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Piotr Czapla c29180a08f Add some results
2019-02-20 10:22:44 +01:00

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# JA
## SP30k LSTM nl 4
### LM
```
python -m ulmfit lm --dataset-path data/wiki/ja-100 --cuda-id=0 --tokenizer='sp' --nl 4 --name 'nl4' --max-vocab 30000 \
--lang ja --qrnn=False - train 10 --bs=50 --drop_mult=0
Max vocab: 30000
Cache dir: data/wiki/ja-100/models/sp30k
Model dir: data/wiki/ja-100/models/sp30k/lstm_nl4.m
Running tokenization
Wiki text was split to 98375 articles
Wiki text was split to 138 articles
Size of vocabulary: 30000
First 20 words in vocab: ['xxunk', 'xxpad', 'xxbos', 'xxfld', 'xxmaj', 'xxup', 'xxrep', 'xxwrep', '<unk>', '▁', '▁、', '▁の', '▁。', '▁に', '▁を', '▁は', '▁年', '▁が', '▁)', '▁(']
Training args: {'tie_weights': True, 'clip': 0.12, 'bptt': 70, 'pretrained_fnames': None, 'pretrained_model': None, 'drop_mult': 0} dps: [0.25 0.1 0.2 0.02 0.15]
Training lm from random weights
epoch train_loss valid_loss accuracy
1 3.211025 3.328014 0.396197
2 3.119410 3.286294 0.395946
3 3.042064 3.247161 0.403915
4 3.023840 3.161323 0.413816
5 2.944752 3.102044 0.423163
6 2.907167 3.015610 0.434095
7 2.796073 2.927566 0.447088
8 2.715568 2.828766 0.461556
9 2.717255 2.747889 0.473289
10 2.619846 2.731164 0.477403
data/wiki/ja-100/models/sp30k
Saving info data/wiki/ja-100/models/sp30k/lstm_nl4.m/info.json
```
### MLDoc
#### CLS 1
MultiCCA 85.35%, ULMFiT 89.20%
```
python -m ulmfit cls --dataset-path data/mldoc/ja-1 --base-lm-path data/wiki/ja-100/models/sp30k/lstm_nl4.m --lang=ja --name 'nl4' --cuda-id=1 - train 20 --bs 40 --num-cls-epochs=8
Max vocab: 30000
Cache dir: /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k
Model dir: /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k/lstm_nl4.m
Loading validation /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/ja.dev.csv
Running tokenization...
Saving tokenized: cls.trn 13500, cls.val 1500
Running tokenization...
Saving tokenized: cls.trn 1000, cls.val 1000
Size of vocabulary: 30000
First 20 words in vocab: ['xxunk', 'xxpad', 'xxbos', 'xxfld', 'xxmaj', 'xxup', 'xxrep', 'xxwrep', '<unk>', '▁', '▁、', '▁の', '▁。', '▁に', '▁を', '▁は', '▁年', '▁が', '▁)', '▁(']
Training args: {'tie_weights': True, 'clip': 0.12, 'bptt': 70, 'pretrained_fnames': [PosixPath('/home/pczapla/workspace/ulmfit-multilingual/data/wiki/ja-100/models/sp30k/lstm_nl4.m/lm_best'), PosixPath('/home/pczapla/workspace/ulmfit-multilingual/data/wiki/ja-100/models/sp30k/lstm_nl4.m/../itos')], 'pretrained_model': None, 'drop_mult': 0.3} dps: [0.25 0.1 0.2 0.02 0.15]
Unknown tokens 0, first 100: []
Training lm from: [PosixPath('/home/pczapla/workspace/ulmfit-multilingual/data/wiki/ja-100/models/sp30k/lstm_nl4.m/lm_best'), PosixPath('/home/pczapla/workspace/ulmfit-multilingual/data/wiki/ja-100/models/sp30k/lstm_nl4.m/../itos')]
epoch train_loss valid_loss accuracy
1 2.828645 2.386208 0.518716
epoch train_loss valid_loss accuracy
1 2.462274 2.191761 0.549783
2 2.229925 1.982564 0.586238
3 2.043816 1.805435 0.616238
4 1.885779 1.674736 0.637964
5 1.773445 1.575366 0.653925
6 1.713029 1.490263 0.667570
7 1.660558 1.419641 0.680072
8 1.579792 1.357093 0.690826
9 1.459628 1.298609 0.701452
10 1.433604 1.251296 0.710232
11 1.439143 1.202794 0.719104
12 1.399083 1.158469 0.728430
13 1.310390 1.120877 0.736382
14 1.322389 1.085479 0.744013
15 1.272924 1.056051 0.750401
16 1.235312 1.034233 0.755225
17 1.227864 1.016682 0.759288
18 1.209589 1.007038 0.761234
19 1.173158 1.001694 0.762281
20 1.189994 1.000854 0.762526
/home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k
