2,
           1,    1,    1,    1,    1,   80,    1,    1,    1,    1,    1,   11,
          26,    1,    1,    1,    1,    1,    1,    1], device='cuda:0') tensor([   1,    1,    1,    1,    1,    1,   35,    5,    1,    4,    5,    3,
           1,    1,    1,    1,    1,   81,    1,    1,    1,    1,    1, 4023,
          13,    1,    1,    1,    1,    1,    1,    1], device='cuda:0') tensor([   1,    1,    1,    1,    1,    1,   34,    4,    1, 1818,    4,  855,
           1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,
          12,    1,    1,    1,    1,    1,    1,    1], device='cuda:0') tensor([   1,    1,    1,    1,    1,    1, 3811,  380,    1,    1,    0,    1,
           1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,
        3451,    1,    1,    1,    1,    1,    1,    1], device='cuda:0') tensor([   1,    1,    1,    1,    1,    1,    1, 1826,    1,    1,    1,    1,
           1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,
          20,    1,    1,    1,    1,    1,    1,    1], device='cuda:0') tensor([   1,    1,    1,    1,    1,    1,    1, 1421,    1,    1,    1,    1,
           1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,
          21,    1,    1,    1,    1,    1,    1,    1], device='cuda:0') tensor([  1,   1,   1,   1,   1,   1,   1,   2,   1,   1,   1,   1,   1,   1,
          1,   1,   1,   1,   1,   1,   1,   1,   1,   1, 178,   1,   1,   1,
          1,   1,   1,   1], device='cuda:0') tensor([  1,   1,   1,   1,   1,   1,   1,   3,   1,   1,   1,   1,   1,   1,
          1,   1,   1,   1,   1,   1,   1,   1,   1,   1, 147,   1,   1,   1,
          1,   1,   1,   1], device='cuda:0') tensor([   1,    1,    1,    1,    1,    1,    1,   51,    1,    1,    1,    1,
           1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,
        1366,    1,    1,    1,    1,    1,    1,    1], device='cuda:0') tensor([ 1,  1,  1,  1,  1,  1,  1, 50,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,
         1,  1,  1,  1,  1,  1, 96,  1,  1,  1,  1,  1,  1,  1],
       device='cuda:0') tensor([  1,   1,   1,   1,   1,   1,   1, 289,   1,   1,   1,   1,   1,   1,
          1,   1,   1,   1,   1,   1,   1,   1,   1,   1, 917,   1,   1,   1,
          1,   1,   1,   1], device='cuda:0') tensor([   1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,
           1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,
        1642,    1,    1,    1,    1,    1,    1,    1], device='cuda:0')  Length -  29

