├── Conv.lua
├── CsDis.lua
├── PaddingReshape.lua
├── README.md
├── data
    ├── msrvid
    │   ├── MSRVID_dev
    │   ├── MSRVID_train
    │   ├── dev
    │   │   ├── a.parents
    │   │   ├── a.rels
    │   │   ├── a.toks
    │   │   ├── a.txt
    │   │   ├── b.parents
    │   │   ├── b.rels
    │   │   ├── b.toks
    │   │   ├── b.txt
    │   │   ├── id.txt
    │   │   └── sim.txt
    │   ├── train
    │   │   ├── a.parents
    │   │   ├── a.rels
    │   │   ├── a.toks
    │   │   ├── a.txt
    │   │   ├── b.parents
    │   │   ├── b.rels
    │   │   ├── b.toks
    │   │   ├── b.txt
    │   │   ├── id.txt
    │   │   └── sim.txt
    │   ├── vocab-cased.txt
    │   └── vocab.txt
    └── sick
    │   ├── SICK_test_annotated.txt
    │   ├── SICK_train.txt
    │   ├── SICK_trial.txt
    │   ├── combinePredictionScoreswithAnnotationforSicData.pl
    │   ├── dev
    │       ├── a.parents
    │       ├── a.rels
    │       ├── a.toks
    │       ├── a.txt
    │       ├── b.parents
    │       ├── b.rels
    │       ├── b.toks
    │       ├── b.txt
    │       ├── id.txt
    │       └── sim.txt
    │   ├── readme.txt
    │   ├── sick_evaluation.R
    │   ├── test
    │       ├── a.parents
    │       ├── a.rels
    │       ├── a.toks
    │       ├── a.txt
    │       ├── b.parents
    │       ├── b.rels
    │       ├── b.toks
    │       ├── b.txt
    │       ├── id.txt
    │       └── sim.txt
    │   ├── train
    │       ├── a.parents
    │       ├── a.rels
    │       ├── a.toks
    │       ├── a.txt
    │       ├── b.parents
    │       ├── b.rels
    │       ├── b.toks
    │       ├── b.txt
    │       ├── id.txt
    │       └── sim.txt
    │   ├── vocab-cased.txt
    │   └── vocab.txt
├── fetch_and_preprocess.sh
├── models.lua
├── scripts
    ├── build_vocab.py
    ├── convert-wordvecs.lua
    ├── download.py
    └── preprocess-sick.py
├── testDeployTrainedModel.lua
├── trainMSRVID.lua
├── trainSIC.lua
└── util
    ├── Vocab.lua
    └── read_data.lua


/Conv.lua:
--------------------------------------------------------------------------------
  1 | local Conv = torch.class('similarityMeasure.Conv')
  2 | 
  3 | function Conv:__init(config)
  4 |   self.mem_dim       = config.mem_dim       or 150
  5 |   self.learning_rate = config.learning_rate or 0.01
  6 |   self.batch_size    = config.batch_size    or 1 --25
  7 |   self.num_layers    = config.num_layers    or 1
  8 |   self.reg           = config.reg           or 1e-4
  9 |   self.structure     = config.structure     or 'lstm' -- {lstm, bilstm}
 10 |   self.sim_nhidden   = config.sim_nhidden   or 150
 11 |   self.task          = config.task          or 'sic'  -- or 'vid'
 12 | 	
 13 |   -- word embedding
 14 |   self.emb_vecs = config.emb_vecs
 15 |   self.emb_dim = config.emb_vecs:size(2)
 16 | 
 17 |   -- number of similarity rating classes
 18 |   if self.task == 'sic' then
 19 |     self.num_classes = 5
 20 |   elseif self.task == 'vid' then
 21 |     self.num_classes = 6
 22 |   else
 23 |     error("not possible task!")
 24 |   end
 25 | 	
 26 |   -- optimizer configuration
 27 |   self.optim_state = { learningRate = self.learning_rate }
 28 | 
 29 |   -- KL divergence optimization objective
 30 |   self.criterion = nn.DistKLDivCriterion()
 31 |   
 32 |   ----------------------------------------Combination of ConvNets.
 33 |   dofile 'models.lua'
 34 |   print('<model> creating a fresh model')
 35 |   
 36 |   -- Type of model; Size of vocabulary; Number of output classes
 37 |   local modelName = 'deepQueryRankingNgramSimilarityOnevsGroupMaxMinMeanLinearExDGpPoinPercpt'
 38 |   print(modelName)
 39 |   self.ngram = 3
 40 |   self.length = self.emb_dim
 41 |   self.convModel = createModel(modelName, 10000, self.length, self.num_classes, self.ngram)  
 42 |   self.softMaxC = self:ClassifierOOne()
 43 | 
 44 |   ----------------------------------------
 45 |   local modules = nn.Parallel()
 46 |     :add(self.convModel) 
 47 |     :add(self.softMaxC) 
 48 |   self.params, self.grad_params = modules:getParameters()
 49 |   --print(self.params:norm())
 50 |   --print(self.convModel:parameters()[1][1]:norm())
 51 |   --print(self.softMaxC:parameters()[1][1]:norm())
 52 | end
 53 | 
 54 | function Conv:ClassifierOOne()
 55 |   local maxMinMean = 3
 56 |   local separator = (maxMinMean+1)*self.mem_dim
 57 |   local modelQ1 = nn.Sequential()	
 58 |   local ngram = self.ngram
 59 |   local items = (ngram+1)*3  		
 60 |   --local items = (ngram+1) -- no Min and Mean
 61 |   local NumFilter = self.length --300
 62 |   local conceptFNum = 20	
 63 |   inputNum = 2*items*items/3+NumFilter*items*items/3+6*NumFilter+(2+NumFilter)*2*ngram*conceptFNum --PoinPercpt model!
 64 |   modelQ1:add(nn.Linear(inputNum, self.sim_nhidden))
 65 |   modelQ1:add(nn.Tanh())	
 66 |   modelQ1:add(nn.Linear(self.sim_nhidden, self.num_classes))
 67 |   modelQ1:add(nn.LogSoftMax())	
 68 |   return modelQ1
 69 | end
 70 | 
 71 | function Conv:trainCombineOnly(dataset)
 72 |   --local classes = {1,2}
 73 |   --local confusion = optim.ConfusionMatrix(classes)
 74 |   --confusion:zero()
 75 |   train_looss = 0.0
 76 |    
 77 |   local indices = torch.randperm(dataset.size)
 78 |   local zeros = torch.zeros(self.mem_dim)
 79 |   for i = 1, dataset.size, self.batch_size do
 80 |     --if i%10 == 1 then
 81 |     --	    xlua.progress(i, dataset.size)
 82 |     --end
 83 | 
 84 |     local batch_size = 1 --math.min(i + self.batch_size - 1, dataset.size) - i + 1
 85 |     -- get target distributions for batch
 86 |     local targets = torch.zeros(batch_size, self.num_classes)
 87 |     for j = 1, batch_size do
 88 |       local sim  = -0.1
 89 |       if self.task == 'sic' or self.task == 'vid' then
 90 |         sim = dataset.labels[indices[i + j - 1]] * (self.num_classes - 1) + 1
 91 |       elseif self.task == 'others' then
 92 |         sim = dataset.labels[indices[i + j - 1]] + 1 
 93 |       else
 94 | 	error("not possible!")
 95 |       end
 96 |       local ceil, floor = math.ceil(sim), math.floor(sim)
 97 |       if ceil == floor then
 98 |         targets[{j, floor}] = 1
 99 |       else
100 |         targets[{j, floor}] = ceil - sim
101 |         targets[{j, ceil}] = sim - floor
102 |       end--]]
103 |     end
104 |     
105 |     local feval = function(x)
106 |       self.grad_params:zero()
107 |       local loss = 0
108 |       for j = 1, batch_size do
109 |         local idx = indices[i + j - 1]
110 |         local lsent, rsent = dataset.lsents[idx], dataset.rsents[idx]
111 |         local linputs = self.emb_vecs:index(1, lsent:long()):double()
112 |         local rinputs = self.emb_vecs:index(1, rsent:long()):double()
113 |     		
114 |    	local part2 = self.convModel:forward({linputs, rinputs})
115 |    	local output = self.softMaxC:forward(part2)
116 | 
117 |         loss = self.criterion:forward(output, targets[1])
118 |         train_looss = loss + train_looss
119 |         local sim_grad = self.criterion:backward(output, targets[1])
120 | 		local gErrorFromClassifier = self.softMaxC:backward(part2, sim_grad)
121 | 		self.convModel:backward({linputs, rinputs}, gErrorFromClassifier)
122 |       end
123 |       -- regularization
124 |       loss = loss + 0.5 * self.reg * self.params:norm() ^ 2
125 |       self.grad_params:add(self.reg, self.params)
126 |       return loss, self.grad_params
127 |     end
128 |     _, fs  = optim.sgd(feval, self.params, self.optim_state)
129 |     --train_looss = train_looss + fs[#fs]
130 |   end
131 |   print('Loss: ' .. train_looss)
132 | end
133 | 
134 | -- Predict the similarity of a sentence pair.
135 | function Conv:predictCombination(lsent, rsent)
136 |   local linputs = self.emb_vecs:index(1, lsent:long()):double()
137 |   local rinputs = self.emb_vecs:index(1, rsent:long()):double()
138 | 
139 |   local part2 = self.convModel:forward({linputs, rinputs})
140 |   local output = self.softMaxC:forward(part2)
141 |   local val = -1.0
142 |   if self.task == 'sic' then
143 |     val = torch.range(1, 5, 1):dot(output:exp())
144 |   elseif self.task == 'vid' then
145 |     val = torch.range(0, 5, 1):dot(output:exp())
146 |   else
147 |     error("not possible task")
148 |   end
149 |   return val
150 | end
151 | 
152 | -- Produce similarity predictions for each sentence pair in the dataset.
153 | function Conv:predict_dataset(dataset)
154 |   local predictions = torch.Tensor(dataset.size)
155 |   for i = 1, dataset.size do
156 |     local lsent, rsent = dataset.lsents[i], dataset.rsents[i]
157 |     predictions[i] = self:predictCombination(lsent, rsent)
158 |   end
159 |   return predictions
160 | end
161 | 
162 | function Conv:print_config()
163 |   local num_params = self.params:nElement()
164 | 
165 |   print('num params: ' .. num_params)
166 |   print('word vector dim: ' .. self.emb_dim)
167 |   print('LSTM memory dim: ' .. self.mem_dim)
168 |   print('regularization strength: ' .. self.reg)
169 |   print('minibatch size: ' .. self.batch_size)
170 |   print('learning rate: ' .. self.learning_rate)
171 |   print('LSTM structure: ' .. self.structure)
172 |   print('LSTM layers: ' .. self.num_layers)
173 |   print('sim module hidden dim: ' .. self.sim_nhidden)
174 | end
175 | 
176 | function Conv:save(path)
177 |   local config = {
178 |     batch_size    = self.batch_size,
179 |     emb_vecs      = self.emb_vecs:float(),
180 |     learning_rate = self.learning_rate,
181 |     num_layers    = self.num_layers,
182 |     mem_dim       = self.mem_dim,
183 |     sim_nhidden   = self.sim_nhidden,
184 |     reg           = self.reg,
185 |     structure     = self.structure,
186 |   }
187 | 
188 |   torch.save(path, {
189 |     params = self.params,
190 |     config = config,
191 |   })
192 | end
193 | 


--------------------------------------------------------------------------------
/CsDis.lua:
--------------------------------------------------------------------------------
 1 | --From original cosine distance codes from nn package before modifications, this is backward compatible.
 2 | 
 3 | local CsDis, parent = torch.class('nn.CsDis', 'nn.Module')
 4 | 
 5 | function CsDis:__init()
 6 |    parent.__init(self)
 7 |    self.gradInput = {torch.Tensor(), torch.Tensor()}
 8 |    self.output=torch.Tensor(1)
 9 | end 
10 |  
11 | function CsDis:updateOutput(input)
12 |    local input1, input2 = input[1], input[2]
13 |    self.w1 = input1:dot(input2)
14 |    self.w22 = input1:dot(input1)
15 |    self.w2 = math.sqrt(self.w22)
16 |    self.w32 = input2:dot(input2)
17 |    self.w3 = math.sqrt(self.w32)
18 |    self.output[1] = self.w1/self.w2/self.w3
19 |    return self.output
20 | end
21 | 
22 | function CsDis:updateGradInput(input, gradOutput)
23 |    local v1  = input[1]
24 |    local v2  = input[2]
25 |    local gw1 = input[1].new()
26 |    local gw2 = input[2].new()
27 |    gw1:resizeAs(v1) 
28 |    gw2:resizeAs(v1)
29 | 
30 |    gw1:zero()
31 |    gw1:add(1/(self.w2*self.w3), v2)
32 |    gw1:add(-self.w1/(self.w22*self.w2*self.w3), v1)
33 |    
34 |    gw2:zero()
35 |    gw2:add(1/(self.w2*self.w3), v1)
36 |    gw2:add(-self.w1/(self.w32*self.w2*self.w3), v2)
37 | 
38 |    gw1:mul(gradOutput[1])
39 |    gw2:mul(gradOutput[1])
40 |    self.gradInput = {gw1, gw2}
41 |    return self.gradInput
42 | end


--------------------------------------------------------------------------------
/PaddingReshape.lua:
--------------------------------------------------------------------------------
 1 | local PaddingReshape, parent = torch.class('nn.PaddingReshape', 'nn.Module')
 2 | 
 3 | function PaddingReshape:__init(...)
 4 |    parent.__init(self)
 5 |    local arg = {...}
 6 | 
 7 |    self.size = torch.LongStorage()
 8 |    self.batchsize = torch.LongStorage()
 9 |    if torch.type(arg[#arg]) == 'boolean' then
10 |       self.batchMode = arg[#arg]
11 |       table.remove(arg, #arg)
12 |    end
13 |    local n = #arg
14 |    if n == 1 and torch.typename(arg[1]) == 'torch.LongStorage' then
15 |       self.size:resize(#arg[1]):copy(arg[1])
16 |    else
17 |       self.size:resize(n) 
18 |       for i=1,n do --modifed index
19 |          self.size[i] = arg[i] --modified shift
20 |       end
21 |    end
22 | 
23 |    self.nelement = 1
24 |    self.batchsize:resize(#self.size+1)
25 |    for i=1,#self.size do
26 |       self.nelement = self.nelement * self.size[i]
27 |       self.batchsize[i+1] = self.size[i]
28 |    end
29 |    
30 |    -- only used for non-contiguous input or gradOutput
31 |    self._input = torch.Tensor()
32 |    self._gradOutput = torch.Tensor()
33 | end
34 | 
35 | function PaddingReshape:updateOutput(input)
36 |    if not input:isContiguous() then
37 |       self._input:resizeAs(input)
38 |       self._input:copy(input)
39 |       input = self._input
40 |    end
41 |    
42 |    argsYoshi = torch.LongStorage()
43 |    local nsi = #input:size() --modified
44 |    argsYoshi:resize(nsi+1) 
45 |    argsYoshi[1] = 1
46 |    for i=2,nsi+1 do --modifed index
47 |    	argsYoshi[i] = input:size()[i-1] --modified shift
48 |    end
49 |    self.batchMode = false
50 |       
51 |    if (self.batchMode == false) or (
52 |          (self.batchMode == nil) and 
53 |          (input:nElement() == self.nelement and input:size(1) ~= 1)
54 |       ) then
55 |       self.output:view(input, argsYoshi) --modified
56 |    else
57 |       self.batchsize[1] = input:size(1)
58 |       self.output:view(input, self.batchsize)
59 |    end
60 |    return self.output
61 | end
62 | 
63 | function PaddingReshape:updateGradInput(input, gradOutput)
64 |    if not gradOutput:isContiguous() then
65 |       self._gradOutput:resizeAs(gradOutput)
66 |       self._gradOutput:copy(gradOutput)
67 |       gradOutput = self._gradOutput
68 |    end
69 |    
70 |    self.gradInput:viewAs(gradOutput, input)
71 |    return self.gradInput
72 | end
73 | 
74 | 
75 | function PaddingReshape:__tostring__()
76 |   return torch.type(self) .. '(Pad ' ..
77 |       table.concat(self.size:totable(), 'x') .. ')'
78 | end


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # Multi-Perspective Convolutional Neural Networks for Modeling Textual Similarity
 2 | 
 3 | **NOTE: This repo contains code for the original Torch implementation from the EMNLP 2015 paper. The code is not being maintained anymore and has been superseded by a PyTorch reimplementation in [Castor](https://github.com/castorini/Castor). This repo exists solely for archival purposes.**
 4 | 
 5 | This repo contains the Torch implementation of multi-perspective convolutional neural networks for modeling textual similarity, described in the following paper:
 6 | 
 7 | + Hua He, Kevin Gimpel, and Jimmy Lin. [Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks.](http://aclweb.org/anthology/D/D15/D15-1181.pdf) *Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP 2015)*, pages 1576-1586.
 8 | 
 9 | This model does not require external resources such as WordNet or parsers, does not use sparse features, and achieves good accuracy on standard public datasets.
10 | 
11 | Installation and Dependencies
12 | ------------
13 | 
14 | - Please install Torch deep learning library. We recommend this local installation which includes all required packages our tool needs, simply follow the instructions here:
15 | https://github.com/torch/distro
16 | 
17 | - Currently our tool only runs on CPUs, therefore it is recommended to use INTEL MKL library (or at least OpenBLAS lib) so Torch can run much faster on CPUs. 
18 | 
19 | - Our tool then requires Glove embeddings by Stanford. Please run fetech_and_preprocess.sh for downloading and preprocessing this data set (around 3 GBs).
20 | 
21 | 
22 | Running
23 | ------------
24 | 
25 | - Command to run (training, tuning and testing all included): 
26 | - ``th trainSIC.lua`` or ``th trainMSRVID.lua``
27 | 
28 | The tool will output pearson scores and also write the predicted similarity scores given each pair of sentences from test data into predictions directory.
29 | 
30 | Adaption to New Dataset
31 | ------------
32 | To run our model on your own dataset, first you need to build the dataset following below format and put it under data folder:
33 | 
34 | - a.toks: sentence A, each sentence per line.
35 | - b.toks: sentence B, each sentence per line.
36 | - id.txt: sentence pair ID
37 | - sim.txt: semantic relatedness gold label, can be in any scale. For binary classification, the set of labels will be {0, 1}.
38 | 
39 | Then build vocabulary for your dataset which writes the vocab-cased.txt into your data folder:
40 | ```
41 | $ python build_vocab.py
42 | ```
43 | The last thing is to change the training and model code slightly to process your dataset:
44 | - change util/read_data.lua to handle your data.
45 | - create a new piece of training code following trainSIC.lua to read in your dataset.
46 | - change Conv.lua in Line 89-102 and 142-148 to handle your own task
47 | - more details can refer to issue https://github.com/castorini/MP-CNN-Torch/issues/6
48 | 
49 | Then you should be able to run your training code.
50 | 
51 | Trained Model
52 | -------------
53 | We also porvide a model which is already trained on STS dataset. So it is easier if you just want to use the model and do not want to re-train the whole thing. 
54 | 
55 | The tarined model download link is [HERE](https://drive.google.com/file/d/0B-lu_eEMkpVxYVdPMldJX3JDVjg/view?usp=sharing). Model file size is 500MB. To use the trained model, then simply use codes below:
56 | ```
57 | modelTrained = torch.load("download_local_location/modelSTS.trained.th", 'ascii')
58 | modelTrained.convModel:evaluate()
59 | modelTrained.softMaxC:evaluate()
60 | local linputs = torch.zeros(rigth_sentence_length, emd_dimension)
61 | linpus = XassignEmbeddingValuesX
62 | local rinputs = torch.zeros(left_sentence_length, emd_dimension)
63 | rinpus = XassignEmbeddingValuesX
64 | 
65 | local part2 = modelTrained.convModel:forward({linputs, rinputs})
66 | local output = modelTrained.softMaxC:forward(part2)
67 | local val = torch.range(0, 5, 1):dot(output:exp()) 
68 | return val/5
69 | ```
70 | The ouput variable 'val' contains a similarity score between [0,1]. The input linputs1/rinputs are torch tensors and you need to fill in the word embedding values for both. 
71 | 
72 | Example Deployment Script with Our Trained Model
73 | -------------
74 | We provide one example file for deployment: testDeployTrainedModel.lua. So it is easier for you to directly use our model. Run:
75 | ```
76 | $ th testDeployTrainedModel.lua
77 | ```
78 | This deployment file will use the trained model (assume you have downloaded the trained model from the above link), and it will generate scores given all test sentences of sick dataset. Please note the trained model is not trained on SICK data.
79 | 
80 | 
81 | Ackowledgement
82 | -------------
83 | We thank Kai Sheng Tai for providing the preprocessing codes. We also thank the public data providers and Torch developers. Thanks.
84 | 


