General Middleware
NLP – Text Generation Using LSTM
Introduction¶
Text Generation is the task of predicting what word comes next, given what words lied ahead of it. It can be thought of as a chain of Auto-Predict task, where we continuously produce the next word, from the current word. While dealing with text, we need to train our model to forget old Context, update new Context, with each new words in the sequence, as each new words received brings some value-addition to the Context. For example, Pronouns are changed when the person we are talking about changes in the sentence.
Therefore,we leverage LSTM Reccurent Neural Networks to perform Text Generation. LSTM has a Context vector, and it has Forget gate to decide which part of the Old Context it should forget, an Input Gate to decide which part of the Input brings what change in the context,and an Output Gate to produce the Output or the next Word in our Text Generation task.
Auto Text Generation is also called Natural Language Modelling, because training Machines to perform Text Generation is an attempt to teach Machines, the mappings between words, and how words in a particular language are related to one another. By learning, which words appear after a sequence of words, or after a significant volume of natural language, all encoded within a context vector, macines learn to replicate human understanding of Natural Language in a sense.
Here are some applications where Language Modelling comes into play.
1) Mobile keyboard text recommandation
2) Whenever we search for something on any search engine, we get many suggestions and, as we type new words in it, we get better recommendations according to our searching context. So, how does this happen?
It is possible because of Natural Language Processing (NLP) techniques. Here, we will use NLP and try to make a prediction model using an LSTM (Long short-term memory) model that will predict the next words of a sentence.
Importing the necessary libraries¶
In [ ]:
import pandas as pd
import os
import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
Dataset Information¶
Import Medium-articles-dataset:
This dataset contains information about randomly chosen medium articles published in 2019 from these 7 Digital Sources. Our assumption here is, by exposing our model to NaturalLanguage in a particular domain, it will see instances of which words usually occur in sequence and therefore on training, it will learn the mapping of which words are likely to occur given a particular context. :
- Towards Data Science
- UX Collective
- The Startup
- The Writing Cooperative
- Data Driven Investor
- Better Humans
- Better Marketing
Dataset link: https://www.kaggle.com/datasets/dorianlazar/medium-articles-dataset
In [ ]:
data = pd.read_csv('/content/medium_data.csv')
data.head()
Out[ ]:
| id | url | title | subtitle | image | claps | responses | reading_time | publication | date | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | https://towardsdatascience.com/a-beginners-gui | A Beginner’s Guide to Word Embedding with Gens | NaN | 1.png | 850 | 8 | 8 | Towards Data Science | 2019-05-30 |
| 1 | 2 | https://towardsdatascience.com/hands-on-graph- | Hands-on Graph Neural Networks with PyTorch | NaN | 2.png | 1100 | 11 | 9 | Towards Data Science | 2019-05-30 |
| 2 | 3 | https://towardsdatascience.com/how-to-use-ggpl | How to Use ggplot2 in Python | A Grammar of Graphics for Python | 3.png | 767 | 1 | 5 | Towards Data Science | 2019-05-30 |
| 3 | 4 | https://towardsdatascience.com/databricks-how- | Databricks: How to Save Files in CSV on Your L | When I work on Python projects dealing… | 4.jpeg | 354 | 0 | 4 | Towards Data Science | 2019-05-30 |
| 4 | 5 | https://towardsdatascience.com/a-step-by-step- | A Step-by-Step Implementation of Gradient Desc | One example of building neural… | 5.jpeg | 211 | 3 | 4 | Towards Data Science | 2019-05-30 |
In [ ]:
print("Number of records: ", data.shape[0])
print("Number of fields: ", data.shape[1])
Number of records: 6508 Number of fields: 10
Here, we have 10 different fields and 6508 records but we will only use the title field to predict the next word.
Displaying the titles of various articles and preprocessing them¶
In [ ]:
data['title']
Out[ ]:
0 A Beginner’s Guide to Word Embedding with Gens...
1 Hands-on Graph Neural Networks with PyTorch & ...
2 How to Use ggplot2 in Python
3 Databricks: How to Save Files in CSV on Your L...
4 A Step-by-Step Implementation of Gradient Desc...
...
6503 “We” vs “I” — How Should You Talk About Yourse...
6504 How Donald Trump Markets Himself
6505 Content and Marketing Beyond Mass Consumption
6506 5 Questions All Copywriters Should Ask Clients...
6507 How To Write a Good Business Blog Post
Name: title, Length: 6508, dtype: object
In [ ]:
data['title'][1]
Out[ ]:
'Hands-on Graph Neural Networks with PyTorch & PyTorch Geometric'
Removing unwanted characters and words in titles¶
Looking at the titles, we can see there are some unwanted characters and words, which may not be useful for us for prediction purposes - in fact these characters and words may decrease our model accuracy. Hence they have to be removed.
In [ ]:
data['title'] = data['title'].apply(lambda x: x.replace(u'\xa0',u' '))
data['title'] = data['title'].apply(lambda x: x.replace('\u200a',' '))
Tokenization¶
Tokenization is the process by which we convert texts into separable units, i.e, words. But here, we are using a pre-built Tokenizer that converts each words to their One-hot encoding indexes, by providing a unique ID to all the words.
In [ ]:
tokenizer = Tokenizer(oov_token='<oov>') # For those words which are not found in word_index
tokenizer.fit_on_texts(data['title'])
total_words = len(tokenizer.word_index) + 1
print("Total number of words: ", total_words)
print("Word: ID")
print("------------")
print("<oov>: ", tokenizer.word_index['<oov>'])
print("Strong: ", tokenizer.word_index['strong'])
print("And: ", tokenizer.word_index['and'])
print("Consumption: ", tokenizer.word_index['consumption'])
Total number of words: 8238 Word: ID ------------ <oov>: 1 Strong: 4 And: 8 Consumption: 8237
Text into sequences and Generating n-gram Sequence for training¶
NLP language models require sequential input data, and the input word/token must be numerical. Here we are generating n-grams in order to train our model for next word prediction.
Let's suppose we have a sentence such as "I am good" - this will convert into a sequence with their respective tokens {'I' : 1, 'am' : 2, 'good' : 3}.
Thus, the output would be: [ '1', '2', '3' ]
Likewise, all our titles will be converted into sequences.
Then, we will make an n_gram model for good predictions.
The image below explains this process.
Since we are training our model on Sequential Text Generation, what we are doing here is essentially increasing the total number of training examples, as if "I am a Data Scientist" is an example text, then so is "I am a".
In [ ]:
input_sequences = []
for line in data['title']:
token_list = tokenizer.texts_to_sequences([line])[0]
print(token_list)
for i in range(1, len(token_list)):
n_gram_sequence = token_list[:i+1]
input_sequences.append(n_gram_sequence)
# print(input_sequences)
print("Total input sequences: ", len(input_sequences))
Streaming output truncated to the last 5000 lines.
[1541, 3, 2944, 818, 5, 21]
[4740, 90, 99, 236]
[30, 46, 864, 232, 217, 6, 284]
[113, 4741, 2945, 59, 1246, 5, 4742, 4743, 83, 172]
[80, 13, 32, 119, 25, 70, 44, 366, 5, 36, 178]
[47, 54, 560, 2, 736, 1872, 1247]
[49, 3, 87, 7, 2, 66, 1748, 9, 12, 105, 5, 34, 244]
[93, 13, 30, 5, 134, 193, 178, 13, 32, 971, 2946, 1749]
[466, 1750, 9, 36, 34, 819, 5, 34, 244]
[175, 34, 308, 34, 9, 303, 596]
[22, 13, 51, 9, 10, 34, 840, 795, 2947]
[3, 885, 811, 6, 115, 2880]
[3, 97, 102, 4744, 2, 4745, 15, 140, 183, 31, 539, 60, 299, 520]
[126, 168, 4746, 494, 4747, 8, 4748, 1454]
[3, 513, 6, 1287, 15, 4749]
[108, 17, 425, 9, 1319, 8, 109, 1018]
[2948, 282]
[22, 117, 291, 2696, 286, 187, 37, 117, 1060, 1248]
[22, 30, 3, 113, 1030, 56, 112, 245]
[3, 4750, 2949, 6, 101, 1152]
[3, 370, 2, 300, 5, 272, 42]
[4, 19, 11, 4, 11, 20, 4, 2, 3, 115, 4751, 776, 1455, 2, 4752, 5, 123, 4753, 4]
[124, 2, 4754, 25, 592, 2, 5, 123, 56]
[5, 4755, 2950, 23, 314, 139, 185, 16, 205, 2782]
[3, 121, 6, 26, 495]
[3, 588, 1958, 6, 559, 573]