English -  tensor([ 412,  128,    4,    7,   22,    6,    8,    4, 2740,   36,    4,  255,
           6,    4,    4,   16,   11,    9,    0,   22,    0,    6,    4,   16,
          43,   13,   16,   69,   16,   17,   20,   30], device='cuda:0') tensor([   9, 3129,    2,  639,   19,  778,  435,   30,   21,  188,   94,   10,
          95,   67, 2821,   78,    6,   68,  750,    2,    2,   39,  486,    2,
         120,    2,    2,  207,    6,   39,   25,  133], device='cuda:0') tensor([  55,    9, 1001,  185,   10, 1469,   10,   10,   41,  486,   51,    2,
         540,   61,   18,   16,   10, 1570,    0,  712,  420,   23,   45, 4012,
        2889,    0,  896,    7,   32,   23,  859,  125], device='cuda:0') tensor([4286,    7,    3,    3,  252, 2470,    2,    5, 1965,    5,   31, 1412,
           6,   31,    8,   67,  322,   43,  127,    4, 2183, 1535,   68, 2162,
          12,    3,  346,  880,  246,  632,   27,   12], device='cuda:0') tensor([3884, 1231,  987, 3757,  126,    3,   91,   12,  646,  147,    9,    6,
          10,    8,  435,    6,    5,  302,  502, 1189,   13,   85,    0,    3,
          67,   36,   18,    5,  163,   14,    2,   33], device='cuda:0') tensor([   1,   40,    3,    1,   47,    2, 1234,  128,    4,    7,    2,  148,
         770,  913,    4,   15,    0,   16,  269,    3,    8,   13,    6,   45,
           6, 1798,   53,    2,    1,  670,   75,   78], device='cuda:0') tensor([   1,   51,  280,    1,   21, 4201, 1265,    2, 2527, 3314, 2196, 3157,
          50,    1,  426, 4419,    1,  200,    2,    2,   71,    2,   15,  304,
        1312,  761, 1284,    0,    1,    1,  633, 2017], device='cuda:0') tensor([   1, 3283,    1,    1,    2, 3129,    4, 1885, 1537,    3,    3,   26,
           2,    1,    1,    9,    1,   16,    0,  118, 1336, 2757,   54,    3,
          58,    1,  463,    1,    1,    1,  347,    1], device='cuda:0') tensor([   1,   26,    1,    1,  166,    6, 2999,    3,    1,  885,    0,   42,
        1845,    1,    1,    2,    1,  367,    1,    1,    1,    3,    0,   20,
        3897,    1, 1121,    1,    1,    1,    1,    1], device='cuda:0') tensor([   1,    7,    1,    1, 1047,   15,   20, 1008,    1,   44, 4056,    3,
           1,    1,    1, 3416,    1,   32,    1,    1,    1,  284,    1,    6,
           9,    1,    1,    1,    1,    1,    1,    1], device='cuda:0') tensor([   1,    0,    1,    1,    1,  593,    6, 2642,    1,   46,    4,    2,
           1,    1,    1,    1,    1,  641,    1,    1,    1,    0,    1,   10,
          64,    1,    1,    1,    1,    1,    1,    1], device='cuda:0') tensor([   1,    1,    1,    1,    1,    1,  648,    3,    1,    3, 3972,   91,
           1,    1,    1,    1,    1,   81,    1,    1,    1,    1,    1, 4635,
           0,    1,    1,    1,    1,    1,    1,    1], device='cuda:0') tensor([  1,   1,   1,   1,   1,   1,  18, 373,   1,   8,   3, 819,   1,   1,
          1,   1,   1,  43,   1,   1,   1,   1,   1,   1,  16,   1,   1,   1,
          1,   1,   1,   1], device='cuda:0') tensor([   1,    1,    1,    1,    1,    1,    0, 1656,    1, 1678,    0,    1,
           1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,
           7,    1,    1,    1,    1,    1,    1,    1], device='cuda:0') tensor([   1,    1,    1,    1,    1,    1,    1, 1251,    1,    1,    1,    1,
           1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,
        4249,    1,    1,    1,    1,    1,    1,    1], device='cuda:0') tensor([ 1,  1,  1,  1,  1,  1,  1, 26,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,
         1,  1,  1,  1,  1,  1, 11,  1,  1,  1,  1,  1,  1,  1],
       device='cuda:0') tensor([ 1,  1,  1,  1,  1,  1,  1,  2,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,
         1,  1,  1,  1,  1,  1, 64,  1,  1,  1,  1,  1,  1,  1],
       device='cuda:0') tensor([   1,    1,    1,    1,    1,    1,    1, 1737,    1,    1,    1,    1,
           1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,
          68,    1,    1,    1,    1,    1,    1,    1], device='cuda:0') tensor([   1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,
           1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,
        1237,    1,    1,    1,    1,    1,    1,    1], device='cuda:0') tensor([ 1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,
         1,  1,  1,  1,  1,  1, 48,  1,  1,  1,  1,  1,  1,  1],
       device='cuda:0') tensor([  1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,
          1,   1,   1,   1,   1,   1,   1,   1,   1,   1, 768,   1,   1,   1,
          1,   1,   1,   1], device='cuda:0') tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
        0, 1, 1, 1, 1, 1, 1, 1], device='cuda:0')  Length -  22

In [28]:

temp_eng_idx = (temp_eng).cpu().detach().numpy()
temp_crowd_idx = (temp_crowd).cpu().detach().numpy()

In [29]:

df_eng_idx = pd.DataFrame(data = temp_eng_idx, columns = [str("S_")+str(x) for x in np.arange(1, 33)])
df_eng_idx.index.name = 'Time Steps'
df_eng_idx.index = df_eng_idx.index + 1 
df_eng_idx