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/data/msrvid/dev/a.parents:
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 43 | 2 4 4 0 4 7 5 4
 44 | 3 3 0 3 6 4 4 9 7 3
 45 | 2 4 4 0 7 7 4 4 4
 46 | 2 3 0 5 7 5 3 3
 47 | 3 3 5 5 0 7 5 5
 48 | 2 3 0 6 6 3 3
 49 | 2 4 4 0 6 4 4
 50 | 2 4 4 0 6 4 4
 51 | 2 4 4 0 4 4 6 9 7 4
 52 | 2 4 4 0 4 4
 53 | 2 4 4 0 6 4 4
 54 | 2 4 4 0 4 4 8 6 4
 55 | 2 4 4 0 4 7 5 4
 56 | 2 4 4 0 6 4 4
 57 | 2 4 4 0 6 4 4
 58 | 2 4 4 0 6 4 4
 59 | 2 4 4 0 4 4
 60 | 2 5 5 5 0 5
 61 | 2 3 0 3 6 4 3
 62 | 2 4 4 0 6 4 4
 63 | 2 4 4 0 4 7 5 4
 64 | 2 4 4 0 6 4 4
 65 | 2 4 4 0 6 4 4
 66 | 2 4 4 0 6 4 4
 67 | 2 4 4 0 6 4 4
 68 | 2 3 0 5 3 3
 69 | 2 4 4 0 6 4 4
 70 | 2 4 4 0 6 4 4
 71 | 2 4 4 0 4 4 4
 72 | 2 4 4 0 6 4 4
 73 | 2 4 4 0 6 4 4
 74 | 2 4 4 0 6 4 4
 75 | 2 4 4 0 4 4
 76 | 2 4 4 0 6 4 4
 77 | 2 4 4 0 6 4 4
 78 | 2 4 4 0 7 7 4 4
 79 | 2 4 4 0 6 4 4
 80 | 3 3 0 3 4 3
 81 | 2 4 4 0 4 4 4
 82 | 2 4 4 0 6 4 4
 83 | 2 4 4 0 6 4 4
 84 | 2 4 4 0 7 7 4 4
 85 | 2 3 0 5 3 3
 86 | 2 4 4 0 6 4 4
 87 | 2 4 4 0 6 4 4
 88 | 2 4 4 0 6 4 4
 89 | 2 4 4 0 4
 90 | 2 3 0 5 3 3 8 6 3
 91 | 3 3 4 0 4 8 8 5 4
 92 | 2 5 5 5 0 5
 93 | 2 4 4 0 6 4 4
 94 | 2 4 4 0 6 4 4
 95 | 2 4 4 0 6 4 4
 96 | 2 4 4 0 6 4 4
 97 | 2 4 4 0 6 4 4
 98 | 2 4 4 0 6 4 4
 99 | 2 4 4 0 4 8 8 5 4
100 | 2 3 0 3 4 5 3
101 | 2 3 0 5 3 3
102 | 2 0 2 5 2 2
103 | 2 4 4 0 4 7 5 4 10 8 4
104 | 2 4 4 0 4
105 | 2 4 4 0 4
106 | 2 4 4 0 6 4 4
107 | 2 4 4 0 4 4 8 6 4
108 | 2 4 4 0 4 7 4 4
109 | 2 4 4 0 4 4
110 | 2 4 4 0 6 4 4
111 | 2 4 4 0 6 4 4
112 | 2 4 4 0 6 4 4
113 | 3 3 5 5 0 7 5 5
114 | 2 4 4 0 6 4 4
115 | 2 3 0 6 6 3 3
116 | 2 4 4 0 4 5 4
117 | 2 3 0 5 3 3
118 | 2 4 4 0 6 4 4
119 | 2 4 4 0 6 4 6 7 4
120 | 2 4 4 0 4 4 8 6 4
121 | 2 3 0 5 3 3
122 | 2 3 0 5 3 3
123 | 2 4 4 0 4 4
124 | 2 4 4 0 4 4
125 | 2 4 4 0 6 4 4
126 | 2 4 4 0 4 4
127 | 2 6 2 2 6 0 6
128 | 2 4 4 0 6 4 4
129 | 2 3 0 3 6 3 3
130 | 2 4 4 0 6 4 4
131 | 2 4 4 0 4 4
132 | 2 4 4 0 4 7 5 4
133 | 2 4 4 0 4 4 8 6 4
134 | 2 4 4 0 4 7 5 4
135 | 2 4 4 0 6 4 4
136 | 2 4 4 0 4
137 | 2 4 4 0 4
138 | 2 3 0 5 3 3
139 | 2 4 4 0 4
140 | 2 4 4 0 6 4 4
141 | 2 4 4 0 4 4
142 | 2 4 4 0 6 4 4
143 | 2 4 4 0 6 4 4
144 | 2 4 4 0 4 4
145 | 2 4 4 0 4 4
146 | 3 3 0 3 3
147 | 2 4 4 0 6 4 4
148 | 2 4 4 0 6 4 4
149 | 2 4 4 0 4 4
150 | 2 4 4 0 7 7 4 4
151 | 2 4 4 0 4 4
152 | 2 4 4 0 6 4 4
153 | 2 4 4 0 6 4 4
154 | 2 4 4 0 4 4
155 | 2 4 4 0 6 4 4
156 | 2 4 4 0 7 7 4 4
157 | 2 4 4 0 4 7 5 4
158 | 2 3 0 3 7 7 3 3
159 | 2 4 4 0 4 4 6 4
160 | 3 3 5 5 0 8 8 5 5
161 | 3 3 0 5 3 5 8 6 3
162 | 2 4 4 0 6 4 4
163 | 2 4 4 0 6 4 4
164 | 2 3 0 3 6 4 3
165 | 2 4 4 0 4 7 5 4
166 | 2 4 4 0 4 4
167 | 2 3 0 5 3 3
168 | 2 4 4 0 4 7 5 4
169 | 3 3 5 5 0 5 5
170 | 2 4 4 0 6 4 4
171 | 2 6 2 2 6 0 6
172 | 2 4 4 0 6 4 4
173 | 2 4 4 0 4 7 4 4
174 | 2 4 4 0 4 4
175 | 2 4 4 0 6 4 4
176 | 2 4 4 0 4 7 5 4
177 | 2 4 4 0 4 7 5 4
178 | 2 4 4 0 4
179 | 2 4 4 0 4
180 | 2 4 4 0 6 4 4
181 | 2 4 4 0 6 4 4
182 | 2 4 4 0 4
183 | 2 4 4 0 4
184 | 2 4 4 0 4 4 8 6 4
185 | 2 4 4 0 4 5 5 7 4
186 | 2 3 0 3 6 3 6 9 7 3
187 | 2 4 4 0 4 4 8 6 4
188 | 2 3 0 3 6 4 3
189 | 2 4 4 0 6 4 4
190 | 2 4 4 0 4 4
191 | 2 3 0 3 3 7 5 3
192 | 2 4 4 0 6 4 4
193 | 2 3 0 3 6 3 3
194 | 2 4 4 0 6 4 4
195 | 2 6 2 3 6 0 6 9 7 6
196 | 2 6 2 2 6 0 6 9 7 6 14 14 14 10 6
197 | 3 3 5 5 0 5 8 6 5
198 | 2 4 4 0 4 4
199 | 2 4 4 0 4 7 5 4
200 | 2 4 4 0 4 8 8 5 4
201 | 2 4 4 0 4
202 | 2 4 4 0 6 4 4
203 | 2 4 4 0 4 4 8 6 4
204 | 2 4 4 0 7 7 4 4
205 | 3 3 0 5 3 3
206 | 2 4 4 0 4
207 | 2 3 0 5 3 3
208 | 2 3 0 5 3 3
209 | 2 4 4 0 6 4 4
210 | 2 4 4 0 4 4 8 6 4
211 | 4 4 4 0 4
212 | 2 11 2 3 6 4 3 9 7 11 0 13 11 11
213 | 2 4 4 0 4 4
214 | 2 4 4 0 4 8 8 5 4
215 | 2 3 0 3 4 3
216 | 2 4 4 0 4 7 5 4
217 | 2 8 2 3 6 4 8 0 10 8 8
218 | 2 4 4 0 6 4 4
219 | 2 4 4 0 4 4
220 | 2 4 4 0 6 4 4
221 | 2 4 4 0 4 4
222 | 2 4 4 0 6 4 4 9 7 4
223 | 2 4 4 0 6 4 4
224 | 3 3 0 5 3 3
225 | 2 4 4 0 4 4 8 10 8 6 4
226 | 3 3 5 5 0 5 8 5 5
227 | 2 3 0 3 3 8 8 5 3
228 | 2 4 4 0 6 4 4
229 | 3 3 5 5 0 5
230 | 2 4 4 0 4 4
231 | 2 4 4 0 6 4 4
232 | 2 6 2 2 6 0 6 6 8 6 13 13 10 6
233 | 2 4 4 0 4 4 8 6 4
234 | 2 3 0 3 7 7 3 3
235 | 2 4 4 0 4
236 | 2 3 0 3 4 7 5 3
237 | 2 4 4 0 6 4 4
238 | 2 4 4 0 4
239 | 2 4 4 0 6 4 4
240 | 2 4 4 0 6 4 4
241 | 2 4 4 0 4 4 6 4
242 | 2 4 4 0 4 4 6 4
243 | 2 4 4 0 6 4 6 7 4
244 | 2 6 2 3 6 0 6
245 | 2 4 4 0 4 4
246 | 2 4 4 0 4 4
247 | 2 4 4 0 4 4
248 | 2 4 4 0 6 4 4
249 | 2 4 4 0 4 4
250 | 2 4 4 0 6 4 4
251 | 2 3 0 5 3 3 8 6 3
252 | 2 4 4 0 4 4 8 6 4
253 | 2 4 4 0 6 4 4
254 | 3 3 0 3 6 4 6 7 3
255 | 2 4 4 0 4 4
256 | 2 4 4 0 4 5 4
257 | 2 4 4 0 4 4 4
258 | 2 4 4 0 6 4 4
259 | 2 4 4 0 6 4 4
260 | 2 4 4 0 6 4 4
261 | 2 4 4 0 4
262 | 2 3 0 5 3 3
263 | 2 4 4 0 6 4 4
264 | 2 4 4 0 4 7 5 4
265 | 2 4 4 0 4 5 4
266 | 2 4 4 0 6 4 4
267 | 2 4 4 0 4 4 8 6 4
268 | 2 4 4 0 4 7 4 4
269 | 2 4 4 0 4 7 4 4
270 | 3 3 0 3 3 5 3
271 | 2 4 4 0 4 4 6 4
272 | 3 3 5 5 0 7 5 5
273 | 2 4 4 0 4 7 5 4
274 | 2 4 4 0 6 4 6 7 4
275 | 2 4 4 0 4 7 5 4
276 | 2 7 2 5 2 7 0 7 10 8 7
277 | 2 6 2 2 6 0 6 9 7 6
278 | 2 4 4 0 7 7 4 4
279 | 3 3 5 5 0 7 5 9 7 5
280 | 2 4 4 0 6 4 8 6 4
281 | 2 4 4 0 6 4 4
282 | 2 4 4 0 6 4 4
283 | 3 3 5 5 0 7 5 5
284 | 2 4 4 0 6 4 4
285 | 3 3 5 5 0 5 9 9 6 5
286 | 4 4 4 5 0 7 5 5
287 | 3 3 0 5 3 3 8 6 8 9 3
288 | 2 4 4 0 6 4 4
289 | 3 3 0 3 6 4 3
290 | 2 4 4 0 6 4 4 9 7 4
291 | 2 0 2 5 3 7 5 7 10 7 2
292 | 2 4 4 0 4 4
293 | 2 6 2 3 6 0 8 6 6
294 | 2 4 4 0 6 4 4
295 | 3 3 0 5 3 3
296 | 3 3 0 5 3 3
297 | 3 3 0 3 3
298 | 2 4 4 0 4 8 8 5 4
299 | 2 4 4 0 4 7 4 4
300 | 2 4 4 0 4 7 5 4
301 | 2 4 4 0 4 4
302 | 3 3 5 5 0 5 8 5 5
303 | 2 4 4 0 4 4 8 6 4
304 | 2 4 4 0 4 4 8 6 8 11 9 4
305 | 2 4 4 0 4 5 4
306 | 3 3 0 5 3 5 6 3 10 8 3
307 | 2 3 0 5 3 3 8 6 3
308 | 2 4 4 0 6 4 4
309 | 2 4 4 0 6 4 4
310 | 2 4 4 0 6 4 4
311 | 2 4 4 0 6 4 4
312 | 2 4 4 0 6 4 4
313 | 2 4 4 0 4 4
314 | 2 4 4 0 6 4 4
315 | 2 4 4 0 4 5 4
316 | 2 4 4 0 4 4 8 6 4
317 | 2 4 4 0 4 4
318 | 2 4 4 0 6 4 4
319 | 3 3 0 3 6 4 3
320 | 2 4 4 0 6 4 4
321 | 3 3 0 5 3 3
322 | 2 4 4 0 4 7 5 4
323 | 2 4 4 0 4
324 | 3 3 0 5 3 3
325 | 2 4 4 0 4 7 5 4
326 | 2 4 4 0 4 4
327 | 2 4 4 0 4
328 | 2 3 0 6 6 3 3
329 | 2 4 4 0 6 4 4
330 | 2 4 4 0 4 4
331 | 2 4 4 0 4 4
332 | 2 4 4 0 4 4 8 6 4
333 | 2 4 4 0 4 7 4 4
334 | 2 4 4 0 6 4 4
335 | 3 3 0 3 3
336 | 2 4 4 0 4 4
337 | 2 4 4 0 4
338 | 2 4 4 0 4 4
339 | 2 4 4 0 6 4 4
340 | 2 4 4 0 4 7 4 4
341 | 2 4 4 0 6 4 4
342 | 2 4 4 0 6 4 4
343 | 2 4 4 0 6 4 4
344 | 2 3 0 5 3 3
345 | 2 4 4 0 6 4 4
346 | 2 4 4 0 4 4
347 | 2 4 4 0 6 4 4
348 | 2 3 0 3 7 7 4 3
349 | 2 0 2 5 3 2
350 | 2 4 4 0 6 4 4
351 | 2 4 4 0 6 4 4
352 | 2 4 4 0 4 4
353 | 2 4 4 0 6 4 4
354 | 2 4 4 0 4 4 8 6 4
355 | 2 4 4 0 6 4 4
356 | 2 4 4 0 6 4 4
357 | 2 4 4 0 6 4 4
358 | 2 5 5 5 0 5 8 6 5
359 | 3 3 0 3 3
360 | 3 3 0 3
361 | 2 4 4 0 4 4
362 | 2 4 4 0 6 4 4
363 | 2 4 4 0 6 4 4
364 | 2 4 4 0 4 7 4 4
365 | 2 4 4 0 4 4 8 6 4
366 | 2 4 4 0 6 4 4
367 | 2 4 4 0 4 5 9 9 6 4
368 | 3 3 0 5 6 3 3
369 | 2 0 4 2 4 7 5 2
370 | 2 3 0 5 3 3
371 | 2 4 4 0 4 4 8 6 4
372 | 2 4 4 0 6 4 4
373 | 2 4 4 0 4 4
374 | 2 4 4 0 6 4 4
375 | 2 4 4 0 6 4 4
376 | 2 4 4 0 4
377 | 2 4 4 0 4 7 5 4
378 | 2 5 2 3 0 5 8 5 5
379 | 2 4 4 0 6 4 4
380 | 2 4 4 0 4 7 5 4
381 | 2 4 4 0 4 4 6 4
382 | 2 4 4 0 4 4 8 6 4
383 | 2 4 4 0 6 4 4
384 | 2 4 4 0 4 4
385 | 3 3 5 5 0 5 8 6 5
386 | 2 4 4 0 4 7 5 4
387 | 2 4 4 0 6 4 4
388 | 2 4 4 0 6 4 4
389 | 2 4 4 0 4
390 | 2 4 4 0 4 4
391 | 2 4 4 0 4 5 4
392 | 2 4 4 0 6 4 4
393 | 2 4 4 0 6 4 4
394 | 2 3 0 3 7 7 4 3
395 | 2 4 4 0 6 4 4
396 | 2 3 0 3 7 7 4 3
397 | 2 4 4 0 4 7 4 4
398 | 3 3 5 5 0 5 9 9 5 5
399 | 2 4 4 0 4 7 4 4
400 | 2 4 4 0 6 4 4 9 7 4
401 | 2 4 4 0 4 4
402 | 2 4 4 0 4 7 5 4
403 | 2 4 4 0 4 4
404 | 2 4 4 0 6 4 4
405 | 2 4 4 0 4 8 8 5 4
406 | 2 3 0 3 6 4 3
407 | 2 4 4 0 4 4 8 6 4
408 | 2 4 4 0 6 4 4 9 7 4
409 | 2 4 4 0 6 4 4
410 | 2 4 4 0 7 7 4 4
411 | 2 4 4 0 6 4 4
412 | 2 4 4 0 6 4 4
413 | 2 4 4 0 4 4
414 | 2 4 4 0 4 7 4 4
415 | 2 4 4 0 4 7 5 4
416 | 2 4 4 0 6 4 4 9 7 4
417 | 2 3 0 3 7 7 3 3
418 | 2 5 5 5 0 5
419 | 2 4 4 0 6 4 4
420 | 2 6 2 5 3 0 8 6 6
421 | 2 3 0 3 3 7 5 3
422 | 2 4 4 0 6 4 4
423 | 2 4 4 0 6 4 4
424 | 2 4 4 0 6 4 4
425 | 2 4 4 0 6 4 4
426 | 2 4 4 0 6 4 4
427 | 3 3 0 5 3 5 8 6 3
428 | 3 3 0 3
429 | 4 4 4 0 4
430 | 2 4 4 0 4 5 5 4 4
431 | 2 0 2 2 4 2
432 | 2 4 4 0 4 7 4 4
433 | 2 4 4 0 4 7 5 4
434 | 3 3 0 5 3 5 8 6 8 9 3
435 | 2 4 4 0 4 7 5 4
436 | 2 4 4 0 4 7 4 4
437 | 2 3 0 3 6 4 3
438 | 2 4 4 0 4 4
439 | 2 4 4 0 6 4 4
440 | 2 4 4 0 6 4 4
441 | 2 4 4 0 4 4 4
442 | 3 3 0 3
443 | 2 4 4 0 4 4
444 | 2 4 4 0 6 4 4
445 | 3 3 0 5 3 3
446 | 2 4 4 0 4 4 4
447 | 2 3 0 5 3 3
448 | 2 4 4 0 6 4 6 7 4 9 4
449 | 2 4 4 0 4 7 5 7 10 8 4
450 | 2 4 4 0 6 4 4 9 7 4 12 10 4
451 | 2 4 4 0 4
452 | 3 3 0 3 6 4 3 9 7 3
453 | 2 3 0 3 6 4 3
454 | 3 3 4 0 4 7 5 4
455 | 2 3 0 3 6 3 3
456 | 2 4 4 0 4 4 8 6 4
457 | 2 4 4 0 6 4 4
458 | 2 4 4 0 7 7 4 4
459 | 2 3 0 5 3 3 8 6 3
460 | 2 4 4 0 4
461 | 2 0 2 6 6 3
462 | 2 4 4 0 7 7 4 4
463 | 3 3 0 3 3 5 3
464 | 2 6 2 3 6 0 6
465 | 2 4 4 0 6 4 4
466 | 3 3 0 5 3 5 6 9 7 3
467 | 2 4 4 0 4 7 5 4
468 | 2 4 4 0 6 4 4
469 | 2 4 4 0 6 4 4
470 | 2 4 4 0 6 4 4
471 | 2 4 4 0 6 4 4
472 | 2 4 4 0 6 4 4 9 7 4
473 | 2 4 4 0 4 7 5 4
474 | 2 4 4 0 4 7 5 4
475 | 2 4 4 0 6 4 4
476 | 2 4 4 0 7 7 4 4
477 | 2 3 0 3 3 5 3 9 7 3
478 | 3 3 5 5 0 5 6 5
479 | 3 3 5 5 0 8 8 5 5
480 | 2 4 4 0 4 4 6 7 10 8 4
481 | 2 4 4 0 6 4 4
482 | 2 3 0 5 3 5 6 3
483 | 2 3 0 5 3 3
484 | 2 4 4 0 4 4
485 | 2 4 4 0 6 4 4
486 | 2 4 4 0 4 8 8 5 4
487 | 3 3 0 3 6 4 3
488 | 2 4 4 0 6 4 4
489 | 2 3 0 6 6 3 3 9 7 3
490 | 2 4 4 0 4
491 | 2 4 4 0 4
492 | 2 3 0 5 3 5 8 6 3
493 | 5 5 5 5 7 7 0 7 11 11 8 7
494 | 2 4 4 0 4 4
495 | 2 3 0 3 7 7 4 3
496 | 2 4 4 0 6 4 4
497 | 2 4 4 0 6 4 4 7 4
498 | 2 4 4 0 4 4
499 | 2 4 4 0 4 4
500 | 2 4 4 0 6 4 4
501 | 2 4 4 0 4 4
502 | 2 4 4 0 4 4
503 | 2 4 4 0 6 4 4
504 | 2 3 0 3 6 4 6 6 3
505 | 2 4 4 0 4 8 8 4 4
506 | 2 4 4 0 4 7 5 4
507 | 2 3 0 3 6 4 3
508 | 2 3 0 5 3 3
509 | 2 4 4 0 6 4 4
510 | 2 3 0 5 3 3
511 | 2 3 0 3
512 | 2 4 4 0 4 7 5 7 10 8 4 11 4
513 | 2 4 4 0 4 4
514 | 2 4 4 0 6 4 4
515 | 2 3 0 3 4 3
516 | 2 4 4 0 4 4 8 6 4
517 | 2 4 4 0 4 7 5 4
518 | 2 4 4 0 6 4 4
519 | 3 3 5 5 0 5 5 9 7 9 12 10 5
520 | 2 4 4 0 4 7 5 4
521 | 2 0 2 3 3 8 8 5 2
522 | 2 4 4 0 6 4 4
523 | 2 4 4 0 4
524 | 2 4 4 0 4 5 8 6 4
525 | 2 4 4 0 4 4
526 | 2 3 0 3 3 7 5 3
527 | 2 4 4 0 6 4 4
528 | 2 4 4 0 6 4 4
529 | 2 4 4 0 7 7 4 4
530 | 2 4 4 0 4 4 6 4
531 | 2 6 6 6 6 0 6
532 | 2 4 4 0 4 7 4 4
533 | 2 3 0 6 6 3 3 9 7 3
534 | 2 3 0 5 3 3
535 | 2 4 4 0 6 4 6 9 6 4
536 | 2 4 4 0 4 7 5 4
537 | 2 4 4 0 6 4 4
538 | 2 4 4 0 4 7 4 4
539 | 2 4 4 0 4 4 4
540 | 2 4 4 0 4 5 4
541 | 2 4 4 0 4 5 6 7 8 9 4
542 | 2 4 4 0 6 4 4
543 | 2 4 4 0 6 4 4
544 | 2 4 4 0 6 4 4 9 7 4
545 | 2 3 0 3 6 4 3
546 | 2 4 4 0 4 7 5 4
547 | 2 4 4 0 4 4
548 | 2 4 4 0 4 4
549 | 2 4 4 0 4 4
550 | 2 4 4 0 6 4 4
551 | 2 4 4 0 6 4 4
552 | 2 4 4 0 4 4 8 6 4
553 | 2 3 0 3 3 8 8 5 3
554 | 2 0 2 5 6 2 6 2
555 | 2 4 4 0 7 7 4 4
556 | 2 4 4 0 4 5 8 6 4
557 | 3 3 0 3 6 7 4 3
558 | 2 3 0 3 7 7 4 3
559 | 2 5 5 5 0 5
560 | 2 4 4 0 6 4 4
561 | 2 4 4 0 6 4 4
562 | 2 4 4 0 6 4 4
563 | 2 4 4 0 6 4 4
564 | 3 3 0 3 3
565 | 2 4 4 0 4
566 | 2 4 4 0 6 4 6 6 4
567 | 2 3 0 5 3 3 8 6 3
568 | 2 4 4 0 4 4 4 7 4
569 | 2 4 4 0 6 4 8 4 4
570 | 2 3 0 6 6 3 3
571 | 2 4 4 0 4
572 | 2 4 4 0 4 7 5 7 8 4
573 | 2 4 4 0 4 4 8 6 4
574 | 2 3 0 3 6 4 3 3 8 8 12 10 3
575 | 2 4 4 0 4 4
576 | 2 4 4 0 7 7 4 4
577 | 2 4 4 0 4 7 5 7 10 8 4
578 | 2 6 2 2 6 0 6
579 | 2 4 4 0 6 4 4 9 7 4
580 | 2 4 4 0 4 4 4
581 | 2 0 2 5 3 2
582 | 2 4 4 0 4 5 4
583 | 2 4 4 0 4 4 8 6 4
584 | 2 0 2 5 3 2
585 | 2 4 4 0 4 4
586 | 2 5 2 5 0 7 5 7 7 11 9 9 5
587 | 2 4 4 0 4 4
588 | 2 3 0 3 6 3 3
589 | 2 4 4 0 6 4 4
590 | 2 3 0 3 3 7 5 3
591 | 2 4 4 0 4 4
592 | 2 4 4 0 4
593 | 2 4 4 0 4 4
594 | 2 3 0 5 3 3
595 | 2 3 0 3 3 7 5 3
596 | 3 3 7 3 4 7 0 7 10 8 7
597 | 3 3 0 5 3 3
598 | 2 4 4 0 6 4 4
599 | 2 4 4 0 7 7 4 4
600 | 2 4 4 0 7 7 4 4 10 8 4
601 | 2 4 4 0 6 4 4 7 11 11 8 4
602 | 2 7 2 3 7 7 0 7
603 | 2 4 4 0 6 4 4
604 | 2 3 0 5 3 3
605 | 2 3 0 5 3 7 5 3
606 | 2 4 4 0 6 4 4
607 | 2 4 4 0 6 4 6 7 4 11 9 4
608 | 2 4 4 0 7 7 4 4 11 11 8 4
609 | 2 3 0 6 6 3 3
610 | 2 4 4 0 4 4 8 6 4
611 | 3 3 4 0 6 4 4
612 | 2 4 4 0 4 4 8 6 4
613 | 2 4 4 0 6 4 4
614 | 2 4 4 0 4
615 | 2 6 2 5 3 0 8 6 6
616 | 3 3 0 3
617 | 4 4 4 6 6 0 6
618 | 2 4 4 0 4 4 9 9 6 4
619 | 3 3 0 3 4 7 9 7 5 3
620 | 3 3 0 5 3 5 6 7 10 8 3
621 | 2 4 4 0 6 4 6 9 7 4 10 13 11 4
622 | 2 4 4 0 7 7 4 4
623 | 2 4 4 0 4 7 4 4
624 | 2 3 0 3 3 8 8 5 3
625 | 2 3 0 5 3 3 8 6 3
626 | 2 0 4 2 2 7 5 7 10 8 2
627 | 2 4 4 0 7 7 4 4
628 | 3 3 5 5 0 5 8 6 5
629 | 2 4 4 0 4 7 4 4
630 | 2 4 4 0 4 4 8 6 4
631 | 2 4 4 0 4 7 5 7 8 4 12 10 12 4
632 | 2 4 4 0 4 7 5 4
633 | 2 4 4 0 6 4 6 9 7 4 4
634 | 3 3 4 0 4 7 5 4
635 | 2 4 4 0 4 7 5 4
636 | 3 3 5 5 0 5
637 | 2 4 4 0 4 4
638 | 2 4 4 0 4 4 8 6 4
639 | 2 4 4 0 4 7 5 4
640 | 2 4 4 0 6 4 4
641 | 2 3 0 5 3 3
642 | 3 3 5 5 0 5 6 5
643 | 2 4 4 0 7 7 4 4 10 8 4
644 | 2 4 4 0 4 8 8 5 4
645 | 2 5 5 5 0 5
646 | 2 4 4 0 4 4 4
647 | 2 4 4 0 7 7 4 4
648 | 2 3 0 5 3 3 8 6 3
649 | 7 1 5 5 2 7 0 9 7 9 10 7
650 | 2 4 4 0 6 4 4 9 7 4
651 | 2 4 4 0 4 7 5 4
652 | 2 4 4 0 6 4 4
653 | 2 4 4 0 6 4 4
654 | 2 4 4 0 4
655 | 2 6 2 3 6 0 6 6 10 8 6
656 | 2 4 4 0 4 7 5 4
657 | 3 3 0 5 3 3
658 | 3 3 0 6 6 3 6 9 7 3
659 | 2 4 4 0 6 4 4
660 | 2 4 4 0 4 4 8 6 4
661 | 2 4 4 0 4
662 | 2 7 2 2 2 7 0 7 7 7 10 7
663 | 3 3 0 3 3
664 | 2 4 4 0 4 8 8 5 4
665 | 2 4 4 0 4 4 8 6 4
666 | 2 4 4 0 6 4 4 9 7 4
667 | 2 4 4 0 6 4 4 7 4
668 | 5 5 2 2 7 7 0 7
669 | 2 3 0 5 3 3 6 9 7 3
670 | 2 4 4 0 4 7 5 4
671 | 2 4 4 0 4 7 5 4
672 | 2 3 0 3 6 4 3 9 7 3
673 | 2 4 4 0 4 4 9 9 6 4
674 | 2 7 2 5 3 7 0 7 10 8 10 11 12 7
675 | 5 1 2 5 0 5 9 9 6 5
676 | 2 6 2 5 2 0 6 6
677 | 2 4 4 0 4 7 5 7 4
678 | 2 4 4 0 4 4 8 6 8 11 8 8 12 4
679 | 2 0 4 2 2
680 | 2 4 4 0 4 4
681 | 2 4 4 0 6 4 4 9 7 9 12 10 4
682 | 2 4 4 0 4
683 | 2 5 5 5 0 5
684 | 2 5 2 3 0 5 8 6 5
685 | 3 3 5 5 0 5 8 6 5
686 | 2 3 0 3 3 7 5 3
687 | 2 4 4 0 4 4
688 | 2 3 0 7 7 7 3 3 3
689 | 2 3 0 5 3 3
690 | 2 4 4 0 4 7 5 4
691 | 2 3 0 5 3 3
692 | 2 5 5 5 0 5
693 | 2 4 4 0 4 7 5 4
694 | 3 3 5 5 0 5
695 | 2 4 4 0 6 4 4
696 | 2 4 4 0 4
697 | 2 4 4 0 6 4 4
698 | 2 4 4 0 4 4 8 6 4
699 | 2 3 0 3 3 5 8 6 3
700 | 3 3 0 5 3 3
701 | 2 5 5 5 0 5
702 | 2 4 4 0 4 4
703 | 2 4 4 0 4 4
704 | 2 4 4 0 6 4 4
705 | 2 4 4 0 6 4 4 9 7 4
706 | 2 4 4 0 4 7 4 4
707 | 2 4 4 0 4 7 5 4
708 | 3 3 5 5 0 7 5 5 11 11 8 5
709 | 3 3 0 3 4 7 5 3
710 | 2 4 4 0 4 4
711 | 2 3 0 3 3 7 5 3
712 | 2 4 4 0 6 4 4
713 | 2 4 4 0 4 7 5 4
714 | 2 4 4 0 7 7 4 7 7 4 12 10 4
715 | 3 3 4 0 4 8 8 5 4
716 | 2 4 4 0 6 4 4
717 | 2 4 4 0 4
718 | 2 3 0 3 7 7 3 7 10 8 3
719 | 2 3 0 3 3 7 5 3
720 | 2 4 4 0 4 7 5 7 10 8 8 13 11 4
721 | 2 4 4 0 6 4 4
722 | 2 4 4 0 4
723 | 2 4 4 0 6 4 4
724 | 2 3 0 3 3 7 5 3
725 | 2 4 4 0 7 7 4 4
726 | 2 3 0 6 6 3 3
727 | 2 4 4 0 7 7 4 4
728 | 2 4 4 0 6 4 4
729 | 2 4 4 0 4 7 5 4
730 | 3 3 0 3 4 7 5 3
731 | 2 4 4 0 4 5 4 9 7 4
732 | 2 4 4 0 7 7 4 4 11 11 8 4
733 | 2 4 4 0 6 4 4 9 7 4
734 | 2 4 4 0 6 4 4 7 4
735 | 2 4 4 0 4 7 9 7 5 4
736 | 3 3 4 0 6 4 4
737 | 3 3 4 0 4 9 8 9 4 9 10 4
738 | 2 4 4 0 6 4 4
739 | 2 3 0 3 6 4 6 6 3
740 | 3 3 0 3 3
741 | 2 4 4 0 4 4
742 | 2 4 4 0 6 4 4 10 10 7 4
743 | 3 3 0 3 6 4 3
744 | 2 6 2 2 6 0 6 6
745 | 2 3 0 3 6 4 3
746 | 2 4 4 0 4 4
747 | 2 3 0 5 3 3
748 | 2 4 4 0 4 7 5 4
749 | 2 4 4 0 6 4 4
750 | 2 4 4 0 6 4 4
751 | 