Saving info /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k/lstm_nl4.m/info.json
Starting classifier training
epoch train_loss valid_loss accuracy
1 0.745803 0.554439 0.819000
epoch train_loss valid_loss accuracy
1 0.620647 0.392026 0.856000
epoch train_loss valid_loss accuracy
1 0.489173 0.369560 0.869000
epoch train_loss valid_loss accuracy
1 0.406491 0.365988 0.872000
2 0.392645 0.351823 0.876000
3 0.386403 0.331737 0.880000
4 0.361338 0.333245 0.882000
5 0.319456 0.347253 0.879000
6 0.295419 0.350348 0.885000
7 0.286144 0.348592 0.879000
8 0.278896 0.358145 0.877000
Saving models at /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k/lstm_nl4.m
Loss and accuracy using (cls_best): [0.29789856, tensor(0.8920)]
$ mv /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k/lstm_nl4.m /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k/lstm_nl4x8.m
$ python -m ulmfit eval --glob="mldoc/ja-1/models/sp30k/lstm_nl4x8.m" --name nl4 --cuda-id=0
Max vocab: 30000
Cache dir: /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k
Model dir: /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k/lstm_nl4.m
Training
Loading validation /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/ja.dev.csv
Tokenized data loaded, lm.trn 13500, lm.val 1500
Tokenized data loaded, cls.trn 1000, cls.val 1000
Size of vocabulary: 30000
First 20 words in vocab: ['xxunk', 'xxpad', 'xxbos', 'xxfld', 'xxmaj', 'xxup', 'xxrep', 'xxwrep', '<unk>', '▁', '▁、', '▁の', '▁。', '▁に', '▁を', '▁は', '▁年', '▁が', '▁)', '▁(']
Training args: {'tie_weights': True, 'clip': 0.12, 'bptt': 70, 'pretrained_fnames': [PosixPath('/home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k/lstm_nl4x8.m/lm_best'), PosixPath('/home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k/lstm_nl4x8.m/../itos')], 'pretrained_model': None, 'alpha': 2, 'beta': 1, 'drop_mult': 0.3} dps: [0.25 0.1 0.2 0.02 0.15]
Unknown tokens 0, first 100: []
/home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k
Saving info /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k/lstm_nl4.m/info.json
Starting classifier training
epoch train_loss valid_loss accuracy
1 0.757615 0.562652 0.825000
epoch train_loss valid_loss accuracy
1 0.609298 0.382412 0.870000
epoch train_loss valid_loss accuracy
1 0.544682 0.379602 0.871000
epoch train_loss valid_loss accuracy
1 0.435453 0.360421 0.885000
2 0.426099 0.350480 0.885000
Saving models at /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k/lstm_nl4.m
Loss and accuracy using (cls_best): [0.32738593, tensor(0.8905)]
OrderedDict([('data/mldoc/ja-1/models/sp30k/lstm_nl4.m', 0.890500009059906)])
python -m ulmfit eval --glob="mldoc/ja-1/models/sp30k/lstm_nl4x8.m" --name nl4x2 --cuda-id=0
Max vocab: 30000
Cache dir: /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k
Model dir: /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k/lstm_nl4x2.m
Training
Loading validation /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/ja.dev.csv
Tokenized data loaded, lm.trn 13500, lm.val 1500
Tokenized data loaded, cls.trn 1000, cls.val 1000
Size of vocabulary: 30000
First 20 words in vocab: ['xxunk', 'xxpad', 'xxbos', 'xxfld', 'xxmaj', 'xxup', 'xxrep', 'xxwrep', '<unk>', '▁', '▁、', '▁の', '▁。', '▁に', '▁を', '▁は', '▁年', '▁が', '▁)', '▁(']
Training args: {'tie_weights': True, 'clip': 0.12, 'bptt': 70, 'pretrained_fnames': [PosixPath('/home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k/lstm_nl4x8.m/lm_best'), PosixPath('/home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k/lstm_nl4x8.m/../itos')], 'pretrained_model': None, 'alpha': 2, 'beta': 1, 'drop_mult': 0.3} dps: [0.25 0.1 0.2 0.02 0.15]