Out[29]:

	S_1	S_2	S_3	S_4	S_5	S_6	S_7	S_8	S_9	S_10	S_11	S_12	S_13	S_14	S_15	S_16	S_17	S_18	S_19	S_20	S_21	S_22	S_23	S_24	S_25	S_26	S_27	S_28	S_29	S_30	S_31	S_32
Time Steps
1	412	128	4	7	22	6	8	4	2740	36	4	255	6	4	4	16	11	9	0	22	0	6	4	16	43	13	16	69	16	17	20	30
2	9	3129	2	639	19	778	435	30	21	188	94	10	95	67	2821	78	6	68	750	2	2	39	486	2	120	2	2	207	6	39	25	133
3	55	9	1001	185	10	1469	10	10	41	486	51	2	540	61	18	16	10	1570	0	712	420	23	45	4012	2889	0	896	7	32	23	859	125
4	4286	7	3	3	252	2470	2	5	1965	5	31	1412	6	31	8	67	322	43	127	4	2183	1535	68	2162	12	3	346	880	246	632	27	12
5	3884	1231	987	3757	126	3	91	12	646	147	9	6	10	8	435	6	5	302	502	1189	13	85	0	3	67	36	18	5	163	14	2	33
6	1	40	3	1	47	2	1234	128	4	7	2	148	770	913	4	15	0	16	269	3	8	13	6	45	6	1798	53	2	1	670	75	78
7	1	51	280	1	21	4201	1265	2	2527	3314	2196	3157	50	1	426	4419	1	200	2	2	71	2	15	304	1312	761	1284	0	1	1	633	2017
8	1	3283	1	1	2	3129	4	1885	1537	3	3	26	2	1	1	9	1	16	0	118	1336	2757	54	3	58	1	463	1	1	1	347	1
9	1	26	1	1	166	6	2999	3	1	885	0	42	1845	1	1	2	1	367	1	1	1	3	0	20	3897	1	1121	1	1	1	1	1
10	1	7	1	1	1047	15	20	1008	1	44	4056	3	1	1	1	3416	1	32	1	1	1	284	1	6	9	1	1	1	1	1	1	1
11	1	0	1	1	1	593	6	2642	1	46	4	2	1	1	1	1	1	641	1	1	1	0	1	10	64	1	1	1	1	1	1	1
12	1	1	1	1	1	1	648	3	1	3	3972	91	1	1	1	1	1	81	1	1	1	1	1	4635	0	1	1	1	1	1	1	1
13	1	1	1	1	1	1	18	373	1	8	3	819	1	1	1	1	1	43	1	1	1	1	1	1	16	1	1	1	1	1	1	1
14	1	1	1	1	1	1	0	1656	1	1678	0	1	1	1	1	1	1	1	1	1	1	1	1	1	7	1	1	1	1	1	1	1
15	1	1	1	1	1	1	1	1251	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	4249	1	1	1	1	1	1	1
16	1	1	1	1	1	1	1	26	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	11	1	1	1	1	1	1	1
17	1	1	1	1	1	1	1	2	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	64	1	1	1	1	1	1	1
18	1	1	1	1	1	1	1	1737	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	68	1	1	1	1	1	1	1
19	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1237	1	1	1	1	1	1	1
20	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	48	1	1	1	1	1	1	1
21	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	768	1	1	1	1	1	1	1
22	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1	1	1	1

In [30]:

df_eng_word = pd.DataFrame(columns = [str("S_")+str(x) for x in np.arange(1, 33)])
df_eng_word = df_eng_idx.replace(idx_2_word)
df_eng_word

Out[30]:

