dtrain: removed old stuff

1 files changed, 0 insertions, 27 deletions

diff --git a/training/dtrain/examples/standard/dtrain.ini b/training/dtrain/examples/standard/dtrain.ini
deleted file mode 100644
index a515db02..00000000
--- a/training/dtrain/examples/standard/dtrain.ini
+++ /dev/null
@@ -1,27 +0,0 @@
-#input=./nc-wmt11.de.gz
-#refs=./nc-wmt11.en.gz
-bitext=./nc-wmt11.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=Glue WordPenalty LanguageModel LanguageModel_OOV PhraseModel_0 PhraseModel_1 PhraseModel_2 PhraseModel_3 PhraseModel_4 PhraseModel_5 PhraseModel_6 PassThrough
-# newer version of the grammar extractor use different feature names: 
-#print_weights= EgivenFCoherent SampleCountF CountEF MaxLexFgivenE MaxLexEgivenF IsSingletonF IsSingletonFE Glue WordPenalty PassThrough LanguageModel LanguageModel_OOV
-stop_after=10 # stop epoch after 10 inputs
-
-# interesting stuff
-epochs=3                 # run over input 3 times
-k=100                    # use 100best lists
-N=4                      # optimize (approx) BLEU4
-scorer=fixed_stupid_bleu # use 'stupid' BLEU+1
-learning_rate=0.1        # learning rate, don't care if gamma=0 (perceptron) and loss_margin=0 (not margin 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            # update if correctly ranked, but within this margin
-repeat=1                 # repeat training on a kbest list 1 times 
-#batch=true              # batch tuning, update after accumulating over all sentences and all kbest lists