[7, 2, 49, 3, 113, 6, 2004, 1751, 9, 202, 185]
[3, 61, 1573, 6, 1456]
[4, 19, 11, 4, 11, 20, 4, 2951, 10, 34, 234, 14, 4756, 4]
[5, 53, 192, 68, 2, 1612, 2723]
[34, 653, 4757, 16, 4758, 8, 4759, 299]
[4, 19, 11, 4, 11, 20, 4, 32, 938, 1752, 2204, 727, 498, 4]
[4760, 15, 109, 183, 725, 30, 1006]
[3, 2205, 14, 388]
[7, 2, 59, 5, 519, 14, 177]
[97, 102, 72, 2, 213, 26, 4761, 2516]
[53, 1133, 419, 161, 432, 45, 59]
[47, 2952, 2, 2170, 44, 491, 2, 173, 10, 169, 5, 1249]
[4762, 370, 1047]
[4763, 31, 679]
[3, 47, 389, 6, 1413, 1655]
[28, 1712, 4764]
[2127, 21, 286, 820, 37, 29]
[440, 2953, 2206, 330, 612, 91, 724, 191, 463]
[62, 420, 214, 2954, 13]
[7, 21, 4765]
[3, 800, 2, 106, 1011, 100]
[112, 1289, 8, 101, 916]
[137, 211, 2207, 6, 5, 996, 2743, 374]
[18, 4766, 15, 4767, 113, 2208, 4768, 16, 122, 393, 305]
[4, 19, 11, 4, 11, 20, 4, 952, 1132, 1452, 2135, 455, 2, 1693, 991, 4769, 287, 4]
[25, 1997, 2, 222, 516, 26, 4770, 6, 78, 2955]
[4, 19, 11, 4, 11, 20, 4, 4771, 13, 32, 277, 2, 1034, 558, 179, 8, 266, 136, 4]
[33, 5, 4772, 268, 12, 29, 807, 176]
[5, 4773, 75, 870, 14, 4774, 1405]
[4, 19, 11, 4, 11, 20, 4, 47, 132, 2, 1373, 5, 1457, 465, 4775, 363, 715, 4]
[51, 12, 782, 14, 275]
[4, 19, 11, 4, 11, 20, 4, 293, 152, 18, 61, 156, 545, 51, 56, 4776, 4]
[137, 245, 2, 410, 2209, 2, 27, 177]
[1061, 1621, 15, 144, 1621]
[438, 4777, 53, 470, 310, 12, 4778, 3, 2210, 4779]
[215, 259, 25, 208, 14, 88, 615, 6, 40, 176]
[500, 1753, 35, 24, 649, 58, 71, 182, 181, 1062, 649]
[4780, 89, 2070, 799]
[7, 2, 67, 2956, 12, 35, 24, 14, 3, 61, 1754, 17, 48, 2957]
[3, 2958, 219, 199, 2959, 1755]
[4781, 14, 40, 969, 2897]
[17, 357, 16, 386, 1012, 45, 21, 67, 2211, 891]
[47, 710, 41, 330]
[25, 326, 2, 887, 14, 2960]
[451, 737, 673, 8, 242, 565]
[3, 358, 289, 181]
[33, 17, 48, 2, 266, 136, 23, 28, 169, 1756, 2, 2961]
[204, 2962, 2676, 2963, 16, 150, 4782, 13, 669, 1757]
[3, 2125, 6, 4783, 428, 447, 23, 4784]
[35, 24, 12, 4785, 1013, 182, 8, 2964, 4786, 4787]
[556, 4788, 73, 412, 1016, 24]
[2965, 99, 453, 1458]
[4789, 4790, 17, 874, 14, 1710]
[18, 45, 13, 625, 2, 4791, 2966]
[4792, 17, 48, 12, 3, 4793]
[2212, 426, 26, 660, 75, 6, 1414, 1380, 2685, 58, 92]
[1519, 998, 1676]
[147, 22, 15, 35, 24]
[75, 6, 2213, 168]
[26, 1214, 1400, 6, 4794]
[371, 176, 2, 4795, 63, 10, 2967, 4796]
[1652, 851, 75, 14, 35, 24]
[175, 81, 103]
[133, 3, 140, 4797, 2968, 2969, 730, 2970]
[102, 72, 2, 2971, 63, 5, 253, 34]
[4798, 10, 4799, 78, 5, 409, 12, 2972]
[7, 2, 34, 1377, 340, 1745]
[4800, 10, 34, 2, 2973]
[246, 2214, 34, 425, 14, 891]
[18, 21, 2022, 361, 2, 213, 5, 36, 178]
[1063, 682, 306, 15, 39, 421, 5, 557, 6, 156]
[34, 9, 3, 401, 6, 41]
[4801, 34, 308, 8, 234, 9, 5, 1336, 16, 5, 36, 87, 129]
[3, 48, 6, 4802, 681]
[7, 2, 66, 2973, 4803, 37, 134, 239]
[134, 1152, 118, 4804, 611, 3, 809, 2202, 6, 3, 2115, 2116]
[3, 84, 6, 3, 2215, 15, 39, 3, 2215]
[206, 12, 1250, 16, 13, 51, 2, 119, 2, 1715]
[3, 154, 577, 1549, 16, 4805, 246, 193, 14, 2974, 8, 782]
[2216, 264, 2, 66, 10, 804, 2, 460, 36]
[7, 2, 66, 3, 113, 95, 6, 4806, 4807]
[15, 31, 5, 4808, 43, 734, 202, 4809, 8, 115, 30, 4810, 9, 163, 431]
[36, 61, 4811, 16, 42, 6, 5, 2730, 58, 71]
[1609, 8, 34, 7, 32, 46, 67, 117, 2975, 2, 27, 79, 254]
[4812, 1992]
[7, 21, 49, 136, 23, 1055, 171, 1890, 57, 4813, 26, 4814]
[4, 19, 11, 4, 11, 20, 4, 624, 616, 2976, 170, 10, 4815, 1649, 56, 4]
[3, 4816, 68, 2, 4817, 4818, 92, 265, 3, 4819, 746, 1222, 1459]
[7, 3, 76, 1329, 6, 10, 128, 32, 49, 13, 5, 79, 542]
[2005, 10, 801, 1460, 14, 4820]
[1563, 23, 921, 925, 5, 354, 6, 4821]
[391, 4822, 55, 4823, 16, 881, 4824, 30, 2977, 52, 1758]
[4825, 14, 2217, 8, 2148, 58, 71]
[198, 109, 992, 2, 196, 127]
[33, 4826, 2, 585, 5, 424, 219]
[4827, 2978, 1007, 3, 4828]
[4829, 3, 4830, 6, 4831, 4832, 9, 77]
[676, 68, 2, 819, 23, 671, 337, 383, 2979]
[805, 728]
[7, 78, 1027, 32, 153, 228, 399, 234]
[2980, 69, 4833, 10, 74, 245]
[3, 1060, 1759, 6, 50]
[13, 212, 669, 410, 31, 94, 633, 341]
[7, 4834, 1038, 821, 109, 504, 1038]
[278, 21, 32, 564, 13, 37, 4835, 9, 5, 92, 1444, 215]
[7, 2, 67, 4836, 2, 1675, 1064]
[4837, 4838, 4839, 16, 26, 1187, 9, 4840, 10, 4841, 8, 2981]
[3, 2660, 87, 6, 39, 1242, 10, 74, 751]
[410, 263, 685, 242, 683, 321]
[3, 47, 475, 6, 395, 4842]
[62, 290, 3, 566, 509]
[145, 319, 1146, 25, 4843, 4844, 4845]
[97, 106, 100, 1251, 9, 208, 8, 597, 344]
[28, 333, 63, 14, 5, 412, 4846, 8, 3, 2218, 6, 922, 4847, 1441]
[7, 433, 236, 70, 2982, 201, 2219]
[7, 2, 816, 1421, 25, 5, 1461, 2983]
[15, 416, 4848, 4849, 57, 5, 4850, 9, 3, 1322]
[2, 1252, 1462, 735, 9, 163, 2718]
[7, 2, 2984, 10, 1760, 1129]
[4851, 56, 5, 408, 87]
[3, 4852, 4853, 15, 299, 16, 183, 13, 62, 59, 2, 27, 2985]
[623, 63, 126, 168, 12, 718, 646]
[2001, 4854, 727, 4855, 12, 94]
[2986, 560, 2, 901, 4856, 9, 3, 4857, 6, 303]
[98, 4858, 10, 364, 645, 96, 5, 227, 4859]
[3, 4860, 6, 111, 1172, 7, 4861, 2987, 692, 2220, 2, 624, 4862]
[15, 468, 94, 37, 79, 827, 8, 4863]
[198, 131, 21, 104, 25, 28, 76, 369, 1463]
[22, 28, 2221, 427, 91, 37, 282]
[1181, 158, 447, 16, 88, 4864, 872, 1165]
[83, 15, 18, 28, 2004, 32, 60, 25, 240, 2988, 316, 1325, 1104]
[22, 30, 1578, 1532, 321]
[4865, 64, 4866]
[384, 72, 2, 830, 89, 387, 44, 13, 59, 144, 136]
[109, 1053, 383, 338, 115, 32, 67, 2, 372, 257, 8, 458, 54, 1026]
[1065, 2989, 8, 73, 24, 723, 72, 2, 266, 3, 2222, 4867]
[7, 2, 4868, 567, 4869, 8, 4870, 14, 10, 477]
[149, 18, 21, 4871, 5, 2990, 71, 4872, 4873, 9, 88, 128]
[7, 13, 32, 476, 2, 39, 27, 1934]
[7, 2, 116, 5, 56, 25, 1761, 183, 2991, 2981]
[33, 35, 24, 2, 2170, 2223, 1440, 394, 23, 922, 668, 127]
[18, 257, 15, 54, 9, 70]
[3, 662, 370, 2992, 484, 6, 4874]
[10, 1762, 2527, 360, 27, 2993, 10, 227, 8, 604, 111]
[22, 15, 70, 405, 8, 18, 13, 51, 31, 16, 3, 1147, 1433, 1356]
[7, 2, 266, 10, 76, 2994, 298, 843]
[1527, 2148, 2572, 4875, 12, 2995, 126, 168, 1066]
[3, 167, 6, 300, 14, 4876]
[3, 2996, 13, 1763, 30, 9, 3, 2997]
[13, 62, 51, 969, 2, 151, 10, 61, 184]
[53, 809, 1764, 6, 1341, 2998, 10, 56, 7, 2, 702, 214]
[4, 19, 11, 4, 11, 20, 4, 2224, 10, 519, 14, 4877, 5, 99, 8, 5, 381, 2512, 1412, 6, 4]
[21, 636, 2, 333, 31, 2, 13, 183, 167, 15, 60]
[22, 323, 867, 225, 25, 240, 12, 92, 331]
[2999, 21, 92]
[7, 2, 212, 66, 637, 1185, 2, 179]
[4, 19, 11, 4, 11, 20, 4, 3000, 10, 263, 18, 13, 51, 2, 8, 7, 2, 38, 31, 4]
[4878, 917, 126, 290]
[45, 13, 2874, 10, 3001, 57, 333, 200, 214]
[225, 55, 240, 203, 91, 4879]
[829, 3002, 3003, 4880, 4881]
[4882, 4883, 7, 13, 235, 59, 4884, 5, 4885, 4886, 23, 10, 3004, 518, 303, 4887]
[9, 1183, 6, 3, 1762, 1212]
[9, 1183, 6, 1765, 4888]
[110, 5, 661, 208, 1253, 4889, 12, 5, 4890, 437, 255, 33, 4891]
[3005, 3, 3006, 3007, 6, 4892]
[3, 4893, 8, 373, 4894, 6, 3008, 95]
[133, 1430, 93, 10, 141, 1251]
[258, 108, 8, 365, 42, 131, 25, 2225]
[4, 19, 11, 4, 11, 20, 4, 10, 2226, 2227, 281, 114, 257, 1400, 170, 13, 258, 31, 165, 4]
[62, 27, 5, 85, 138, 27, 5, 140, 2228]
[32, 13, 458, 93, 13, 62, 65, 161, 128]
[819, 3009, 1464, 14, 4895]
[26, 1018, 4896, 68, 2, 154, 2229]
[165, 4897, 29, 49, 85, 295]
[22, 29, 2230, 32, 564, 13]
[198, 152, 18, 3, 4898, 4899, 15, 5, 201, 369]
[384, 132, 2, 49, 10, 813, 2168, 139, 135, 3, 76, 50]
[294, 49, 26, 41, 4900, 3010, 563, 16, 1465, 64, 81, 164]
[7, 21, 67, 83, 270, 247, 2, 173, 4901, 634]
[4, 19, 11, 4, 11, 20, 4, 1211, 8, 765, 4902, 41, 8, 2130, 185, 4]
[3, 383, 808, 613, 3, 3011, 9, 3, 84]
[47, 1170, 2038, 43, 4903, 5, 196, 4904, 353, 39, 60, 9, 10, 74, 1579]
[18, 13, 669, 27, 1448, 6, 108, 17]
[4, 19, 11, 4, 11, 20, 4, 945, 451, 737, 673, 14, 40, 4]
[76, 132, 9, 35, 24]
[22, 15, 262, 663, 220, 403]
[4905, 4906, 1383, 12, 1406, 2231, 728]
[56, 773, 3012, 982, 103, 4907]
[4908, 15, 39, 5, 822]
[1210, 15, 5, 123, 1067, 238]
[3, 1619, 113, 1054, 35]
[7, 38, 3013, 173, 8, 1201, 10, 112, 3, 137, 928, 3014, 6, 446, 994]
[3015, 4909, 3016, 2118, 4910, 400, 12, 2231, 728, 478]
[1088, 3, 2949, 4911]
[3, 3017, 847, 498, 16, 122, 393, 305]
[667, 257, 4912, 57, 1466, 257, 1669, 13, 27, 3, 3018, 8, 5, 211, 4913, 6, 10, 160, 443, 4914]
[699, 16, 666, 483, 2232, 2059, 77]
[3019, 12, 975, 4915, 10, 114, 2, 4916]
[124, 2, 4917, 49, 1151, 518, 363, 13, 51, 2, 49, 5, 1151, 518, 78, 149, 7]
[7, 2, 362, 63, 10, 238, 12, 2233, 394, 25, 3, 151]
[157, 131, 9, 155, 115, 2, 4918, 2, 10, 2234]
[165, 2987, 4919, 70, 522, 1118, 63]
[4, 19, 11, 4, 11, 20, 4, 53, 951, 2, 3020, 54, 226, 363, 4]
[3, 1682, 114, 2, 66, 54, 946, 23, 10, 260, 1066]