	S_1	S_2	S_3	S_4	S_5	S_6	S_7	S_8	S_9	S_10	S_11	S_12	S_13	S_14	S_15	S_16	S_17	S_18	S_19	S_20	S_21	S_22	S_23	S_24	S_25	S_26	S_27	S_28	S_29	S_30	S_31	S_32
Time Steps
1	except	like	and	a	but	i	my	and	guided	her	and	neither	i	and	and	as	that	in	<unk>	but	<unk>	i	and	as	you	with	as	about	as	he	which	this
2	in	visions	the	further	it	grew	bed	this	by	majesty	found	was	now	if	linen	much	i	other	brother	the	the	could	used	the	must	the	the	half	i	could	they	thought
3	very	in	pay	account	was	heartily	was	was	an	used	we	the	perceived	these	for	as	was	studies	<unk>	noise	proper	not	some	mechanical	pardon	<unk>	period	a	have	not	eat	gave
4	rare	a	of	of	carried	ashamed	the	to	ardent	to	were	covering	i	were	my	if	forced	you	has	and	keys	rank	other	impulse	me	of	fixed	league	already	continue	at	me
5	instances	dream	ministers	glubbdubdrib.	against	of	same	me	imagination	put	in	i	was	my	bed	i	to	go	just	astonishment	with	myself	<unk>	of	if	her	for	to	said	his	the	so
6	<pad>	when	of	<pad>	him	the	dry	like	and	a	the	sometimes	fallen	sensations	and	had	<unk>	as	got	of	my	with	i	some	i	neglected	our	the	<pad>	tale	most	much
7	<pad>	we	state	<pad>	by	pleasing	grass	the	childish	bit	latitude	wore	into	<pad>	table	shared	<pad>	far	the	the	two	the	had	power	regard	daughter	marriage	<unk>	<pad>	<pad>	convenient	comfort
8	<pad>	awake	<pad>	<pad>	the	visions	and	torture	reasoning	of	of	on	the	<pad>	<pad>	in	<pad>	as	<unk>	people	sticks	herd	been	of	any	<pad>	drew	<pad>	<pad>	<pad>	hours	<pad>
9	<pad>	on	<pad>	<pad>	whole	i	sea-weed	of	<pad>	meat	<unk>	them	sea.	<pad>	<pad>	the	<pad>	others	<pad>	<pad>	<pad>	of	<unk>	which	interruption	<pad>	nearer	<pad>	<pad>	<pad>	<pad>	<pad>
10	<pad>	a	<pad>	<pad>	board	had	which	single	<pad>	upon	n.	of	<pad>	<pad>	<pad>	command.”	<pad>	have	<pad>	<pad>	<pad>	common	<pad>	i	in	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>
11	<pad>	<unk>	<pad>	<pad>	<pad>	formed	i	drops	<pad>	one	and	the	<pad>	<pad>	<pad>	<pad>	<pad>	gone	<pad>	<pad>	<pad>	<unk>	<pad>	was	your	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>
12	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	intended	of	<pad>	of	longitude	same	<pad>	<pad>	<pad>	<pad>	<pad>	before	<pad>	<pad>	<pad>	<pad>	<pad>	unconscious	<unk>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>
13	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	for	water	<pad>	my	of	shape	<pad>	<pad>	<pad>	<pad>	<pad>	you	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	as	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>
14	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<unk>	continually	<pad>	dishes	<unk>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	a	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>
15	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	falling	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	proof	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>
16	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	on	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	that	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>
17	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	the	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	your	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>
18	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	head.	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	other	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>
19	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	duties	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>
20	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	are	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>
21	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	equally	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>
22	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<unk>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>	<pad>

Model Building:¶

Encoder for LSTM:¶

In [31]:

class EncoderLSTM(nn.Module):
  def __init__(self, input_size, embedding_size, hidden_size, num_layers, p):
    super(EncoderLSTM, self).__init__()

    # Dimension of the NN's inside the lstm cell/ (hs,cs)'s dimension.
    self.hidden_size = hidden_size

    # Number of layers in the lstm
    self.num_layers = num_layers

    # Regularization parameter
    self.dropout = nn.Dropout(p)
    self.tag = True

    # Shape --------------------> (5376, 300) [input size, embedding dims]
    self.embedding = nn.Embedding(input_size, embedding_size)
    
    # Shape -----------> (300, 2, 1024) [embedding dims, hidden size, num layers]
    self.LSTM = nn.LSTM(embedding_size, hidden_size, num_layers, dropout = p)

  # Shape of x (26, 32) [Sequence_length, batch_size]
  def forward(self, x):

    # Shape -----------> (26, 32, 300) [Sequence_length , batch_size , embedding dims]
    embedding = self.dropout(self.embedding(x))
    
