epfml / sent2vec

General purpose unsupervised sentence representations
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SegFault while training on Toronto Corpus #21

Closed prerna135 closed 6 years ago

prerna135 commented 6 years ago

I'm encountering a segfault while training on the Toronto Book Corpus. For training I'm using: ./fasttext sent2vec -input ../input.txt -output my_model -minCount 5 -dim 700 -epoch 12 -lr 0.2 -wordNgrams 2 -loss ns -neg 10 -thread 4 -t 0.000005 -dropoutK 7 -bucket 2000000

It runs for a few minutes and then this shows up: Progress: 0.0% words/sec/thread: 5160 lr: 0.199927 loss: 3.388311 eta: 170h56m Segmentation fault: 11

I'm working on a Macbook Air mid 2012 model having 4 cores.

mpagli commented 6 years ago

If I recall correctly, the toronto book corpus has some very long sentences corresponding to crawling errors (or very personal writing style). We removed those using this script:

#!/usr/bin/python3.5

import sys

if __name__ == "__main__":

    if len(sys.argv) != 4:
        print("\nUsage: python3 ./remove_long_sentences <in> <out> <max_length>\n")
        sys.exit(0)

    input_file = sys.argv[1]
    output_file = sys.argv[2]
    max_length = int(sys.argv[3])

    cpt = 0

    with open(output_file, 'w') as out_stream:
        with open(input_file, 'r') as in_stream:
            for line in in_stream:
                num_t = len(line.split(' '))
                if num_t <= max_length:
                    out_stream.write(line)
                else:
                    print(line)
                    cpt += 1

    print("%d sentences discarded." % (cpt))

If you run this script with a generous threshold like 300 or 500 you will see the sentences being discarded. For instance (more than 1000 tokens):

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or:

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1 keeps 1 mud 1 anguish 1 revelation 1 centuries-long 1 ancestry 1 throat 1 honestly 1 squeak 1 lizards 1 shooting 1 words 1 infinitely 1 shallow 1 arrayed 1 enlightenment 1 macedonian 1 minutes 1 corpse 1 inbreeding 1 porch 1 box 1 distant 1 drops 1 suspended 1 rusty 1 macy 1 amontillado 1 whilst 1 essential 1 march 1 fellow 1 drip 1 personnel 1 establish 1 ice 1 imbrium 1 stages 1 fool 1 notion 1 hunger 1 mean 1 polyglot 1 whisper 1 stick 1 idiot 1 intuitive 1 fleshy 1 wicker 1 masturbating 1 socks 1 enclave 1 continual 1 primacy 1 weren 1 effect 1 pop 1 bahamas 1 saps 1 perspective 1 shaving 1 bessy 1 lay 1 react 1 major 1 replicants 1 place 1 intergalactic 1 above 1 help 1 vagaries 1 play 1 particles 1 learned 1 circuits 1 mortal 1 eldest 1 modest 1 materials 1 overlords 1 previous 1 ichor 1 overflowed 1 prince 1 cracked 1 choked 1 empathic 1 lights 1 guts 1 claw 1 birds 1 ganglion 1 catapults 1 perpetual 1 sulfurous 1 type 1 parchment 1 eulogies 1 lonely 1 perpetrate 1 uninterruptable 1 brink 1 eons 1 signified 1 south 1 neutron 1 homogenize 1 partially 1 astronomy 1 doubtless 1 yeasty 1 trinity 1 amount 1 laser 1 rival 1 jagged 1 venerated 1 government 1 told 1 york 1 sci-fi 1 contact 1 bombs 1 purge 1 fighting 1 snuffed 1 interest 1 poor 1 smiling 1 semaphore 1 sensibilities 1 astronomical 1 nano-cloud 1 come-back 1 build 1 sliced 1 descending 1 empire 1 horse 1 jars 1 reproductive 1 era 1 majesty 1 whimpers 1 stare 1 debunked 1 person 1 aimed 1 shell 1 regrettable 1 overpopulation 1 monsters 1 homicidal 1 slain 1 buffoon 1 crooked 1 rage 1 grief 1 antiquity 1 cowered 1 impressed 1 biology 1 hooted 1 fries 1 persist 1 outside 1 immortality 1 process 1 tong 1 young 1 witchcraft 1 herald 1 slipped 1 spiral 1 laugh 1 decipher 1 relentless 1 cylinders 1 reborn 1 bump 1 functionally 1 knowing 1 wiped 1 halloween 1 threw 1 power 1 rendered 1 anomalous 1 bizarre 1 adopts 1 notable 1 grip 1 mine 1 snowflake 1 nanoseconds 1 support 1 zen 1 ants 1 honorable 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mpagli commented 6 years ago

@prerna135 : any news? Did it solve your issue ?

prerna135 commented 6 years ago

@mpagli Yeah, it did! I set the threshold to 300 and the model trained fine. Thanks a lot.