[7, 2, 27, 1457]
[3, 257, 15, 54, 268, 2, 29]
[3021, 1146, 2, 4920, 10, 388]
[53, 72, 2, 49, 406, 147, 217, 831, 779, 124, 2, 2735, 818]
[3, 92, 4921, 6, 140, 395, 4922, 1068]
[3, 167, 6, 1343, 177, 90, 10, 60]
[3, 87, 12, 3, 2656, 138]
[17, 48, 89, 427, 118, 843]
[120, 2, 4923, 1727, 236, 658, 9, 40]
[35, 24, 25, 422, 4924]
[3, 113, 3022, 40, 1446]
[454, 63, 4925, 4926, 12, 1467, 247]
[490, 10, 2235, 8, 234, 1561, 1283, 14, 40]
[26, 1214, 1400, 6, 1520, 296, 8, 1766, 764]
[7, 2, 116, 10, 160, 345, 81, 164, 4927, 25, 422]
[2829, 9, 498, 1051]
[183, 22, 15, 5, 546, 2699]
[424, 35, 24, 14, 1291, 8, 4928]
[2967, 9, 208, 403, 14, 4929, 40]
[5, 4930, 68, 2, 116, 5, 3023, 691]
[205, 3, 446, 14, 1174, 1663, 1385, 8, 3024]
[1135, 158, 2908, 17, 14, 326, 8, 4931, 58, 21]
[326, 25, 487, 2, 927, 12, 17, 327]
[3025, 712, 3026]
[3, 2236, 6, 513]
[67, 3, 4932, 1422, 14, 4933]
[22, 21, 104, 25, 4934, 14, 97, 17, 48, 668, 16, 86, 12, 1622, 17, 327]
[28, 76, 382, 184, 9, 40, 12, 4935, 1623, 9, 1468, 4936]
[30, 41, 4937, 4938, 212, 308]
[294, 4939, 8, 4940, 5, 35, 24, 99]
[1241, 782, 1361, 4941, 4942, 8, 4943, 4944]
[155, 231, 14, 416, 17, 23, 826, 14, 40, 8, 923]
[3, 4945, 356, 6, 4946, 4947]
[1040, 262, 9, 437, 94, 25, 3, 2237, 615]
[4948, 4949, 1727, 24, 2, 585, 3, 4950, 1386, 4951]
[158, 529, 358]
[4, 19, 11, 4, 11, 20, 4, 1767, 64, 4952, 4953, 2, 223, 4954, 3, 41, 4955, 4]
[49, 10, 1177, 140, 481, 9, 157, 109, 132]
[4, 19, 11, 4, 11, 20, 4, 66, 3, 78, 34, 3027, 13, 2238, 16, 58, 71, 4]
[3, 381, 3028, 2, 26, 1469, 34, 4956]
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[206, 12, 115, 8, 312]
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[3, 680, 354, 6, 4959]
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[1652, 682, 306, 185]
[26, 120, 2, 3030, 4962]
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[98, 252, 3, 84, 6, 10, 176]
[7, 2, 49, 10, 158, 447, 60, 4967]
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[18, 21, 1305, 9, 4976]
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[4978, 4979, 15, 319, 70, 189, 117, 862]
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[15, 31, 201, 2, 890, 169, 2, 5, 477, 2243, 4981, 13, 343]
[7, 2, 98, 1688, 8, 66, 79, 55, 413, 121]
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[9, 108, 17, 3, 1771, 15, 3, 78]
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[7, 2, 67, 1310, 9, 10, 716, 139]
[105, 5, 158, 2244, 14, 601, 8, 921, 925]
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[18, 13, 51, 5, 377, 3034, 3035]
[4986, 403, 4987, 14, 4988, 4989]
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[7, 2, 49, 836, 95, 6, 2478, 1364]
[3, 1034, 12, 1033, 16, 18, 311, 45, 1200, 9, 121, 115, 488]
[34, 1256, 56]
[7, 2, 243, 10, 618, 373]
[7, 126, 168, 823, 4991, 163, 1130]
[3, 381, 6, 3036]
[1985, 29, 2, 10, 42, 44, 13, 59, 5, 414, 50, 101]
[161, 3037, 1593, 42, 52, 5, 3038, 2228, 23, 4992, 4993]
[3, 142, 6, 2853]
[5, 900, 23, 4994, 363, 8, 3039, 12, 1374, 249, 4995]
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[283, 1430, 14, 111, 3, 3040]
[881, 3041, 1013, 190, 14, 41, 5, 68, 12, 2245]
[1692, 69, 179, 10, 42, 9, 47, 1054, 72]
[3, 2246, 6, 226, 806, 554]
[18, 38, 1257, 277, 163, 160, 859]
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[38, 46, 51, 2, 1208, 14, 1168]
[3, 1407, 558, 4999, 32, 119, 25, 145, 1070, 441]
[97, 47, 5000, 12, 981]
[404, 15, 39, 5, 336, 238, 1755]
[38, 13, 51, 2, 1020, 2, 173, 177]
[3042, 5001, 43, 69, 362, 63, 5, 5002, 368]
[105, 89, 792, 7, 2, 971, 10, 664, 52, 5, 178]
[18, 318, 10, 1472, 15, 2509]
[149, 18, 638, 94, 201, 5003]
[294, 49, 158, 447, 1377, 2, 656]
[1969, 2133, 15, 2065, 9, 163, 3043]
[10, 112, 281, 34, 282]
[4, 19, 11, 4, 11, 20, 4, 133, 50, 2, 66, 672, 37, 141, 218, 4]
[3044, 5004, 8, 112, 245]
[22, 46, 32, 119, 25, 5005, 1924, 6, 1395, 3045, 25, 5006]
[7, 2, 625, 1772, 44, 13, 30, 684, 5007, 8, 5008, 735]
[42, 131, 25, 28, 1671]
[3, 3046, 381, 6, 2247, 5009, 5010]
[4, 19, 11, 4, 11, 20, 4, 7, 217, 525, 31, 277, 12, 13, 2, 1305, 9, 177, 4]
[7, 2, 362, 430, 96, 5, 804]
[1874, 672, 8, 66, 2012, 22, 21, 104, 55, 5, 5011, 5012]
[149, 18, 21, 221, 111, 5, 3047]
[7, 2, 390, 14, 1773, 137, 2073, 2, 153, 449, 3, 956, 1168, 43, 561, 14, 1700]
[817, 295, 613]
[50, 12, 815, 47, 132, 2, 1774, 151, 10, 306, 6, 3, 172, 768, 3048]
[1736, 14, 459]
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[1453, 5014, 9, 3, 999, 1000, 235, 3050, 27, 3, 973]
[5015, 15, 5, 159, 22, 1258, 2, 3, 480, 287, 975]
[7, 2, 5016, 1464, 96, 9, 536, 201, 748, 62, 286]
[105, 5017, 14, 10, 540, 18, 133, 1133, 7, 2, 38, 31]
[32, 1290, 8, 1123, 49, 91, 5, 79, 138]
[44, 111, 1186, 15, 359]
[2569, 15, 39, 3, 5018, 6, 614]
[46, 351, 585, 1017, 407, 14, 26, 395, 3051]
[3, 74, 152, 2, 65, 10, 594, 83, 647]
[7, 2, 67, 5019, 3052, 2, 622, 363, 12, 10, 56]
[5020, 3, 128]
[1218, 2242, 1365]
[3053, 6, 5, 2770]
[7, 2, 27, 26, 845, 39, 26, 5021, 55, 2248, 10, 1216, 55, 60]
[3, 443, 2112, 795, 2947]
[684, 250, 321, 2, 358]
[2, 839, 786, 459, 632, 54]
[278, 13, 51, 2, 82, 2, 65, 5, 188, 1970]
[3, 535, 6, 3054, 10, 29]
[94, 3, 72, 357, 32, 153, 10, 551, 29]
[133, 39, 37, 22, 13, 1616, 183, 7, 13, 203, 214, 392]
[3055, 500, 3039, 1103, 3056, 64, 3057, 23, 348, 394, 194, 2612]
[914, 329, 2, 343, 189, 121, 5, 3058, 369]
[35, 24, 121, 41, 114, 257]
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[38, 13, 1463, 10, 17, 143, 12, 1174, 3045, 93, 13, 62, 13, 360, 27, 508, 5022, 2249, 980]
[5023, 5024, 1139, 12, 5025, 1297]
[3, 974, 37, 377, 453, 12, 5026]
[7, 2, 67, 1259, 302, 2, 66, 3, 113, 95, 6, 10, 146, 1066]
[662, 80, 21, 104, 1233, 28, 76, 172, 52, 5, 35, 24, 897]
[175, 1106, 1022, 419, 75]
[413, 1071, 182]
[15, 546, 1422, 79, 170, 1014, 181, 5, 1260]
[325, 12, 61, 8, 3059, 17, 327]
[18, 5027, 507, 30, 2250, 9, 580, 8, 7, 2, 702, 214]
[546, 1422, 182, 8, 242, 485]
[5028, 8, 3, 380, 14, 81, 103]
[334, 24, 16, 99, 236, 617, 560]
[2182, 16, 5, 1607, 220, 658, 973]
[1698, 6, 5029, 764]
[451, 737, 673, 3060, 400]
[1022, 419, 6, 1106]
[3, 1341, 6, 17, 48, 9, 70]
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[1398, 5, 1334, 119, 737, 673, 99, 9, 204, 1051]
[1376, 5033]
[1308, 405, 95, 6, 3, 1140]
[186, 92, 265, 16, 299, 15, 22, 323, 104, 25, 5, 21, 128, 55, 125]
[723, 2251, 1981, 1982, 14, 713, 2252]
[5, 21, 8, 3061, 89, 5034]
[5035, 6, 5036, 5037, 64, 5038, 3003, 597, 344, 223, 2756, 1775]
[18, 254, 45, 173, 3, 1058, 229, 312, 308, 8, 34, 308]
[1006, 134, 681]
[115, 234, 78]
[4, 19, 11, 4, 11, 20, 4, 34, 5039, 5040, 3062, 340, 2253, 16, 8, 54, 36, 83, 368, 4]
[417, 824, 43, 30, 95, 6, 83, 145, 5041]
[2254, 5042, 5043, 330, 135, 2, 10, 477, 16, 5, 36, 87, 129]
[34, 1153, 27, 26, 5044, 12, 10, 34]
[206, 12, 701, 8, 946]
[3, 5045, 46, 2238, 8, 3, 1244, 46, 51, 135, 195]
[7, 2, 67, 10, 1642, 307, 2, 10, 2175]
[2192, 921, 925, 16, 5046, 5047, 5048]
[21, 5049, 3, 5050, 5051, 712, 149, 22, 21, 104]
[63, 8, 534, 14, 5052, 5053, 8, 237]
[4, 19, 11, 4, 11, 20, 4, 256, 118, 2255, 5054, 590, 1072, 9, 77, 4]
[40, 326, 1473, 17, 1135, 5055, 13, 79, 5056]
[97, 1359, 13, 51, 2, 82, 37, 1178, 176, 5057]
[5058, 5059, 8, 3, 5060, 1928, 1006]
[206, 5, 2256, 9, 92, 1043]
[65, 10, 160, 315, 335, 346, 1108]
[205, 31, 96, 225, 9, 156, 55, 3063]
[316, 30, 195, 5061]
[18, 21, 1021, 55, 592, 12, 147, 479]
[110, 5, 857, 2249, 14, 315]
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[7, 404, 125, 8, 302, 941, 349, 2257]
[5064, 1227, 5, 253, 87, 129]
[18, 721, 1470, 3065, 2, 27, 54, 5065, 170, 1073, 1470]
[332, 2, 47, 518, 517, 9, 162, 1074, 1043]
[3066, 667, 416, 8, 3, 1162, 5066, 2258, 1129]
[1127, 16, 5, 7, 2, 68]
[3, 1776, 2259, 1617, 6, 145, 105]
[5067, 3067, 8, 3, 3068, 12, 455]
[2260, 5068, 3069, 3070]
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[18, 21, 1778, 2, 66, 5, 1474, 8, 13, 45, 38, 52, 604]
[180, 5, 5070, 611, 255, 9, 437, 14, 5071]
[32, 1394, 5072]
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[1198, 10, 2262, 715]
[5074, 3, 1779, 6, 2086, 5075]
[3, 5076, 6, 144, 5077, 5078]
[2, 3071, 57, 39, 2, 3071]
[124, 2, 1075, 10, 430, 856, 12, 3, 1762, 1261]
[264, 12, 5079, 14, 179]
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[22, 21, 104, 23, 28, 800, 1756, 1769, 3, 2264, 37, 1076, 223]
[10, 356, 15, 1550, 5, 382, 58, 6, 5, 225, 56, 43, 31, 32, 27, 3072, 5, 3073, 980]