    # Shape --> outputs (26, 32, 1024) [Sequence_length , batch_size , hidden_size]
    # Shape --> (hs, cs) (2, 32, 1024) , (2, 32, 1024) [num_layers, batch_size size, hidden_size]
    outputs, (hidden_state, cell_state) = self.LSTM(embedding)

    return hidden_state, cell_state

input_size_encoder = len(crowdtalk.vocab)
encoder_embedding_size = 300
hidden_size = 1024
num_layers = 2
encoder_dropout = 0.5

encoder_lstm = EncoderLSTM(input_size_encoder, encoder_embedding_size,
                           hidden_size, num_layers, encoder_dropout).to(device)
print(encoder_lstm)

EncoderLSTM(
  (dropout): Dropout(p=0.5, inplace=False)
  (embedding): Embedding(4530, 300)
  (LSTM): LSTM(300, 1024, num_layers=2, dropout=0.5)
)

Decoder for LSTM:¶

In [32]:

class DecoderLSTM(nn.Module):
  def __init__(self, input_size, embedding_size, hidden_size, num_layers, p, output_size):
    super(DecoderLSTM, self).__init__()

    # Dimension of the NN's inside the lstm cell/ (hs,cs)'s dimension.
    self.hidden_size = hidden_size

    # Number of layers in the lstm
    self.num_layers = num_layers

    # Size of the one hot vectors that will be the output to the encoder (English Vocab Size)
    self.output_size = output_size

    # Regularization parameter
    self.dropout = nn.Dropout(p)
    self.embedding = nn.Embedding(input_size, embedding_size)
    self.LSTM = nn.LSTM(embedding_size, hidden_size, num_layers, dropout = p)
    self.fc = nn.Linear(hidden_size, output_size)

  # Shape of x (32) [batch_size]
  def forward(self, x, hidden_state, cell_state):
    x = x.unsqueeze(0)
    embedding = self.dropout(self.embedding(x))
    outputs, (hidden_state, cell_state) = self.LSTM(embedding, (hidden_state, cell_state))

    # Shape --> predictions (1, 32, 4556) [ 1, batch_size , output_size]
    predictions = self.fc(outputs)

    # Shape --> predictions (32, 4556) [batch_size , output_size]
    predictions = predictions.squeeze(0)

    return predictions, hidden_state, cell_state

input_size_decoder = len(english.vocab)
decoder_embedding_size = 300
hidden_size = 1024
num_layers = 2
decoder_dropout = 0.5
output_size = len(english.vocab)

decoder_lstm = DecoderLSTM(input_size_decoder, decoder_embedding_size,
                           hidden_size, num_layers, decoder_dropout,
                           output_size).to(device)
print(decoder_lstm)

DecoderLSTM(
  (dropout): Dropout(p=0.5, inplace=False)
  (embedding): Embedding(4763, 300)
  (LSTM): LSTM(300, 1024, num_layers=2, dropout=0.5)
  (fc): Linear(in_features=1024, out_features=4763, bias=True)
)

In [33]:

for batch in train_iterator:
  print(batch.crowd.shape)
  print(batch.eng.shape)
  break

x = batch.eng[1]
print(x)

torch.Size([30, 32])
torch.Size([19, 32])
tensor([   2,    8,    6, 3274,   80, 1010, 2152,   62,    7,   32, 4548,   60,
          10, 1531,  115,    7,   15,    6,   43,   18,  104,   67,   34, 1188,
          20,  918,   64,    8, 3396,   19, 1373,  930], device='cuda:0')

Seq2Seq (Encoder + Decoder):¶

In [34]:

class Seq2Seq(nn.Module):
  def __init__(self, Encoder_LSTM, Decoder_LSTM):
    super(Seq2Seq, self).__init__()
    self.Encoder_LSTM = Encoder_LSTM
    self.Decoder_LSTM = Decoder_LSTM

  def forward(self, source, target, tfr=0.5):
    batch_size = source.shape[1]
    target_len = target.shape[0]
    target_vocab_size = len(english.vocab)

    outputs = torch.zeros(target_len, batch_size, target_vocab_size).to(device)
    hidden_state, cell_state = self.Encoder_LSTM(source)