Unknown tokens 0, first 100: []
/home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k
Saving info /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k/lstm_nl4x2.m/info.json
Starting classifier training
epoch train_loss valid_loss accuracy
1 0.778722 0.690746 0.805000
epoch train_loss valid_loss accuracy
1 0.574789 0.386483 0.862000
epoch train_loss valid_loss accuracy
1 0.518843 0.361983 0.869000
epoch train_loss valid_loss accuracy
1 0.435260 0.350808 0.869000
2 0.386701 0.352221 0.875000
Saving models at /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k/lstm_nl4x2.m
Loss and accuracy using (cls_best): [0.31783763, tensor(0.8892)]
OrderedDict([('data/mldoc/ja-1/models/sp30k/lstm_nl4x2.m', 0.8892499804496765)])
```
### JA on 100 elements
```
python -m ulmfit cls --dataset-path data/mldoc/ja-1 --base-lm-path data/wiki/ja-100/models/sp30k/lstm_nl4.m --lang=ja --name 'nl4-100' --cuda-id=1 - train 20 --bs 40 --num-cls-epochs=8 --limit=100
Max vocab: 30000
Cache dir: /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k
Model dir: /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k/lstm_nl4-100.m
Loading validation /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/ja.dev.csv
Tokenized data loaded, lm.trn 13500, lm.val 1500
Limiting data set to: 100
Running tokenization...
Saving tokenized: cls.trn 100, cls.val 100
Size of vocabulary: 30000
First 20 words in vocab: ['xxunk', 'xxpad', 'xxbos', 'xxfld', 'xxmaj', 'xxup', 'xxrep', 'xxwrep', '<unk>', '▁', '▁、', '▁の', '▁。', '▁に', '▁を', '▁は', '▁年', '▁が', '▁)', '▁(']
Training args: {'tie_weights': True, 'clip': 0.12, 'bptt': 70, 'pretrained_fnames': [PosixPath('/home/pczapla/workspace/ulmfit-multilingual/data/wiki/ja-100/models/sp30k/lstm_nl4.m/lm_best'), PosixPath('/home/pczapla/workspace/ulmfit-multilingual/data/wiki/ja-100/models/sp30k/lstm_nl4.m/../itos')], 'pretrained_model': None, 'drop_mult': 0.3} dps: [0.25 0.1 0.2 0.02 0.15]
Unknown tokens 0, first 100: []
Training lm from: [PosixPath('/home/pczapla/workspace/ulmfit-multilingual/data/wiki/ja-100/models/sp30k/lstm_nl4.m/lm_best'), PosixPath('/home/pczapla/workspace/ulmfit-multilingual/data/wiki/ja-100/models/sp30k/lstm_nl4.m/../itos')]
epoch train_loss valid_loss accuracy
1 2.837937 2.387255 0.518590
epoch train_loss valid_loss accuracy
1 2.466900 2.193583 0.549492
2 2.232762 1.983981 0.586658
3 2.026505 1.810167 0.615649
4 1.918111 1.679784 0.636613
5 1.748909 1.577095 0.653108
6 1.708709 1.491436 0.667657
7 1.640415 1.420449 0.679619
8 1.577434 1.359511 0.690194
9 1.551961 1.302819 0.700306
10 1.475623 1.252393 0.710039
11 1.435565 1.208159 0.718740
12 1.354910 1.161781 0.727927
13 1.351157 1.123244 0.736009
14 1.299070 1.086383 0.743896
15 1.258739 1.055745 0.750383
16 1.210775 1.035209 0.754965
17 1.228421 1.018373 0.758963
18 1.179444 1.007714 0.761158
19 1.197443 1.003041 0.762068
20 1.163223 1.001939 0.762211
/home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k
Saving info /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k/lstm_nl4-100.m/info.json
Starting classifier training
epoch train_loss valid_loss accuracy
1 1.269222 1.360420 0.340000
epoch train_loss valid_loss accuracy
1 0.969350 1.314497 0.400000
epoch train_loss valid_loss accuracy
1 0.832396 1.263416 0.550000
epoch train_loss valid_loss accuracy
1 0.780991 1.225439 0.600000
2 0.765755 1.183010 0.600000
3 0.749420 1.139053 0.600000
4 0.731800 1.093319 0.610000
5 0.711152 1.054695 0.610000
6 0.694611 1.029465 0.580000
7 0.680276 1.004366 0.580000
8 0.668421 0.984848 0.590000