[3, 87, 12, 3, 41, 5081]
[18, 38, 2265, 5082, 23, 126, 168]
[174, 442, 21, 8, 174, 30, 13]
[151, 64, 2266, 2110, 10, 114, 63]
[15, 5083, 3, 135, 122, 12, 10, 112]
[102, 80, 13, 350, 5084, 8, 5085, 189, 1659, 10, 432]
[226, 1023, 362, 2, 5086, 5087, 5088, 64, 5089]
[5090, 5091, 6, 2562, 25, 5092]
[4, 19, 11, 4, 11, 20, 4, 124, 2, 27, 5, 906, 138, 5093, 31, 96, 5, 101, 4]
[5094, 425, 200, 3, 3074, 6, 3075, 5095]
[5096, 2244, 8, 151, 2267, 489, 5097, 5098, 970, 18, 1978, 6, 3, 151, 2267, 372]
[3076, 906, 537, 15, 359, 2, 561, 64]
[5, 407, 6, 3077]
[7, 2, 67, 260, 2, 122, 10, 1597, 56]
[22, 13, 62, 82, 37, 1099, 5099]
[83, 15, 18, 13, 1780, 55, 2268, 10, 160, 29]
[22, 15, 10, 1642, 5100, 5101, 9]
[3, 330, 43, 21, 124, 2, 27, 5102, 5103]
[792, 15, 616, 46, 116, 25, 3, 524, 95]
[4, 19, 11, 4, 11, 20, 4, 154, 5104, 4]
[28, 2671, 130]
[205, 3, 76, 615, 6, 10, 5105]
[434, 30, 144, 2269, 5106, 3, 1542]
[39, 28, 295, 2, 286]
[101, 2025, 1777, 5107]
[892, 23, 1262, 5108, 39, 23, 322]
[7, 2, 5109, 115, 148, 1215]
[261, 115, 51, 5, 3078]
[7, 489, 21, 173, 3, 473, 1331, 12, 28, 188]
[136, 3, 140, 969, 814]
[5110, 1140, 6, 805, 728]
[1705, 10, 841, 14, 5111]
[3079, 1582, 2123, 2230]
[32, 5112, 110, 26, 5113, 650, 6, 1421, 12, 5114]
[4, 19, 11, 4, 11, 20, 4, 1387, 3, 41, 521, 16, 869, 57, 5, 192, 217, 947, 58, 441, 4]
[269, 30, 3, 2473, 5115, 8, 5116]
[3, 47, 182, 1062, 649, 161, 17, 445, 350, 82]
[3, 607, 6, 3, 794, 5117]
[3, 501, 68, 6, 428, 12, 17, 327]
[74, 220, 12, 2082, 1255, 299, 30, 28, 2508]
[120, 2, 663, 220, 403, 262]
[105, 5, 984, 658, 691, 14, 5118, 8, 2455]
[4, 19, 11, 4, 11, 20, 4, 7, 2, 848, 3, 127, 6, 10, 70, 1476, 4]
[7, 2, 1521, 1170, 2038, 9, 5, 17, 48, 101, 1431]
[15, 226, 2132, 1409]
[98, 33, 1167, 2, 2270, 967, 17]
[2140, 75, 514]
[5, 1661, 1381, 90, 3, 5119, 5120, 17, 362]
[18, 1096, 15, 3, 211, 2, 24, 17, 48]
[4, 19, 11, 4, 11, 20, 4, 97, 2260, 2, 1077, 189, 697, 40, 12, 185, 4]
[22, 15, 5, 3080]
[507, 118, 5121, 3081, 164, 12, 208, 403]
[15, 3, 204, 108, 17, 2271, 589, 31]
[7, 217, 17, 399, 165, 5, 17, 445, 51, 2, 82]
[22, 2, 304, 396, 355, 23, 17, 48, 478]
[1218, 5122, 84, 6, 5123, 14, 41]
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[15, 10, 238, 1654, 17, 251]
[7, 17, 48, 69, 394, 84, 6, 545]
[7, 165, 5, 81, 164, 49, 1226]
[185, 879, 43, 17, 327, 45, 67, 195]
[197, 35, 24, 9, 3, 1781, 123, 50, 208, 182]
[5127, 5, 73, 24, 236, 480, 5128, 43, 15, 309, 650]
[829, 5129, 5130, 5131, 651, 3085, 14, 5132, 785]
[1174, 448, 3086, 9, 17, 48, 2141, 742]
[3087, 541, 5133, 436, 17]
[5134, 5135, 14, 186, 92, 265]
[62, 1450, 3, 41, 133, 3, 725, 174, 30, 2995]
[913, 1095, 23, 2275, 190, 312]
[26, 605, 6, 262, 865, 12, 3088]
[32, 73, 24, 2052, 79, 170, 5136]
[3, 773, 6, 26, 2275, 190, 244, 731, 913, 164, 5137]
[18, 15, 2114, 2842, 5138]
[5, 741, 5139, 87, 129, 2213, 5140, 1977]
[1477, 8, 5141, 874, 211, 419, 6, 5, 17, 48, 5142]
[5, 17, 75, 6, 3, 3089, 3090, 1397]
[3, 1208, 6, 559, 128, 2275, 190, 312]
[252, 3, 306, 3, 2276, 130, 5143, 1016]
[3, 3091, 1326, 6, 455, 16, 1461, 1146]
[5144, 825, 5145, 9, 41, 30, 46, 434, 733]
[5146, 5147, 320, 5148, 12, 2828, 880, 100]
[1242, 139, 1782, 565, 185]
[2087, 5149, 9, 36, 34]
[384, 749, 12, 473, 292, 34]
[49, 3, 113, 6, 10, 134, 239, 8, 5150, 96, 5, 775]
[18, 165, 278, 209, 3, 896]
[47, 475, 6, 2277, 7, 2, 67, 31, 2, 736, 1783]
[22, 3092, 518, 134, 678, 5151, 37, 1428, 253, 456]
[5152, 5153, 12, 543, 1249, 5154, 9, 292, 790]
[2278, 63, 12, 738, 1478, 16, 5, 36, 75]
[111, 5, 272, 698, 3093]
[3, 211, 2, 206, 140, 714, 15, 1127]
[5155, 5156]
[384, 5157, 812, 43, 69, 277, 13, 25, 2063, 2, 845]
[3, 202, 399, 328, 3094]
[22, 18, 44, 3095]
[498, 480, 5158, 3096, 3097, 3098, 5159]
[3, 5160, 6, 1460, 5161]
[180, 5, 858, 1048, 247]
[1261, 5162, 1394, 1045, 1631, 91]
[4, 19, 11, 4, 11, 20, 4, 18, 21, 5163, 5164, 331, 2, 65, 28, 188, 37, 739, 4]
[22, 15, 221, 5, 4, 3099, 6, 22, 15]
[4, 19, 11, 4, 11, 20, 4, 7, 2526, 1479, 652, 320, 5165, 5, 5166, 4]
[7, 2, 34, 5, 1784, 85, 156, 112, 715, 43, 5167, 5168]
[22, 30, 13, 2259, 2, 5169, 63, 44, 148, 3100, 10, 238]
[5, 1976, 1162, 5170, 9, 1480]
[735, 9, 1078]
[423, 1411, 62, 557]
[1408, 5171, 57, 3037, 386, 88, 69, 10, 238, 27]
[7, 2, 266, 136, 1015]
[5172, 1477, 1680, 3101]
[137, 270, 464, 29, 310, 12, 279, 191]
[85, 3102, 247, 8, 7, 46, 110, 31]
[18, 248, 48, 1580, 576, 2, 397, 805, 515]
[2914, 3103, 176, 5173, 8, 1263]
[3, 167, 611, 6, 467]
[47, 380, 14, 491, 652]
[3, 140, 112, 869]
[62, 2759, 3, 5174, 3104]
[175, 3, 5175, 6, 1785, 5176]
[7, 38, 13, 1770, 25, 893]
[159, 5177, 313, 12, 499]
[1101, 54, 6, 10, 136]
[634, 8, 2780, 425, 33, 5, 5178, 774]
[1697, 5179, 16, 7, 2, 771, 10, 5180, 1116]
[385, 9, 5181, 5182, 1952, 2610]
[258, 1032, 14, 3105, 763, 8, 757]
[3, 142, 6, 1348, 12, 127]
[45, 13, 1774, 23, 3, 5183, 3106]
[638, 162, 299, 12, 3, 5184]
[5185, 43, 222, 671, 337, 8, 3, 5186, 6, 3, 291, 30, 3107, 31]
[2, 3, 5187, 43, 3108, 91, 1360, 13]
[5188, 3, 3109, 5189, 6, 2054, 578]
[10, 175, 6, 300, 5190, 42, 2, 3, 5191, 353, 27, 233]
[13, 62, 51, 1783, 1576, 325]
[667, 1093, 8, 2566, 2034, 6, 5192, 5193, 8, 5194]
[293, 3110, 2, 209, 12, 29, 1079]
[7, 2, 757, 3, 721, 1633, 6, 2279, 171, 2279, 257]
[38, 249, 51, 56, 218]
[3, 562, 6, 1776, 5195, 2280]
[7, 2, 27, 5, 297, 495, 9, 274, 197, 132]
[1207, 250, 6, 1365, 9, 3111, 6, 227]
[18, 1005, 203, 558, 179, 15, 5, 1194, 5196]
[3, 167, 6, 415, 1761]
[119, 2, 3112, 10, 1481, 200, 167, 1786]
[133, 39, 338, 2, 27, 85, 16, 13, 3113, 51, 2, 27, 2771]
[4, 19, 11, 4, 11, 20, 4, 53, 109, 132, 2, 488, 23, 886, 44, 148, 9, 5, 1944, 5197, 5198, 4]
[39, 147, 487, 358, 6, 1885]
[18, 5199, 465, 3114, 30, 398, 5, 85, 367]
[18, 141, 191, 51, 2, 119, 3115]
[18, 323, 2716, 63, 5200]
[7, 5201, 5202, 15, 5203, 10, 56, 23, 5, 364, 2786]
[592, 1706, 13, 7, 2, 60]
[146, 1767, 2281, 330, 2, 153, 191, 8, 1163, 397, 3, 579]
[22, 21, 104, 23, 3, 362, 6, 28, 76, 462, 2282]
[2283, 8, 3, 167, 6, 5204, 3116]
[15, 26, 5205, 127, 5206, 64, 163, 1683, 2, 1314]
[2672, 429, 5207, 18, 13, 51, 26, 2284, 1201]
[410, 177, 2209, 2, 2109]
[1787, 14, 3, 753, 3117, 44, 3118, 261, 444]
[18, 491, 637, 14, 5208, 235, 1306, 10, 238]
[1482, 27, 373, 5209]
[201, 1152, 30, 85, 12, 13]
[22, 5210, 5, 5211, 5212, 5213, 5214, 32, 564, 13, 37, 17, 567]
[1124, 72, 48, 1788, 1290, 32, 1139, 5215, 1350, 83, 883]
[5216, 9, 1789]
[38, 5217, 59, 2, 27, 5218, 5219]
[1763, 5220, 3, 101, 5221, 443, 5222, 1350, 2140]
[309, 1144, 5223]
[534, 5, 56, 14, 10, 5224, 15, 96, 591, 5, 1137]
[3097, 1091]
[7, 2, 221, 10, 1481, 461, 298, 1611]
[1264, 8, 842]
[5, 600, 13, 360, 210, 59, 3119, 16, 407, 902, 57, 407, 1483]
[318, 5225, 9, 1789]
[684, 9, 3, 1261, 481]
[1238, 30, 3120, 5226, 5227]
[269, 3, 1339, 378, 30]
[80, 2, 243, 9, 307, 44, 5228, 5, 280, 3121]
[22, 13, 212, 51, 9, 993, 2, 213, 5, 54, 272, 138]
[5229, 3, 378]
[5230, 3122, 808, 2081, 5231, 5232, 1056, 6, 5233]
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[47, 152, 2, 119, 5, 61, 423, 220, 9, 230]
[17, 48, 532, 12, 230]
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[35, 24, 184, 617]
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[73, 24, 14, 1025, 2040, 862]
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[7, 32, 13, 243, 10, 112, 25, 2289]
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[174, 859, 174, 5318, 174, 1486, 3, 130, 16, 697, 10, 2741]
[277, 50, 2, 27, 522]
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[3154, 3009, 1464]
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[3, 648, 2, 5325, 654]
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[7, 69, 156, 49, 13, 5, 79, 495, 8, 542, 9, 3, 5327]
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[7, 21, 5346, 3, 5347, 12, 28, 169, 184]
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[7, 2, 215, 827]
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[998, 2757, 5374, 334, 24]
[965]
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[112, 2304, 13, 51, 2, 82]
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[3, 566, 6, 80]
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[18, 1343, 5, 5378, 5379, 2305, 23, 10, 238, 730, 273, 49, 13, 5, 513, 542]
[89, 1871]
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[155, 767, 371, 14, 35, 24, 8, 402, 122, 1226]
[4, 19, 11, 4, 11, 20, 4, 175, 907, 15, 31, 5, 276, 2700, 9, 262, 4]