--------------------------------------------------------------------------------
/data/msrvid/dev/id.txt:
--------------------------------------------------------------------------------
  1 | 10001
  2 | 10002
  3 | 10003
  4 | 10004
  5 | 10005
  6 | 10006
  7 | 10007
  8 | 10008
  9 | 10009
 10 | 10010
 11 | 10011
 12 | 10012
 13 | 10013
 14 | 10014
 15 | 10015
 16 | 10016
 17 | 10017
 18 | 10018
 19 | 10019
 20 | 10020
 21 | 10021
 22 | 10022
 23 | 10023
 24 | 10024
 25 | 10025
 26 | 10026
 27 | 10027
 28 | 10028
 29 | 10029
 30 | 10030
 31 | 10031
 32 | 10032
 33 | 10033
 34 | 10034
 35 | 10035
 36 | 10036
 37 | 10037
 38 | 10038
 39 | 10039
 40 | 10040
 41 | 10041
 42 | 10042
 43 | 10043
 44 | 10044
 45 | 10045
 46 | 10046
 47 | 10047
 48 | 10048
 49 | 10049
 50 | 10050
 51 | 10051
 52 | 10052
 53 | 10053
 54 | 10054
 55 | 10055
 56 | 10056
 57 | 10057
 58 | 10058
 59 | 10059
 60 | 10060
 61 | 10061
 62 | 10062
 63 | 10063
 64 | 10064
 65 | 10065
 66 | 10066
 67 | 10067
 68 | 10068
 69 | 10069
 70 | 10070
 71 | 10071
 72 | 10072
 73 | 10073
 74 | 10074
 75 | 10075
 76 | 10076
 77 | 10077
 78 | 10078
 79 | 10079
 80 | 10080
 81 | 10081
 82 | 10082
 83 | 10083
 84 | 10084
 85 | 10085
 86 | 10086
 87 | 10087
 88 | 10088
 89 | 10089
 90 | 10090
 91 | 10091
 92 | 10092
 93 | 10093
 94 | 10094
 95 | 10095
 96 | 10096
 97 | 10097
 98 | 10098
 99 | 10099
100 | 10100
101 | 10101
102 | 10102
103 | 10103
104 | 10104
105 | 10105
106 | 10106
107 | 10107
108 | 10108
109 | 10109
110 | 10110
111 | 10111
112 | 10112
113 | 10113
114 | 10114
115 | 10115
116 | 10116
117 | 10117
118 | 10118
119 | 10119
120 | 10120
121 | 10121
122 | 10122
123 | 10123
124 | 10124
125 | 10125
126 | 10126
127 | 10127
128 | 10128
129 | 10129
130 | 10130
131 | 10131
132 | 10132
133 | 10133
134 | 10134
135 | 10135
136 | 10136
137 | 10137
138 | 10138
139 | 10139
140 | 10140
141 | 10141
142 | 10142
143 | 10143
144 | 10144
145 | 10145
146 | 10146
147 | 10147
148 | 10148
149 | 10149
150 | 10150
151 | 10151
152 | 10152
153 | 10153
154 | 10154
155 | 10155
156 | 10156
157 | 10157
158 | 10158
159 | 10159
160 | 10160
161 | 10161
162 | 10162
163 | 10163
164 | 10164
165 | 10165
166 | 10166
167 | 10167
168 | 10168
169 | 10169
170 | 10170
171 | 10171
172 | 10172
173 | 10173
174 | 10174
175 | 10175
176 | 10176
177 | 10177
178 | 10178
179 | 10179
180 | 10180
181 | 10181
182 | 10182
183 | 10183
184 | 10184
185 | 10185
186 | 10186
187 | 10187
188 | 10188
189 | 10189
190 | 10190
191 | 10191
192 | 10192
193 | 10193
194 | 10194
195 | 10195
196 | 10196
197 | 10197
198 | 10198
199 | 10199
200 | 10200
201 | 10201
202 | 10202
203 | 10203
204 | 10204
205 | 10205
206 | 10206
207 | 10207
208 | 10208
209 | 10209
210 | 10210
211 | 10211
212 | 10212
213 | 10213
214 | 10214
215 | 10215
216 | 10216
217 | 10217
218 | 10218
219 | 10219
220 | 10220
221 | 10221
222 | 10222
223 | 10223
224 | 10224
225 | 10225
226 | 10226
227 | 10227
228 | 10228
229 | 10229
230 | 10230
231 | 10231
232 | 10232
233 | 10233
234 | 10234
235 | 10235
236 | 10236
237 | 10237
238 | 10238
239 | 10239
240 | 10240
241 | 10241
242 | 10242
243 | 10243
244 | 10244
245 | 10245
246 | 10246
247 | 10247
248 | 10248
249 | 10249
250 | 10250
251 | 10251
252 | 10252
253 | 10253
254 | 10254
255 | 10255
256 | 10256
257 | 10257
258 | 10258
259 | 10259
260 | 10260
261 | 10261
262 | 10262
263 | 10263
264 | 10264
265 | 10265
266 | 10266
267 | 10267
268 | 10268
269 | 10269
270 | 10270
271 | 10271
272 | 10272
273 | 10273
274 | 10274
275 | 10275
276 | 10276
277 | 10277
278 | 10278
279 | 10279
280 | 10280
281 | 10281
282 | 10282
283 | 10283
284 | 10284
285 | 10285
286 | 10286
287 | 10287
288 | 10288
289 | 10289
290 | 10290
291 | 10291
292 | 10292
293 | 10293
294 | 10294
295 | 10295
296 | 10296
297 | 10297
298 | 10298
299 | 10299
300 | 10300
301 | 10301
302 | 10302
303 | 10303
304 | 10304
305 | 10305
306 | 10306
307 | 10307
308 | 10308
309 | 10309
310 | 10310
311 | 10311
312 | 10312
313 | 10313
314 | 10314
315 | 10315
316 | 10316
317 | 10317
318 | 10318
319 | 10319
320 | 10320
321 | 10321
322 | 10322
323 | 10323
324 | 10324
325 | 10325
326 | 10326
327 | 10327
328 | 10328
329 | 10329
330 | 10330
331 | 10331
332 | 10332
333 | 10333
334 | 10334
335 | 10335
336 | 10336
337 | 10337
338 | 10338
339 | 10339
340 | 10340
341 | 10341
342 | 10342
343 | 10343
344 | 10344
345 | 10345
346 | 10346
347 | 10347
348 | 10348
349 | 10349
350 | 10350
351 | 10351
352 | 10352
353 | 10353
354 | 10354
355 | 10355
356 | 10356
357 | 10357
358 | 10358
359 | 10359
360 | 10360
361 | 10361
362 | 10362
363 | 10363
364 | 10364
365 | 10365
366 | 10366
367 | 10367
368 | 10368
369 | 10369
370 | 10370
371 | 10371
372 | 10372
373 | 10373
374 | 10374
375 | 10375
376 | 10376
377 | 10377
378 | 10378
379 | 10379
380 | 10380
381 | 10381
382 | 10382
383 | 10383
384 | 10384
385 | 10385
386 | 10386
387 | 10387
388 | 10388
389 | 10389
390 | 10390
391 | 10391
392 | 10392
393 | 10393
394 | 10394
395 | 10395
396 | 10396
397 | 10397
398 | 10398
399 | 10399
400 | 10400
401 | 10401
402 | 10402
403 | 10403
404 | 10404
405 | 10405
406 | 10406
407 | 10407
408 | 10408
409 | 10409
410 | 10410
411 | 10411
412 | 10412
413 | 10413
414 | 10414
415 | 10415
416 | 10416
417 | 10417
418 | 10418
419 | 10419
420 | 10420
421 | 10421
422 | 10422
423 | 10423
424 | 10424
425 | 10425
426 | 10426
427 | 10427
428 | 10428
429 | 10429
430 | 10430
431 | 10431
432 | 10432
433 | 10433
434 | 10434
435 | 10435
436 | 10436
437 | 10437
438 | 10438
439 | 10439
440 | 10440
441 | 10441
442 | 10442
443 | 10443
444 | 10444
445 | 10445
446 | 10446
447 | 10447
448 | 10448
449 | 10449
450 | 10450
451 | 10451
452 | 10452
453 | 10453
454 | 10454
455 | 10455
456 | 10456
457 | 10457
458 | 10458
459 | 10459
460 | 10460
461 | 10461
462 | 10462
463 | 10463
464 | 10464
465 | 10465
466 | 10466
467 | 10467
468 | 10468
469 | 10469
470 | 10470
471 | 10471
472 | 10472
473 | 10473
474 | 10474
475 | 10475
476 | 10476
477 | 10477
478 | 10478
479 | 10479
480 | 10480
481 | 10481
482 | 10482
483 | 10483
484 | 10484
485 | 10485
486 | 10486
487 | 10487
488 | 10488
489 | 10489
490 | 10490
491 | 10491
492 | 10492
493 | 10493
494 | 10494
495 | 10495
496 | 10496
497 | 10497
498 | 10498
499 | 10499
500 | 10500
501 | 10501
502 | 10502
503 | 10503
504 | 10504
505 | 10505
506 | 10506
507 | 10507
508 | 10508
509 | 10509
510 | 10510
511 | 10511
512 | 10512
513 | 10513
514 | 10514
515 | 10515
516 | 10516
517 | 10517
518 | 10518
519 | 10519
520 | 10520
521 | 10521
522 | 10522
523 | 10523
524 | 10524
525 | 10525
526 | 10526
527 | 10527
528 | 10528
529 | 10529
530 | 10530
531 | 10531
532 | 10532
533 | 10533
534 | 10534
535 | 10535
536 | 10536
537 | 10537
538 | 10538
539 | 10539
540 | 10540
541 | 10541
542 | 10542
543 | 10543
544 | 10544
545 | 10545
546 | 10546
547 | 10547
548 | 10548
549 | 10549
550 | 10550
551 | 10551
552 | 10552
553 | 10553
554 | 10554
555 | 10555
556 | 10556
557 | 10557
558 | 10558
559 | 10559
560 | 10560
561 | 10561
562 | 10562
563 | 10563
564 | 10564
565 | 10565
566 | 10566
567 | 10567
568 | 10568
569 | 10569
570 | 10570
571 | 10571
572 | 10572
573 | 10573
574 | 10574
575 | 10575
576 | 10576
577 | 10577
578 | 10578
579 | 10579
580 | 10580
581 | 10581
582 | 10582
583 | 10583
584 | 10584
585 | 10585
586 | 10586
587 | 10587
588 | 10588
589 | 10589
590 | 10590
591 | 10591
592 | 10592
593 | 10593
594 | 10594
595 | 10595
596 | 10596
597 | 10597
598 | 10598
599 | 10599
600 | 10600
601 | 10601
602 | 10602
603 | 10603
604 | 10604
605 | 10605
606 | 10606
607 | 10607
608 | 10608
609 | 10609
610 | 10610
611 | 10611
612 | 10612
613 | 10613
614 | 10614
615 | 10615
616 | 10616
617 | 10617
618 | 10618
619 | 10619
620 | 10620
621 | 10621
622 | 10622
623 | 10623
624 | 10624
625 | 10625
626 | 10626
627 | 10627
628 | 10628
629 | 10629
630 | 10630
631 | 10631
632 | 10632
633 | 10633
634 | 10634
635 | 10635
636 | 10636
637 | 10637
638 | 10638
639 | 10639
640 | 10640
641 | 10641
642 | 10642
643 | 10643
644 | 10644
645 | 10645
646 | 10646
647 | 10647
648 | 10648
649 | 10649
650 | 10650
651 | 10651
652 | 10652
653 | 10653
654 | 10654
655 | 10655
656 | 10656
657 | 10657
658 | 10658
659 | 10659
660 | 10660
661 | 10661
662 | 10662
663 | 10663
664 | 10664
665 | 10665
666 | 10666
667 | 10667
668 | 10668
669 | 10669
670 | 10670
671 | 10671
672 | 10672
673 | 10673
674 | 10674
675 | 10675
676 | 10676
677 | 10677
678 | 10678
679 | 10679
680 | 10680
681 | 10681
682 | 10682
683 | 10683
684 | 10684
685 | 10685
686 | 10686
687 | 10687
688 | 10688
689 | 10689
690 | 10690
691 | 10691
692 | 10692
693 | 10693
694 | 10694
695 | 10695
696 | 10696
697 | 10697
698 | 10698
699 | 10699
700 | 10700
701 | 10701
702 | 10702
703 | 10703
704 | 10704
705 | 10705
706 | 10706
707 | 10707
708 | 10708
709 | 10709
710 | 10710
711 | 10711
712 | 10712
713 | 10713
714 | 10714
715 | 10715
716 | 10716
717 | 10717
718 | 10718
719 | 10719
720 | 10720
721 | 10721
722 | 10722
723 | 10723
724 | 10724
725 | 10725
726 | 10726
727 | 10727
728 | 10728
729 | 10729
730 | 10730
731 | 10731
732 | 10732
733 | 10733
734 | 10734
735 | 10735
736 | 10736
737 | 10737
738 | 10738
739 | 10739
740 | 10740
741 | 10741
742 | 10742
743 | 10743
744 | 10744
745 | 10745
746 | 10746
747 | 10747
748 | 10748
749 | 10749
750 | 10750
751 | 


--------------------------------------------------------------------------------
/data/msrvid/dev/sim.txt:
--------------------------------------------------------------------------------
  1 | 5.000
  2 | 5.000
  3 | 4.750
  4 | 5.000
  5 | 3.800
  6 | 3.800
  7 | 2.600
  8 | 2.400
  9 | 2.750
 10 | 4.250
 11 | 2.615
 12 | 4.250
 13 | 0.500
 14 | 1.600
 15 | 2.200
 16 | 5.000
 17 | 5.000
 18 | 4.200
 19 | 4.600
 20 | 3.867
 21 | 4.667
 22 | 1.667
 23 | 3.750
 24 | 2.333
 25 | 2.500
 26 | 5.000
 27 | 0.500
 28 | 3.750
 29 | 3.800
 30 | 5.000
 31 | 3.200
 32 | 2.800
 33 | 4.600
 34 | 3.600
 35 | 3.000
 36 | 5.000
 37 | 5.000
 38 | 3.200
 39 | 4.800
 40 | 1.583
 41 | 5.000
 42 | 5.000
 43 | 4.200
 44 | 5.000
 45 | 4.200
 46 | 5.000
 47 | 4.000
 48 | 4.000
 49 | 4.909
 50 | 4.909
 51 | 3.000
 52 | 0.800
 53 | 2.400
 54 | 4.200
 55 | 3.400
 56 | 5.000
 57 | 3.750
 58 | 2.750
 59 | 5.000
 60 | 4.000
 61 | 2.400
 62 | 3.600
 63 | 1.600
 64 | 4.200
 65 | 1.750
 66 | 5.000
 67 | 1.500
 68 | 5.000
 69 | 1.000
 70 | 1.000
 71 | 2.375
 72 | 3.800
 73 | 3.200
 74 | 3.200
 75 | 1.800
 76 | 4.400
 77 | 3.500
 78 | 3.750
 79 | 4.750
 80 | 4.000
 81 | 3.200
 82 | 1.556
 83 | 2.200
 84 | 3.938
 85 | 5.000
 86 | 5.000
 87 | 4.000
 88 | 1.600
 89 | 0.636
 90 | 4.750
 91 | 3.500
 92 | 3.000
 93 | 2.200
 94 | 1.400
 95 | 1.400
 96 | 1.714
 97 | 1.714
 98 | 1.714
 99 | 3.200
100 | 4.000
101 | 5.000
102 | 5.000
103 | 3.833
104 | 0.600
105 | 0.600
106 | 2.917
107 | 4.200
108 | 4.400
109 | 2.000
110 | 2.600
111 | 1.600
112 | 2.000
113 | 4.200
114 | 2.000
115 | 4.800
116 | 4.400
117 | 5.000
118 | 3.000
119 | 4.250
120 | 4.250
121 | 3.800
122 | 2.400
123 | 2.167
124 | 1.600
125 | 2.000
126 | 2.000
127 | 1.600
128 | 1.800
129 | 4.400
130 | 4.000
131 | 2.200
132 | 4.400
133 | 3.600
134 | 3.600
135 | 3.600
136 | 0.500
137 | 0.800
138 | 3.600
139 | 0.600
140 | 1.200
141 | 1.000
142 | 2.600
143 | 2.000
144 | 2.200
145 | 2.400
146 | 3.600
147 | 2.400
148 | 1.917
149 | 2.200
150 | 4.800
151 | 0.200
152 | 1.643
153 | 1.750
154 | 4.250
155 | 2.250
156 | 4.000
157 | 4.200
158 | 4.800
159 | 3.200
160 | 3.000
161 | 4.000
162 | 4.400
163 | 4.400
164 | 4.600
165 | 3.800
166 | 4.800
167 | 4.857
168 | 5.000
169 | 2.533
170 | 2.250
171 | 2.000
172 | 0.750
173 | 1.000
174 | 1.000
175 | 2.200
176 | 1.000
177 | 2.000
178 | 0.600
179 | 0.143
180 | 2.000
181 | 1.600
182 | 0.800
183 | 1.600
184 | 2.200
185 | 3.400
186 | 4.000
187 | 2.600
188 | 4.800
189 | 2.500
190 | 5.000
191 | 1.750
192 | 1.000
193 | 5.000
194 | 1.400
195 | 4.600
196 | 4.000
197 | 3.800
198 | 3.200
199 | 4.000
200 | 4.000
201 | 4.800
202 | 0.600
203 | 4.750
204 | 2.200
205 | 3.000
206 | 0.000
207 | 5.000
208 | 2.200
209 | 0.400
210 | 4.800
211 | 4.800
212 | 4.800
213 | 4.800
214 | 3.800
215 | 3.800
216 | 3.000
217 | 4.000
218 | 5.000
219 | 5.000
220 | 5.000
221 | 4.200
222 | 3.800
223 | 1.400
224 | 3.000
225 | 4.400
226 | 3.600
227 | 3.800
228 | 3.000
229 | 2.800
230 | 1.400
231 | 0.667
232 | 4.000
233 | 4.250
234 | 3.750
235 | 4.133
236 | 4.000
237 | 1.600
238 | 3.600
239 | 1.200
240 | 1.600
241 | 4.000
242 | 4.000
243 | 3.400
244 | 3.200
245 | 0.533
246 | 1.000
247 | 1.000
248 | 0.600
249 | 0.400
250 | 0.400
251 | 3.400
252 | 1.200
253 | 3.600
254 | 5.000
255 | 3.000
256 | 3.000
257 | 4.000
258 | 1.200
259 | 0.538
260 | 0.600
261 | 1.600
262 | 1.400
263 | 2.600
264 | 3.600
265 | 0.250
266 | 0.250
267 | 3.750
268 | 3.500
269 | 3.765
270 | 2.750
271 | 2.250
272 | 2.750
273 | 3.800
274 | 4.800
275 | 3.600
276 | 1.200
277 | 3.000
278 | 0.400
279 | 2.400
280 | 3.600
281 | 0.000
282 | 0.500
283 | 3.941
284 | 0.500
285 | 3.750
286 | 4.000
287 | 4.000
288 | 3.000
289 | 4.200
290 | 4.500
291 | 2.000
292 | 5.000
293 | 3.000
294 | 3.800
295 | 0.000
296 | 0.000
297 | 0.250
298 | 4.000
299 | 3.250
300 | 0.750
301 | 1.500
302 | 0.500
303 | 1.500
304 | 3.500
305 | 3.000
306 | 3.800
307 | 4.800
308 | 0.200
309 | 0.800
310 | 0.800
311 | 0.800
312 | 4.200
313 | 3.800
314 | 0.600
315 | 0.400
316 | 5.000
317 | 0.800
318 | 1.200
319 | 2.800
320 | 3.200
321 | 5.000
322 | 4.400
323 | 0.000
324 | 1.000
325 | 0.250
326 | 0.000
327 | 1.750
328 | 3.750
329 | 0.400
330 | 0.400
331 | 0.400
332 | 0.600
333 | 4.000
334 | 1.400
335 | 0.400
336 | 0.400
337 | 0.400
338 | 0.400
339 | 1.200
340 | 4.800
341 | 0.600
342 | 0.800
343 | 0.600
344 | 2.000
345 | 0.800
346 | 0.800
347 | 0.133
348 | 3.200
349 | 3.200
350 | 0.600
351 | 0.600
352 | 0.000
353 | 1.400
354 | 4.000
355 | 3.800
356 | 2.200
357 | 0.267
358 | 3.600
359 | 3.400
360 | 3.111
361 | 0.600
362 | 0.400
363 | 0.000
364 | 1.200
365 | 3.500
366 | 4.250
367 | 3.000
368 | 4.500
369 | 5.000
370 | 5.000
371 | 5.000
372 | 3.750
373 | 2.800
374 | 0.200
375 | 0.400
376 | 3.800
377 | 1.286
378 | 4.600
379 | 0.600
380 | 2.000
381 | 5.000
382 | 3.800
383 | 1.800
384 | 0.000
385 | 2.400
386 | 2.400
387 | 0.400
388 | 0.000
389 | 4.200
390 | 3.800
391 | 0.000
392 | 0.000
393 | 4.000
394 | 0.800
395 | 1.400
396 | 1.500
397 | 0.750
398 | 2.500
399 | 2.500
400 | 3.500
401 | 3.400
402 | 3.400
403 | 3.800
404 | 3.800
405 | 4.250
406 | 2.812
407 | 1.500
408 | 0.500
409 | 0.000
410 | 0.000
411 | 0.800
412 | 0.000
413 | 0.200
414 | 0.850
415 | 3.400
416 | 0.400
417 | 1.750
418 | 3.250
419 | 0.750
420 | 4.250
421 | 3.000
422 | 4.000
423 | 0.000
424 | 0.000
425 | 1.000
426 | 0.500
427 | 3.750
428 | 4.250
429 | 1.800
430 | 3.200
431 | 3.600
432 | 0.000
433 | 2.600
434 | 3.200
435 | 0.400
436 | 3.800
437 | 2.400
438 | 2.800
439 | 4.600
440 | 0.000
441 | 0.250
442 | 3.923
443 | 2.750
444 | 0.000
445 | 3.000
446 | 3.800
447 | 4.000
448 | 4.600
449 | 2.600
450 | 3.600
451 | 3.600
452 | 2.800
453 | 4.750
454 | 3.750
455 | 1.154
456 | 1.000
457 | 2.750
458 | 1.250
459 | 2.750
460 | 0.000
461 | 0.000
462 | 0.000
463 | 2.750
464 | 3.750
465 | 2.800
466 | 2.600
467 | 2.400
468 | 2.200
469 | 0.000
470 | 0.200
471 | 0.000
472 | 2.800
473 | 4.000
474 | 0.000
475 | 0.000
476 | 0.750
477 | 5.000
478 | 0.000
479 | 3.400
480 | 3.200
481 | 3.500
482 | 1.750
483 | 1.250
484 | 0.833
485 | 0.000
486 | 3.750
487 | 2.750
488 | 0.000
489 | 5.000
490 | 3.800
491 | 0.200
492 | 3.800
493 | 3.400
494 | 1.400
495 | 3.800
496 | 0.800
497 | 0.400
498 | 0.000
499 | 0.000
500 | 0.000
501 | 0.200
502 | 0.000
503 | 0.000
504 | 3.800
505 | 2.250
506 | 2.250
507 | 2.750
508 | 4.500
509 | 2.400
510 | 2.800
511 | 3.200
512 | 1.600
513 | 0.400
514 | 0.000
515 | 2.600
516 | 0.400
517 | 0.600
518 | 1.000
519 | 3.800
520 | 4.600
521 | 4.250
522 | 0.000
523 | 1.500
524 | 3.500
525 | 2.800
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527 | 0.000
528 | 0.000
529 | 0.333
530 | 0.333
531 | 3.333
532 | 4.333
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534 | 0.800
535 | 0.800
536 | 0.200
537 | 0.000
538 | 0.000
539 | 0.000
540 | 1.333
541 | 3.000
542 | 2.600
543 | 2.800
544 | 0.800
545 | 1.000
546 | 0.000
547 | 0.000
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555 | 5.000
556 | 1.750
557 | 2.667
558 | 2.333
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561 | 0.000
562 | 1.000
563 | 0.800
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565 | 0.000
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567 | 4.000
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570 | 3.200
571 | 5.000
572 | 4.400
573 | 0.750
574 | 2.000
575 | 2.000
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577 | 2.800
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579 | 3.091
580 | 3.538
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594 | 3.000
595 | 3.000
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598 | 0.000
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603 | 4.750
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615 | 0.000
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628 | 0.000
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630 | 1.400
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632 | 2.200
633 | 2.533
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643 | 2.800
644 | 0.000
645 | 0.000
646 | 0.417
647 | 0.000
648 | 0.400
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651 | 2.000
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653 | 0.500
654 | 0.000
655 | 2.500
656 | 2.000
657 | 2.600
658 | 0.000
659 | 3.600
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661 | 0.600
662 | 2.818
663 | 3.000
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665 | 1.600
666 | 1.250
667 | 3.533
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671 | 4.000
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680 | 3.600
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682 | 0.000
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684 | 2.800
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686 | 0.000
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688 | 3.800
689 | 0.643
690 | 0.000
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692 | 1.250
693 | 3.400
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696 | 0.500
697 | 0.000
698 | 1.000
699 | 4.400
700 | 4.000
701 | 0.000
702 | 1.000
703 | 0.000
704 | 0.000
705 | 0.000
706 | 0.000
707 | 0.000
708 | 0.000
709 | 1.800
710 | 2.400
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712 | 0.600
713 | 0.250
714 | 3.250
715 | 3.750
716 | 1.250
717 | 0.400
718 | 1.800
719 | 4.000
720 | 3.250
721 | 2.500
722 | 0.750
723 | 3.250
724 | 2.500
725 | 1.200
726 | 0.083
727 | 0.000
728 | 3.800
729 | 3.400
730 | 0.000
731 | 0.800
732 | 0.000
733 | 1.000
734 | 1.000
735 | 0.000
736 | 3.500
737 | 3.750
738 | 4.500
739 | 4.000
740 | 0.250
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742 | 0.400
743 | 0.000
744 | 0.000
745 | 0.000
746 | 0.000
747 | 0.800
748 | 0.000
749 | 0.000
750 | 0.000
751 | 