    # Shape of x (32 elements)
    x = target[0] # Trigger token <SOS>

    for i in range(1, target_len):
      # Shape --> output (32, 5766) 
      output, hidden_state, cell_state = self.Decoder_LSTM(x, hidden_state, cell_state)
      outputs[i] = output
      best_guess = output.argmax(1) # 0th dimension is batch size, 1st dimension is word embedding
      x = target[i] if random.random() < tfr else best_guess # Either pass the next word correctly from the dataset or use the earlier predicted word
    return outputs

In [53]:

learning_rate = 0.001
writer = SummaryWriter(f"runs/loss_plot")
step = 0

model = Seq2Seq(encoder_lstm, decoder_lstm).to(device)
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

pad_idx = english.vocab.stoi["<pad>"]
criterion = nn.CrossEntropyLoss(ignore_index=pad_idx)

In [54]:

def translate_sentence(model, sentence, crowdtalk, english, device, max_length=100):

    if type(sentence) == str:
        tokens = [token.text.lower() for token in tokenizer(sentence)]
    else:
        tokens = [token.lower() for token in sentence]
    tokens.insert(0, crowdtalk.init_token)
    tokens.append(crowdtalk.eos_token)
    text_to_indices = [crowdtalk.vocab.stoi[token] for token in tokens]
    sentence_tensor = torch.LongTensor(text_to_indices).unsqueeze(1).to(device)

    # Build encoder hidden, cell state
    with torch.no_grad():
        hidden, cell = model.Encoder_LSTM(sentence_tensor)

    outputs = [english.vocab.stoi["<sos>"]]

    for _ in range(max_length):
        previous_word = torch.LongTensor([outputs[-1]]).to(device)

        with torch.no_grad():
            output, hidden, cell = model.Decoder_LSTM(previous_word, hidden, cell)
            best_guess = output.argmax(1).item()

        outputs.append(best_guess)

        # Model predicts it's the end of the sentence
        if output.argmax(1).item() == english.vocab.stoi["<eos>"]:
            break

    translated_sentence = [english.vocab.itos[idx] for idx in outputs]
    return translated_sentence[1:]

def bleu(data, model, crowdtalk, english, device):
    targets = []
    outputs = []

    for example in data:
        crowd = vars(example)["crowd"]
        eng = vars(example)["eng"]

        prediction = translate_sentence(model, crowd, crowdtalk, english, device)
        prediction = prediction[:-1]  # remove <eos> token

        targets.append([eng])
        outputs.append(prediction)

    return bleu_score(outputs, targets)

def checkpoint_and_save(model, best_loss, epoch, optimizer, epoch_loss):
    print('saving')
    print()
    state = {'model': model,'best_loss': best_loss,'epoch': epoch,'rng_state': torch.get_rng_state(), 'optimizer': optimizer.state_dict(),}
    torch.save(state, '/content/Model-V1')
    torch.save(model.state_dict(),'/content/Model-V1_SD')

In [58]:

train_df.english[0]

Out[58]:

'upon this ladder one of them mounted'

In [59]:

epoch_loss = 0.0
num_epochs = 5
best_loss = 999999
best_epoch = -1
sentence1 = "wraov driourth wreury hyuirf schneiald chix loir schloors rhiuny pfaiacts"
ts1  = []

for epoch in range(num_epochs):
  print("Epoch - {} / {}".format(epoch+1, num_epochs))
  model.eval()
  translated_sentence1 = translate_sentence(model, sentence1, crowdtalk, english, device, max_length=100)
  print(f"Original Sentence: \n {sentence1}")
  print(f"Translated example sentence 1: \n {translated_sentence1}")
  ts1.append(translated_sentence1)

  model.train(True)
  for batch_idx, batch in enumerate(train_iterator):
    input = batch.crowd.to(device)
    target = batch.eng.to(device)

    # Pass the input and target for model's forward method
    output = model(input, target)
    output = output[1:].reshape(-1, output.shape[2])
    target = target[1:].reshape(-1)