[7, 2, 1042, 17, 48, 193, 189, 10, 76, 17, 48, 101]
[150, 73, 24, 143, 16, 44, 45, 13, 67, 214]
[53, 927, 40, 379, 12, 17, 327]
[120, 2, 742, 5380]
[2482, 10, 35, 24, 14, 320, 339]
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[345, 71, 53, 16, 205, 61]
[7, 2, 488, 3172, 8, 272, 44, 24, 17, 48]
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[819, 5, 5381, 743, 244, 200, 742, 2457]
[125, 248, 5382, 5383, 5384, 57, 59, 316]
[5, 68, 2, 2183, 259, 426, 90, 2184]
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[120, 2, 5386]
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[567, 271, 1202, 1766, 1799, 8, 5387, 1799]
[190, 8, 182, 6, 3173, 5388, 14, 73, 24, 58, 92, 16, 453, 8, 1062]
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[541, 498, 5389, 17]
[2309, 669, 27, 5390]
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[22, 13, 51, 2, 82, 37, 17, 1487, 8, 2900, 5391, 12, 663, 220, 403, 262]
[7, 284, 30, 1800, 26, 959, 337, 16, 5, 5392, 75]
[5393, 790, 16, 3174, 6, 3, 309, 77]
[658, 5394, 3175, 9, 1801, 8, 196, 450, 33, 302, 1267]
[1188, 408, 15, 52, 2208, 52, 31, 15, 3176]
[7, 2, 1120, 10, 76, 36, 635]
[5, 209, 321, 55, 5, 50, 189, 134, 193, 34]
[4, 19, 11, 4, 11, 20, 4, 633, 2864, 10, 599, 34, 5395, 22, 4]
[112, 5396, 5, 5397, 68]
[3, 717, 3177, 818, 2057, 651, 9, 10, 34]
[61, 207, 127, 668, 294, 98, 3178, 46, 59, 144, 356, 22, 638, 415]
[3, 84, 6, 2567, 5398]
[5, 207, 1256, 78, 369, 409]
[3179, 24, 15, 3179, 2715]
[5, 462, 130, 37, 221, 36, 8, 473, 3180]
[1739, 2117, 1802, 3181]
[3115, 2310, 14, 3182, 1178, 5399, 8, 5400, 33, 601, 1288]
[5401, 208, 8, 1675, 31, 2, 26, 247, 9, 716]
[225, 14, 3183, 33, 40, 1189]
[24, 250, 64, 105, 26, 247]
[1253, 5402, 2078, 2058, 3184]
[18, 28, 112, 5403]
[1427, 5404, 514]
[7, 3185, 5405, 163, 2311, 5406, 64, 391, 5407]
[3186, 1799, 8, 5408, 1799, 748, 33, 3187, 801, 247, 9, 314]
[18, 437, 3188, 5, 5409]
[1034, 1803, 5410, 500, 22, 5411, 5412, 341, 135, 8, 233]
[5413, 3, 941, 1233, 1980]
[417, 26, 2312, 2313, 172]
[18, 2314, 3189, 8, 1268, 3189, 622, 3, 142, 6, 5, 706, 436]
[5414, 2256, 2480, 797, 822, 5415]
[33, 204, 5416, 193]
[5417, 140, 1005, 15, 352, 3190, 2, 27, 5418]
[22, 525, 5419, 38]
[5, 109, 5420, 3191, 5421, 12, 5422, 33, 40]
[205, 61, 9, 544, 1003]
[1019, 484, 12, 918, 10, 878]
[918, 5423, 778, 860, 2, 34, 308]
[5424, 58, 6, 3, 130]
[5425, 535, 71, 34, 308]
[47, 5426, 152, 2, 213, 5, 527]
[21, 513, 28, 5427, 203, 91, 5428]
[1061, 3100, 5, 56, 14, 10, 5429]
[5, 109, 68, 2, 2540, 23, 2149, 949, 247, 237, 385]
[5430, 539, 490, 10, 5431]
[1675, 2315, 25, 5432, 2, 3010, 9, 53, 506]
[43, 50, 21, 285, 5, 2316, 5433]
[4, 19, 11, 4, 11, 20, 4, 5434, 8, 2971, 8, 205, 79, 170, 3192, 4]
[5435, 5436, 23, 29]
[18, 254, 45, 215, 54, 48, 551]
[1320, 5437, 2706, 45, 1221, 54, 170, 504, 1038, 1752]
[18, 46, 1208, 14, 89, 271, 16, 3, 219, 6, 274, 1490]
[5438, 5439, 3, 1414, 5440, 6, 5441, 2225, 8, 2545, 167, 9]
[22, 30, 1237, 3193]
[3, 5442, 3, 2747, 174, 5443, 1758]
[377, 1117, 5444, 12, 44, 148, 162, 83, 5445, 13, 82]
[26, 5446, 68, 2, 154, 577]
[171, 17, 10, 56, 356, 15, 162, 26, 3194]
[3, 50, 1804, 7, 46, 1423, 64, 707, 50]
[911, 969, 235, 27, 88, 6, 10, 1090, 3195]
[7, 2, 228, 10, 317, 14, 83, 109, 3196]
[453, 28, 291, 2, 552, 64, 3197, 313]
[294, 119, 5447, 8, 2680]
[3, 3198, 1562, 229, 3199, 8, 5448]
[131, 25, 28, 1021, 5449, 550, 3200]
[237, 8, 438, 58, 71]
[5450]
[3, 1804, 6, 225, 8, 5451]
[591, 1186, 1546, 18, 5, 1186, 1760, 2812, 674]
[5452, 1491]
[5453, 1447, 37, 349]
[3, 570, 6, 3, 76, 303, 5454]
[3, 358, 6, 5455, 2317, 3201]
[23, 111, 5456, 3202, 1028, 95, 128]
[13, 62, 51, 685, 2, 27, 373]
[7, 2, 5457, 5458, 15, 3, 1047, 6, 3, 5459]
[227, 83, 227, 43, 16, 98]
[38, 314, 5460, 599, 6, 1388, 5461]
[29, 28, 114, 95, 6, 3, 5462]
[7, 2112, 5463, 28, 166, 52, 5, 5464]
[5465, 5466, 2056, 54, 170, 13, 38, 37, 155, 80, 376]
[102, 80, 43, 49, 926, 39, 124, 2, 60, 12, 13]
[966, 2318, 6, 3, 2314, 5467, 16, 5468, 14, 28, 302, 652, 123, 50, 17]
[7, 2, 49, 5, 857, 3203]
[5469, 47, 1447, 37, 698, 618, 14, 1359]
[22, 248, 2, 250]
[18, 1492, 177, 69, 1937, 13, 914, 825, 301]
[13, 62, 51, 5, 5470, 784, 2, 49, 5, 831, 324, 5471, 493]
[7, 2, 5472, 5473]
[5474, 3, 5475, 3078]
[9, 5476, 6, 5477]
[213, 5, 263]
[274, 951, 2, 3204, 309, 2, 3, 74, 8, 788, 6, 42]
[577, 5478]
[180, 5, 3081, 2121]
[22, 3205, 32, 564, 13, 37, 29]
[3, 76, 3206, 3207, 3, 1720]
[1680, 615, 8, 2319, 10, 5479, 465]
[2200, 3, 735, 167, 6, 1050, 9, 29]
[3, 194, 6, 1644, 29]
[15, 159, 223, 2208]
[7, 41, 5480, 9, 5, 274, 1249, 276]
[1321, 96, 5, 2528, 5481, 5482, 1776, 5483, 5484, 5485]
[3, 5486, 5487]
[22, 5488, 5, 5489, 41, 184]
[5, 1756, 90, 3, 145, 6, 106, 100]
[142, 8, 3, 5490, 7, 1227, 3208, 2320, 171, 3209]
[15, 3210, 3210, 121, 136, 16, 122, 393, 305]
[69, 1269, 1270, 25, 159]
[44, 15, 1805, 1479, 2, 2066]
[298, 5491, 5492, 156, 3211, 2, 277, 1627, 3161]
[5493, 8, 787, 5, 5494, 348, 448, 3212, 216]
[5495, 102, 507, 3031]
[120, 2, 208, 405, 14, 451, 598, 694]
[262, 12, 499, 1135, 1487, 259, 17]
[3, 1806, 6, 35, 24]
[157, 132, 2, 2321, 8, 5496, 17, 567]
[4, 19, 11, 4, 11, 20, 4, 17, 399, 4]
[3213, 63, 14, 1106, 26, 120, 2, 1022, 419, 75]
[22, 15, 274, 2231, 89, 465, 9, 2182]
[3, 501, 68, 2, 460, 1203, 35, 2695]
[270, 17, 1538, 12, 35, 24]
[2005, 10, 41, 239, 14, 345, 3214]
[155, 231, 14, 933, 602, 16, 361, 8, 255, 9, 437]
[1180, 276, 515, 14, 5497]
[41, 9, 5498]
[105, 5, 289, 181, 99, 9, 275, 2, 848, 5499, 5500]
[26, 2322, 255, 12, 3, 2143, 2831, 1628]
[1187, 182, 14, 35, 24, 58, 21]
[1905, 6, 17, 1493]
[35, 24, 874, 5501, 99, 5502]
[1700, 966, 3094]
[3, 142, 6, 3215, 3216]
[262, 14, 691, 5503]
[1187, 182, 14, 35, 24, 58, 441]
[4, 19, 11, 4, 11, 20, 4, 3, 736, 6, 17, 1355, 23, 35, 24, 284, 4]
[746, 1222, 69, 210, 27, 61, 1729, 183, 283, 1150, 23]
[5504, 48, 8, 17, 48, 14, 5505]
[2166, 2871, 478]
[339, 6, 17, 357, 2, 5506, 1449, 1209]
[105, 3217, 35, 24, 874, 14, 204, 1051, 8, 5507, 267]
[1807, 123, 467, 14, 73, 24]
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[33]
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[7, 38, 13, 82, 10, 70, 15, 398, 225]
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[3, 1559, 114, 2, 119, 17, 48]
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[97, 102, 108, 17, 310, 12, 108, 17, 6509]
[2423, 56, 6510, 23, 799, 33, 872, 2423, 865, 9, 40]
[3, 519, 229, 3467, 8, 106, 100]
[102, 1484, 40, 3133, 1432, 45, 82]
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[3, 126, 7557, 6, 2890]
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[1503, 379, 9, 81, 103]
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[536, 81, 103, 14, 913, 1095]
[887, 9, 125, 3491]
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[289, 181, 8, 1068, 6, 7567]
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[157, 109, 560, 2, 66, 79, 55, 510, 253, 34]
[3493, 105, 5, 811, 3546, 78, 16, 5, 36, 87, 129]
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[7, 21, 867, 2, 49, 3, 74, 6, 28, 1452, 52, 5, 36, 178]
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[42, 199, 1984, 149, 7, 2, 639, 3, 745, 1410]
[3, 152, 18, 21, 1778, 2, 972, 111, 5, 3383, 168, 3632]
[70, 2, 1716, 7640, 12, 1038, 627]
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[13, 62, 51, 2, 27, 261, 2, 110]
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[18, 13, 45, 98, 29, 55, 240]
[3, 142, 6, 3007, 3, 370, 2, 4, 29]
[86, 12, 1230, 2119, 29]
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[7, 2, 304, 111, 7671, 226]
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[7672, 1652, 2032]
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[117, 108, 17, 7673, 17, 251, 689]
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[123, 582, 15, 175, 3, 7674, 6, 3449, 7675]
[5, 109, 120, 2, 451, 737, 673, 400]
[18, 38, 46, 51, 204, 1051]
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[81, 361, 340, 7676, 3, 84, 6, 73, 24]
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[904, 26, 7678, 197, 114, 2, 49, 10, 7679, 17, 807, 14, 2184]
[1798, 24, 16, 41, 7680]
[7, 35, 24, 32, 153, 1515, 1417, 8, 7681, 6, 5, 2130]
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[7, 2, 1571, 61, 480, 23, 1080, 14, 106, 100]
[7682, 1376, 9, 1480, 12, 186]
[1371, 2869, 16, 7, 1713, 7683, 42, 2707]
[7, 32, 115, 213, 54, 17, 251, 9, 163, 60]
[1312, 12, 81, 103, 14, 7684, 87, 129]