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492 | 2 4 4 0 6 4 4
493 | 3 3 5 5 0 5 10 10 10 6 5
494 | 2 6 6 6 6 0 6
495 | 5 5 5 5 0 5
496 | 2 5 5 5 0 5 8 6 5
497 | 3 3 0 5 3 3 9 9 6 3
498 | 2 3 0 5 3 5 8 6 3 9 12 10 3
499 | 2 4 4 0 6 4 4 10 10 7 10 11 4
500 | 2 3 0 3 6 4 3
501 | 2 5 5 5 0 7 5 5
502 | 2 4 4 0 4 4 4
503 | 2 4 4 0 6 4 4 9 7 4
504 | 2 3 0 3 3 5 3 9 7 3
505 | 2 4 4 0 4
506 | 2 4 4 0 4 4 4
507 | 2 3 0 5 3 3
508 | 2 4 4 0 7 7 4 4
509 | 4 4 4 0 4 7 5 7 10 8 4
510 | 2 4 4 0 4 4
511 | 2 4 4 0 6 4 4
512 | 2 4 4 0 6 4 4
513 | 2 4 4 0 4 4 9 9 6 4
514 | 2 4 4 0 4 7 4 4
515 | 2 4 4 0 4 7 5 4
516 | 2 4 4 0 4
517 | 2 3 0 3 7 7 3 7 10 8 8 14 14 11 14 14 18 16 16 21 19 3
518 | 2 4 4 0 4 7 5 4
519 | 2 4 4 0 7 7 4 4
520 | 2 4 4 0 4 4
521 | 2 4 4 0 6 4 4
522 | 2 4 4 0 6 4 4
523 | 2 4 4 0 6 4 4
524 | 2 4 4 7 6 4 0 7
525 | 3 3 5 5 0 5 5 9 7 5
526 | 2 3 0 5 3 3
527 | 2 4 4 0 6 4 4 7 4
528 | 2 3 0 3 4 5 5 9 5 9 12 10 3
529 | 2 4 4 0 6 4 4 9 7 4 12 10 4
530 | 2 4 4 0 4 7 5 4
531 | 3 3 0 3 4 3 8 6 3
532 | 2 3 0 5 3 3 8 6 3
533 | 2 4 4 0 6 4 6 7 4
534 | 2 4 4 0 6 4 4 9 7 4
535 | 2 4 4 0 4 4
536 | 2 4 4 0 6 4 4
537 | 2 4 4 0 6 4 4
538 | 3 3 0 3 7 7 4 3
539 | 2 4 4 0 6 4 4 9 7 4
540 | 2 5 5 5 0 5 5
541 | 2 4 4 0 4 7 5 4
542 | 2 4 4 0 4 7 5 4
543 | 2 3 0 5 7 5 3 3
544 | 3 3 0 3 3
545 | 2 4 4 0 4 4
546 | 3 3 0 5 3 5 6 3
547 | 2 3 0 3 6 4 6 9 7 3
548 | 2 4 4 0 4
549 | 2 4 4 0 7 7 4 4
550 | 2 4 4 0 6 4 4
551 | 2 4 4 0 6 4 4
552 | 2 4 4 0 6 4 4
553 | 2 4 4 0 4 4 8 6 4
554 | 2 4 4 0 7 7 4 4
555 | 4 4 4 0 4 7 5 9 7 9 4
556 | 2 3 0 3 3
557 | 2 4 4 0 6 4 4 9 7 4
558 | 2 4 4 0 4 4
559 | 2 4 4 0 6 4 4
560 | 2 4 4 0 4 4 4
561 | 2 4 4 0 6 4 4
562 | 2 4 4 0 6 4 4 9 7 4
563 | 3 3 0 5 3 5 6 3
564 | 2 4 4 0 6 4 4
565 | 2 4 4 0 4 7 5 4
566 | 3 3 4 0 6 4 4
567 | 3 3 0 3 3 7 5 3
568 | 2 4 4 0 7 7 4 4 10 8 4
569 | 2 3 0 5 3 3 8 6 3
570 | 3 3 0 5 3 3
571 | 2 4 4 0 7 7 4 4
572 | 3 3 5 5 0 5 8 6 5
573 | 2 4 4 0 4 4
574 | 2 4 4 0 4 4 4
575 | 2 3 0 3 3
576 | 2 4 4 0 4 7 5 4
577 | 2 4 4 0 4 7 5 4
578 | 3 3 5 5 0 5 5 7 5 9 12 10 5
579 | 2 4 4 0 6 4 4
580 | 2 4 4 0 6 4 4
581 | 2 5 5 5 0 8 8 5 5
582 | 2 0 2 2 6 4 2
583 | 2 4 4 0 6 4 6 7 4
584 | 2 4 4 0 4 7 4 7 10 8 4
585 | 2 4 4 0 6 4 4
586 | 2 4 4 0 6 4 4 9 7 4 13 13 10 4
587 | 2 4 4 0 4 4 8 6 4
588 | 2 4 4 0 4 7 4 4
589 | 2 4 4 0 4 7 5 4
590 | 2 4 4 0 4 7 5 4
591 | 2 4 4 0 4 5 6 7 4
592 | 2 4 4 0 6 4 6 10 10 7 4
593 | 2 3 0 6 6 3 3
594 | 2 4 4 0 6 4 4
595 | 2 4 4 0 6 4 4
596 | 2 4 4 0 6 4 4
597 | 2 3 0 5 3 3 8 6 3
598 | 2 3 0 5 3 5 8 6 3 11 9 11 12 3
599 | 2 4 4 0 6 4 4 9 7 9 12 10 12 13 4
600 | 2 4 4 0 6 4 4 9 7 4
601 | 3 3 0 3 6 4 3
602 | 2 3 0 5 3 3
603 | 3 3 4 0 4
604 | 2 4 4 0 4
605 | 2 4 4 0 4 5 6 9 7 9 12 10 4
606 | 2 4 4 0 4 4 6 9 7 4
607 | 2 4 4 0 6 4 4
608 | 2 4 4 0 4 5 6 4
609 | 2 4 4 0 6 4 4
610 | 2 4 4 0 6 4 4
611 | 2 4 4 0 6 4 4 9 7 4
612 | 2 4 4 0 4 4 8 6 4
613 | 2 3 0 3 7 7 4 3
614 | 2 4 4 0 6 4 4
615 | 2 4 4 0 6 4 4
616 | 2 4 4 0 4 5 4
617 | 2 4 4 0 6 4 4
618 | 2 4 4 0 6 4 4
619 | 2 4 4 0 6 4 4
620 | 2 4 4 0 6 4 6 9 7 4
621 | 2 4 4 0 4
622 | 2 4 4 0 4 7 5 4
623 | 2 4 4 0 6 4 4
624 | 2 4 4 0 6 4 4
625 | 2 3 0 3 7 7 4 3
626 | 2 4 4 0 4 7 5 4
627 | 2 4 4 0 6 4 4
628 | 3 3 5 5 0 5 8 6 5
629 | 2 4 4 0 6 4 4
630 | 2 4 4 0 6 4 8 6 4
631 | 2 4 4 0 4 5 9 9 6 4
632 | 2 4 4 0 4 4
633 | 2 3 0 3 3 7 5 3
634 | 2 4 4 0 6 4 4
635 | 2 4 4 0 6 4 4
636 | 2 4 4 0 6 4 4
637 | 2 0 2 2 4 5 2
638 | 2 4 4 0 6 4 4
639 | 2 4 4 0 4
640 | 2 4 4 0 4 4
641 | 2 4 4 0 4 7 5 4
642 | 2 7 2 5 3 7 0 7 10 8 10 10 7
643 | 2 4 4 0 4 4
644 | 3 3 5 5 0 5
645 | 2 4 4 0 7 7 4 4
646 | 2 4 4 0 4 7 5 4
647 | 2 4 4 0 4 7 4 4 10 8 4
648 | 2 4 4 0 6 4 4 9 7 4
649 | 2 4 4 0 4 4 8 6 4
650 | 2 4 4 0 4 4 8 6 4
651 | 2 4 4 0 6 4 4
652 | 3 3 0 5 3 3
653 | 2 4 4 0 4 5 8 6 8 8 4
654 | 2 4 4 0 6 4 4
655 | 2 3 0 3 3 7 5 3
656 | 2 4 4 0 4
657 | 2 3 0 5 3 3
658 | 2 4 4 0 6 4 4
659 | 2 4 4 0 4
660 | 2 4 4 0 6 4 4
661 | 2 4 4 0 4 4 8 6 4
662 | 3 3 0 3 6 4 3
663 | 2 4 4 0 6 4 6 7 4
664 | 2 4 4 0 7 7 4 4 4 11 4 13 11 4
665 | 2 4 4 0 4
666 | 2 4 4 0 6 4 4
667 | 2 4 4 0 4 4 4
668 | 2 4 4 0 6 4 4
669 | 2 4 4 0 6 8 6 4 4
670 | 2 3 0 5 3 3 9 9 6 3
671 | 2 4 4 0 4 4 4
672 | 2 3 0 6 6 3 3
673 | 2 4 4 0 6 4 4
674 | 2 3 0 5 3 3 6 10 10 7 3
675 | 2 4 4 0 4 7 5 4
676 | 2 4 4 0 4 4 8 6 4
677 | 2 3 0 3 6 4 3
678 | 2 4 4 0 6 4 4
679 | 2 3 0 7 7 7 3 3
680 | 5 1 1 5 0 5 5
681 | 2 3 0 6 6 3 3 9 7 3
682 | 2 4 4 0 4 7 5 4
683 | 2 4 4 0 4 5 8 6 4
684 | 2 4 4 0 4 7 5 4
685 | 2 4 4 0 6 4 4
686 | 3 3 0 7 7 7 3 3 11 11 8 3
687 | 2 3 0 5 3 3 8 6 3
688 | 2 3 0 5 3 3
689 | 2 4 4 0 6 4 4
690 | 2 4 4 0 6 4 4
691 | 2 4 4 0 4 7 5 4
692 | 3 3 0 5 3 3
693 | 2 4 4 0 7 7 4 4
694 | 4 4 4 5 0 5 6 9 7 9 12 10 5
695 | 2 4 4 0 4 7 5 7 10 7 4 13 11 4 14 14 16 4
696 | 2 4 4 0 4 4 4 4
697 | 2 4 4 0 6 4 4 9 7 4
698 | 2 4 4 0 4 7 5 7 10 8 4
699 | 2 4 4 0 4 4
700 | 2 4 4 0 4 4
701 | 2 4 4 0 6 4 4 9 7 4
702 | 2 7 2 6 6 3 0 9 7 12 12 7 7
703 | 3 3 0 3 4 5 6 3
704 | 2 4 4 0 6 4 4
705 | 2 4 4 0 4 4
706 | 2 3 0 5 3 5 6 10 10 5 10 14 14 11 14 18 18 15 3
707 | 2 3 0 5 3 5 6 3 10 8 10 11 3
708 | 2 3 0 5 3 3
709 | 2 4 4 0 4 4 4
710 | 3 3 4 0 4 7 5 4
711 | 2 3 0 6 6 3 3 7 10 8 3
712 | 2 4 4 0 4
713 | 2 4 4 0 6 4 4
714 | 2 4 4 0 4 4 8 6 4
715 | 3 3 0 5 3 7 5 3
716 | 2 3 0 5 3 3 8 6 3
717 | 2 4 4 0 4 4 8 6 4
718 | 2 4 4 0 4
719 | 2 3 0 5 3 7 5 5 10 8 3
720 | 2 4 4 0 6 4 6 4
721 | 2 4 4 0 6 4 4
722 | 2 4 4 0 6 4 4
723 | 2 3 0 5 3 3 8 6 8 9 3
724 | 2 3 0 3 3
725 | 2 4 4 0 4 4 8 6 4
726 | 2 4 4 0 4 4
727 | 3 3 0 3 6 4 3
728 | 2 4 4 0 6 4 4 7 4
729 | 2 4 4 0 6 4 4 7 4
730 | 2 4 4 0 6 4 4
731 | 2 4 4 0 6 4 6 9 7 4
732 | 2 3 0 3 6 4 3 7 3
733 | 5 5 5 5 0 5
734 | 3 3 5 5 0 7 5 5
735 | 2 4 4 0 6 4 4
736 | 2 4 4 0 6 4 6 7 4
737 | 2 4 4 0 6 4 4
738 | 2 5 5 5 0 5 8 6 5
739 | 2 3 0 3 7 7 4 3
740 | 2 4 4 0 6 4 4 9 7 4
741 | 2 4 4 0 6 4 4
742 | 3 3 5 5 0 7 5 5
743 | 2 3 0 3 6 3 3
744 | 2 4 4 0 4 8 8 5 4
745 | 2 4 4 0 6 4 4
746 | 2 3 0 5 3 3
747 | 2 4 4 0 4 7 5 4
748 | 2 0 2 5 3 2
749 | 2 3 0 6 6 3 3
750 | 2 4 4 0 4 4
751 | 


--------------------------------------------------------------------------------
/data/msrvid/train/id.txt:
--------------------------------------------------------------------------------
  1 | 1
  2 | 2
  3 | 3
  4 | 4
  5 | 5
  6 | 6
  7 | 7
  8 | 8
  9 | 9
 10 | 10
 11 | 11
 12 | 12
 13 | 13
 14 | 14
 15 | 15
 16 | 16
 17 | 17
 18 | 18
 19 | 19
 20 | 20
 21 | 21
 22 | 22
 23 | 23
 24 | 24
 25 | 25
 26 | 26
 27 | 27
 28 | 28
 29 | 29
 30 | 30
 31 | 31
 32 | 32
 33 | 33
 34 | 34
 35 | 35
 36 | 36
 37 | 37
 38 | 38
 39 | 39
 40 | 40
 41 | 41
 42 | 42
 43 | 43
 44 | 44
 45 | 45
 46 | 46
 47 | 47
 48 | 48
 49 | 49
 50 | 50
 51 | 51
 52 | 52
 53 | 53
 54 | 54
 55 | 55
 56 | 56
 57 | 57
 58 | 58
 59 | 59
 60 | 60
 61 | 61
 62 | 62
 63 | 63
 64 | 64
 65 | 65
 66 | 66
 67 | 67
 68 | 68
 69 | 69
 70 | 70
 71 | 71
 72 | 72
 73 | 73
 74 | 74
 75 | 75
 76 | 76
 77 | 77
 78 | 78
 79 | 79
 80 | 80
 81 | 81
 82 | 82
 83 | 83
 84 | 84
 85 | 85
 86 | 86
 87 | 87
 88 | 88
 89 | 89
 90 | 90
 91 | 91
 92 | 92
 93 | 93
 94 | 94
 95 | 95
 96 | 96
 97 | 97
 98 | 98
 99 | 99
100 | 100
101 | 101
102 | 102
103 | 103
104 | 104
105 | 105
106 | 106
107 | 107
108 | 108
109 | 109
110 | 110
111 | 111
112 | 112
113 | 113
114 | 114
115 | 115
116 | 116
117 | 117
118 | 118
119 | 119
120 | 120
121 | 121
122 | 122
123 | 123
124 | 124
125 | 125
126 | 126
127 | 127
128 | 128
129 | 129
130 | 130
131 | 131
132 | 132
133 | 133
134 | 134
135 | 135
136 | 136
137 | 137
138 | 138
139 | 139
140 | 140
141 | 141
142 | 142
143 | 143
144 | 144
145 | 145
146 | 146
147 | 147
148 | 148
149 | 149
150 | 150
151 | 151
152 | 152
153 | 153
154 | 154
155 | 155
156 | 156
157 | 157
158 | 158
159 | 159
160 | 160
161 | 161
162 | 162
163 | 163
164 | 164
165 | 165
166 | 166
167 | 167
168 | 168
169 | 169
170 | 170
171 | 171
172 | 172
173 | 173
174 | 174
175 | 175
176 | 176
177 | 177
178 | 178
179 | 179
180 | 180
181 | 181
182 | 182
183 | 183
184 | 184
185 | 185
186 | 186
187 | 187
188 | 188
189 | 189
190 | 190
191 | 191
192 | 192
193 | 193
194 | 194
195 | 195
196 | 196
197 | 197
198 | 198
199 | 199
200 | 200
201 | 201
202 | 202
203 | 203
204 | 204
205 | 205
206 | 206
207 | 207
208 | 208
209 | 209
210 | 210
211 | 211
212 | 212
213 | 213
214 | 214
215 | 215
216 | 216
217 | 217
218 | 218
219 | 219
220 | 220
221 | 221
222 | 222
223 | 223
224 | 224
225 | 225
226 | 226
227 | 227
228 | 228
229 | 229
230 | 230
231 | 231
232 | 232
233 | 233
234 | 234
235 | 235
236 | 236
237 | 237
238 | 238
239 | 239
240 | 240
241 | 241
242 | 242
243 | 243
244 | 244
245 | 245
246 | 246
247 | 247
248 | 248
249 | 249
250 | 250
251 | 251
252 | 252
253 | 253
254 | 254
255 | 255
256 | 256
257 | 257
258 | 258
259 | 259
260 | 260
261 | 261
262 | 262
263 | 263
264 | 264
265 | 265
266 | 266
267 | 267
268 | 268
269 | 269
270 | 270
271 | 271
272 | 272
273 | 273
274 | 274
275 | 275
276 | 276
277 | 277
278 | 278
279 | 279
280 | 280
281 | 281
282 | 282
283 | 283
284 | 284
285 | 285
286 | 286
287 | 287
288 | 288
289 | 289
290 | 290
291 | 291
292 | 292
293 | 293
294 | 294
295 | 295
296 | 296
297 | 297
298 | 298
299 | 299
300 | 300
301 | 301
302 | 302
303 | 303
304 | 304
305 | 305
306 | 306
307 | 307
308 | 308
309 | 309
310 | 310
311 | 311
312 | 312
313 | 313
314 | 314
315 | 315
316 | 316
317 | 317
318 | 318
319 | 319
320 | 320
321 | 321
322 | 322
323 | 323
324 | 324
325 | 325
326 | 326
327 | 327
328 | 328
329 | 329
330 | 330
331 | 331
332 | 332
333 | 333
334 | 334
335 | 335
336 | 336
337 | 337
338 | 338
339 | 339
340 | 340
341 | 341
342 | 342
343 | 343
344 | 344
345 | 345
346 | 346
347 | 347
348 | 348
349 | 349
350 | 350
351 | 351
352 | 352
353 | 353
354 | 354
355 | 355
356 | 356
357 | 357
358 | 358
359 | 359
360 | 360
361 | 361
362 | 362
363 | 363
364 | 364
365 | 365
366 | 366
367 | 367
368 | 368
369 | 369
370 | 370
371 | 371
372 | 372
373 | 373
374 | 374
375 | 375
376 | 376
377 | 377
378 | 378
379 | 379
380 | 380
381 | 381
382 | 382
383 | 383
384 | 384
385 | 385
386 | 386
387 | 387
388 | 388
389 | 389
390 | 390
391 | 391
392 | 392
393 | 393
394 | 394
395 | 395
396 | 396
397 | 397
398 | 398
399 | 399
400 | 400
401 | 401
402 | 402
403 | 403
404 | 404
405 | 405
406 | 406
407 | 407
408 | 408
409 | 409
410 | 410
411 | 411
412 | 412
413 | 413
414 | 414
415 | 415
416 | 416
417 | 417
418 | 418
419 | 419
420 | 420
421 | 421
422 | 422
423 | 423
424 | 424
425 | 425
426 | 426
427 | 427
428 | 428
429 | 429
430 | 430
431 | 431
432 | 432
433 | 433
434 | 434
435 | 435
436 | 436
437 | 437
438 | 438
439 | 439
440 | 440
441 | 441
442 | 442
443 | 443
444 | 444
445 | 445
446 | 446
447 | 447
448 | 448
449 | 449
450 | 450
451 | 451
452 | 452
453 | 453
454 | 454
455 | 455
456 | 456
457 | 457
458 | 458
459 | 459
460 | 460
461 | 461
462 | 462
463 | 463
464 | 464
465 | 465
466 | 466
467 | 467
468 | 468
469 | 469
470 | 470
471 | 471
472 | 472
473 | 473
474 | 474
475 | 475
476 | 476
477 | 477
478 | 478
479 | 479
480 | 480
481 | 481
482 | 482
483 | 483
484 | 484
485 | 485
486 | 486
487 | 487
488 | 488
489 | 489
490 | 490
491 | 491
492 | 492
493 | 493
494 | 494
495 | 495
496 | 496
497 | 497
498 | 498
499 | 499
500 | 500
501 | 501
502 | 502
503 | 503
504 | 504
505 | 505
506 | 506
507 | 507
508 | 508
509 | 509
510 | 510
511 | 511
512 | 512
513 | 513
514 | 514
515 | 515
516 | 516
517 | 517
518 | 518
519 | 519
520 | 520
521 | 521
522 | 522
523 | 523
524 | 524
525 | 525
526 | 526
527 | 527
528 | 528
529 | 529
530 | 530
531 | 531
532 | 532
533 | 533
534 | 534
535 | 535
536 | 536
537 | 537
538 | 538
539 | 539
540 | 540
541 | 541
542 | 542
543 | 543
544 | 544
545 | 545
546 | 546
547 | 547
548 | 548
549 | 549
550 | 550
551 | 551
552 | 552
553 | 553
554 | 554
555 | 555
556 | 556
557 | 557
558 | 558
559 | 559
560 | 560
561 | 561
562 | 562
563 | 563
564 | 564
565 | 565
566 | 566
567 | 567
568 | 568
569 | 569
570 | 570
571 | 571
572 | 572
573 | 573
574 | 574
575 | 575
576 | 576
577 | 577
578 | 578
579 | 579
580 | 580
581 | 581
582 | 582
583 | 583
584 | 584
585 | 585
586 | 586
587 | 587
588 | 588
589 | 589
590 | 590
591 | 591
592 | 592
593 | 593
594 | 594
595 | 595
596 | 596
597 | 597
598 | 598
599 | 599
600 | 600
601 | 601
602 | 602
603 | 603
604 | 604
605 | 605
606 | 606
607 | 607
608 | 608
609 | 609
610 | 610
611 | 611
612 | 612
613 | 613
614 | 614
615 | 615
616 | 616
617 | 617
618 | 618
619 | 619
620 | 620
621 | 621
622 | 622
623 | 623
624 | 624
625 | 625
626 | 626
627 | 627
628 | 628
629 | 629
630 | 630
631 | 631
632 | 632
633 | 633
634 | 634
635 | 635
636 | 636
637 | 637
638 | 638
639 | 639
640 | 640
641 | 641
642 | 642
643 | 643
644 | 644
645 | 645
646 | 646
647 | 647
648 | 648
649 | 649
650 | 650
651 | 651
652 | 652
653 | 653
654 | 654
655 | 655
656 | 656
657 | 657
658 | 658
659 | 659
660 | 660
661 | 661
662 | 662
663 | 663
664 | 664
665 | 665
666 | 666
667 | 667
668 | 668
669 | 669
670 | 670
671 | 671
672 | 672
673 | 673
674 | 674
675 | 675
676 | 676
677 | 677
678 | 678
679 | 679
680 | 680
681 | 681
682 | 682
683 | 683
684 | 684
685 | 685
686 | 686
687 | 687
688 | 688
689 | 689
690 | 690
691 | 691
692 | 692
693 | 693
694 | 694
695 | 695
696 | 696
697 | 697
698 | 698
699 | 699
700 | 700
701 | 701
702 | 702
703 | 703
704 | 704
705 | 705
706 | 706
707 | 707
708 | 708
709 | 709
710 | 710
711 | 711
712 | 712
713 | 713
714 | 714
715 | 715
716 | 716
717 | 717
718 | 718
719 | 719
720 | 720
721 | 721
722 | 722
723 | 723
724 | 724
725 | 725
726 | 726
727 | 727
728 | 728
729 | 729
730 | 730
731 | 731
732 | 732
733 | 733
734 | 734
735 | 735
736 | 736
737 | 737
738 | 738
739 | 739
740 | 740
741 | 741
742 | 742
743 | 743
744 | 744
745 | 745
746 | 746
747 | 747
748 | 748
749 | 749
750 | 750
751 | 


