“Gigabye LM”版本间的差异

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3. word-based 3-gram
3. word-based 3-gram
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== 3. word-based 3-gram ==
 
== 3. word-based 3-gram ==
 
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||        org          th-7    th-6.5  th-6|
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|       org          th-7    th-6.5  th-6|
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|10k: 52M        23M    8M      4M|
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|20k: 57M        24M    9M      4M|
 
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||10k: 52M        23M    8M      4M|
 
||20k: 57M        24M    9M      4M|
 

2012年9月13日 (四) 05:08的版本

1. very initial, without any prunning, character based. Here is the size and perplexity.

The training is with Gigabytes except the cna data, and ppl testing is based on a sub set from the cna data (big52gb applied)

2gram:

25M 2gram.4000.gz: 0 zeroprobs, logprob= -9.39983e+06 ppl= 161.965 ppl1= 177.141


3gram:

47M 3gram.500.gz:0 zeroprobs, logprob= -6.34868e+06 ppl= 85.1361 ppl1= 94.2525

117M 3gram.1000.gz  :0 zeroprobs, logprob= -7.43809e+06 ppl= 80.6408 ppl1= 87.7439

195M 3gram.2000.gz:0 zeroprobs, logprob= -7.95872e+06 ppl= 79.9875 ppl1= 86.5196

221M 3gram.3000.gz:0 zeroprobs, logprob= -8.04799e+06 ppl= 80.2418 ppl1= 86.7277

229M 3gram.4000.gz:0 zeroprobs, logprob= -8.15697e+06 ppl= 82.6585 ppl1= 89.3392

4gram:

205M 4gram.500.gz:0 zeroprobs, logprob= -6.25395e+06 ppl= 79.6739 ppl1= 88.0716

472M 4gram.1000.gz:0 zeroprobs, logprob= -7.21607e+06 ppl= 70.737 ppl1= 76.774


2. pruning the 4k 3gram LM.

Model 1gram 2gram 3gram              size        ppl
1           1e-7  1e-7      1e-7               30M     logprob= -8.55982e+06 ppl= 102.796 ppl1= 111.532
2           1e-6  1e-6      1e-6               5M       logprob= -9.26982e+06 ppl= 150.96   ppl1= 164.9
3           1e-7  1e-6.5    1e-6.5           11M      logprob= -9.09681e+06 ppl= 137.467 ppl1= 149.913


3. word-based 3-gram

|- | org th-7 th-6.5 th-6| |- |10k: 52M 23M 8M 4M| |20k: 57M 24M 9M 4M| |-