[109, 660, 17, 75, 123, 1067, 7685, 236, 23, 7686, 7687, 7688, 1055, 17]
[33, 7689, 2, 1724, 9, 1166]
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[693, 650, 236, 220, 24, 33, 1746, 400]
[28, 1309, 275, 423, 712, 16, 17, 1992, 91]
[3636, 17, 241, 22, 15, 17, 48, 12, 2429]
[78, 34, 216, 234]
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[23, 3, 84, 6, 36, 206, 12, 621]
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[7, 2, 1310, 5, 237, 184, 14, 7694, 7695, 34, 244, 25, 422]
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[47, 86, 25, 5, 3202, 2433, 7701]
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[7702, 71, 265, 1012, 7703, 8, 2713]
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[13, 258, 415, 140, 60, 7704, 2320, 258, 481]
[7, 1623, 353, 266, 2377, 961]
[22, 32, 7705, 38, 12, 1394]
[45, 13, 1169, 1046, 2439, 12, 10, 112]
[7706, 12, 2113, 7707]
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[155, 3, 113, 95, 6, 10, 986, 361]
[18, 46, 51, 54, 7709, 249, 7710, 9, 2055, 455]
[3066, 977, 3, 2155, 112, 1279]
[15, 84, 778, 3, 61, 1089, 1501]
[44, 89, 387, 1439, 438, 2370, 7711]
[34, 14, 538, 7712, 980, 832]
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[10, 42, 15, 39, 26, 369]
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[7714, 21, 631, 63, 21, 124, 2, 27, 26, 260, 3632]
[3, 7715, 322, 6, 5, 7716, 3299, 57, 365, 7717, 6, 546, 1033]
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[7, 348, 32, 739, 1169, 581]
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[18, 38, 1018, 378, 59, 147, 588, 7725, 1077, 7726]
[293, 1488, 13, 30, 5, 1656]
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[404, 1420, 31, 7728, 14, 3638, 77]
[198, 80, 21, 221, 37, 300, 9, 3, 187]
[62, 640, 3, 466, 80, 44, 31, 1232, 2, 920]
[283, 39, 3243, 2, 10, 1575, 1176]
[180, 5, 246, 7729, 145]
[781, 89, 7730, 90, 89, 2404]
[41, 1627, 9, 7731, 8, 7732]
[7, 544, 3624, 7733, 3, 142, 8, 7734, 6, 237, 7735]
[4, 19, 11, 4, 11, 20, 4, 7, 2, 972, 10, 101, 624, 10, 7736, 8, 7737, 96, 26, 845, 4]
[7738, 7739, 291, 335, 182, 7740, 3050, 12, 1498, 2245, 7741]
[137, 2351, 795, 322, 21, 104, 7742]
[22, 113, 6, 187, 66, 233, 37, 2186, 70]
[7743, 118, 750]
[7, 2, 458, 9, 606, 126, 3302, 44, 7744, 5, 7745]
[53, 475, 6, 954, 386, 30, 13]
[7, 1810, 85, 317, 32, 7746, 10, 307, 2, 2030, 54]
[3, 955, 6, 3, 211, 2243, 23, 3, 7747]
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[7, 2, 1515, 8, 304, 7749, 308]
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[18, 21, 65, 28, 74, 70, 1064, 2, 88, 686, 55, 5, 50]
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[2029, 8, 26, 7752, 42]
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[3, 1682, 114, 2, 228, 10, 363, 234, 8002, 23, 53, 329]
[70, 8003, 3504, 13, 595, 62, 82, 183, 45, 38]
[81, 164, 565]
[7, 2, 116, 5, 1453, 344, 563, 14, 40]
[59, 46, 3647, 3, 219, 6, 2657, 344]
[4, 19, 11, 4, 11, 20, 4, 49, 17, 1517, 197, 14, 204, 722, 1841, 40, 9, 662, 132, 4]
[320, 339, 14, 81, 164]
[8004, 61, 17, 48, 2957, 16, 5, 288, 8, 1916]
[53, 140, 8005, 12, 10, 1087, 2427]
[3437, 345, 14, 1179, 1503, 379]
[7, 2, 67, 262, 2, 988, 3625, 2452]
[175, 1013, 33, 35, 24]
[7, 2, 1451, 89, 8006, 2, 3312, 17, 508, 8007]
[8008, 2830, 9, 345]
[41, 1465, 8009, 8, 3, 61, 145, 993]
[334, 24, 9, 101, 1741]
[5, 371, 482, 200, 6, 8010, 8011, 3642, 8, 8012, 1267]
[3023, 419, 8, 874]
[8013, 100, 524, 3, 3391, 3392]
[7, 106, 100, 8014, 567, 8015]
[3324, 208, 340, 33, 1660, 3604]
[73, 24, 8, 1230, 208, 75, 12, 8016, 190, 14, 2677]
[73, 24, 12, 8017, 190, 9, 2824, 657]
[35, 24, 1840, 1679, 16, 17, 403]
[1136, 426, 9, 873, 568]
[3220, 6, 469, 25, 8018, 406, 17, 16, 58, 92]
[2355, 577, 8019, 12, 17, 8020, 165, 39, 557]
[53, 8021, 12, 105, 17, 3425]
[510, 344, 33, 1012]
[22, 165, 3, 84, 6, 17, 48, 1118, 12, 13]
[252, 3041, 1013, 8022, 14, 35, 24]
[471, 1404, 8, 1312]
[571, 8, 168, 471, 190, 33, 35, 24, 16, 5, 773, 114, 2, 173, 8023, 8024]
[35, 24, 1840, 1679, 16, 1173, 24]
[3636, 17, 241, 22, 15, 8025]
[33, 208, 405, 2, 8026, 657]
[1412, 31, 95, 14, 284]
[1358, 1800, 3, 1255, 287]
[206, 12, 134, 1263]
[2620, 515, 8027, 1194, 8, 3507, 8028]
[86, 12, 191, 155, 90, 141, 218]
[7, 2, 3297, 140, 134, 1152]
[3, 88, 750, 3609, 51, 2, 119]
[7, 2, 449, 150, 1492, 1262, 475, 9, 700, 3, 2673, 8029]
[18, 113, 513, 282, 199, 513, 1264]
[8030, 8031, 1048, 5, 109, 158, 139, 58, 71]
[8032, 8033, 23, 3474, 8, 282]
[2183, 8034, 8, 8035, 9, 544]
[15, 3648, 3, 240, 6, 8036, 8037, 5, 4, 87, 12, 8038, 8039, 8040, 8041]
[3, 2001, 1029, 1323, 6, 141, 70]
[7, 2, 215, 8, 277, 824, 79, 170, 8042, 3458]
[781, 26, 716, 139, 90, 5, 3490]
[8043, 17, 2252, 14, 798, 385, 8, 3637]
[1528, 21, 59, 2, 27, 612]
[8044, 55, 60, 133, 2286]
[346, 47, 132, 2, 5, 373, 216]
[220, 15, 319, 8045, 1316, 135, 189, 117, 862]
[3, 339, 6, 1738, 57, 8046, 8047]
[8048, 3648, 57, 8049, 2003, 386, 88, 12, 28, 226, 56]
[102, 80, 21, 867, 528, 300, 9, 5, 8050, 8051, 2264]
[35, 24, 14, 88, 615, 6, 176]
[4, 19, 11, 4, 11, 20, 4, 35, 24, 731, 118, 8052, 143, 4]
[18, 294, 8053, 15, 5, 212, 201, 356, 58, 441]
[28, 843, 2518, 285, 5, 855, 6, 136]
[7, 2, 1935, 8054, 9, 682, 6, 26, 8055, 540]
[21, 2384, 8056, 9, 28, 8057]
[18, 8058, 193, 3, 145, 2257]
[5, 1840, 1679, 2, 491, 26, 1219, 279, 216]
[3, 1341, 2, 29, 37, 1250, 55, 28, 401]
[7, 928, 8059, 8060, 51, 2, 625, 990, 720, 52, 5, 439]
[125, 1510, 46, 3, 115]
[3, 339, 6, 121, 1328, 8061]
[4, 19, 11, 4, 11, 20, 4, 7, 3, 1759, 6, 8062, 32, 1797, 1819, 457, 526, 4]
[284, 59, 2083, 8063, 232]
[8064, 3, 2127, 117, 1490, 286, 187]
[4, 19, 11, 4, 11, 20, 4, 1247, 251, 185, 8, 436, 251, 185, 4]
[8065, 8, 91]
[7, 2, 8066]
[7, 21, 821, 3, 71, 377, 263, 8067, 23, 125]
[4, 19, 11, 4, 11, 20, 4, 2406, 721, 1048, 3564, 4]
[2488, 55, 3, 8068]
[7, 2, 724, 471, 9, 5, 2898, 8069, 145]
[13, 59, 144, 135, 2, 349]
[1355, 117, 112]
[8070, 39, 1028, 2, 5, 8071, 2868, 13]
[3, 167, 6, 8072, 63, 8073, 14, 8074]
[315, 1814, 991, 8075]
[3, 951, 2, 8076, 63, 3539, 8, 1948, 161, 744, 128]
[26, 8077, 6, 28, 8078, 37, 309, 1468, 3274, 3646]
[7, 525, 13, 392, 93, 13, 939, 8079, 272]
[21, 513, 707, 859, 285, 28, 1248]
[8080, 3, 8081]
[82, 10, 540, 3, 1494, 167, 6, 8082]
[3512, 617, 624, 10, 56, 908, 8, 12, 54, 136]
[3, 339, 6, 5, 1435, 645]
[2, 49, 5, 300, 52, 5, 138, 13, 51, 2, 173, 26, 2272, 1189]
[7, 2, 65, 528, 13, 1294]
[662, 80, 323, 104, 25, 29, 3649, 730, 1066]
[2331, 9, 3, 619, 16, 709, 86, 23, 7, 46, 32, 458, 117, 114]
[434, 15, 144, 1550, 367, 52, 464]
[1856, 1568, 52, 5, 648, 2, 179]
[7, 2, 458, 23, 146, 16, 58, 92]
[7, 2, 110, 5, 2388, 42, 9, 5, 145, 269, 13, 59, 466, 271]
[7, 2, 65, 5, 730, 550, 16, 8083, 141, 203, 197]
[32, 46, 2906, 2315, 1104]
[44, 28, 698, 282, 322, 2403, 429]
[219, 665, 15, 8084, 22, 2, 38, 675]
[18, 302, 15, 28, 1309, 1190, 2, 332, 1076]
[291, 2548, 10, 114, 2, 257, 834]
[291, 222, 1424, 1117, 2536]
[3600, 3644, 324, 8085, 64, 8086, 1160, 8087]
[3, 194, 3341]
[78, 216, 8, 363, 425, 2372]
[44, 21, 8088, 95, 6, 80, 2, 65, 23, 146]
[719, 688, 21, 155, 28, 8089, 23, 146]
[62, 343, 177, 2835, 2, 65, 133, 3, 233, 600]
[1448, 2, 179, 3354, 38, 31, 1235]
[2376, 16, 10, 113, 224, 750]
[147, 217, 227, 325, 2537, 3, 620]
[18, 141, 199, 2206, 8, 22, 69, 13, 38, 37, 31]
[198, 80, 8090, 8091, 427, 91, 37, 847, 606, 750]
[8092, 8093, 260, 8094, 12, 56]
[22, 70, 341, 233, 147, 803, 8, 22, 32, 46, 38, 37, 31]
[3240, 2333, 688, 586, 3, 2333, 8095, 2, 1082, 23, 8096, 8097]
[89, 153, 16, 32, 31, 212, 60, 12, 377, 185]
[30, 13, 300, 3, 8098, 42]
[7, 2, 67, 260, 340, 2, 66, 61, 1597, 2807]
[42, 15, 5, 375, 162, 27, 1052, 2, 66, 231]
[22, 28, 1257, 427, 91, 37, 29, 8099, 300, 42, 9, 1011]
[1776, 3172, 13, 32, 2453, 586, 9, 3, 556]
[70, 12, 89, 2012, 1363]
[91, 820, 8, 841, 8100, 8101, 8, 8102, 515]
[5, 8103, 1002, 23, 784, 43, 497]
[24, 2, 221, 3, 1261]
[127, 199, 628, 183, 18, 39, 59, 1206]
[3465, 953, 8, 7, 2, 1050, 10, 369, 850, 1574]
[3, 535, 6, 697, 22, 2, 65]
[3, 2784, 14, 5, 1196]
[3, 1425, 719, 8104, 43, 2448, 2, 28, 8105, 8106, 56]
[7, 2, 65, 14, 2379, 8, 632, 1026]
[21, 2201, 64, 1589, 2, 552, 5, 462, 130, 2047]
[884, 15, 3, 211, 2, 1097, 127]
[7, 38, 13, 49, 136, 25, 630, 13, 49, 1300, 12, 5, 479, 50]
[3, 1680, 615, 8, 8107, 268, 2, 29]
[65, 54, 8108, 33, 3, 29, 3028, 8109]
[623, 5, 593, 2, 10, 29]
[98, 755, 10, 29, 2, 3, 497, 6, 1495, 1363]