--------------------------------------------------------------------------------
/data/msrvid/train/sim.txt:
--------------------------------------------------------------------------------
  1 | 5.000
  2 | 5.000
  3 | 0.300
  4 | 0.600
  5 | 4.200
  6 | 3.600
  7 | 5.000
  8 | 2.750
  9 | 5.000
 10 | 3.750
 11 | 5.000
 12 | 1.250
 13 | 5.000
 14 | 4.250
 15 | 3.400
 16 | 1.600
 17 | 5.000
 18 | 3.600
 19 | 5.000
 20 | 2.600
 21 | 5.000
 22 | 3.200
 23 | 4.800
 24 | 5.000
 25 | 5.000
 26 | 2.200
 27 | 5.000
 28 | 4.250
 29 | 5.000
 30 | 2.500
 31 | 2.800
 32 | 2.800
 33 | 2.800
 34 | 2.800
 35 | 5.000
 36 | 4.800
 37 | 2.400
 38 | 4.200
 39 | 5.000
 40 | 5.000
 41 | 5.000
 42 | 3.750
 43 | 3.800
 44 | 2.000
 45 | 2.000
 46 | 1.583
 47 | 1.778
 48 | 2.000
 49 | 2.000
 50 | 2.000
 51 | 3.200
 52 | 3.800
 53 | 4.400
 54 | 5.000
 55 | 5.000
 56 | 3.500
 57 | 4.000
 58 | 3.500
 59 | 4.400
 60 | 3.800
 61 | 2.600
 62 | 3.000
 63 | 5.000
 64 | 5.000
 65 | 3.667
 66 | 2.333
 67 | 2.500
 68 | 5.000
 69 | 3.824
 70 | 4.000
 71 | 4.400
 72 | 0.200
 73 | 5.000
 74 | 5.000
 75 | 4.000
 76 | 1.600
 77 | 5.000
 78 | 4.600
 79 | 5.000
 80 | 2.375
 81 | 2.600
 82 | 2.600
 83 | 2.600
 84 | 2.200
 85 | 1.700
 86 | 3.400
 87 | 2.000
 88 | 4.000
 89 | 3.250
 90 | 4.500
 91 | 2.600
 92 | 2.000
 93 | 1.800
 94 | 1.556
 95 | 4.500
 96 | 4.000
 97 | 4.500
 98 | 5.000
 99 | 4.200
100 | 1.600
101 | 0.500
102 | 1.400
103 | 1.600
104 | 4.400
105 | 1.800
106 | 2.200
107 | 2.600
108 | 2.400
109 | 1.600
110 | 3.000
111 | 2.000
112 | 2.000
113 | 3.800
114 | 4.000
115 | 3.000
116 | 4.400
117 | 4.800
118 | 4.800
119 | 2.500
120 | 2.000
121 | 0.750
122 | 0.727
123 | 0.000
124 | 1.250
125 | 1.250
126 | 1.250
127 | 0.500
128 | 3.250
129 | 1.333
130 | 1.000
131 | 1.400
132 | 1.333
133 | 5.000
134 | 4.000
135 | 3.000
136 | 4.400
137 | 2.000
138 | 2.167
139 | 1.400
140 | 1.200
141 | 1.200
142 | 2.200
143 | 1.250
144 | 5.000
145 | 4.000
146 | 4.000
147 | 3.200
148 | 2.000
149 | 2.200
150 | 3.200
151 | 1.500
152 | 4.750
153 | 0.500
154 | 0.067
155 | 1.600
156 | 2.600
157 | 4.875
158 | 5.000
159 | 2.000
160 | 1.400
161 | 1.400
162 | 3.600
163 | 0.800
164 | 3.800
165 | 2.000
166 | 1.600
167 | 3.000
168 | 3.615
169 | 3.750
170 | 0.500
171 | 4.000
172 | 4.000
173 | 3.333
174 | 1.500
175 | 4.400
176 | 4.857
177 | 2.875
178 | 0.800
179 | 5.000
180 | 2.200
181 | 0.200
182 | 0.400
183 | 0.200
184 | 1.800
185 | 1.600
186 | 1.300
187 | 1.200
188 | 1.200
189 | 0.200
190 | 1.200
191 | 3.000
192 | 3.500
193 | 3.500
194 | 2.000
195 | 4.400
196 | 5.000
197 | 0.000
198 | 3.200
199 | 3.250
200 | 0.000
201 | 1.000
202 | 3.667
203 | 3.000
204 | 4.200
205 | 5.000
206 | 4.091
207 | 1.800
208 | 2.000
209 | 2.800
210 | 1.000
211 | 3.800
212 | 4.000
213 | 1.400
214 | 1.800
215 | 5.000
216 | 0.400
217 | 4.400
218 | 1.000
219 | 3.000
220 | 1.200
221 | 3.200
222 | 2.600
223 | 4.600
224 | 3.400
225 | 2.000
226 | 4.000
227 | 4.200
228 | 3.800
229 | 3.400
230 | 3.800
231 | 4.400
232 | 4.200
233 | 4.600
234 | 1.200
235 | 1.200
236 | 0.800
237 | 3.200
238 | 4.000
239 | 2.600
240 | 4.400
241 | 4.200
242 | 3.200
243 | 3.400
244 | 4.000
245 | 4.800
246 | 0.200
247 | 1.800
248 | 2.600
249 | 2.800
250 | 5.000
251 | 0.250
252 | 3.750
253 | 1.750
254 | 3.500
255 | 3.400
256 | 1.400
257 | 1.400
258 | 1.000
259 | 1.800
260 | 4.600
261 | 0.400
262 | 3.600
263 | 3.250
264 | 0.778
265 | 1.250
266 | 0.750
267 | 1.500
268 | 1.000
269 | 3.750
270 | 3.250
271 | 4.000
272 | 3.000
273 | 1.667
274 | 3.333
275 | 2.250
276 | 0.500
277 | 2.750
278 | 2.000
279 | 0.833
280 | 3.750
281 | 4.000
282 | 3.250
283 | 2.750
284 | 1.000
285 | 3.500
286 | 3.750
287 | 4.400
288 | 0.400
289 | 0.400
290 | 4.200
291 | 2.500
292 | 0.250
293 | 0.250
294 | 0.250
295 | 3.800
296 | 2.600
297 | 2.600
298 | 2.000
299 | 1.800
300 | 0.000
301 | 1.200
302 | 3.800
303 | 3.600
304 | 1.800
305 | 1.200
306 | 3.200
307 | 2.200
308 | 0.800
309 | 1.600
310 | 2.800
311 | 0.583
312 | 3.200
313 | 0.800
314 | 3.800
315 | 0.000
316 | 0.667
317 | 5.000
318 | 4.333
319 | 4.214
320 | 1.800
321 | 0.400
322 | 2.000
323 | 2.800
324 | 0.800
325 | 0.800
326 | 0.400
327 | 0.250
328 | 0.750
329 | 0.750
330 | 1.000
331 | 0.000
332 | 0.800
333 | 3.400
334 | 2.769
335 | 2.800
336 | 3.200
337 | 4.200
338 | 2.400
339 | 4.400
340 | 1.800
341 | 0.800
342 | 1.400
343 | 2.250
344 | 0.250
345 | 3.250
346 | 4.500
347 | 4.200
348 | 0.800
349 | 0.800
350 | 0.800
351 | 0.250
352 | 3.500
353 | 2.750
354 | 4.250
355 | 0.500
356 | 0.500
357 | 0.000
358 | 0.500
359 | 0.600
360 | 2.200
361 | 3.200
362 | 4.400
363 | 3.000
364 | 2.250
365 | 2.583
366 | 0.000
367 | 0.000
368 | 1.800
369 | 0.000
370 | 1.400
371 | 0.000
372 | 0.400
373 | 0.000
374 | 0.000
375 | 3.000
376 | 2.000
377 | 2.200
378 | 1.200
379 | 0.000
380 | 1.000
381 | 1.533
382 | 1.667
383 | 1.000
384 | 0.308
385 | 0.000
386 | 0.200
387 | 4.200
388 | 2.600
389 | 3.000
390 | 3.800
391 | 0.750
392 | 0.250
393 | 0.750
394 | 3.000
395 | 0.400
396 | 0.400
397 | 0.400
398 | 4.200
399 | 3.500
400 | 3.500
401 | 3.000
402 | 3.750
403 | 3.400
404 | 3.400
405 | 3.600
406 | 3.800
407 | 1.000
408 | 1.400
409 | 3.400
410 | 3.600
411 | 2.800
412 | 4.000
413 | 3.200
414 | 3.400
415 | 2.333
416 | 2.667
417 | 3.000
418 | 4.667
419 | 3.200
420 | 1.600
421 | 3.000
422 | 0.200
423 | 0.800
424 | 1.200
425 | 0.800
426 | 0.800
427 | 0.750
428 | 0.000
429 | 0.250
430 | 3.250
431 | 4.000
432 | 3.000
433 | 3.333
434 | 3.333
435 | 2.000
436 | 3.000
437 | 3.750
438 | 4.000
439 | 3.000
440 | 2.000
441 | 3.750
442 | 0.000
443 | 0.400
444 | 0.400
445 | 3.200
446 | 0.400
447 | 2.000
448 | 0.000
449 | 0.000
450 | 0.000
451 | 0.500
452 | 4.500
453 | 0.500
454 | 0.500
455 | 0.500
456 | 4.000
457 | 3.750
458 | 4.250
459 | 3.750
460 | 2.750
461 | 3.250
462 | 3.000
463 | 0.800
464 | 0.400
465 | 0.400
466 | 0.400
467 | 0.250
468 | 0.250
469 | 0.750
470 | 0.250
471 | 4.800
472 | 0.800
473 | 0.000
474 | 3.000
475 | 0.750
476 | 3.750
477 | 2.500
478 | 0.000
479 | 0.250
480 | 0.000
481 | 3.500
482 | 3.250
483 | 0.400
484 | 0.800
485 | 1.800
486 | 1.600
487 | 1.200
488 | 4.200
489 | 0.200
490 | 0.200
491 | 0.500
492 | 0.000
493 | 4.500
494 | 0.000
495 | 2.500
496 | 0.750
497 | 4.000
498 | 2.750
499 | 3.400
500 | 3.400
501 | 0.400
502 | 0.400
503 | 3.200
504 | 2.400
505 | 3.000
506 | 0.400
507 | 0.500
508 | 3.929
509 | 3.000
510 | 1.750
511 | 0.000
512 | 0.400
513 | 2.200
514 | 2.600
515 | 3.500
516 | 0.500
517 | 3.500
518 | 0.000
519 | 0.000
520 | 0.000
521 | 1.000
522 | 2.000
523 | 1.200
524 | 0.600
525 | 0.800
526 | 4.000
527 | 0.500
528 | 2.500
529 | 2.750
530 | 3.250
531 | 2.000
532 | 0.800
533 | 0.000
534 | 0.400
535 | 0.600
536 | 0.800
537 | 0.800
538 | 0.800
539 | 0.400
540 | 3.000
541 | 0.400
542 | 0.000
543 | 0.000
544 | 0.000
545 | 0.400
546 | 0.000
547 | 3.600
548 | 0.231
549 | 3.200
550 | 0.000
551 | 0.500
552 | 0.300
553 | 1.250
554 | 0.000
555 | 2.750
556 | 5.000
557 | 0.750
558 | 1.250
559 | 0.000
560 | 0.000
561 | 0.750
562 | 3.750
563 | 2.333
564 | 5.000
565 | 0.000
566 | 3.200
567 | 0.750
568 | 0.500
569 | 2.500
570 | 1.750
571 | 4.000
572 | 0.000
573 | 1.333
574 | 4.600
575 | 4.000
576 | 1.200
577 | 4.200
578 | 3.800
579 | 3.000
580 | 0.200
581 | 3.000
582 | 2.200
583 | 1.400
584 | 0.000
585 | 2.800
586 | 3.800
587 | 3.800
588 | 4.400
589 | 0.000
590 | 3.000
591 | 0.500
592 | 2.750
593 | 1.750
594 | 0.000
595 | 0.000
596 | 1.000
597 | 1.800
598 | 1.600
599 | 2.333
600 | 2.000
601 | 0.333
602 | 1.000
603 | 0.000
604 | 0.000
605 | 3.000
606 | 1.500
607 | 0.400
608 | 0.400
609 | 2.800
610 | 0.167
611 | 0.500
612 | 3.250
613 | 0.000
614 | 0.000
615 | 0.000
616 | 0.000
617 | 0.400
618 | 0.000
619 | 1.000
620 | 1.250
621 | 0.250
622 | 4.250
623 | 0.118
624 | 1.000
625 | 0.400
626 | 0.000
627 | 0.000
628 | 2.500
629 | 0.750
630 | 3.000
631 | 1.600
632 | 2.600
633 | 2.600
634 | 0.000
635 | 0.000
636 | 0.000
637 | 4.250
638 | 0.000
639 | 0.000
640 | 1.000
641 | 0.000
642 | 3.500
643 | 2.400
644 | 2.600
645 | 0.600
646 | 0.100
647 | 0.000
648 | 3.750
649 | 0.000
650 | 0.000
651 | 0.400
652 | 0.400
653 | 2.600
654 | 2.000
655 | 1.750
656 | 3.750
657 | 0.000
658 | 0.750
659 | 0.000
660 | 0.000
661 | 4.000
662 | 0.000
663 | 0.800
664 | 3.400
665 | 3.100
666 | 0.000
667 | 2.750
668 | 2.000
669 | 0.250
670 | 2.692
671 | 3.333
672 | 3.000
673 | 0.667
674 | 3.500
675 | 4.429
676 | 5.000
677 | 3.200
678 | 0.333
679 | 3.333
680 | 3.333
681 | 1.000
682 | 0.188
683 | 2.200
684 | 0.000
685 | 0.000
686 | 0.400
687 | 0.000
688 | 0.250
689 | 2.250
690 | 0.250
691 | 0.800
692 | 2.600
693 | 1.200
694 | 3.200
695 | 1.400
696 | 0.364
697 | 0.200
698 | 2.750
699 | 3.200
700 | 0.600
701 | 3.800
702 | 3.400
703 | 1.500
704 | 2.500
705 | 0.000
706 | 5.000
707 | 4.333
708 | 1.333
709 | 0.333
710 | 3.333
711 | 4.200
712 | 0.800
713 | 1.000
714 | 0.000
715 | 2.800
716 | 1.500
717 | 0.000
718 | 0.000
719 | 0.600
720 | 1.200
721 | 0.000
722 | 0.000
723 | 3.000
724 | 0.000
725 | 0.600
726 | 1.000
727 | 1.750
728 | 0.000
729 | 0.000
730 | 0.000
731 | 1.000
732 | 0.000
733 | 3.500
734 | 0.000
735 | 0.000
736 | 0.000
737 | 2.750
738 | 2.500
739 | 3.400
740 | 2.200
741 | 1.800
742 | 0.000
743 | 0.750
744 | 0.000
745 | 0.000
746 | 0.000
747 | 0.000
748 | 0.000
749 | 0.000
750 | 0.800
751 | 


