“Gigabye LM”版本间的差异
（→2. pruning the 4k 3gram LM.） |
（→2. pruning the 4k 3gram LM.） |
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{|class="wikitable" | {|class="wikitable" | ||
| − | ! Model ||2gram ||3gram || size || ppl | + | ! Model ||2gram ||3gram || size || ppl || fst size |
|- | |- | ||
| − | | 1 || 1e-7 || 1e-7 || 30M | + | | 1 || 1e-7 || 1e-7 || 30M || ppl= 102.796 || 860M |
|- | |- | ||
| − | |2 || 1e-6 || 1e-6 || 5M | + | |2 || 1e-6 || 1e-6 || 5M || ppl= 150.96 || 152M |
|- | |- | ||
| − | | 3 || 1e-7 || 1e-6 || 11M | + | | 3 || 1e-7 || 1e-6 || 11M || ppl= 137.467 || 224M |
|- | |- | ||
|} | |} | ||
2012年9月13日 (四) 06:02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 | 2gram | 3gram | size | ppl | fst size |
|---|---|---|---|---|---|
| 1 | 1e-7 | 1e-7 | 30M | ppl= 102.796 | 860M |
| 2 | 1e-6 | 1e-6 | 5M | ppl= 150.96 | 152M |
| 3 | 1e-7 | 1e-6 | 11M | ppl= 137.467 | 224M |
3. word-based 3-gram
tri-gram size:
| org | th-7 | th-7/6 | th-6 |
| 10k: 52M | 23M | 8M | 4M |
| 20k: 57M | 24M | 9M | 4M |
final fst size:
| org | th-7 | th-7/6 | th-6 |
| 10k: - | 770M | 193M | 135M |
| 20k: - | - | - | - |