[149, 269, 148, 391, 2, 173, 10, 169, 108, 188, 356]
[665, 8110, 33, 2105, 1071, 548, 8111, 50, 1987, 71]
[97, 152, 18, 106, 100, 444, 372]
[8112, 10, 3516, 528, 300, 9, 3, 2925]
[2965, 8113, 340, 284, 16, 8114, 8115]
[3248, 186, 8, 580, 3572]
[213, 845, 6, 33, 2080, 9, 40]
[7, 2, 67, 8116, 150, 411, 6, 8117, 2, 490, 363, 3650]
[4, 19, 11, 4, 11, 20, 4, 3, 84, 6, 60, 141, 346, 264, 722, 408, 1535, 8118, 8119, 1465, 64, 1140, 256, 141, 4]
[8120, 16, 123, 50, 935, 405]
[5, 1193, 8121, 8, 3262, 2407, 23, 3, 659, 3651, 1307, 81, 103]
[120, 2, 3129, 181]
[27, 5, 17, 8122, 58, 1539]
[711, 8123, 14, 1618, 439]
[3079, 534, 8124]
[3649, 309, 1825, 12, 35, 24, 3091, 90, 102, 67, 1830]
[4, 19, 11, 4, 11, 20, 4, 150, 42, 707, 682, 306, 817, 2039, 4]
[727, 701, 9, 1023, 3, 219, 14, 3, 1991, 1268]
[15, 237, 661, 139, 185, 1001, 212, 562, 324]
[8125, 3, 2221, 196, 2034, 9, 455]
[7, 2, 622, 10, 1096, 9, 92, 506, 929, 128]
[5, 128, 9, 3, 42, 6, 5, 279, 138]
[3, 443, 68, 2, 347, 10, 29, 166]
[904, 5, 642, 229, 513, 486, 8, 823]
[7, 2, 2432, 5, 302, 3651, 8, 67, 31, 2, 886, 3650]
[15, 8126, 141, 70, 2271, 589, 31]
[7, 2, 8127, 10, 8128, 9, 5, 145, 8129, 23, 2338]
[3, 113, 9, 958, 156, 322, 12, 17, 327]
[848, 416, 581, 14, 73, 24]
[181, 16, 18, 1167, 2999, 980]
[7, 2, 871, 8130, 143, 23, 1754, 2090, 2091, 33, 8131]
[26, 605, 6, 99, 2144, 9, 559, 35, 24]
[2798, 5, 50, 241, 75, 23, 3, 8132, 402, 1725]
[24, 334, 24, 8133, 14, 345]
[8134, 1202, 14, 2211, 8, 40]
[1975, 72, 2, 2790, 888, 2916, 9, 2390, 12, 8135, 550]
[877, 41, 118, 559, 41]
[541, 406, 8136, 200, 262, 484]
[665, 8137, 33, 849, 423]
[3, 1110, 6, 26, 880, 1891]
[402, 122, 796, 12, 3, 3541, 3628, 3369, 8138, 14, 73, 24, 33, 780, 8139]
[7, 2, 243, 10, 322, 8140, 528, 17, 48, 101, 2440]
[2198, 6, 176, 428, 923]
[17, 357, 14, 40, 33, 2211, 8, 1166, 3379, 929, 3380, 42, 8141, 761]
[3, 955, 6, 8142, 1599]
[508, 8143, 26, 713, 608, 12, 50, 241, 695]
[33, 125, 532, 17, 2, 1534, 10, 1136, 99]
[25, 8144, 2, 17, 445]
[278, 13, 509, 1906, 2, 82, 37, 451, 737, 673]
[5, 900, 9, 222, 516, 14, 1241]
[2451, 8145, 24, 14, 731, 8146]
[8147, 9, 1018, 3607, 1243, 1786, 1001, 22, 32, 3, 17, 286, 187]
[927, 99, 105, 33, 3, 691, 3489]
[133, 73, 24, 825, 5, 61, 1995, 6, 17]
[3635, 2732, 14, 73, 2752]
[47, 330, 2, 228, 10, 504, 1228, 308]
[3, 1590, 868, 418, 509]
[649, 8, 40, 441]
[677, 5, 17, 48, 924, 16, 1403, 8148, 533, 75]
[17, 48, 8, 679, 2927, 44, 7, 8, 269, 2, 173, 3, 74, 446]
[695, 84, 581, 6, 590, 33, 1461, 17]
[22, 15, 3, 74, 8149, 99, 9, 8150, 17, 267, 16, 131, 25, 5, 348, 2177, 8151, 61, 188]
[71, 1444, 726, 15, 265, 461, 57, 8152]
[94, 13, 51, 2, 82, 189, 347, 14, 868]
[3138, 8, 1843, 582, 1012, 33, 933, 8153, 8154, 1489]
[28, 78, 15, 672, 170, 13, 38, 13, 96, 31]
[7, 3350, 3647, 8155, 2601, 14, 5, 962, 1508]
[17, 48, 8156, 12, 8157]
[105, 874, 14, 3552, 265, 265, 157, 8158, 9, 1241]
[53, 710, 8159, 947, 2, 552, 5, 1336]
[291, 5, 2316]
[35, 24, 3067, 285, 549, 6, 922, 50]
[41, 8, 187, 16, 26, 8160, 193]
[154, 322, 52, 5, 439, 8161]
[900, 23, 3, 339, 6, 1708, 1979, 17, 1564, 1712]
[3222, 5, 109, 1175, 1191, 9, 237, 661]
[432, 621, 8, 36, 7, 30, 316, 2180]
[662, 132, 12, 3, 230, 154, 2912, 16, 8, 2, 1513, 10, 8162, 261, 773]
[53, 72, 2, 1452, 134, 239, 2, 3, 113, 3515, 3496]
[3, 370, 6, 575, 9, 3, 34, 234]
[30, 3633, 649, 3, 74, 46, 32, 38, 14, 1703, 2943]
[7, 1592, 320, 8163, 32, 228, 10, 78]
[22, 32, 78, 486, 119, 25, 8164, 8165, 8166]
[7, 2, 66, 3, 113, 95, 6, 8167, 651]
[175, 115, 6, 3, 84]
[352, 38, 10, 36, 2032]
[134, 296, 3, 648, 2, 3534, 8168]
[36, 273, 8169, 31, 3566, 8170]
[1390, 16, 66, 17, 25, 5, 801, 247, 8, 266, 8171, 9, 5, 8172, 1205]
[1080, 253, 1251]
[2851, 10, 2368, 247, 25, 408, 3184]
[4, 19, 11, 4, 11, 20, 4, 18, 1527, 6, 981, 30, 2075, 163, 3326, 3, 233, 114, 4]
[18, 5, 8173, 8174, 3151, 353, 27, 1430, 461, 93, 1886, 8175]
[454, 63, 3573, 8, 8176, 23, 5, 921, 925]
[7, 2, 458, 9, 10, 166, 3152, 64, 239]
[3, 8177, 219, 14, 1822, 554]
[7, 3, 1844, 6, 3, 989, 2356, 2, 3, 570, 6, 1738]
[18, 404, 281, 5, 1192, 12, 1257]
[22, 93, 21, 351, 352, 27, 272]
[5, 8178, 68, 2, 1462, 8179, 298, 890]
[949, 666, 3251, 3652, 358]
[7, 2, 715, 10, 889, 2, 26, 2228]
[22, 2, 38, 9, 5, 2426, 2447]
[98, 33, 10, 1781, 8180, 52, 5, 3615, 2983]
[44, 881, 15, 232, 881, 26, 1171, 550, 3200, 12, 159]
[47, 2322, 80, 2, 38, 44, 148, 1462, 684]
[53, 152, 18, 957, 15, 5, 2295, 624, 2, 382, 1823, 917, 3460]
[7, 2, 116, 2524, 55, 60]
[7, 21, 67, 5, 3643, 3038, 2, 8181, 8182, 1263]
[7, 2, 2435, 110, 5, 218, 9, 10, 56]
[1181, 1460, 3, 3157, 1802, 26, 120]
[3, 217, 908, 114, 2, 2432, 1390, 23, 8183]
[47, 544, 8184, 865, 43, 69, 2176, 10, 253, 36]
[1499, 23, 1659, 1074, 881, 1144, 249, 9, 662, 3614]
[18, 1227, 281, 2, 98, 8185, 744, 115]
[8186, 296, 12, 383, 2428, 5, 192, 64, 192, 68]
[782, 63, 10, 8187, 924, 14, 8188]
[833, 10, 430, 90, 5, 1978, 128, 1722, 12, 127]
[228, 10, 1417, 64, 918, 10, 664]
[4, 19, 11, 4, 11, 20, 4, 7, 2, 2008, 2, 721, 537, 4]
[8189, 9, 303]
[66, 54, 432, 851, 68, 2, 1103, 10, 464, 1632]
[465, 1647, 16, 13, 350, 76, 213, 8190, 55, 145, 105]
[18, 44, 8, 7, 6, 1281, 144]
[144, 176, 17, 2513, 247]
[124, 2, 151, 300, 3, 42, 13, 124, 49, 3, 1774]
[293, 211, 1209, 25, 2247, 3555, 1476]
[5, 371, 68, 2, 8191, 3570]
[30, 293, 8192, 212, 338, 12, 747, 2138]
[2606, 8, 519, 16, 5, 289, 1853, 664]
[30, 46, 8193, 26, 41, 1134, 3443, 3, 41, 1930, 200, 2126]
[18, 2504, 8, 24, 15, 211, 2, 324, 282]
[976, 494, 597, 344, 8194, 8195, 12, 292, 477, 651]
[8196, 3652, 26, 596, 6, 2173, 8, 223]
[640, 635, 143, 13, 51, 26, 1171, 99, 162, 343, 2223, 1440]
[18, 202, 1640, 235, 1270, 25, 417, 5, 36, 34, 712]
[15, 31, 50, 12, 5, 166, 179]
[738, 8197, 5, 8198, 8199, 3627, 25, 2450]
[3, 8200, 2066, 44, 10, 3338, 1244, 213, 8201]
[1049, 627, 823, 353, 39, 27, 589, 31, 183, 191, 30]
[899, 8202, 169, 3267, 1218, 508, 242, 381, 2, 1354]
[7, 2, 8203]
[1472, 69, 153, 13, 65, 10, 130]
[1781, 3623, 8204, 57, 751]
[2431, 142, 194, 5, 8205, 268, 2, 8206, 634]
[1016, 2972]
[180, 3, 1195, 2, 372]
[3, 717, 754, 9, 10, 3403, 29]
[21, 66, 64, 14, 5, 466, 153, 25, 28, 29, 652]
[7, 2, 67, 537, 2, 2454, 166, 336]
[7, 223, 15, 1041, 2465, 2768, 8207, 493, 8208]
[7, 224, 15, 2404, 9, 3, 619]
[5, 8209, 362, 6, 322]
[374, 898, 44, 13, 32, 144, 2169, 89, 1201, 10, 112]
[111, 1091, 14, 1073, 430, 15, 522, 26, 1496]
[3, 452, 8210, 248, 8211, 95, 242, 797]
[8212, 31, 94, 44, 13, 65]
[1198, 8, 252, 3, 84, 6, 1478, 2015]
[38, 245, 1224, 13, 93, 13, 62, 67, 214]
[3, 8213, 6, 180, 8214, 167]
[7, 2, 1034, 321, 727, 125, 722, 52, 5, 158, 134, 8, 5, 158, 770]
[7, 217, 6, 10, 141, 45, 13, 410, 95, 12, 270]
[7, 165, 1055, 288, 2938, 3640, 2, 928, 1364, 9, 3164, 2618, 1291]
[3, 979, 8215, 2, 28, 2778, 8216, 8217]
[137, 152, 1010, 45, 27, 8218, 2, 1211, 9, 8219, 271]
[286, 295, 2, 8220, 10, 540]
[45, 10, 1641, 1221, 37, 1033]
[8221, 31, 94]
[395, 70, 32, 934, 127, 12, 10, 56]
[2737, 12, 108, 17, 75, 9, 70]
[30, 13, 637, 174, 1439, 76]
[971, 3, 3598]
[8222, 514, 2113, 5, 407]
[7, 440, 10, 60, 8223, 8224, 10, 29]
[130, 2359, 14, 1081]
[3, 433, 6, 3, 715]
[29, 5, 2332, 1848, 62, 49, 342, 355]
[28, 3019, 12, 29, 17, 295]
[402, 122, 694, 14, 451, 598, 694, 9, 40]
[437, 1184, 560, 16, 22, 30, 342]
[451, 737, 673]
[84, 6, 202, 399, 73, 24]
[7, 13, 833, 10, 1514, 90, 5, 1149, 2175]
[7, 2, 82, 10, 583, 15, 135]
[30, 13, 8225, 63, 14, 479, 534, 1086, 299, 30, 3, 264, 2, 271, 31]
[8226, 126, 164, 12, 2742]
[18, 3584, 2341, 321, 2, 5, 166, 9, 156, 8227, 17, 48]
[1173, 24, 451, 598, 694]
[119, 44, 2, 250, 14, 3, 994]
[38, 13, 51, 154, 696, 193, 2, 27, 5, 85, 542]
[371, 1875, 2048]
[97, 8228, 634, 2, 1122, 195, 47, 1202, 2, 209, 55]
[125, 464, 8, 290]
[8229, 505]
[8230, 1213, 1157, 2, 3399]
[5, 452, 23, 3, 2450, 6, 1846, 1114]
[159, 5, 8231, 2, 2028, 9, 3, 1570, 857, 287, 16, 3568]
[3, 97, 150, 253, 8, 36, 532, 12, 230]
[8232, 118, 8233, 16, 7, 45, 13, 332, 37, 177, 23, 10, 432]
[7, 8234, 1036, 450, 8235]
[141, 8, 70, 438, 8236, 8237]
[47, 329, 94, 2577, 45, 343, 574, 189, 163, 1196, 3400, 3, 659]
[7, 2, 65, 5, 85, 56, 730, 550]
Total input sequences: 48461
Make all titles the same length through padding¶
The length of every title has to be the same - using padding we make every sequence have the same length. To do this, we need to find a title that has a maximum length, and based on that length, we have to pad the rest of the titles.