--------------------------------------------------------------------------------
/data/msrvid/vocab.txt:
--------------------------------------------------------------------------------
   1 | 's
   2 | ,
   3 | .
   4 | a
   5 | aardvark
   6 | about
   7 | abs
   8 | abulance
   9 | abusing
  10 | acoustic
  11 | across
  12 | added
  13 | adding
  14 | addresses
  15 | adds
  16 | adoring
  17 | adult
  18 | african
  19 | after
  20 | against
  21 | agitating
  22 | aiming
  23 | aimlessly
  24 | aims
  25 | air
  26 | airplane
  27 | airways
  28 | all
  29 | alligator
  30 | along
  31 | american
  32 | amount
  33 | an
  34 | and
  35 | anger
  36 | animal
  37 | animals
  38 | animated
  39 | ankle
  40 | another
  41 | ant
  42 | antenna
  43 | anti-freeze
  44 | apart
  45 | apollo
  46 | apple
  47 | applied
  48 | applies
  49 | applying
  50 | are
  51 | area
  52 | arena
  53 | arm
  54 | arms
  55 | around
  56 | arts
  57 | as
  58 | asian
  59 | asleep
  60 | assembling
  61 | astro
  62 | at
  63 | ate
  64 | attached
  65 | attacks
  66 | audience
  67 | automatic
  68 | away
  69 | axe
  70 | baby
  71 | back
  72 | backing
  73 | backs
  74 | backwards
  75 | bacon
  76 | badger
  77 | bag
  78 | bags
  79 | balancing
  80 | ball
  81 | ballerina
  82 | ballet
  83 | balling
  84 | bamboo
  85 | banana
  86 | band
  87 | banging
  88 | barbecue
  89 | barbell
  90 | barbells
  91 | barked
  92 | barking
  93 | baseball
  94 | basket
  95 | basketball
  96 | bath
  97 | bath-tub
  98 | bathing
  99 | bathtub
 100 | batman
 101 | batter
 102 | bays
 103 | beach
 104 | beak
 105 | beans
 106 | bear
 107 | bears
 108 | beating
 109 | beats
 110 | beauty
 111 | beaver
 112 | bed
 113 | beef
 114 | before
 115 | behind
 116 | being
 117 | bell
 118 | belly
 119 | bending
 120 | beside
 121 | between
 122 | bicycle
 123 | bicycles
 124 | bicyclists
 125 | big
 126 | bike
 127 | biker
 128 | bill
 129 | bin
 130 | bird
 131 | bites
 132 | biting
 133 | black
 134 | blades
 135 | blanket
 136 | blender
 137 | block
 138 | blocked
 139 | blocks
 140 | blow
 141 | blowing
 142 | blowtorch
 143 | blue
 144 | bmx
 145 | board
 146 | boarding
 147 | boards
 148 | boat
 149 | body
 150 | boiled
 151 | boiling
 152 | boils
 153 | bollywood
 154 | bomb
 155 | bonfire
 156 | bongo
 157 | book
 158 | booming
 159 | boot
 160 | bottle
 161 | bottles
 162 | bounces
 163 | bowl
 164 | bowling
 165 | box
 166 | boxer
 167 | boxing
 168 | boy
 169 | boys
 170 | braiding
 171 | branch
 172 | bread
 173 | breaded
 174 | breading
 175 | break
 176 | breaking
 177 | breaks
 178 | brick
 179 | bridge
 180 | broccoli
 181 | broke
 182 | broom
 183 | broth
 184 | brush
 185 | brushes
 186 | brushing
 187 | bug
 188 | bull
 189 | bullets
 190 | bun
 191 | bunch
 192 | bunny
 193 | burning
 194 | burrata
 195 | burrowing
 196 | bus
 197 | busting
 198 | butter
 199 | buttered
 200 | buttering
 201 | buttons
 202 | by
 203 | cabbage
 204 | cabinet
 205 | cage
 206 | cake
 207 | calendar
 208 | calender
 209 | call
 210 | camera
 211 | cameras
 212 | can
 213 | canoe
 214 | cantaloupe
 215 | capsicum
 216 | car
 217 | card
 218 | cards
 219 | caribou
 220 | carpet
 221 | carries
 222 | carrot
 223 | carrots
 224 | carrying
 225 | cartoon
 226 | carts
 227 | carving
 228 | castle
 229 | cat
 230 | catches
 231 | catching
 232 | cats
 233 | cattle
 234 | catwalk
 235 | cd
 236 | ceiling
 237 | celebrating
 238 | cell
 239 | cello
 240 | cement
 241 | cereal
 242 | chain
 243 | chair
 244 | chalk
 245 | chases
 246 | chasing
 247 | checking
 248 | checks
 249 | cheese
 250 | cheetah
 251 | chef
 252 | chess
 253 | chest
 254 | chewed
 255 | chewing
 256 | chicken
 257 | child
 258 | children
 259 | chili
 260 | chimp
 261 | chimpanzee
 262 | chop
 263 | chopped
 264 | chopper
 265 | chopping
 266 | chops
 267 | church
 268 | cigarette
 269 | cilantro
 270 | cinder
 271 | circle
 272 | circles
 273 | clarinet
 274 | classes
 275 | clay
 276 | cleaned
 277 | cleaner
 278 | cleaning
 279 | cleans
 280 | cleaver
 281 | cliff
 282 | climb
 283 | climbing
 284 | climbs
 285 | clip
 286 | close
 287 | closes
 288 | closet
 289 | clove
 290 | cloves
 291 | clown
 292 | club
 293 | coating
 294 | cob
 295 | coke
 296 | collecting
 297 | colored
 298 | combing
 299 | come
 300 | comedic
 301 | coming
 302 | communicating
 303 | compete
 304 | computer
 305 | concert
 306 | concrete
 307 | container
 308 | containing
 309 | contents
 310 | cook
 311 | cooked
 312 | cooker
 313 | cooking
 314 | cord
 315 | coriander
 316 | corn
 317 | cosmetics
 318 | costume
 319 | costumes
 320 | couch
 321 | cougar
 322 | could
 323 | counter
 324 | couple
 325 | couples
 326 | covers
 327 | cow
 328 | cowboy
 329 | cracked
 330 | cracking
 331 | cracks
 332 | crashes
 333 | crawled
 334 | crawling
 335 | crawls
 336 | cream
 337 | creature
 338 | creed
 339 | creek
 340 | cricket
 341 | crochet
 342 | crocodile
 343 | crossing
 344 | crowd
 345 | crushing
 346 | crying
 347 | cub
 348 | cubes
 349 | cucumber
 350 | cucumbers
 351 | cup
 352 | cupcake
 353 | curb
 354 | cut
 355 | cuts
 356 | cutting
 357 | cycling
 358 | cyclone
 359 | dance
 360 | danced
 361 | dancer
 362 | dances
 363 | dancing
 364 | dangling
 365 | dead
 366 | deboning
 367 | deep
 368 | deer
 369 | delivering
 370 | descaling
 371 | desert
 372 | desk
 373 | deveining
 374 | dices
 375 | dicing
 376 | did
 377 | dieing
 378 | different
 379 | digging
 380 | digital
 381 | diner
 382 | dips
 383 | dirt
 384 | dish
 385 | dishes
 386 | disk
 387 | dives
 388 | diving
 389 | doctor
 390 | doctors
 391 | does
 392 | dog
 393 | doggy
 394 | doghouse
 395 | dogs
 396 | doing
 397 | doll
 398 | dollar
 399 | donkey
 400 | door
 401 | doorway
 402 | dough
 403 | doused
 404 | down
 405 | downhill
 406 | dragged
 407 | dragging
 408 | dragon
 409 | draining
 410 | drawer
 411 | drawing
 412 | draws
 413 | drew
 414 | dried
 415 | dries
 416 | drill
 417 | drilled
 418 | drilling
 419 | drills
 420 | drinking
 421 | drinks
 422 | driven
 423 | drives
 424 | driving
 425 | drivong
 426 | drool
 427 | dropped
 428 | dropping
 429 | droppped
 430 | drops
 431 | drove
 432 | drum
 433 | drums
 434 | dry
 435 | duck
 436 | dummy
 437 | dumping
 438 | dunking
 439 | each
 440 | eagle
 441 | earth
 442 | eat
 443 | eating
 444 | eats
 445 | edge
 446 | egg
 447 | eggplant
 448 | eggs
 449 | elderly
 450 | electric
 451 | electronic
 452 | elephant
 453 | elephants
 454 | email
 455 | emery
 456 | empty
 457 | emptying
 458 | enclosed
 459 | end
 460 | erase
 461 | erased
 462 | erasing
 463 | escalator
 464 | escelator
 465 | event
 466 | exercise
 467 | exercises
 468 | exercising
 469 | exiting
 470 | exploded
 471 | explodes
 472 | explosion
 473 | eye
 474 | eyelid
 475 | eyeliner
 476 | eyes
 477 | eyeshadow
 478 | face
 479 | fall
 480 | falling
 481 | falls
 482 | fan
 483 | faucet
 484 | fed
 485 | feed
 486 | feeding
 487 | feeds
 488 | feet
 489 | fell
 490 | female
 491 | fence
 492 | ferret
 493 | ferrets
 494 | field
 495 | fighting
 496 | figure
 497 | figures
 498 | filing
 499 | filling
 500 | finding
 501 | finely
 502 | finger
 503 | fingernails
 504 | fingers
 505 | fire
 506 | fired
 507 | fires
 508 | firetruck
 509 | firing
 510 | fish
 511 | fished
 512 | fishes
 513 | fishing
 514 | fistfight
 515 | fistfighting
 516 | fitting
 517 | five
 518 | fixes
 519 | flag
 520 | flew
 521 | flies
 522 | flip
 523 | flipped
 524 | flipping
 525 | flips
 526 | float
 527 | floating
 528 | floor
 529 | flour
 530 | flouring
 531 | flower
 532 | flowers
 533 | flute
 534 | fly
 535 | flying
 536 | folding
 537 | folds
 538 | follow
 539 | followed
 540 | following
 541 | fondles
 542 | food
 543 | foods
 544 | football
 545 | for
 546 | fork
 547 | form
 548 | forth
 549 | fought
 550 | four
 551 | foxes
 552 | freezer
 553 | fridge
 554 | fried
 555 | friend
 556 | frog
 557 | frogs
 558 | from
 559 | front
 560 | fruit
 561 | frying
 562 | full
 563 | funny
 564 | furniture
 565 | game
 566 | gameboy
 567 | games
 568 | garage
 569 | garden
 570 | garland
 571 | garlic
 572 | garlics
 573 | gasoline
 574 | gate
 575 | gel
 576 | get
 577 | gets
 578 | getting
 579 | giant
 580 | gibbon
 581 | giggling
 582 | ginger
 583 | girl
 584 | girls
 585 | giving
 586 | glass
 587 | glasses
 588 | gloved
 589 | gloves
 590 | gnawing
 591 | goal
 592 | goes
 593 | going
 594 | golf
 595 | goo
 596 | gorilla
 597 | got
 598 | grabs
 599 | grand
 600 | grass
 601 | grates
 602 | grating
 603 | gray
 604 | grazed
 605 | grazing
 606 | greasing
 607 | greating
 608 | green
 609 | grilling
 610 | ground
 611 | group
 612 | guinea
 613 | guitar
 614 | guitars
 615 | gun
 616 | guns
 617 | guts
 618 | guy
 619 | hackysack
 620 | had
 621 | hair
 622 | haki
 623 | half
 624 | half-pipe
 625 | hallway
 626 | halter
 627 | hammer
 628 | hamster
 629 | hand
 630 | handgun
 631 | handheld
 632 | handling
 633 | hands
 634 | hanging
 635 | hangs
 636 | happily
 637 | hard
 638 | hardwood
 639 | hare
 640 | harp
 641 | hat
 642 | hay
 643 | he
 644 | head
 645 | heading
 646 | headphones
 647 | heavy
 648 | hedgehog
 649 | helicopter
 650 | helipad
 651 | helping
 652 | her
 653 | herb
 654 | herbs
 655 | herd
 656 | here
 657 | high
 658 | highway
 659 | hiking
 660 | hill
 661 | himself
 662 | his
 663 | hit
 664 | hitchhiking
 665 | hits
 666 | hitting
 667 | hoe
 668 | hold
 669 | holding
 670 | holds
 671 | hole
 672 | holes
 673 | hopping
 674 | hops
 675 | horse
 676 | hose
 677 | hot
 678 | hotel
 679 | house
 680 | how
 681 | huge
 682 | hugging
 683 | hunting
 684 | hurdles
 685 | hurting
 686 | in
 687 | inflatable
 688 | ingrediants
 689 | ingredients
 690 | inner
 691 | inside
 692 | insides
 693 | instrument
 694 | instruments
 695 | int
 696 | internet
 697 | interview
 698 | into
 699 | is
 700 | it
 701 | items
 702 | its
 703 | itself
 704 | jalepeno
 705 | jeep
 706 | jet
 707 | jetski
 708 | jogging
 709 | joker
 710 | jugs
 711 | juice
 712 | jump
 713 | jumped
 714 | jumping
 715 | jumps
 716 | jungle
 717 | kangaroo
 718 | kangroo
 719 | karaoke
 720 | karate
 721 | kept
 722 | kettle
 723 | key
 724 | key-board
 725 | keyboard
 726 | keyboards
 727 | kick
 728 | kicked
 729 | kicking
 730 | kicks
 731 | kid
 732 | kids
 733 | kill
 734 | kind
 735 | kiss
 736 | kissing
 737 | kitchen
 738 | kite
 739 | kitten
 740 | kittens
 741 | kneading
 742 | knife
 743 | knives
 744 | knocked
 745 | knocks
 746 | komodo
 747 | ladies
 748 | lady
 749 | lake
 750 | lamp
 751 | landing
 752 | lands
 753 | lane
 754 | language
 755 | laptop
 756 | large
 757 | laughed
 758 | laughing
 759 | lawn
 760 | lay
 761 | laying
 762 | lays
 763 | leader
 764 | leading
 765 | leaf
 766 | leak
 767 | leather
 768 | leave
 769 | leaves
 770 | leaving
 771 | lecture
 772 | leek
 773 | leg
 774 | legs
 775 | lemon
 776 | lemons
 777 | lemur
 778 | lense
 779 | letter
 780 | levitating
 781 | library
 782 | licking
 783 | licks
 784 | lid
 785 | lies
 786 | lifestyle
 787 | lifted
 788 | lifting
 789 | lifts
 790 | light
 791 | like
 792 | limb
 793 | line
 794 | liner
 795 | lion
 796 | lionel
 797 | lips
 798 | lipstick
 799 | liquid
 800 | lit
 801 | little
 802 | live
 803 | living
 804 | loading
 805 | loaf
 806 | loaves
 807 | log
 808 | logs
 809 | long
 810 | looking
 811 | looks
 812 | lori
 813 | loris
 814 | lorises
 815 | loses
 816 | lot
 817 | lying
 818 | machine
 819 | made
 820 | magazine
 821 | magic
 822 | make
 823 | make-up
 824 | makes
 825 | makeup
 826 | making
 827 | male
 828 | man
 829 | mandolin
 830 | maneuvering
 831 | mango
 832 | mans
 833 | marching
 834 | marsh
 835 | martial
 836 | masked
 837 | massage
 838 | mat
 839 | math
 840 | meal
 841 | measured
 842 | measures
 843 | measuring
 844 | meat
 845 | mechanical
 846 | medics
 847 | meditating
 848 | melon
 849 | melting
 850 | men
 851 | metal
 852 | mice
 853 | microphone
 854 | microwave
 855 | military
 856 | milk
 857 | mimes
 858 | mincing
 859 | missile
 860 | mix
 861 | mixed
 862 | mixer
 863 | mixes
 864 | mixing
 865 | mixture
 866 | mo-pad
 867 | models
 868 | monitor
 869 | monkey
 870 | mooing
 871 | moon-walking
 872 | moose
 873 | mother
 874 | motion
 875 | motor
 876 | motorcar
 877 | motorcycle
 878 | motorcyclist
 879 | motorcyle
 880 | motorized
 881 | mouse
 882 | mouth
 883 | moves
 884 | moving
 885 | mowing
 886 | mud
 887 | mug
 888 | mushrooms
 889 | music
 890 | musical
 891 | mustard
 892 | nail
 893 | nails
 894 | near
 895 | net
 896 | newspaper
 897 | noodles
 898 | noose
 899 | nose
 900 | not
 901 | nuclear
 902 | nunchucks
 903 | object
 904 | obstacles
 905 | ocean
 906 | octopus
 907 | of
 908 | off
 909 | office
 910 | officer
 911 | oil
 912 | okapi
 913 | okra
 914 | old
 915 | older
 916 | olive
 917 | on
 918 | one
 919 | onion
 920 | onions
 921 | onstage
 922 | onto
 923 | oots
 924 | open
 925 | opening
 926 | opens
 927 | operating
 928 | orange
 929 | orangutan
 930 | oriental
 931 | other
 932 | ouf
 933 | out
 934 | outside
 935 | oven
 936 | over
 937 | overweight
 938 | pacifier
 939 | pacing
 940 | package
 941 | packet
 942 | packing
 943 | paddle
 944 | paddling
 945 | paino
 946 | painting
 947 | pan
 948 | pancakes
 949 | panda
 950 | pandas
 951 | pankda
 952 | paper
 953 | papers
 954 | parking
 955 | parrot
 956 | partially
 957 | parts
 958 | passionately
 959 | pasta
 960 | paste
 961 | pasture
 962 | path
 963 | patient
 964 | patio
 965 | patting
 966 | paw
 967 | pawing
 968 | paws
 969 | peas
 970 | pecking
 971 | pecks
 972 | peddling
 973 | peeled
 974 | peeler
 975 | peeling
 976 | peels
 977 | pen
 978 | pencil
 979 | pencilling
 980 | penguins
 981 | people
 982 | pepper
 983 | pepperoni
 984 | peppers
 985 | perched
 986 | performed
 987 | performing
 988 | performs
 989 | perimeter
 990 | person
 991 | persons
 992 | pet
 993 | petted
 994 | petting
 995 | phone
 996 | piano
 997 | pianos
 998 | picked
 999 | picking
1000 | picks
1001 | picture
1002 | pictures
1003 | piece
1004 | pieces
1005 | pig
1006 | pilots
1007 | pineapple
1008 | pineapples
1009 | ping
1010 | pink
1011 | pipe
1012 | pistol
1013 | pitcher
1014 | pizza
1015 | pizzas
1016 | place
1017 | places
1018 | placing
1019 | plane
1020 | plant
1021 | plantain
1022 | plants
1023 | plastic
1024 | plate
1025 | platform
1026 | play
1027 | played
1028 | player
1029 | players
1030 | playing
1031 | plays
1032 | podium
1033 | poking
1034 | polar
1035 | pole
1036 | policewomen
1037 | pong
1038 | pony
1039 | pool
1040 | pooping
1041 | pops
1042 | pork
1043 | pot
1044 | potato
1045 | potatoe
1046 | potatoes
1047 | potatos
1048 | pots
1049 | pouncing
1050 | poured
1051 | pouring
1052 | pours
1053 | powder
1054 | powdering
1055 | power
1056 | practicing
1057 | pratices
1058 | prawn
1059 | praying
1060 | prepared
1061 | preparing
1062 | pressing
1063 | prey
1064 | processor
1065 | prone
1066 | protective
1067 | pudding
1068 | puddle
1069 | pull-ups
1070 | pulled
1071 | pulling
1072 | pulls
1073 | pumpkin
1074 | punching
1075 | puncturing
1076 | puppies
1077 | puppy
1078 | push
1079 | push-ups
1080 | pushed
1081 | pushes
1082 | pushing
1083 | pushups
1084 | put
1085 | puts
1086 | putting
1087 | quail
1088 | quickly
1089 | rabbi
1090 | rabbit
1091 | rabbits
1092 | raccoons
1093 | race
1094 | racers
1095 | racing
1096 | rack
1097 | racks
1098 | raft
1099 | railway
1100 | rain
1101 | ran
1102 | rapidly
1103 | rat
1104 | raw
1105 | read
1106 | reading
1107 | recruits
1108 | red
1109 | reels
1110 | refrigerator
1111 | relaxes
1112 | relaxing
1113 | removes
1114 | removing
1115 | repeatedly
1116 | resting
1117 | retrieves
1118 | reverse
1119 | revive
1120 | rhino
1121 | ribs
1122 | ric-a-roni
1123 | rice
1124 | richie
1125 | ride
1126 | rider
1127 | rides
1128 | riding
1129 | ridint
1130 | rifle
1131 | rihanna
1132 | rind
1133 | ring
1134 | ringers
1135 | rinsing
1136 | rises
1137 | rising
1138 | ritchie
1139 | river
1140 | road
1141 | robin
1142 | robot
1143 | robots
1144 | rock
1145 | rocket
1146 | rocks
1147 | rocky
1148 | rod
1149 | rode
1150 | roll
1151 | rolling
1152 | roni
1153 | roof
1154 | room
1155 | rooster
1156 | roosters
1157 | rope
1158 | rowing
1159 | rubbing
1160 | rubs
1161 | run
1162 | runners
1163 | running
1164 | runs
1165 | runway
1166 | rushes
1167 | sails
1168 | sak
1169 | salt
1170 | same
1171 | sand
1172 | sang
1173 | sapling
1174 | sat
1175 | sauce
1176 | saucer
1177 | sausages
1178 | sawing
1179 | say
1180 | scales
1181 | scaling
1182 | scallions
1183 | scallop
1184 | scared
1185 | scissors
1186 | scooter
1187 | scores
1188 | scrambles
1189 | scraping
1190 | scratched
1191 | scratches
1192 | scratching
1193 | screaming
1194 | screwing
1195 | scrubbing
1196 | sea
1197 | seadoo
1198 | seafood
1199 | seasoning
1200 | seasons
1201 | seat
1202 | seated
1203 | seeds
1204 | selling
1205 | serves
1206 | serving
1207 | set
1208 | sets
1209 | several
1210 | severs
1211 | sewing
1212 | shadow
1213 | shake
1214 | shaking
1215 | shallot
1216 | shappens
1217 | share
1218 | sharpening
1219 | sharpens
1220 | shaved
1221 | she
1222 | sheet
1223 | shelf
1224 | shimmied
1225 | shimp
1226 | shirt
1227 | shirtless
1228 | shiso
1229 | shoe
1230 | shooting
1231 | shoots
1232 | shopping
1233 | short
1234 | shot
1235 | shotgun
1236 | shoulder
1237 | shoulders
1238 | shower
1239 | showing
1240 | shown
1241 | shows
1242 | shredded
1243 | shredding
1244 | shreded
1245 | shrimp
1246 | shrimps
1247 | side
1248 | sidewalk
1249 | sign
1250 | silencer
1251 | sing
1252 | singing
1253 | sings
1254 | sink
1255 | sitiing
1256 | sitring
1257 | sits
1258 | sitting
1259 | skate
1260 | skateboard
1261 | skateboarder
1262 | skateboards
1263 | skating
1264 | skewered
1265 | skewers
1266 | skiing
1267 | skillet
1268 | skin
1269 | skipping
1270 | skit
1271 | skull
1272 | skunk
1273 | sky
1274 | slab
1275 | slabs
1276 | slam
1277 | slaps
1278 | slced
1279 | sledge
1280 | sledghammer
1281 | slice
1282 | sliced
1283 | slicer
1284 | slices
1285 | slicing
1286 | slide
1287 | slides
1288 | sliding
1289 | slime
1290 | slipping
1291 | slow
1292 | slowly
1293 | small
1294 | smashing
1295 | smearing
1296 | smelling
1297 | smeone
1298 | smoked
1299 | smoking
1300 | snake
1301 | sneezed
1302 | sniffing
1303 | snow
1304 | soccer
1305 | socializing
1306 | soda
1307 | solenodon
1308 | some
1309 | someoen
1310 | someone
1311 | something
1312 | song
1313 | sound
1314 | soup
1315 | south
1316 | space
1317 | spanked
1318 | spanks
1319 | speaker
1320 | speaking
1321 | speaks
1322 | speech
1323 | spices
1324 | spinning
1325 | spins
1326 | spitting
1327 | splashes
1328 | split
1329 | splits
1330 | spoon
1331 | spray
1332 | sprayed
1333 | spraying
1334 | sprays
1335 | spread
1336 | spreading
1337 | spreads
1338 | sprinkled
1339 | sprinkler
1340 | sprinkling
1341 | spun
1342 | square
1343 | squash
1344 | squeezing
1345 | squirrel
1346 | stabbing
1347 | stage
1348 | staircase
1349 | stairs
1350 | stand
1351 | standing
1352 | stared
1353 | stares
1354 | staring
1355 | starting
1356 | station
1357 | statue
1358 | steak
1359 | stealing
1360 | stenograph
1361 | stenographers
1362 | step
1363 | steps
1364 | stick
1365 | stickers
1366 | sticky
1367 | still
1368 | stirred
1369 | stirring
1370 | stirs
1371 | stone
1372 | store
1373 | storm
1374 | stove
1375 | straightening
1376 | straining
1377 | straw
1378 | street
1379 | streets
1380 | stretcher
1381 | strikes
1382 | striking
1383 | string
1384 | strip
1385 | strips
1386 | stroking
1387 | strumming
1388 | stuck
1389 | studies
1390 | stuffed
1391 | stuffing
1392 | stunts
1393 | styling
1394 | submarine
1395 | substance
1396 | sucker
1397 | sucking
1398 | sugar
1399 | suit
1400 | suitcases
1401 | sumo
1402 | sun
1403 | sunflowers
1404 | sunglasses
1405 | sunset
1406 | surf
1407 | surfing
1408 | swamp
1409 | swats
1410 | sweeping
1411 | swept
1412 | swim
1413 | swimmers
1414 | swimming
1415 | swims
1416 | swing
1417 | swinging
1418 | swings
1419 | sword
1420 | swung
1421 | t
1422 | t-shirt
1423 | table
1424 | tables
1425 | tail
1426 | take
1427 | takes
1428 | taking
1429 | talk
1430 | talked
1431 | talking
1432 | talks
1433 | tap
1434 | tape
1435 | tapping
1436 | target
1437 | tased
1438 | taxi
1439 | teams
1440 | tearing
1441 | teased
1442 | teasing
1443 | teenage
1444 | teenager
1445 | telephone
1446 | television
1447 | tennis
1448 | that
1449 | the
1450 | their
1451 | them
1452 | themselves
1453 | then
1454 | there
1455 | thick
1456 | thin
1457 | things
1458 | thinking
1459 | thinly
1460 | three
1461 | threw
1462 | through
1463 | throwing
1464 | throws
1465 | tiding
1466 | tied
1467 | ties
1468 | tiger
1469 | tight
1470 | tiles
1471 | tiny
1472 | tire
1473 | tires
1474 | to
1475 | toad
1476 | toaster
1477 | toddler
1478 | toe
1479 | tofu
1480 | together
1481 | toilet
1482 | tomato
1483 | tomatoe
1484 | tomatoes
1485 | tomatos
1486 | too
1487 | took
1488 | top
1489 | torching
1490 | tortila
1491 | tortilla
1492 | tortoise
1493 | tosses
1494 | touch
1495 | touched
1496 | touching
1497 | touchscreen
1498 | toward
1499 | towards
1500 | towel
1501 | towels
1502 | town
1503 | toy
1504 | toys
1505 | track
1506 | tracking
1507 | tractor
1508 | traffic
1509 | trail
1510 | train
1511 | trainer
1512 | trampoline
1513 | transport
1514 | trash
1515 | trashcan
1516 | traveling
1517 | tray
1518 | trays
1519 | treatment
1520 | tree
1521 | trees
1522 | trick
1523 | tricks
1524 | tricycle
1525 | tried
1526 | tries
1527 | trotted
1528 | truck
1529 | trumpet
1530 | trunk
1531 | trying
1532 | trys
1533 | tub
1534 | tube
1535 | tuber
1536 | tummy
1537 | tuna
1538 | turf
1539 | turning
1540 | turns
1541 | turtle
1542 | tv
1543 | twice
1544 | two
1545 | tying
1546 | typed
1547 | typing
1548 | ukulele
1549 | uncooked
1550 | under
1551 | underwater
1552 | up
1553 | ups
1554 | upset
1555 | upside
1556 | upside-down
1557 | used
1558 | uses
1559 | using
1560 | vacuuming
1561 | van
1562 | vase
1563 | vegetable
1564 | vegetables
1565 | vehicle
1566 | very
1567 | vice
1568 | video
1569 | violin
1570 | vodka
1571 | volleyballs
1572 | wading
1573 | wake
1574 | waking
1575 | walk
1576 | walked
1577 | walking
1578 | walks
1579 | wall
1580 | wallaby
1581 | walruses
1582 | was
1583 | washing
1584 | washington
1585 | waste
1586 | watching
1587 | water
1588 | water-tub
1589 | watermelon
1590 | waterskiing
1591 | wave
1592 | waves
1593 | waving
1594 | way
1595 | weapon
1596 | wearing
1597 | webcam
1598 | wedges
1599 | weight
1600 | weights
1601 | were
1602 | whacks
1603 | what
1604 | wheel
1605 | wheelies
1606 | while
1607 | whipping
1608 | whisk
1609 | whisked
1610 | whisking
1611 | white
1612 | who
1613 | wilderness
1614 | wind
1615 | window
1616 | windows
1617 | winds
1618 | wine
1619 | wipes
1620 | wiping
1621 | wire
1622 | with
1623 | wok
1624 | wolves
1625 | woman
1626 | women
1627 | wood
1628 | wooded
1629 | wooden
1630 | woods
1631 | work
1632 | working
1633 | wrapping
1634 | wrestling
1635 | writing
1636 | yard
1637 | yellow
1638 | yoga
1639 | you
1640 | young
1641 | youngster
1642 | zebras
1643 | zoo
1644 | zucchini
1645 | 


--------------------------------------------------------------------------------
/data/sick/combinePredictionScoreswithAnnotationforSicData.pl:
--------------------------------------------------------------------------------
 1 | use strict;
 2 | use warnings;
 3 | 
 4 | 
 5 | #20      There is no man in a black jacket doing tricks on a motorbike   A person in a black jacket is doing tricks on a motorbike       3.6     CONTRADICTION
 6 | 
 7 | my $anno = shift;
 8 | my $pred = shift;
 9 | 
10 | open IN1, "< $anno" or die $!;
11 | open IN2, "< $pred" or die $!;
12 | 
13 | my @annoo = <IN1>;
14 | my @predd = <IN2>;
15 | 
16 | chomp(@annoo);
17 | chomp(@predd);
18 | 
19 | if(@annoo-1 != @predd){
20 | 	print("error!\n");
21 | 	exit;
22 | }
23 | 
24 | print "pair_ID\tsentence_A\tsentence_B\trelatedness_score\tentailment_judgment\n";
25 | 
26 | for(my $i = 0; $i < @predd; $i++){
27 | 	if($annoo[$i+1] =~ /^(\d+\t(.*\t){2})(\d+\.*\d*)(\t.*)$/){
28 | 		print $1.$predd[$i].$4."\n";
29 | 	}else{
30 | 		print "Noooooooooooooooooooooot possible\n";
31 | 		print $annoo[$i+1]."\n";
32 | 		exit;
33 | 	}
34 | }
35 | 


