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Commit 4451bc3

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Nico Colic
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prettier layout, more comments, more try / except
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accessor.py

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example_queries.txt

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curl 127.0.0.1:5000/spacy_rest?text=This+is+a+test
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curl -d text="Induction of chromosome banding by trypsin/EDTA for gene mapping by in situ hybridization. We describe an easy and reproducible procedure that utilizes trypsin/EDTA for the induction of chromosome banding in conjunction with in situ hybridization. The high quality banding resolution required for grain localization is obtained on both elongated and contracted chromosomes derived from synchronized or nonsynchronized human lymphocytes or fibroblasts. This procedure can also be useful for gene localization on chromosomes from cancer cells." 127.0.0.1:5000/spacy_rest/
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curl -H "content-type:application/json" -d '{"text":"Induction of chromosome banding by trypsin/EDTA for gene mapping by in situ hybridization."}' 127.0.0.1:5000/spacy_rest/
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curl 127.0.0.1:5000/spacy_rest?text=This+is+a+test

g.py

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stanford/Tagger.class

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stanford/TaggerConsole.class

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stanford/lib/slf4j-api.jar

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stanford/lib/slf4j-simple.jar

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stanford/models/.DS_Store

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## tagger training invoked at Tue Feb 25 01:33:39 PST 2014 with arguments:
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model = english-bidirectional-distsim.tagger
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arch = bidirectional5words,naacl2003unknowns,allwordshapes(-1,1),distsim(/u/nlp/data/pos_tags_are_useless/egw4-reut.512.clusters,-1,1),distsimconjunction(/u/nlp/data/pos_tags_are_useless/egw4-reut.512.clusters,-1,1)
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wordFunction = edu.stanford.nlp.process.AmericanizeFunction
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trainFile = /u/nlp/data/pos-tagger/english/train-wsj-0-18;/u/nlp/data/pos-tagger/english/train-extra-english;/u/nlp/data/pos-tagger/english/train-tech-english
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closedClassTags =
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closedClassTagThreshold = 40
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curWordMinFeatureThresh = 2
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debug = false
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debugPrefix =
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tagSeparator = _
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encoding = UTF-8
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iterations = 100
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lang = english
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learnClosedClassTags = false
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minFeatureThresh = 2
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openClassTags =
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rareWordMinFeatureThresh = 5
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rareWordThresh = 5
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search = owlqn
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sgml = false
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sigmaSquared = 0.5
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regL1 = 0.75
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tagInside =
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tokenize = true
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tokenizerFactory =
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tokenizerOptions =
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verbose = false
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verboseResults = true
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veryCommonWordThresh = 250
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xmlInput =
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outputFile =
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outputFormat = slashTags
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outputFormatOptions =
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nthreads = 1

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