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-rw-r--r--dtrain/test/example/README6
-rw-r--r--dtrain/test/example/cdec.ini24
-rw-r--r--dtrain/test/example/dtrain.ini21
-rw-r--r--dtrain/test/example/nc-wmt11.1k.gzbin0 -> 21185883 bytes
-rw-r--r--dtrain/test/example/nc-wmt11.en.srilm.gzbin0 -> 16017291 bytes
5 files changed, 51 insertions, 0 deletions
diff --git a/dtrain/test/example/README b/dtrain/test/example/README
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+Small example of input format for distributed training.
+Call dtrain from cdec/dtrain/ with ./dtrain -c test/example/dtrain.ini .
+
+For this to work, disable '#define DTRAIN_LOCAL' from dtrain.h
+and recompile.
+
diff --git a/dtrain/test/example/cdec.ini b/dtrain/test/example/cdec.ini
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+formalism=scfg
+add_pass_through_rules=true
+scfg_max_span_limit=15
+intersection_strategy=cube_pruning
+cubepruning_pop_limit=30
+feature_function=WordPenalty
+feature_function=KLanguageModel test/example/nc-wmt11.en.srilm.gz
+# all currently working feature functions for translation:
+# (with those features active that were used in the ACL paper)
+#feature_function=ArityPenalty
+#feature_function=CMR2008ReorderingFeatures
+#feature_function=Dwarf
+#feature_function=InputIndicator
+#feature_function=LexNullJump
+#feature_function=NewJump
+#feature_function=NgramFeatures
+#feature_function=NonLatinCount
+#feature_function=OutputIndicator
+feature_function=RuleIdentityFeatures
+feature_function=RuleNgramFeatures
+feature_function=RuleShape
+#feature_function=SourceSpanSizeFeatures
+#feature_function=SourceWordPenalty
+#feature_function=SpanFeatures
diff --git a/dtrain/test/example/dtrain.ini b/dtrain/test/example/dtrain.ini
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+input=test/example/nc-wmt11.1k.gz # use '-' for STDIN
+output=- # 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=20 # stop epoch after 20 inputs
+
+# interesting stuff
+epochs=3 # run over input 3 times
+k=100 # use 100best lists
+N=4 # optimize (approx) BLEU4
+scorer=stupid_bleu # use 'stupid' BLEU+1
+learning_rate=0.0001 # learning rate
+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 (this will still only use pairs with diff > 0)
+select_weights=VOID # don't output weights
diff --git a/dtrain/test/example/nc-wmt11.1k.gz b/dtrain/test/example/nc-wmt11.1k.gz
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diff --git a/dtrain/test/example/nc-wmt11.en.srilm.gz b/dtrain/test/example/nc-wmt11.en.srilm.gz
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