--------------------------------------------------------------------------------
/data/sick/dev/a.parents:
--------------------------------------------------------------------------------
  1 | 3 3 5 5 11 5 6 9 6 11 0 11
  2 | 2 8 2 6 6 3 8 0 8 8 12 10
  3 | 2 4 4 0 4 4 8 6
  4 | 2 4 4 0 6 4
  5 | 2 4 4 0 4 5 6 10 10 7
  6 | 2 4 4 0 4 7 5 4 10 8 4 11
  7 | 2 0 2 8 8 5 5 3 2 11 2 11 14 12
  8 | 2 4 4 0 4 7 5
  9 | 2 6 2 3 6 0
 10 | 2 0 4 2 4 7 5
 11 | 2 11 2 3 4 9 9 9 5 11 0
 12 | 2 6 2 3 6 0
 13 | 2 7 2 5 3 7 0 7 7
 14 | 2 3 0 3 4 5 8 6 3 11 3 14 14 11
 15 | 2 4 4 0 4 7 5
 16 | 3 3 5 5 0 7 5
 17 | 7 7 7 5 7 7 0 7 8
 18 | 2 8 2 3 4 5 8 0 8 11 9 8 14 8 16 14 16 19 17
 19 | 5 5 2 2 7 7 0 7 11 11 8
 20 | 5 5 2 2 7 7 0 7 13 13 10 10 8
 21 | 2 4 4 0 4 7 5 4 10 4 12 10
 22 | 2 6 2 3 6 0 6 9 7
 23 | 2 4 4 0 6 4 6 9 7 9 13 13 10
 24 | 2 4 4 0 4 4 8 6
 25 | 2 14 2 5 3 5 8 5 5 12 12 5 14 0 16 14
 26 | 2 14 2 5 3 5 8 5 5 12 12 5 14 0 16 14
 27 | 2 4 4 0 4 8 8 5
 28 | 2 4 4 0 4 7 4 7 12 12 12 7 4 4 4 18 18 15
 29 | 2 4 4 0 4 7 4 9 7 9 10 11 14 11 14 17 15
 30 | 2 5 5 5 0 7 5 5 11 11 8 13 11 13 16 14
 31 | 2 4 4 0 6 4 4 9 7
 32 | 2 4 4 0 6 4 4 9 7 9 13 13 9
 33 | 3 3 5 5 0 5 9 9 6
 34 | 2 8 5 5 2 5 5 0 8 12 12 9 8 15 8 17 15 15 20 18
 35 | 3 3 8 3 6 4 8 0 8
 36 | 3 3 8 3 6 4 8 0 8
 37 | 2 5 5 5 0 5 8 6
 38 | 2 8 2 6 6 2 2 0 8 12 12 9
 39 | 2 6 2 3 6 0 6 9 7
 40 | 3 3 9 3 7 7 3 9 0 9 9 11 12 15 13
 41 | 3 3 6 3 4 0 6 11 11 11 7
 42 | 2 10 2 8 8 5 5 3 10 0
 43 | 2 5 5 5 0 5 8 6
 44 | 2 8 2 6 6 3 8 0 10 8
 45 | 2 5 5 5 0 5 8 6 10 8 10 13 11 13 18 18 18 13
 46 | 2 4 4 0 4 7 5 4 10 8 10 11 14 12
 47 | 2 3 0 3 6 4 3 9 3 9 12 10
 48 | 2 10 2 5 3 5 8 5 10 0 10 13 11
 49 | 2 5 5 5 0 5 8 6
 50 | 2 4 4 0 6 4
 51 | 2 4 4 0 4 7 5 7 10 8
 52 | 2 5 5 5 0 5 8 6
 53 | 2 4 4 0 6 4
 54 | 2 4 4 0 6 4
 55 | 2 7 2 5 2 7 0 7 11 11 8
 56 | 2 7 2 5 2 7 0 7 7 11 9
 57 | 2 4 4 0 6 4
 58 | 2 4 4 0 4 4 4
 59 | 2 4 4 0 4 4
 60 | 2 4 4 0 4 4 6 9 7
 61 | 2 4 4 0 4 7 5
 62 | 2 4 4 0 4
 63 | 2 4 4 0 7 7 4
 64 | 2 4 4 0 4 7 5
 65 | 2 4 4 0
 66 | 3 3 5 5 0
 67 | 2 4 4 0 4
 68 | 3 3 0 3
 69 | 2 4 4 0 4 7 5
 70 | 3 3 5 5 0 5 5 7 7 9 13 13 10
 71 | 2 4 4 0 4 7 4
 72 | 2 5 5 5 0 7 5 5 10 8
 73 | 2 4 4 0 7 7 4
 74 | 2 4 4 0 6 4
 75 | 2 8 2 5 3 8 8 0
 76 | 2 4 4 0 6 4
 77 | 2 4 4 0 6 4
 78 | 2 5 5 5 0 5 8 6
 79 | 2 5 5 5 0 7 5 5 10 8
 80 | 2 5 5 5 0 5 8 6 5 11 9
 81 | 2 4 4 0 4 4 8 6
 82 | 2 5 5 5 0 5 8 6
 83 | 2 4 4 0 6 4 6 7 8 4
 84 | 2 4 4 0 6 4 6 7 8
 85 | 2 5 5 5 0 5 9 9 6 5 12 10
 86 | 2 4 4 0 6 4 4
 87 | 2 4 4 0 6 4 4
 88 | 2 10 2 6 6 2 2 10 10 0 12 10 10 16 16 13
 89 | 2 4 4 0 4 7 5
 90 | 2 11 2 3 4 8 8 5 11 11 0
 91 | 2 8 2 5 3 8 8 0 8 9 10 13 11
 92 | 2 8 2 5 3 8 8 0 8 11 9
 93 | 2 4 4 0 6 4 6 6
 94 | 2 5 5 5 0 5 8 6
 95 | 2 5 5 5 0 5 5 9 7
 96 | 2 4 4 0 4 7 4 4
 97 | 2 4 4 0 6 4
 98 | 2 4 4 0 6 4
 99 | 3 3 5 5 0 7 5
100 | 2 6 2 5 3 0 6 10 10 6 10 13 11 13 16 14 10 19 17
101 | 2 4 4 0 4 7 5
102 | 3 3 0 3 6 3
103 | 2 4 4 0 6 4 4 9 7
104 | 2 4 4 0 6 4
105 | 2 4 4 0 4 4 8 6
106 | 2 4 4 0 4 4 8 6
107 | 2 4 4 0 6 4 4 10 10 7
108 | 2 5 5 5 0 5 8 6 8 12 12 9
109 | 2 7 2 5 3 7 0 7 11 11 8
110 | 2 4 4 0 6 4 4 7
111 | 2 5 5 5 0 7 5
112 | 3 3 0 5 3
113 | 2 4 4 0 6 4
114 | 2 4 4 0 6 4
115 | 2 4 4 0 6 4
116 | 2 4 4 0 4 7 4
117 | 2 4 4 0
118 | 2 4 4 0 6 4
119 | 3 3 5 5 0 5 8 6
120 | 3 3 0 3 3 7 5
121 | 2 0 4 2 4 5 5 9 7
122 | 2 4 4 0 6 4
123 | 3 3 0 5 3
124 | 2 4 4 0
125 | 3 3 6 6 6 0 6 9 7
126 | 2 4 4 0 7 7 4
127 | 2 5 5 5 0 7 5 7 10 8
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220 | 2 4 4 0 6 4
221 | 2 4 4 0 6 4
222 | 2 4 4 0 6 4
223 | 2 4 4 0 4 7 4
224 | 2 4 4 0 4 4 6 9 7
225 | 2 0 4 2 4 8 8 5
226 | 2 4 4 0 6 4
227 | 2 4 4 0 6 4 6 9 7
228 | 2 4 4 0 6 4
229 | 2 4 4 0 4 7 5
230 | 2 4 4 0 4
231 | 2 4 4 0 4 7 4
232 | 2 0 4 2 2
233 | 2 0 4 2 2
234 | 2 4 4 0 6 4
235 | 2 4 4 0 6 4
236 | 2 0 4 2 4 7 5
237 | 2 4 4 0 4 7 5 9 7
238 | 3 3 5 5 0
239 | 2 4 4 0 6 4 6 9 7
240 | 2 4 4 0 6 4
241 | 2 4 4 0 6 4
242 | 2 4 4 0 6 4
243 | 2 4 4 0 6 4
244 | 2 0 4 2 4 7 5 5 10 8
245 | 2 4 4 0 6 4
246 | 2 6 2 3 6 0 6 6 10 8
247 | 2 4 4 0 6 4 4 9 7
248 | 2 0 4 2 4 7 5 5 10 8
249 | 2 0 4 2 7 7 4
250 | 2 4 4 0 4
251 | 2 4 4 0 6 4 4 9 7
252 | 2 4 4 0 6 4 4 9 7
253 | 2 4 4 0 6 4
254 | 2 4 4 0 6 4
255 | 2 5 5 5 0 7 5
256 | 2 0 4 2 4 7 5 5 10 8
257 | 2 4 4 0 6 4
258 | 2 4 4 0 6 4
259 | 2 4 4 0
260 | 2 4 4 0 4 4 8 6
261 | 2 4 4 0 4 4 9 9 6
262 | 2 4 4 0 4 7 5
263 | 2 4 4 0 4
264 | 2 0 4 5 2 7 5
265 | 2 5 5 5 0 5 8 6
266 | 2 4 4 0 6 4 4 4 4
267 | 2 4 4 0 4 7 5
268 | 2 4 4 0 6 4
269 | 2 0 4 2 4 7 5
270 | 2 4 4 0 6 4
271 | 2 0 4 2 4 7 5
272 | 2 4 4 0 6 4
273 | 3 3 5 5 0
274 | 2 4 4 0 7 7 4 4 10 8
275 | 2 4 4 0 6 4 6 7
276 | 4 4 4 0 4 7 5 4 10 8
277 | 2 5 5 5 0 5 5 9 7
278 | 2 4 4 0 6 4
279 | 2 4 4 0 6 4
280 | 2 0 4 2 4 7 5
281 | 2 4 4 0 6 4
282 | 2 5 5 5 0 7 5
283 | 2 4 4 0 6 4
284 | 2 4 4 0 4
285 | 2 6 2 3 6 0
286 | 2 4 4 0 6 4
287 | 2 4 4 0 6 4
288 | 2 4 4 0 4 4 8 6
289 | 2 0 2 3 6 4
290 | 2 4 4 0 4 7 5
291 | 3 3 5 5 0 5 8 6
292 | 2 5 5 5 0 5 8 6
293 | 2 4 4 0 6 4
294 | 2 7 2 5 2 7 0 7 10 8 7 14 14 11
295 | 2 4 4 0 6 4
296 | 2 4 4 0 6 4
297 | 2 4 4 0 4
298 | 2 4 4 0 4
299 | 2 4 4 0
300 | 3 3 0 3 3 7 5
301 | 2 4 4 0 6 4 4 7
302 | 2 6 2 5 3 0 6 9 7
303 | 3 3 9 3 7 7 4 9 0 9 13 13 9 9 16 9 16 19 17
304 | 3 3 5 5 0 5 8 6
305 | 2 6 2 5 2 0 6 10 10 7 6 6 15 15 12 12 18 12 12 19 22 20
306 | 2 6 2 3 6 0 6 9 7 9 12 10
307 | 5 5 2 2 7 7 0 9 7 7 12 10
308 | 2 7 2 5 3 7 0 7 11 11 8
309 | 3 3 0 3 6 4 3 3 10 8
310 | 4 4 4 6 6 0 6 6 10 6
311 | 2 8 2 6 6 3 8 0 11 11 8
312 | 2 5 5 5 0 5 8 6 8 11 9 11 12 13 16 14
313 | 2 4 4 0 4 7 5 7 8 9 12 10 12 15 13
314 | 2 3 0 3 6 4 6 7 8 11 9
315 | 2 4 4 0 7 7 4 10 10 7 13 13 10
316 | 2 4 4 0 4 4 8 6
317 | 3 3 5 5 0 5 8 6
318 | 3 3 5 5 0 5 8 6
319 | 3 3 5 5 0 5 8 6 5 12 12 9
320 | 3 3 9 3 7 7 4 9 0 9 12 10 12 15 13
321 | 3 3 7 3 6 4 0 7 11 11 8
322 | 2 9 2 6 6 3 9 9 0 9 13 13 10 9 17 17 9 19 17
323 | 2 4 4 0 4 7 5 4 11 11 8
324 | 2 4 4 0 6 4 6 9 7 4 13 13 10
325 | 3 3 4 0 4 8 8 5 4 11 4 13 11
326 | 2 4 4 0 4 8 8 5 8 11 9
327 | 2 0 5 5 2 2 9 9 6 9 12 10
328 | 2 7 7 7 7 7 0 7 10 7 10 11 12 13
329 | 3 3 5 5 0
330 | 3 3 5 5 0
331 | 2 0 6 6 6 2 6 9 7 9 12 10
332 | 3 3 5 5 0 5 8 6
333 | 2 7 2 5 2 7 0 7 10 8
334 | 2 7 2 5 3 7 0 9 7 7 12 10
335 | 2 8 2 3 4 5 8 0 8 8 12 10 12 15 13
336 | 3 3 6 6 6 0 6 6 11 11 8
337 | 3 3 6 6 6 0 6 6 11 11 8
338 | 3 3 6 6 6 0 6 9 7
339 | 2 4 4 0 4 7 5 4 11 11 8
340 | 4 4 4 11 4 9 9 9 4 11 0 11 15 15 12
341 | 2 4 4 0 4 5 6 9 7
342 | 2 7 2 3 4 7 0 11 11 11 7
343 | 2 4 4 14 6 4 4 9 7 9 12 9 14 0
344 | 2 0 5 5 2 5 6 10 10 7
345 | 2 4 4 0 7 7 4 4 11 11 8 11 15 15 11 4 18 4 18 18
346 | 3 3 0 3 6 4 3 9 7
347 | 2 0 5 5 2 5 8 6 2 9 9 11 14 12
348 | 2 8 2 6 6 3 8 0 8 8 10
349 | 2 8 2 6 6 3 8 0 10 8 10 15 15 15 11
350 | 3 3 6 6 6 0 6 9 7
351 | 3 3 5 5 0 5 8 6
352 | 2 8 2 6 6 3 8 0 8 11 9 8 12
353 | 2 6 2 2 6 0 6 10 10 7
354 | 2 15 2 6 6 3 6 9 6 6 13 13 6 15 0 15 16 15 21 21 15
355 | 2 15 2 6 6 3 6 9 6 6 13 13 6 15 0 15 16 15 21 21 15
356 | 2 4 4 0 6 4 4 9 7
357 | 2 4 4 0 7 7 4
358 | 2 6 2 3 6 0 6 6 6 9 12 10
359 | 2 0 5 5 2 5 6 9 7 6 12 10 12 15 13
360 | 2 8 2 6 6 3 8 0 8 9 12 10
361 | 2 4 4 0 7 7 4 7 7 11 7
362 | 2 4 4 0 8 8 8 4 4 4 12 10 10 15 13
363 | 2 4 4 0 4 7 5
364 | 2 4 4 0 4 7 5 7 10 8
365 | 3 3 0 3 7 7 4
366 | 2 4 4 0 4 4 8 6
367 | 3 3 5 5 0 7 5
368 | 2 9 2 6 6 2 2 9 0 9 12 14 14 9 9
369 | 2 9 2 6 6 2 2 9 0 9 12 14 14 9 9
370 | 2 4 4 0 4 7 4 7 10 8
371 | 2 6 2 3 6 0 6 9 7 12 12 9
372 | 2 6 2 3 6 0 6 10 10 7
373 | 2 7 2 5 3 7 0 7 10 8
374 | 2 19 2 6 6 3 6 6 2 11 2 11 15 15 12 15 15 19 0 19 22 20 22 26 26 23
375 | 2 4 4 0 7 7 4 4 4 11 9
376 | 2 4 4 15 6 4 4 7 10 8 10 13 10 15 0
377 | 3 3 5 5 0 5 8 6 8 11 9
378 | 3 3 9 3 7 7 4 9 0 9 12 10 9 15 13
379 | 3 3 11 3 6 4 6 7 8 11 0 11 14 12
380 | 3 3 7 3 4 7 0 7 10 8
381 | 2 4 4 0 4
382 | 3 3 5 5 0 5 8 6 8 12 12 9
383 | 3 3 5 5 0 9 9 9 5 5 12 5 12 15 13
384 | 2 4 4 0 6 4 4 10 10 7 4 13 11 13 16 14 4 19 17
385 | 3 3 5 5 0 5 8 6 5 11 9
386 | 2 0 4 2 4 8 8 5 5 11 9 11 14 12
387 | 2 4 4 0 4 4 8 6 8 12 12 9 4 16 16 13
388 | 2 13 2 6 6 2 9 9 6 2 13 13 0 13 16 14 13 19 17
389 | 2 4 4 0 7 7 4
390 | 2 4 4 0 4 7 5
391 | 2 4 4 0 7 7 4
392 | 3 3 5 5 0 5 8 6
393 | 2 5 5 5 0 5 8 6
394 | 2 6 2 3 6 0 8 6 6 12 12 9
395 | 3 3 5 5 0 5 9 9 6 9 12 9
396 | 2 3 0 3 6 4 6 10 10 7
397 | 2 7 2 3 6 4 0 7 10 8 10 11 7 15 7 15
398 | 3 3 4 0 4 7 5 7 8 4 12 4 12
399 | 2 4 4 0 4 7 5 4 10 8
400 | 2 8 2 6 6 3 8 0 8 11 9
401 | 2 4 4 0 4 7 5 4 10 8
402 | 6 1 4 2 6 0 6 9 7
403 | 3 3 5 5 0 5 6 7 8 9 9 17 17 13 13 17 9 9 9 22 22 19 19 25 23
404 | 2 4 4 0
405 | 2 3 0 5 3 5 5 3 10 3 10 13 11 13 16 13 16 19 17
406 | 2 9 2 5 3 5 8 5 0 9 10
407 | 2 4 4 0 6 4 4 9 11 11 4 11 14 12 14 15
408 | 2 7 2 2 7 7 0 7 12 12 12 8
409 | 2 4 4 0 4 7 5 4 10 4 13 13 10 13 16 13
410 | 2 4 4 0 4 7 5
411 | 2 5 2 2 0 5 10 9 10 6
412 | 4 4 4 0 4 5 4 4 10 8
413 | 3 3 5 5 0 7 5 5 8 9 12 10 12 13
414 | 2 4 4 0 6 4 4 9 7
415 | 2 8 2 6 6 3 8 0 10 8 10 10 15 15 10
416 | 2 8 2 6 6 3 8 0 8 8 10 14 14 11
417 | 2 4 4 0 4 7 5 7 7
418 | 3 3 7 3 4 7 0 9 7
419 | 3 3 5 5 0 5 6 9 7 9 13 13 10
420 | 2 8 2 6 6 3 8 0 8 11 9
421 | 2 7 2 5 3 7 0 10 10 7 7 13 7 13 16 14 16 19 17 19 22 20
422 | 2 4 4 0 4 7 5
423 | 2 4 4 0 4 8 8 5 4 12 12 9
424 | 2 4 4 0 6 4 6 11 11 11 7
425 | 3 3 5 5 0 8 8 5 8 9 13 13 10
426 | 3 3 9 3 7 7 3 9 0 9 10 14 14 11 9 17 9
427 | 2 5 5 5 0 5 8 12 8 11 8 5 12 15 13
428 | 2 5 5 5 0 5 5 7 7 11 7
429 | 2 4 4 0 4 7 5
430 | 2 6 2 3 6 0 6 6 10 8
431 | 2 4 4 0 6 4 6 9 7
432 | 3 3 9 3 7 7 4 9 0 9 13 13 10
433 | 3 3 5 5 0 5
434 | 2 4 4 0 4 7 5
435 | 2 4 4 0 4 4 6 9 7
436 | 2 4 4 0 4 4 6 9 7
437 | 2 4 4 0 4 7 5 7 8
438 | 2 4 4 0 4 7 5 7 10 8
439 | 4 4 4 6 6 0 6 9 7
440 | 3 3 8 3 6 4 8 0 8 11 9
441 | 2 4 4 0 4 8 8 5 4 12 12 4 12 15 13
442 | 2 8 2 6 6 3 8 0 8
443 | 3 3 8 3 6 4 8 0 8 11 9
444 | 2 7 2 5 3 7 0 7 10 7 10 10 12 15 12
445 | 3 3 5 5 0 5 9 9 6 9 13 13 10
446 | 2 4 4 0 4 8 8 5
447 | 2 4 4 0 4 10 6 6 6 4
448 | 2 4 4 0 4 7 5 4 8 11 9
449 | 2 4 4 0 6 4 4 9 7 4 12 10
450 | 3 3 5 5 0 8 8 5 5 11 9 11 15 15 12
451 | 2 4 4 0 7 7 4 7 10 7 10 11
452 | 2 8 2 6 6 3 8 0 8 11 9
453 | 2 4 4 0 4 7 5 4 10 4 12 10
454 | 6 3 6 3 3 8 8 0 8 11 9
455 | 3 3 5 5 0 7 5
456 | 2 4 4 0 4 7 5 4 10 4 10 13 11
457 | 2 7 2 5 3 7 0 7 8 9 12 10 12 13 7 17 19 19 15
458 | 2 4 4 0 4 7 5 4 11 11 8
459 | 3 3 5 5 0 10 10 7 7 5 5 13 11
460 | 2 4 4 0 4 7 5 7 12 12 12 8 14 12 16 14 16 16 4
461 | 2 4 4 0 4 7 4 7 10 8
462 | 2 4 4 0 4 7 5
463 | 2 4 4 0 4 4 8 6
464 | 2 4 4 0 4 8 8 5 4 11 17 11 15 15 12 17 4 17
465 | 2 4 4 0 7 7 4
466 | 2 11 2 6 6 3 6 9 6 11 0 11 15 15 12
467 | 2 6 2 3 6 0 9 9 6 6 12 18 12 13 14 14 18 6 18
468 | 2 4 4 0 7 7 4 7 11 11 7 4 14 4 14 17 15
469 | 2 3 0 3 6 4 9 9 3 9 12 10
470 | 2 3 0 3 7 7 4 7 10 8 14 14 14 3 14 14 18 16 18 21 18 14 22 25 23
471 | 5 5 2 2 7 7 0 7
472 | 2 4 4 0 4 7 5 7 10 8
473 | 2 4 4 0 4 4 8 6 4 11 9
474 | 2 5 5 5 0 5 9 9 6
475 | 2 4 4 0 6 4 6 9 7
476 | 2 7 2 3 4 7 0 9 7 7 12 10
477 | 3 3 5 5 0 5 8 6
478 | 2 4 4 0 6 4 4 7 10 12 10 8
479 | 2 4 4 0 7 7 4 4 4 9 9 14 14 11
480 | 3 3 5 5 0 5 8 6 8 11 9 5 15 15 17 17 5 17
481 | 3 3 13 3 6 3 6 10 10 7 13 13 0 13 17 17 14
482 | 5 5 2 2 11 5 9 9 6 11 0 11 14 12
483 | 5 5 2 2 11 5 9 9 6 11 0 11 14 12
484 | 2 8 2 6 6 3 8 0 8 8 13 13 10
485 | 3 3 5 5 16 7 5 5 5 11 9 11 14 11 16 0 16
486 | 2 4 4 0 4 4 8 6 4 12 12 4 12 15 12
487 | 2 8 2 6 6 3 8 0 8 13 13 13 9
488 | 2 5 5 5 0 5 8 6
489 | 2 4 4 0 4 7 5 4 10 4 12 10
490 | 2 6 2 3 6 0 6 6 6 9 12 10
491 | 2 4 4 0 4 5 4 4 10 8
492 | 3 3 5 5 0 7 9 7 5 5 12 10
493 | 5 5 5 5 7 7 0 7 8
494 | 2 7 2 5 3 7 0 7 10 8 10 10
495 | 2 4 4 0 7 7 4 4 11 11 8
496 | 2 4 4 0 4 7 5
497 | 2 4 4 0 4
498 | 2 4 4 0 4 8 8 5
499 | 2 7 2 5 3 7 0 9 7
500 | 2 4 4 0 4 8 8 5
501 | 