Saving models at /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k/lstm_nl4-100.m
Loss and accuracy using (cls_best): [0.81621724, tensor(0.7437)]
```
### Japanese fixed sentence piece
```
python -m ulmfit cls --dataset-path data/mldoc/ja-1 --base-lm-path data/mldoc/ja-1/models/bsp30k/lstm_nl4.m --lang=ja --name 'nl4' --cuda-id=0 - train 1 --bs 40 --num-cls-epochs=2
Max vocab: 30000
Cache dir: /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k
Model dir: /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k/lstm_nl4.m
Loading validation /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/ja.dev.csv
Tokenized data loaded, lm.trn 13500, lm.val 1500
Tokenized data loaded, cls.trn 1000, cls.val 1000
Size of vocabulary: 30000
First 20 words in vocab: ['xxunk', 'xxpad', 'xxbos', 'xxfld', 'xxmaj', 'xxup', 'xxrep', 'xxwrep', '<unk>', '▁', '▁、', '▁の', '▁。', '▁に', '▁を', '▁は', '▁年', '▁が', '▁)', '▁(']
Training args: {'tie_weights': True, 'clip': 0.12, 'bptt': 70, 'pretrained_fnames': [PosixPath('/home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/bsp30k/lstm_nl4.m/lm_best'), PosixPath('/home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/bsp30k/lstm_nl4.m/../itos')], 'pretrained_model': None, 'alpha': 2, 'beta': 1, 'drop_mult': 0.3} dps: [0.25 0.1 0.2 0.02 0.15]
Unknown tokens 0, first 100: []
Training lm from: [PosixPath('/home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/bsp30k/lstm_nl4.m/lm_best'), PosixPath('/home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/bsp30k/lstm_nl4.m/../itos')]
epoch train_loss valid_loss accuracy
1 1.222162 0.986234 0.765830
epoch train_loss valid_loss accuracy
1 1.237291 0.983976 0.766659
/home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k
Saving info /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k/lstm_nl4.m/info.json
Starting classifier training
epoch train_loss valid_loss accuracy
1 0.759230 0.619306 0.826000
epoch train_loss valid_loss accuracy
1 0.599281 0.423162 0.841000
epoch train_loss valid_loss accuracy
1 0.485808 0.360609 0.869000
epoch train_loss valid_loss accuracy
1 0.415960 0.390202 0.872000
2 0.371651 0.365374 0.876000
Saving models at /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k/lstm_nl4.m
Loss and accuracy using (cls_best): [0.330675, tensor(0.8873)]
0.33067500591278076
0.8872500061988831
(fastaiv1) pczapla@galatea ~/w/ulmfit-multilingual ❯❯❯ python -m ulmfit eval --glob="mldoc/ja-1/models/sp30k/lstm_nl4.m" --name nl4-2nd --cuda-id=0
Max vocab: 30000
Cache dir: /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k
Model dir: /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k/lstm_nl4-2nd.m
Training
Loading validation /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/ja.dev.csv
Tokenized data loaded, lm.trn 13500, lm.val 1500
Tokenized data loaded, cls.trn 1000, cls.val 1000
Size of vocabulary: 30000
First 20 words in vocab: ['xxunk', 'xxpad', 'xxbos', 'xxfld', 'xxmaj', 'xxup', 'xxrep', 'xxwrep', '<unk>', '▁', '▁、', '▁の', '▁。', '▁に', '▁を', '▁は', '▁年', '▁が', '▁)', '▁(']
Training args: {'tie_weights': True, 'clip': 0.12, 'bptt': 70, 'pretrained_fnames': [PosixPath('/home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k/lstm_nl4.m/lm_best'), PosixPath('/home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k/lstm_nl4.m/../itos')], 'pretrained_model': None, 'alpha': 2, 'beta': 1, 'drop_mult': 0.3} dps: [0.25 0.1 0.2 0.02 0.15]
Unknown tokens 0, first 100: []
/home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k
Saving info /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k/lstm_nl4-2nd.m/info.json
Starting classifier training
