From 867bca3e5fa0cdd63bf032e5859fb5092d9a4ca1 Mon Sep 17 00:00:00 2001
From: Patrick Simianer <p@simianer.de>
Date: Tue, 13 Mar 2012 09:15:46 +0100
Subject: polish

---
 dtrain/test/example/dtrain.ini | 28 ++++++++++++++--------------
 1 file changed, 14 insertions(+), 14 deletions(-)

(limited to 'dtrain/test/example/dtrain.ini')

diff --git a/dtrain/test/example/dtrain.ini b/dtrain/test/example/dtrain.ini
index ed1b7e5f..68173e11 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=w.gz                             # a weights file
-decoder_config=test/example/cdec.ini # a ini for cdec
+output=-                             # a weights file or stdout
+decoder_config=test/example/cdec.ini # ini for cdec
 # these 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_after=10 # stop iteration after 10 inputs 
 
 # interesting stuff
-epochs=1
-k=100
-N=4
-learning_rate=0.0001
-gamma=0.00001
-scorer=stupid_bleu
-sample_from=kbest
-filter=uniq
-pair_sampling=108010
-pair_threshold=0.01
-select_weights=last
+epochs=3                # run over input 3 times
+k=200                   # 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.
+sample_from=kbest       # use kbest lists (as opposed to forest)
+filter=uniq             # only uniq entries in kbest
+pair_sampling=108010    # 10 vs 80 vs 10 and 80 vs 10
+pair_threshold=0        # minimum distance in BLEU
+select_weights=last     # just output last weights
-- 
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