- max_sequence_len: is the sequence length.
- pad_sequences: provided by Keras is used to pad an array of tokens to a given length.
In [ ]:
# Pad sequences
max_sequence_len = max([len(x) for x in input_sequences])
input_sequences = np.array(pad_sequences(input_sequences, maxlen = max_sequence_len, padding = 'pre'))
input_sequences[1]
Out[ ]:
array([ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 676,
68], dtype=int32)
In [ ]:
input_sequences.shape
Out[ ]:
(48461, 40)
In [ ]:
input_sequences[0]
Out[ ]:
array([ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5,
676], dtype=int32)
Preparing Predictors and Labels¶
Here, we consider the last element of all the sequences as a label.
We need to perform one-hot encoding on labels corresponding to total_words.
In [ ]:
# Create features and label
predictors, target = input_sequences[:,:-1],input_sequences[:,-1]
labels = tf.keras.utils.to_categorical(target, num_classes=total_words)
- predictors : these are tokens that will be used as an input for predicting the next word.
- label: is the next word to be predicted.
In [ ]:
#inputs i.e, predictors
predictors[3]
Out[ ]:
array([ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 676, 68, 2],
dtype=int32)
In [ ]:
print(labels[5][14])
1.0
Architecture of the LSTM Neural Network¶
Long Short-Term Memory networks (LSTMs) are advanced Recurrent Neural Networks (RNNs), capable of storing order states through their cell state feature.
LSTM Neural Network Model Training¶
In [ ]:
model = Sequential()
inp_len = max_sequence_len - 1
# Adding Input Embedding Layer
model.add(Embedding(total_words, 100, input_length = inp_len))
# Adding Hidden Layer 1 - LSTM Layer
model.add(LSTM(150,return_sequences=True))
model.add(LSTM(100,activation='relu'))
# Adding Output Layer
model.add(Dense(total_words, activation = 'softmax'))
opt = Adam(learning_rate = 0.001,decay=1e-6)
model.compile(loss='categorical_crossentropy', optimizer = opt, metrics = ['accuracy'])
print(model.summary())
Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding_1 (Embedding) (None, 39, 100) 823800
lstm_2 (LSTM) (None, 39, 150) 150600
lstm_3 (LSTM) (None, 100) 100400
dense_1 (Dense) (None, 8238) 832038
=================================================================
Total params: 1,906,838
Trainable params: 1,906,838
Non-trainable params: 0
_________________________________________________________________
None
In [ ]:
from tensorflow.keras.callbacks import EarlyStopping,ModelCheckpoint
es = EarlyStopping(monitor='loss', mode='min', verbose=1, patience=5)
mcp_save = ModelCheckpoint('.mdl_wts.h5', save_best_only=True, monitor='loss', mode='min')
In [ ]:
history = model.fit(predictors, labels, epochs = 50, callbacks=[es,mcp_save], verbose = 1)
Epoch 1/50 1515/1515 [==============================] - 271s 176ms/step - loss: 7.0183 - accuracy: 0.0728 Epoch 2/50 1515/1515 [==============================] - 268s 177ms/step - loss: 6.4804 - accuracy: 0.1157 Epoch 3/50 1515/1515 [==============================] - 268s 177ms/step - loss: 6.0813 - accuracy: 0.1330 Epoch 4/50 1515/1515 [==============================] - 267s 176ms/step - loss: 5.7126 - accuracy: 0.1477 Epoch 5/50 1515/1515 [==============================] - 269s 178ms/step - loss: 5.3670 - accuracy: 0.1609 Epoch 6/50 1515/1515 [==============================] - 267s 176ms/step - loss: 5.0217 - accuracy: 0.1776 Epoch 7/50 1515/1515 [==============================] - 267s 176ms/step - loss: 4.6667 - accuracy: 0.1942 Epoch 8/50 1515/1515 [==============================] - 276s 182ms/step - loss: 4.2945 - accuracy: 0.2142 Epoch 9/50 1515/1515 [==============================] - 269s 178ms/step - loss: 3.9080 - accuracy: 0.2463 Epoch 10/50 1515/1515 [==============================] - 268s 177ms/step - loss: 3.5482 - accuracy: 0.2881 Epoch 11/50 1515/1515 [==============================] - 268s 177ms/step - loss: 3.2119 - accuracy: 0.3354 Epoch 12/50 1515/1515 [==============================] - 269s 178ms/step - loss: 2.9214 - accuracy: 0.3812 Epoch 13/50 1515/1515 [==============================] - 270s 179ms/step - loss: 2.6723 - accuracy: 0.4236 Epoch 14/50 1515/1515 [==============================] - 267s 176ms/step - loss: 2.4599 - accuracy: 0.4588 Epoch 15/50 1515/1515 [==============================] - 271s 179ms/step - loss: 2.2714 - accuracy: 0.4941 Epoch 16/50 1515/1515 [==============================] - 271s 179ms/step - loss: 2.1009 - accuracy: 0.5260 Epoch 17/50 1515/1515 [==============================] - 271s 179ms/step - loss: 1.9475 - accuracy: 0.5561 Epoch 18/50 1515/1515 [==============================] - 269s 178ms/step - loss: 1.8086 - accuracy: 0.5843 Epoch 19/50 1515/1515 [==============================] - 267s 176ms/step - loss: 1.6902 - accuracy: 0.6069 Epoch 20/50 1515/1515 [==============================] - 267s 176ms/step - loss: 1.5817 - accuracy: 0.6304 Epoch 21/50 1515/1515 [==============================] - 269s 178ms/step - loss: 1.4864 - accuracy: 0.6502 Epoch 22/50 1515/1515 [==============================] - 266s 175ms/step - loss: 1.4006 - accuracy: 0.6707 Epoch 23/50 1515/1515 [==============================] - 266s 176ms/step - loss: 1.3309 - accuracy: 0.6840 Epoch 24/50 1515/1515 [==============================] - 269s 177ms/step - loss: 1.2662 - accuracy: 0.6986 Epoch 25/50 1515/1515 [==============================] - 268s 177ms/step - loss: 1.1969 - accuracy: 0.7136 Epoch 26/50 1515/1515 [==============================] - 267s 176ms/step - loss: 1.1504 - accuracy: 0.7247 Epoch 27/50 1515/1515 [==============================] - 266s 175ms/step - loss: 1.1032 - accuracy: 0.7337 Epoch 28/50 1515/1515 [==============================] - 266s 175ms/step - loss: 1.0546 - accuracy: 0.7476 Epoch 29/50 1515/1515 [==============================] - 245s 162ms/step - loss: 1.0213 - accuracy: 0.7547 Epoch 30/50 1515/1515 [==============================] - 242s 160ms/step - loss: 0.9893 - accuracy: 0.7622 Epoch 31/50 1515/1515 [==============================] - 243s 161ms/step - loss: 0.9505 - accuracy: 0.7700 Epoch 32/50 1515/1515 [==============================] - 244s 161ms/step - loss: 0.9319 - accuracy: 0.7734 Epoch 33/50 1515/1515 [==============================] - 244s 161ms/step - loss: 0.8978 - accuracy: 0.7832 Epoch 34/50 1515/1515 [==============================] - 244s 161ms/step - loss: 0.8694 - accuracy: 0.7887 Epoch 35/50 1515/1515 [==============================] - 245s 162ms/step - loss: 0.8510 - accuracy: 0.7943 Epoch 36/50 1515/1515 [==============================] - 245s 161ms/step - loss: 0.8370 - accuracy: 0.7968 Epoch 37/50 1515/1515 [==============================] - 247s 163ms/step - loss: 0.8219 - accuracy: 0.7996 Epoch 38/50 1515/1515 [==============================] - 244s 161ms/step - loss: 0.8000 - accuracy: 0.8048 Epoch 39/50 1515/1515 [==============================] - 242s 160ms/step - loss: 0.7860 - accuracy: 0.8089 Epoch 40/50 1515/1515 [==============================] - 244s 161ms/step - loss: 0.7726 - accuracy: 0.8121 Epoch 41/50 1515/1515 [==============================] - 244s 161ms/step - loss: 0.7663 - accuracy: 0.8118 Epoch 42/50 1515/1515 [==============================] - 244s 161ms/step - loss: 0.7460 - accuracy: 0.8170 Epoch 43/50 1515/1515 [==============================] - 247s 163ms/step - loss: 0.7393 - accuracy: 0.8180 Epoch 44/50 1515/1515 [==============================] - 246s 162ms/step - loss: 0.7320 - accuracy: 0.8207 Epoch 45/50 1515/1515 [==============================] - 247s 163ms/step - loss: 0.7109 - accuracy: 0.8245 Epoch 46/50 1515/1515 [==============================] - 247s 163ms/step - loss: 0.7068 - accuracy: 0.8256 Epoch 47/50 1515/1515 [==============================] - 248s 164ms/step - loss: 0.7013 - accuracy: 0.8263 Epoch 48/50 1515/1515 [==============================] - 247s 163ms/step - loss: 0.6974 - accuracy: 0.8271 Epoch 49/50 1515/1515 [==============================] - 248s 164ms/step - loss: 0.6870 - accuracy: 0.8282 Epoch 50/50 1515/1515 [==============================] - 248s 164ms/step - loss: 0.6720 - accuracy: 0.8311
Plotting the model accuracy and loss¶
In [ ]:
import matplotlib.pyplot as plt
def plot_graphs(history, string):
plt.plot(history.history[string])
plt.xlabel("Epochs")
plt.ylabel(string)
plt.show()
In [ ]:
plot_graphs(history, 'accuracy')
In [ ]:
plot_graphs(history, 'loss')
Text Generation (Prediction)¶
In [ ]:
def generate_text(seed_text, next_words, model, max_sequence_len):
for _ in range(next_words):
token_list = tokenizer.texts_to_sequences([seed_text])[0]
token_list = pad_sequences([token_list], maxlen = max_sequence_len - 1, padding='pre')
predicted = model.predict(token_list, verbose=0)
classes_x = np.argmax(predicted,axis=1)
output_word = ""
for word,index in tokenizer.word_index.items():
if index == classes_x:
output_word = word
break
seed_text += " "+output_word
return seed_text.title()
- seed_text: the initial words that will be passed for text generation.
- predict_classes: it will return the token id for the predicted word.
- predicted: It's the token id for a predicted word and this will be converted back into a word using the - dictionary tokenizer.word_index .items().
- next_words: It’s the number of next words we want to be predicted.
Generation of Text Using Initial Text or "seed_text" and Maximum Sequence Length"¶
In [ ]:
print (generate_text("implementation of ", 2, model, max_sequence_len))
print (generate_text("implementation of ", 4, model, max_sequence_len))
Implementation Of Rnn Lstm Implementation Of Rnn Lstm And Gru
In [ ]:
print (generate_text("How to use", 5, model, max_sequence_len))
How To Use A Time Before Medium Learn
In [ ]:
print (generate_text("Forecasting ", 7, model, max_sequence_len))
Forecasting Future Prices Of Cryptocurrency Using Historical Data
In [ ]:
print (generate_text("Data", 3, model, max_sequence_len))
Data Science Recruitment —
In [ ]:
print (generate_text("Deep Learning is a ", 1, model, max_sequence_len))
Deep Learning Is A Budget
Observation¶
As we can see, all text generated by our Model is basically replicating how humans would type out a Headline. That means, our model has learned how to generate text like the data it was trained on.
Saving the Model as a Pickle file¶
In [ ]:
model.save("text_generator.h5")