epoch train_loss valid_loss accuracy
1 0.844110 0.700549 0.743000
epoch train_loss valid_loss accuracy
1 0.610796 0.400912 0.853000
epoch train_loss valid_loss accuracy
1 0.449974 0.358793 0.870000
epoch train_loss valid_loss accuracy
1 0.407609 0.397678 0.868000
2 0.367383 0.373168 0.869000
Saving models at /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k/lstm_nl4-2nd.m
Loss and accuracy using (cls_best): [0.33044776, tensor(0.8863)]
OrderedDict([('data/mldoc/ja-1/models/sp30k/lstm_nl4-2nd.m',
0.8862500190734863)])
```
```
python -m ulmfit cls --dataset-path data/mldoc/ja-1 --base-lm-path data/mldoc/ja-1/models/bsp30k/lstm_nl4.m --lang=ja --name '2nd-nl4' --cuda-id=0 - train 1 --bs 40 --num-cls-epochs=2
Max vocab: 30000
Cache dir: /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k
Model dir: /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k/lstm_2nd-nl4.m
Loading validation /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/ja.dev.csv
Tokenized data loaded, lm.trn 13500, lm.val 1500
Tokenized data loaded, cls.trn 1000, cls.val 1000
Size of vocabulary: 30000
First 20 words in vocab: ['xxunk', 'xxpad', 'xxbos', 'xxfld', 'xxmaj', 'xxup', 'xxrep', 'xxwrep', '<unk>', '▁', '▁、', '▁の', '▁。', '▁に', '▁を', '▁は', '▁年', '▁が', '▁)', '▁(']
Training args: {'tie_weights': True, 'clip': 0.12, 'bptt': 70, 'pretrained_fnames': [PosixPath('/home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/bsp30k/lstm_nl4.m/lm_best'), PosixPath('/home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/bsp30k/lstm_nl4.m/../itos')], 'pretrained_model': None, 'alpha': 2, 'beta': 1, 'drop_mult': 0.3} dps: [0.25 0.1 0.2 0.02 0.15]
Unknown tokens 0, first 100: []
Training lm from: [PosixPath('/home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/bsp30k/lstm_nl4.m/lm_best'), PosixPath('/home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/bsp30k/lstm_nl4.m/../itos')]
epoch train_loss valid_loss accuracy
1 1.208774 0.984775 0.766183
epoch train_loss valid_loss accuracy
1 1.198147 0.984786 0.766730
/home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k
Saving info /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k/lstm_2nd-nl4.m/info.json
Starting classifier training
epoch train_loss valid_loss accuracy
1 0.735084 0.613895 0.803000
epoch train_loss valid_loss accuracy
1 0.550159 0.406097 0.867000
epoch train_loss valid_loss accuracy
1 0.468788 0.404081 0.862000
epoch train_loss valid_loss accuracy
1 0.395969 0.380797 0.870000
2 0.349470 0.386497 0.866000
Saving models at /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k/lstm_2nd-nl4.m
Loss and accuracy using (cls_best): [0.32550755, tensor(0.8857)]
0.3255075514316559
0.8857499957084656
```
```bash
python -m ulmfit cls --dataset-path data/mldoc/ja-1 --base-lm-path data/mldoc/ja-1/models/bsp30k/lstm_nl4.m --lang=ja --name '3nd-nl4' --cuda-id=0 - train 0 --bs 40 --num-cls-epochs=2
Max vocab: 30000
Cache dir: /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k
Model dir: /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k/lstm_3nd-nl4.m
Loading validation /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/ja.dev.csv
Tokenized data loaded, lm.trn 13500, lm.val 1500
Tokenized data loaded, cls.trn 1000, cls.val 1000
Size of vocabulary: 30000
First 20 words in vocab: ['xxunk', 'xxpad', 'xxbos', 'xxfld', 'xxmaj', 'xxup', 'xxrep', 'xxwrep', '<unk>', '▁', '▁、', '▁の', '▁。', '▁に', '▁を', '▁は', '▁年', '▁が', '▁)', '▁(']
