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-rw-r--r--dtrain/test/example/dtrain.ini22
1 files changed, 11 insertions, 11 deletions
diff --git a/dtrain/test/example/dtrain.ini b/dtrain/test/example/dtrain.ini
index 68173e11..66be6bf2 100644
--- a/dtrain/test/example/dtrain.ini
+++ b/dtrain/test/example/dtrain.ini
@@ -1,20 +1,20 @@
input=test/example/nc-wmt11.1k.gz # use '-' for stdin
-output=- # a weights file or stdout
-decoder_config=test/example/cdec.ini # ini for cdec
-# these will be printed on each iteration
+output=weights.gz # a weights file (add .gz for gzip compression) or STDOUT '-'
+decoder_config=test/example/cdec.ini # config for cdec
+# weights for these features will be printed on each iteration
print_weights=Glue WordPenalty LanguageModel LanguageModel_OOV PhraseModel_0 PhraseModel_1 PhraseModel_2 PhraseModel_3 PhraseModel_4 PhraseModel_5 PhraseModel_6 PassThrough
tmp=/tmp
-stop_after=10 # stop iteration after 10 inputs
+stop_after=100 # stop epoch after 10 inputs
# interesting stuff
-epochs=3 # run over input 3 times
-k=200 # use 100best lists
-N=4 # optimize (approx) BLEU4
+epochs=100 # run over input 3 times
+k=100 # use 100best lists
+N=4 # optimize (approx) BLEU4
learning_rate=0.0001 # learning rate
-gamma=0.00001 # use SVM reg
-scorer=stupid_bleu # use stupid BLEU+1 approx.
+gamma=0 # use SVM reg
+scorer=smooth_bleu # use smooth BLEU of (Liang et al. '06)
sample_from=kbest # use kbest lists (as opposed to forest)
-filter=uniq # only uniq entries in kbest
+filter=uniq # only unique entries in kbest (surface form)
pair_sampling=108010 # 10 vs 80 vs 10 and 80 vs 10
-pair_threshold=0 # minimum distance in BLEU
+pair_threshold=0 # minimum distance in BLEU (this will still only use pairs with diff > 0)
select_weights=last # just output last weights