    # Clear the accumulating gradients
    optimizer.zero_grad()

    # Calculate the loss value for every epoch
    loss = criterion(output, target)

    # Calculate the gradients for weights & biases using back-propagation
    loss.backward()

    # Clip the gradient value if it exceeds > 1
    torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)

    # Update the weights values using the gradients we calculated using bp 
    optimizer.step()
    step += 1
    epoch_loss += loss.item()
    writer.add_scalar("Training loss", loss, global_step=step)

  if epoch_loss < best_loss:
    best_loss = epoch_loss
    best_epoch = epoch
    checkpoint_and_save(model, best_loss, epoch, optimizer, epoch_loss) 
    if ((epoch - best_epoch) >= 10):
      print("no improvement in 10 epochs, break")
      break
  print("Epoch_Loss - {}".format(loss.item()))
  print()
  
print(epoch_loss / len(train_iterator))

score = bleu(test_data[1:100], model, crowdtalk, english, device)
print(f"Bleu score {score*100:.2f}")

Epoch - 1 / 5
Original Sentence: 
 wraov driourth wreury hyuirf schneiald chix loir schloors rhiuny pfaiacts
Translated example sentence 1: 
 ['this', 'this', 'of', 'them', 'them', 'them', '<unk>']
saving

Epoch_Loss - 1.5663260221481323

Epoch - 2 / 5
Original Sentence: 
 wraov driourth wreury hyuirf schneiald chix loir schloors rhiuny pfaiacts
Translated example sentence 1: 
 ['this', 'this', 'this', 'of', 'them', '<unk>']
Epoch_Loss - 1.742606520652771

Epoch - 3 / 5
Original Sentence: 
 wraov driourth wreury hyuirf schneiald chix loir schloors rhiuny pfaiacts
Translated example sentence 1: 
 ['this', 'this', 'this', 'of', 'them', 'among', 'them', '<unk>']
Epoch_Loss - 1.4115256071090698

Epoch - 4 / 5
Original Sentence: 
 wraov driourth wreury hyuirf schneiald chix loir schloors rhiuny pfaiacts
Translated example sentence 1: 
 ['this', 'this', 'this', 'of', 'them', 'among', 'them', '<unk>']
Epoch_Loss - 1.3121858835220337

Epoch - 5 / 5
Original Sentence: 
 wraov driourth wreury hyuirf schneiald chix loir schloors rhiuny pfaiacts
Translated example sentence 1: 
 ['this', 'this', 'this', 'of', 'them', 'among', 'them', '<unk>']
Epoch_Loss - 0.962820291519165

6.642704698963097
Bleu score 2.99

Prediction Phase ✈¶

In [60]:

from nltk.tokenize.treebank import TreebankWordDetokenizer
data = test_df.crowdtalk.values
prediction = []
progress = []
for i in data:
  model.eval()
  translated_sentence = translate_sentence(model, i, crowdtalk, english, device, max_length=50)
  progress.append(TreebankWordDetokenizer().detokenize(translated_sentence))
  prediction.append(progress[-1])

In [62]:

test_df['prediction'] = prediction

In [63]:

test_df.head()

Out[63]:

	id	crowdtalk	prediction
0	27226	treuns schleangly throuys praests qeipp cyclui...	and made two account of my <unk>
1	31034	feosch treuns schleangly gliath spluiey gheuck...	and <unk>
2	35270	scraocs knaedly squiend sriohl clield whaioght...	this i had on on my <unk>
3	23380	sqaups schlioncy yoik gnoirk cziourk schnaunk ...	me the honour to to not able to <unk>
4	92117	schlioncy yoik psycheiancy mcountz pously mcna...	the old princes that that <unk>

In [64]:

!rm -rf assets
!mkdir assets
test_df.to_csv(os.path.join("assets", "submission.csv"), index=False)

In [ ]:

%aicrowd notebook submit -c lingua-franca-translation -a assets --no-verify

In [ ]:

Content

3530

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victorkras2008

Almost 3 years ago

Score = 0.000 Graded successfully! Why?

Bryon_Diaz

Almost 3 years ago

Comment deleted by Bryon_Diaz.

Bryon_Diaz

Almost 3 years ago

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