Training args: {'tie_weights': True, 'clip': 0.12, 'bptt': 70, 'pretrained_fnames': [PosixPath('/home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/bsp30k/lstm_nl4.m/lm_best'), PosixPath('/home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/bsp30k/lstm_nl4.m/../itos')], 'pretrained_model': None, 'alpha': 2, 'beta': 1, 'drop_mult': 0.3} dps: [0.25 0.1 0.2 0.02 0.15]
Unknown tokens 0, first 100: []
/home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k
Saving info /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k/lstm_3nd-nl4.m/info.json
Starting classifier training
epoch train_loss valid_loss accuracy
1 0.777661 0.617013 0.786000
epoch train_loss valid_loss accuracy
1 0.603897 0.388985 0.867000
epoch train_loss valid_loss accuracy
1 0.510845 0.374942 0.874000
epoch train_loss valid_loss accuracy
1 0.468642 0.379503 0.872000
2 0.430415 0.365797 0.880000
Saving models at /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp30k/lstm_3nd-nl4.m
Loss and accuracy using (cls_best): [0.33084384, tensor(0.8882)]
0.33084383606910706
0.8882499933242798
python -m ulmfit cls --dataset-path data/mldoc/ja-1 --base-lm-path data/mldoc/ja-1/models/bsp30k/lstm_nl4.m --lang=ja --tokenizer=bsp --name '3nd-nl4' --cuda-id=0 - train 0 --bs 40 --num-cls-epochs=2
Max vocab: 30000
Cache dir: /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/bsp30k
Model dir: /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/bsp30k/lstm_3nd-nl4.m
Loading validation /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/ja.dev.csv
Tokenized data loaded, lm.trn 13500, lm.val 1500
Tokenized data loaded, cls.trn 1000, cls.val 1000
Size of vocabulary: 30000
First 20 words in vocab: ['xxunk', 'xxpad', 'xxbos', 'xxfld', 'xxmaj', 'xxup', 'xxrep', 'xxwrep', '<unk>', '▁', '▁、', '▁の', '▁。', '▁に', '▁を', '▁は', '▁年', '▁が', '▁)', '▁(']
Training args: {'tie_weights': True, 'clip': 0.12, 'bptt': 70, 'pretrained_fnames': [PosixPath('/home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/bsp30k/lstm_nl4.m/lm_best'), PosixPath('/home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/bsp30k/lstm_nl4.m/../itos')], 'pretrained_model': None, 'alpha': 2, 'beta': 1, 'drop_mult': 0.3} dps: [0.25 0.1 0.2 0.02 0.15]
Unknown tokens 0, first 100: []
/home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/bsp30k
Saving info /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/bsp30k/lstm_3nd-nl4.m/info.json
Starting classifier training
epoch train_loss valid_loss accuracy
1 0.824216 0.604706 0.825000
epoch train_loss valid_loss accuracy
1 0.606317 0.409647 0.854000
epoch train_loss valid_loss accuracy
1 0.500782 0.381826 0.862000
epoch train_loss valid_loss accuracy
1 0.403516 0.366863 0.866000
2 0.394599 0.357580 0.874000
Saving models at /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/bsp30k/lstm_3nd-nl4.m
Loss and accuracy using (cls_best): [0.32903105, tensor(0.8848)]
0.3290310502052307
0.8847500085830688
python -m ulmfit cls --dataset-path data/mldoc/ja-1 --base-lm-path data/mldoc/ja-1/models/bsp30k/lstm_nl4.m --lang=ja --tokenizer=bsp --name '3nd-nl4' --cuda-id=0 - train 0 --bs 40 --num-cls-epochs=2
Max vocab: 30000
Cache dir: /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/bsp30k
Model dir: /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/bsp30k/lstm_3nd-nl4.m
Loading validation /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/ja.dev.csv
Tokenized data loaded, lm.trn 13500, lm.val 1500
Tokenized data loaded, cls.trn 1000, cls.val 1000
Size of vocabulary: 30000
First 20 words in vocab: ['xxunk', 'xxpad', 'xxbos', 'xxfld', 'xxmaj', 'xxup', 'xxrep', 'xxwrep', '<unk>', '▁', '▁、', '▁の', '▁。', '▁に', '▁を', '▁は', '▁年', '▁が', '▁)', '▁(']
Loading last classifier
Starting classifier training
