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Lingua Franca Translation

Getting Started Notebook for Lingua Franca Translation

A getting started notebook for Lingua Franca Translation challenge using SeqSeq.

divyanshu_kumar

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: seq2seq.png

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 [ ]:


Comments

victorkras2008
About 2 years ago

Score = 0.000 Graded successfully! Why?

Bryon_Diaz
About 2 years ago

Comment deleted by Bryon_Diaz.

Bryon_Diaz
About 2 years ago

Comment deleted by Bryon_Diaz.

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