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Diffstat (limited to 'training/dtrain/examples/standard/dtrain.ini')
| -rw-r--r-- | training/dtrain/examples/standard/dtrain.ini | 24 |
1 files changed, 24 insertions, 0 deletions
diff --git a/training/dtrain/examples/standard/dtrain.ini b/training/dtrain/examples/standard/dtrain.ini new file mode 100644 index 00000000..a05e9c29 --- /dev/null +++ b/training/dtrain/examples/standard/dtrain.ini @@ -0,0 +1,24 @@ +input=./nc-wmt11.de.gz +refs=./nc-wmt11.en.gz +output=- # a weights file (add .gz for gzip compression) or STDOUT '-' +select_weights=avg # output average (over epochs) weight vector +decoder_config=./cdec.ini # config for cdec +# weights for these features will be printed on each iteration +print_weights= EgivenFCoherent SampleCountF CountEF MaxLexFgivenE MaxLexEgivenF IsSingletonF IsSingletonFE Glue WordPenalty PassThrough LanguageModel LanguageModel_OOV +# newer version of the grammar extractor use different feature names: +#print_weights=Glue WordPenalty LanguageModel LanguageModel_OOV PhraseModel_0 PhraseModel_1 PhraseModel_2 PhraseModel_3 PhraseModel_4 PhraseModel_5 PhraseModel_6 PassThrough +stop_after=10 # stop epoch after 10 inputs + +# interesting stuff +epochs=2 # run over input 2 times +k=100 # use 100best lists +N=4 # optimize (approx) BLEU4 +scorer=stupid_bleu # use 'stupid' BLEU+1 +learning_rate=1.0 # learning rate, don't care if gamma=0 (perceptron) +gamma=0 # use SVM reg +sample_from=kbest # use kbest lists (as opposed to forest) +filter=uniq # only unique entries in kbest (surface form) +pair_sampling=XYX # +hi_lo=0.1 # 10 vs 80 vs 10 and 80 vs 10 here +pair_threshold=0 # minimum distance in BLEU (here: > 0) +loss_margin=0 |