epoch train_loss valid_loss accuracy
1 0.460803 0.451998 0.855000
epoch train_loss valid_loss accuracy
1 0.460069 0.421900 0.867000
epoch train_loss valid_loss accuracy
1 0.361791 0.447982 0.859000
epoch train_loss valid_loss accuracy
1 0.301233 0.404477 0.868000
2 0.269350 0.406427 0.870000
Saving models at /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/bsp30k/lstm_3nd-nl4.m
Loss and accuracy using (cls_best): [0.34159982, tensor(0.8925)]
0.34159982204437256
0.8924999833106995
```
## SP60k
```
python -m ulmfit lm --dataset-path data/wiki/ja-100 --cuda-id=1 --tokenizer='sp' --nl 4 --name 'nl4' --max-vocab 60000 \
--lang ja --qrnn=False - train 10 --bs=50 --drop_mult=0
Running tokenization
Wiki text was split to 98375 articles
Wiki text was split to 138 articles
Size of vocabulary: 60000
First 20 words in vocab: ['xxunk', 'xxpad', 'xxbos', 'xxfld', 'xxmaj', 'xxup', 'xxrep', 'xxwrep', '<unk>', '▁', '▁、', '▁の', '▁。', '▁に', '▁を', '▁は', '▁年', '▁が', '▁)', '▁(']
Training args: {'tie_weights': True, 'clip': 0.12, 'bptt': 70, 'pretrained_fnames': None, 'pretrained_model': None, 'alpha': 2, 'beta': 1, 'drop_mult': 0} dps: [0.25 0.1 0.2 0.02 0.15]
Training lm from random weights
epoch train_loss valid_loss accuracy
1 3.557822 3.682454 0.366108
2 3.377493 3.614226 0.369889
3 3.391634 3.562171 0.377114
4 3.328160 3.497388 0.385236
5 3.290285 3.424971 0.394655
6 3.159867 3.337317 0.407095
7 3.139091 3.250999 0.417750
8 3.103923 3.153146 0.433443
9 2.979789 3.092179 0.443405
10 2.984099 3.077171 0.446887
data/wiki/ja-100/models/sp60k
Saving info data/wiki/ja-100/models/sp60k/lstm_nl4.m/info.json
```
## MLDoc
````bash
python -m ulmfit cls --dataset-path data/mldoc/ja-1 --base-lm-path data/wiki/ja-100/models/sp60k/lstm_nl4.m --lang=ja --name 'nl4' --cuda-id=1 - train 20 --bs 40 --num-cls-epochs=2
Max vocab: 60000
Cache dir: /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp60k
Model dir: /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/models/sp60k/lstm_nl4.m
Loading validation /home/pczapla/workspace/ulmfit-multilingual/data/mldoc/ja-1/ja.dev.csv
Tokenized data loaded, lm.trn 13500, lm.val 1500
Running tokenization...
Saving tokenized: cls.trn 1000, cls.val 1000
Size of vocabulary: 60000
First 20 words in vocab: ['xxunk', 'xxpad', 'xxbos', 'xxfld', 'xxmaj', 'xxup', 'xxrep', 'xxwrep', '<unk>', '▁', '▁、', '▁の', '▁。', '▁に', '▁を', '▁は', '▁年', '▁が', '▁)', '▁(']
Training args: {'tie_weights': True, 'clip': 0.12, 'bptt': 70, 'pretrained_fnames': [PosixPath('/home/pczapla/workspace/ulmfit-multilingual/data/wiki/ja-100/models/sp60k/lstm_nl4.m/lm_best'), PosixPath('/home/pczapla/workspace/ulmfit-multilingual/data/wiki/ja-100/models/sp60k/lstm_nl4.m/../itos')], 'pretrained_model': None, 'alpha': 2, 'beta': 1, 'drop_mult': 0.3} dps: [0.25 0.1 0.2 0.02 0.15]
Unknown tokens 0, first 100: []
Training lm from: [PosixPath('/home/pczapla/workspace/ulmfit-multilingual/data/wiki/ja-100/models/sp60k/lstm_nl4.m/lm_best'), PosixPath('/home/pczapla/workspace/ulmfit-multilingual/data/wiki/ja-100/models/sp60k/lstm_nl4.m/../itos')]
epoch train_loss valid_loss accuracy
1 3.019972 2.548868 0.503754
epoch train_loss valid_loss accuracy
1 2.643698 2.363415 0.532341
2 2.403588 2.149524 0.567359
3 2.218298 1.969651 0.597484
4 2.059648 1.829897 0.619758
5 1.941803 1.722339 0.636215
6 1.862969 1.630191 0.650293
7 1.796515 1.551929 0.663782
8 1.727768 1.481659 0.675489
9 1.667709 1.417764 0.687287
10 1.606343 1.357994 0.697264
11 1.553344 1.303901 0.707811
12 1.539182 1.251784 0.718038
Traceback (most recent call last):
````
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