--------------------------------------------------------------------------------
/data/sick/dev/id.txt:
--------------------------------------------------------------------------------
  1 | 4
  2 | 24
  3 | 105
  4 | 116
  5 | 119
  6 | 185
  7 | 197
  8 | 211
  9 | 218
 10 | 219
 11 | 225
 12 | 228
 13 | 236
 14 | 253
 15 | 280
 16 | 285
 17 | 304
 18 | 317
 19 | 382
 20 | 384
 21 | 394
 22 | 402
 23 | 417
 24 | 450
 25 | 520
 26 | 526
 27 | 530
 28 | 592
 29 | 619
 30 | 630
 31 | 647
 32 | 651
 33 | 687
 34 | 698
 35 | 717
 36 | 718
 37 | 765
 38 | 780
 39 | 816
 40 | 860
 41 | 873
 42 | 882
 43 | 913
 44 | 933
 45 | 947
 46 | 980
 47 | 987
 48 | 988
 49 | 1039
 50 | 1102
 51 | 1137
 52 | 1170
 53 | 1177
 54 | 1178
 55 | 1190
 56 | 1192
 57 | 1232
 58 | 1236
 59 | 1237
 60 | 1241
 61 | 1266
 62 | 1302
 63 | 1333
 64 | 1390
 65 | 1396
 66 | 1400
 67 | 1410
 68 | 1412
 69 | 1443
 70 | 1469
 71 | 1470
 72 | 1487
 73 | 1493
 74 | 1495
 75 | 1503
 76 | 1506
 77 | 1525
 78 | 1584
 79 | 1608
 80 | 1628
 81 | 1676
 82 | 1690
 83 | 1703
 84 | 1711
 85 | 1754
 86 | 1807
 87 | 1811
 88 | 1839
 89 | 1847
 90 | 1861
 91 | 1877
 92 | 1881
 93 | 1964
 94 | 2001
 95 | 2019
 96 | 2058
 97 | 2089
 98 | 2096
 99 | 2130
100 | 2157
101 | 2166
102 | 2221
103 | 2229
104 | 2231
105 | 2284
106 | 2286
107 | 2362
108 | 2364
109 | 2366
110 | 2385
111 | 2392
112 | 2402
113 | 2404
114 | 2405
115 | 2410
116 | 2430
117 | 2476
118 | 2485
119 | 2525
120 | 2527
121 | 2535
122 | 2544
123 | 2557
124 | 2566
125 | 2604
126 | 2606
127 | 2662
128 | 2668
129 | 2678
130 | 2679
131 | 2687
132 | 2704
133 | 2712
134 | 2720
135 | 2721
136 | 2722
137 | 2726
138 | 2738
139 | 2766
140 | 2772
141 | 2791
142 | 2795
143 | 2812
144 | 2822
145 | 2829
146 | 2842
147 | 2886
148 | 2898
149 | 2911
150 | 2932
151 | 2936
152 | 2965
153 | 2967
154 | 2968
155 | 2972
156 | 2996
157 | 3002
158 | 3036
159 | 3061
160 | 3107
161 | 3122
162 | 3123
163 | 3143
164 | 3174
165 | 3181
166 | 3222
167 | 3250
168 | 3258
169 | 3275
170 | 3291
171 | 3292
172 | 3322
173 | 3338
174 | 3364
175 | 3373
176 | 3384
177 | 3398
178 | 3420
179 | 3433
180 | 3459
181 | 3485
182 | 3501
183 | 3505
184 | 3518
185 | 3536
186 | 3586
187 | 3598
188 | 3625
189 | 3626
190 | 3657
191 | 3661
192 | 3670
193 | 3680
194 | 3711
195 | 3721
196 | 3733
197 | 3747
198 | 3790
199 | 3812
200 | 3855
201 | 3861
202 | 3862
203 | 3880
204 | 3897
205 | 3909
206 | 3910
207 | 3922
208 | 3941
209 | 3982
210 | 3996
211 | 4001
212 | 4006
213 | 4011
214 | 4015
215 | 4031
216 | 4054
217 | 4060
218 | 4066
219 | 4067
220 | 4079
221 | 4086
222 | 4114
223 | 4135
224 | 4140
225 | 4155
226 | 4157
227 | 4164
228 | 4187
229 | 4191
230 | 4196
231 | 4288
232 | 4321
233 | 4330
234 | 4342
235 | 4360
236 | 4376
237 | 4411
238 | 4429
239 | 4477
240 | 4505
241 | 4520
242 | 4614
243 | 4638
244 | 4650
245 | 4661
246 | 4707
247 | 4734
248 | 4755
249 | 4767
250 | 4776
251 | 4803
252 | 4814
253 | 4816
254 | 4823
255 | 4831
256 | 4843
257 | 4851
258 | 4852
259 | 4866
260 | 4891
261 | 4916
262 | 4934
263 | 4963
264 | 4966
265 | 4983
266 | 5003
267 | 5030
268 | 5047
269 | 5051
270 | 5052
271 | 5082
272 | 5110
273 | 5113
274 | 5119
275 | 5148
276 | 5203
277 | 5206
278 | 5310
279 | 5349
280 | 5352
281 | 5374
282 | 5454
283 | 5461
284 | 5498
285 | 5540
286 | 5588
287 | 5657
288 | 5698
289 | 5706
290 | 5745
291 | 5747
292 | 5799
293 | 5806
294 | 5904
295 | 5909
296 | 5914
297 | 5920
298 | 5924
299 | 5930
300 | 5938
301 | 5955
302 | 6001
303 | 6005
304 | 6042
305 | 6047
306 | 6078
307 | 6119
308 | 6131
309 | 6146
310 | 6153
311 | 6158
312 | 6203
313 | 6213
314 | 6219
315 | 6228
316 | 6231
317 | 6280
318 | 6287
319 | 6315
320 | 6330
321 | 6344
322 | 6358
323 | 6394
324 | 6396
325 | 6450
326 | 6482
327 | 6544
328 | 6568
329 | 6590
330 | 6592
331 | 6598
332 | 6625
333 | 6634
334 | 6649
335 | 6673
336 | 6705
337 | 6709
338 | 6729
339 | 6740
340 | 6756
341 | 6764
342 | 6784
343 | 6849
344 | 6866
345 | 6912
346 | 6932
347 | 6955
348 | 6961
349 | 6971
350 | 7002
351 | 7007
352 | 7018
353 | 7027
354 | 7043
355 | 7044
356 | 7064
357 | 7088
358 | 7093
359 | 7214
360 | 7228
361 | 7311
362 | 7319
363 | 7356
364 | 7363
365 | 7374
366 | 7394
367 | 7410
368 | 7445
369 | 7448
370 | 7464
371 | 7466
372 | 7471
373 | 7475
374 | 7477
375 | 7501
376 | 7533
377 | 7541
378 | 7544
379 | 7546
380 | 7548
381 | 7582
382 | 7590
383 | 7649
384 | 7667
385 | 7677
386 | 7718
387 | 7720
388 | 7726
389 | 7734
390 | 7743
391 | 7772
392 | 7795
393 | 7820
394 | 7877
395 | 7889
396 | 7932
397 | 7937
398 | 7938
399 | 7947
400 | 7950
401 | 7951
402 | 7997
403 | 8057
404 | 8122
405 | 8138
406 | 8141
407 | 8163
408 | 8193
409 | 8212
410 | 8220
411 | 8263
412 | 8313
413 | 8326
414 | 8334
415 | 8343
416 | 8362
417 | 8375
418 | 8383
419 | 8389
420 | 8461
421 | 8471
422 | 8500
423 | 8505
424 | 8548
425 | 8558
426 | 8576
427 | 8632
428 | 8633
429 | 8658
430 | 8660
431 | 8678
432 | 8700
433 | 8714
434 | 8727
435 | 8786
436 | 8792
437 | 8806
438 | 8832
439 | 8841
440 | 8855
441 | 8938
442 | 8963
443 | 8986
444 | 9014
445 | 9029
446 | 9036
447 | 9056
448 | 9069
449 | 9093
450 | 9102
451 | 9125
452 | 9132
453 | 9136
454 | 9169
455 | 9188
456 | 9198
457 | 9200
458 | 9211
459 | 9224
460 | 9242
461 | 9287
462 | 9303
463 | 9314
464 | 9336
465 | 9359
466 | 9371
467 | 9380
468 | 9401
469 | 9424
470 | 9425
471 | 9433
472 | 9452
473 | 9453
474 | 9490
475 | 9500
476 | 9545
477 | 9571
478 | 9584
479 | 9623
480 | 9669
481 | 9677
482 | 9706
483 | 9713
484 | 9722
485 | 9745
486 | 9747
487 | 9759
488 | 9763
489 | 9771
490 | 9788
491 | 9789
492 | 9822
493 | 9838
494 | 9843
495 | 9869
496 | 9880
497 | 9931
498 | 9963
499 | 9975
500 | 9988
501 | 


--------------------------------------------------------------------------------
/data/sick/dev/sim.txt:
--------------------------------------------------------------------------------
  1 | 3.6
  2 | 3.4
  3 | 3.8
  4 | 2.9
  5 | 4.2
  6 | 2.6
  7 | 3.165
  8 | 4.6
  9 | 4.9
 10 | 4.2
 11 | 2.7
 12 | 3.3
 13 | 1.9
 14 | 4.7
 15 | 3.135
 16 | 3.9
 17 | 3.8
 18 | 1.3
 19 | 4.4
 20 | 4.35
 21 | 3.6
 22 | 4.1
 23 | 4
 24 | 3.3
 25 | 3.3
 26 | 2.4
 27 | 3.1
 28 | 3.5
 29 | 3.5
 30 | 3.5
 31 | 3.3
 32 | 4.6
 33 | 3.4
 34 | 2.785
 35 | 3.9
 36 | 3.9
 37 | 4
 38 | 3.1
 39 | 5
 40 | 4.2
 41 | 3.5
 42 | 3.1
 43 | 4.2
 44 | 3.8
 45 | 4.8
 46 | 2.1
 47 | 4.3
 48 | 3.1
 49 | 3.3
 50 | 1
 51 | 3.5
 52 | 3
 53 | 4.1
 54 | 3.6
 55 | 4.3
 56 | 4.6
 57 | 3.6
 58 | 3.285
 59 | 3.8
 60 | 4.1
 61 | 5
 62 | 1.5
 63 | 4.9
 64 | 1.2
 65 | 3.6
 66 | 2.9
 67 | 1.4
 68 | 4.5
 69 | 1.7
 70 | 3.8
 71 | 2.5
 72 | 4
 73 | 4.9
 74 | 4.9
 75 | 3.3
 76 | 3.8
 77 | 1.2
 78 | 4.5
 79 | 4.7
 80 | 4.6
 81 | 1.1
 82 | 5
 83 | 4.7
 84 | 1.1
 85 | 4.6
 86 | 2.4
 87 | 2.4
 88 | 4.6
 89 | 1
 90 | 4
 91 | 3.5
 92 | 4.2
 93 | 4.9
 94 | 5
 95 | 3.3
 96 | 4.8
 97 | 3.2
 98 | 1.1
 99 | 3.4
100 | 3
101 | 4.8
102 | 3.9
103 | 4.1
104 | 3.1
105 | 4.7
106 | 4.5
107 | 3.8
108 | 4.2
109 | 3.3
110 | 3.4
111 | 2.7
112 | 3.5
113 | 3
114 | 4.7
115 | 1.3
116 | 4.4
117 | 4.7
118 | 2.2
119 | 3.8
120 | 4.2
121 | 2.5
122 | 3.1
123 | 4.5
124 | 4.6
125 | 4.8
126 | 3.9
127 | 2.335
128 | 3.1
129 | 4.1
130 | 3.4
131 | 3
132 | 5
133 | 4.3
134 | 3
135 | 4
136 | 5
137 | 2.7
138 | 3
139 | 2.6
140 | 5
141 | 4.8
142 | 4.5
143 | 2.7
144 | 2.7
145 | 5
146 | 3.8
147 | 3.2
148 | 4.3
149 | 4
150 | 2.4
151 | 3
152 | 3.3
153 | 4.8
154 | 3.5
155 | 3.4
156 | 3.3
157 | 2.3
158 | 3.2
159 | 4.7
160 | 2.3
161 | 4.6
162 | 3.1
163 | 3.6
164 | 2.8
165 | 3.8
166 | 3.7
167 | 4.9
168 | 3.7
169 | 4.9
170 | 1.7
171 | 2.1
172 | 3.2
173 | 3.4
174 | 4.9
175 | 4.1
176 | 4.8
177 | 3.2
178 | 1.9
179 | 5
180 | 4.4
181 | 4.1
182 | 4.8
183 | 3.6
184 | 3.3
185 | 4.9
186 | 4.6
187 | 4.3
188 | 3.4
189 | 4.7
190 | 5
191 | 2.7
192 | 4.9
193 | 3.7
194 | 3.8
195 | 4.9
196 | 4.3
197 | 3.2
198 | 5
199 | 4.8
200 | 3.2
201 | 3.4
202 | 3.285
203 | 4.4
204 | 3.4
205 | 4.3
206 | 3.4
207 | 3.8
208 | 3.9
209 | 3.7
210 | 2.2
211 | 4.3
212 | 4.8
213 | 4.8
214 | 4.9
215 | 2.9
216 | 4.5
217 | 2.6
218 | 4.6
219 | 3.3
220 | 4.7
221 | 3.9
222 | 3.2
223 | 4.7
224 | 3.4
225 | 3.7
226 | 4
227 | 3.7
228 | 3.8
229 | 3.6
230 | 1.7
231 | 4.4
232 | 3.8
233 | 3.4
234 | 4.6
235 | 4.4
236 | 2.7
237 | 3.7
238 | 3
239 | 1.2
240 | 4.2
241 | 3.6
242 | 4.4
243 | 2.4
244 | 3.6
245 | 4.8
246 | 4.4
247 | 4.8
248 | 1.4
249 | 3.7
250 | 3.8
251 | 2.935
252 | 4.4
253 | 3.8
254 | 3
255 | 4
256 | 3.8
257 | 5
258 | 3.8
259 | 3
260 | 4
261 | 4.5
262 | 3.7
263 | 4.7
264 | 2.7
265 | 5
266 | 4.4
267 | 4.9
268 | 3.5
269 | 3.6
270 | 1.2
271 | 2.1
272 | 4.8
273 | 4
274 | 1
275 | 3.5
276 | 5
277 | 2.2
278 | 3.6
279 | 4.3
280 | 4.1
281 | 5
282 | 4.2
283 | 3.7
284 | 4.2
285 | 2.3
286 | 5
287 | 1.2
288 | 4
289 | 3.7
290 | 1.4
291 | 3.2
292 | 5
293 | 4.2
294 | 4
295 | 3.1
296 | 5
297 | 1
298 | 3.8
299 | 2.3
300 | 4
301 | 3.7
302 | 2.8
303 | 3.4
304 | 3.4
305 | 5
306 | 4.2
307 | 3.3
308 | 4
309 | 2.5
310 | 3
311 | 1.6
312 | 3.4
313 | 3.8
314 | 4.1
315 | 2.6
316 | 3.3
317 | 5
318 | 4.9
319 | 4.6
320 | 2.8
321 | 2.2
322 | 3.2
323 | 2.3
324 | 3.8
325 | 3.5
326 | 3.1
327 | 4
328 | 3.1
329 | 3.3
330 | 2.7
331 | 3.9
332 | 2.7
333 | 4.5
334 | 4.8
335 | 2.8
336 | 4.9
337 | 3.5
338 | 3.3
339 | 3.2
340 | 4.8
341 | 3.8
342 | 3.4
343 | 2.2
344 | 4
345 | 3.4
346 | 3.8
347 | 3.185
348 | 3
349 | 3.4
350 | 2.5
351 | 3.8
352 | 5
353 | 3
354 | 2.6
355 | 3.2
356 | 5
357 | 2.6
358 | 5
359 | 3.8
360 | 2.6
361 | 4.8
362 | 4.3
363 | 4.1
364 | 3.3
365 | 3.1
366 | 4.1
367 | 2.4
368 | 4.9
369 | 3.6
370 | 3.5
371 | 4.9
372 | 3.6
373 | 2.5
374 | 4.9
375 | 2.4
376 | 3
377 | 3.3
378 | 2.9
379 | 5
380 | 5
381 | 4.5
382 | 3.1
383 | 3.6
384 | 3.7
385 | 4.5
386 | 3.8
387 | 2.9
388 | 4.3
389 | 1.5
390 | 3.2
391 | 3.9
392 | 4.4
393 | 3.7
394 | 3.9
395 | 4.7
396 | 2
397 | 3.1
398 | 2.2
399 | 3.4
400 | 2.3
401 | 3.5
402 | 2.8
403 | 4.8
404 | 3
405 | 4.4
406 | 3
407 | 4.8
408 | 4.4
409 | 2.4
410 | 3.335
411 | 4.2
412 | 3.1
413 | 4.4
414 | 4.4
415 | 4.8
416 | 4.2
417 | 2.4
418 | 4.3
419 | 3.3
420 | 4
421 | 4.6
422 | 4.1
423 | 4.3
424 | 3.1
425 | 3.2
426 | 4.2
427 | 4.4
428 | 4.8
429 | 3.8
430 | 4.9
431 | 4.8
432 | 4
433 | 4.6
434 | 3.5
435 | 4.3
436 | 3.7
437 | 4.5
438 | 3.8
439 | 3.1
440 | 4.5
441 | 4
442 | 3.8
443 | 4.4
444 | 3.7
445 | 2.3
446 | 2.9
447 | 3.6
448 | 4.4
449 | 2.8
450 | 3.4
451 | 2.4
452 | 4.9
453 | 4.5
454 | 3.7
455 | 4.4
456 | 3.2
457 | 2
458 | 4.3
459 | 4
460 | 3.2
461 | 3.1
462 | 4.9
463 | 3.3
464 | 4.2
465 | 4.9
466 | 3
467 | 3.5
468 | 3.3
469 | 4.1
470 | 4
471 | 4.4
472 | 3.7
473 | 1.6
474 | 4
475 | 2.9
476 | 2.8
477 | 4.5
478 | 4.9
479 | 3.6
480 | 4.8
481 | 3.5
482 | 4.2
483 | 4.5
484 | 3.9
485 | 4.4
486 | 4.4
487 | 1.1
488 | 1
489 | 1.4
490 | 1
491 | 1
492 | 2
493 | 1
494 | 1
495 | 2.8
496 | 1.2
497 | 3
498 | 1
499 | 1
500 | 1
501 | 


--------------------------------------------------------------------------------
/data/sick/readme.txt:
--------------------------------------------------------------------------------
 1 | ::::::::::::::::::::::: University of Trento - Italy ::::::::::::::::::::::::::::::::
 2 | 
 3 | :::::::::::: SICK (Sentences Involving Compositional Knowledge) data set ::::::::::::
 4 | 
 5 | 
 6 | The SICK data set consists of 10,000 English sentence pairs, built starting from two existing 
 7 | paraphrase sets: the 8K ImageFlickr data set (http://nlp.cs.illinois.edu/HockenmaierGroup/data.html) 
 8 | and the SEMEVAL-2012 Semantic Textual Similarity Video Descriptions data set 
 9 | (http://www.cs.york.ac.uk/semeval-2012/task6/index.php?id=data). Each sentence pair is annotated 
10 | for relatedness in meaning and for the entailment relation between the two elements.
11 | 
12 | 
13 | The SICK data set is released under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 
14 | Unported License (http://creativecommons.org/licenses/by-nc-sa/3.0/deed.en_US)
15 | 
16 | 
17 | The SICK data set is used in SemEval 2014 - Task 1: Evaluation of compositional distributional 
18 | semantic models on full sentences through semantic relatedness and textual entailment
19 | 
20 | 
21 | The current release is a subset of the data set representing Task 1 Test data (4927 sentence pairs)
22 | 
23 | 
24 | File Structure: tab-separated text file
25 | 
26 | 
27 | Fields:
28 | 
29 | - sentence pair ID
30 | 
31 | - sentence A
32 | 
33 | - sentence B
34 | 
35 | - semantic relatedness gold label (on a 1-5 continuous scale)
36 | 
37 | - textual entailment gold label (NEUTRAL, ENTAILMENT, or CONTRADICTION)
38 | 
39 | 


--------------------------------------------------------------------------------
/data/sick/sick_evaluation.R:
--------------------------------------------------------------------------------
 1 | ## Use this R script as follows:
 2 | 
 3 | ## R --no-save --slave --vanilla --args your-scores gold < sick_evaluation.R 
 4 | 
 5 | ## where your-scores is the file with your system output and gold is
 6 | ## the gold standard file (for example, the SICK_trial.txt and
 7 | ## SICK_train.txt files we already released).
 8 | 
 9 | ## Your file must contain the following 3 tab-delimited columns:
10 | 
11 | ## -pair_ID (the ids, that should match those in the relevant the
12 | ## -trial, train or test data),
13 | 
14 | ## - entailment_judgment (predictions of your system for the
15 | ## entailment sub-task; possible values: ENTAILMENT, CONTRADICTION,
16 | ## NEUTRAL) and
17 | 
18 | ## - relatedness_score (numerical predictions of your system for the
19 | ## sentence relatedness sub-task).
20 | 
21 | ## Note that the first line of the file must be a "header" naming the
22 | ## 3 columns exactly with the 3 strings above (pair_ID,
23 | ## entailment_judgment and relatedness_score).
24 | 
25 | ## The order of the columns and rows does not matter: of course, the
26 | ## ids must match those in the relevant data sets.
27 | 
28 | ## If you do not participate in the entailment or relatedness task,
29 | ## please provide a column of NA (by that, we mean that you should
30 | ## literally enter the string NA in each row).
31 | ## Note that, for either subtask you want to evaluate, you must 
32 | ## provide a value for each test pair: if your scores contain 1) NAs 
33 | ## or 2) missing values, the script will either ignore the subtask 
34 | ## or return an error. 
35 | 
36 | ## The script returns system (percentage) accuracy for the entailment
37 | ## task and Pearson and Spearman correlation scores and mean squared
38 | ## error for the relatedness task.
39 | 
40 | 
41 | ifile = commandArgs()[6];
42 | gold = commandArgs()[7];
43 | 
44 | read.delim(ifile, sep="\t", header=T, stringsAsFactors=F) -> score;
45 | read.delim(gold, sep="\t", header=T) -> gold;
46 | 
47 | score <- score[order(score$pair_ID), ];
48 | gold <- gold[order(gold$pair_ID), ];
49 | 
50 | 
51 | 
52 | if (FALSE %in% (score$pair_ID==gold$pair_ID)){
53 | 	print("ERROR: pair IDs in score set and gold set do not correspond")
54 | 	quit()
55 | 	}
56 | 
57 | 
58 | print(paste("Processing ", ifile, sep=""));
59 | 
60 | if (TRUE %in% is.na(score$entailment_judgment)){
61 | 	print("No data for the entailment task: evaluation on relatedness only")
62 | 	}else if (FALSE %in% (unique(score$entailment_judgment) %in% unique(gold$entailment_judgment))){
63 | 	print("ERROR: wrong level(s) in entailment judgments")
64 | 	}else{
65 | 	accuracy <- sum(score$entailment_judgment == gold$entailment_judgment) / length(score$entailment_judgment)*100
66 | 	print(paste(paste("Entailment: accuracy ", accuracy, sep=""),"%",sep=""))
67 | 	}
68 | 	
69 | if (TRUE %in% is.na(score$relatedness_score)){
70 | 	print("No data for the relatedness task: evaluation on entailment only ")
71 | 	}else if (is.numeric(score$relatedness_score)==FALSE){
72 | 	print("ERROR: wrong format for relatedness scores")
73 | 	}else{
74 | 	pearson <- cor(score$relatedness_score, gold$relatedness_score)
75 | 	print(paste("Relatedness: Pearson correlation ", pearson, sep=""))
76 | 	spearman <- cor(score$relatedness_score, gold$relatedness_score, method = "spearman")
77 | 	print(paste("Relatedness: Spearman correlation ", spearman, sep=""))
78 | 	MSE <- sum((score$relatedness_score - gold$relatedness_score)^2) / length(score$relatedness_score)
79 | 	print(paste("Relatedness: MSE ", MSE, sep=""))
80 | 	}
81 | 
82 | quit()	
83 | 


--------------------------------------------------------------------------------
/fetch_and_preprocess.sh:
--------------------------------------------------------------------------------
 1 | #!/bin/bash
 2 | python2.7 scripts/download.py
 3 | 
 4 | glove_dir="data/glove"
 5 | glove_pre="glove.840B"
 6 | glove_dim="300d"
 7 | if [ ! -f $glove_dir/$glove_pre.$glove_dim.th ]; then
 8 |     th scripts/convert-wordvecs.lua $glove_dir/$glove_pre.$glove_dim.txt \
 9 |         $glove_dir/$glove_pre.vocab $glove_dir/$glove_pre.$glove_dim.th
10 | fi
11 | 


--------------------------------------------------------------------------------
/models.lua:
--------------------------------------------------------------------------------
  1 | 
  2 | function createModel(mdl, vocsize, Dsize, nout, KKw)
  3 |     	-- define model to train
  4 |     	local network = nn.Sequential()
  5 |     	local featext = nn.Sequential()
  6 |     	local classifier = nn.Sequential()
  7 | 
  8 |     	local conCon1 = nn.Sequential()
  9 |     	local conCon2 = nn.Sequential()
 10 |     	local conCon3 = nn.Sequential()
 11 |     	local conCon4 = nn.Sequential()
 12 | 
 13 |     	local parallelConcat1 = nn.Concat(1)
 14 |     	local parallelConcat2 = nn.Concat(1)
 15 |     	local parallelConcat3 = nn.Concat(1)
 16 |     	local parallelConcat4 = nn.Concat(1)
 17 |     	local parallelConcat5 = nn.Concat(1)
 18 | 
 19 |     	local D     = Dsize 
 20 |     	local kW    = KKw 
 21 |     	local dW    = 1 
 22 |     	local noExtra = false
 23 |     	local nhid1 = 250 
 24 |     	local nhid2 = 250 
 25 |     	local NumFilter = D
 26 |     	local pR = 2 
 27 |     	local layers=1
 28 | 	    
 29 |     	if mdl == 'deepQueryRankingNgramSimilarityOnevsGroupMaxMinMeanLinearExDGpPoinPercpt' then
 30 | 		dofile "PaddingReshape.lua"
 31 | 		
 32 | 		deepQuery=nn.Sequential()
 33 |    		D = Dsize 
 34 | 		local incep1max = nn.Sequential()
 35 | 		incep1max:add(nn.TemporalConvolution(D,NumFilter,1,dw))
 36 | 		if pR == 1 then
 37 | 			incep1max:add(nn.PReLU())
 38 | 		else 
 39 | 		  	incep1max:add(nn.Tanh())
 40 | 		end		  
 41 | 		incep1max:add(nn.Max(1))
 42 | 		incep1max:add(nn.Reshape(NumFilter,1))		  
 43 | 		local incep2max = nn.Sequential()
 44 | 		incep2max:add(nn.Max(1))
 45 | 		incep2max:add(nn.Reshape(NumFilter,1))			  
 46 | 		local combineDepth = nn.Concat(2)
 47 | 		combineDepth:add(incep1max)
 48 | 		combineDepth:add(incep2max)
 49 | 		  
 50 | 		local ngram = kW                
 51 | 		for cc = 2, ngram do
 52 | 		    local incepMax = nn.Sequential()
 53 | 		    if not noExtra then
 54 | 		    	incepMax:add(nn.TemporalConvolution(D,D,1,dw)) --set
 55 | 		    	if pR == 1 then
 56 | 				incepMax:add(nn.PReLU())
 57 | 			else 
 58 | 				incepMax:add(nn.Tanh())
 59 | 			end
 60 | 		    end
 61 | 		    incepMax:add(nn.TemporalConvolution(D,NumFilter,cc,dw))
 62 | 		    if pR == 1 then
 63 | 			  	incepMax:add(nn.PReLU())
 64 | 			else 
 65 | 			  	incepMax:add(nn.Tanh())
 66 | 			end
 67 | 		    incepMax:add(nn.Max(1))
 68 | 		    incepMax:add(nn.Reshape(NumFilter,1))		    		    
 69 | 		    combineDepth:add(incepMax)		    
 70 | 		end  		  
 71 | 		  		  
 72 | 		local incep1min = nn.Sequential()
 73 | 		incep1min:add(nn.TemporalConvolution(D,NumFilter,1,dw))
 74 | 		if pR == 1 then
 75 | 			incep1min:add(nn.PReLU())
 76 | 		else 
 77 | 			incep1min:add(nn.Tanh())
 78 | 		end		  
 79 | 		incep1min:add(nn.Min(1))
 80 | 		incep1min:add(nn.Reshape(NumFilter,1))		  
 81 | 		local incep2min = nn.Sequential()
 82 | 		incep2min:add(nn.Min(1))
 83 | 		incep2min:add(nn.Reshape(NumFilter,1))		  
 84 | 		combineDepth:add(incep1min)
 85 | 		combineDepth:add(incep2min)
 86 | 		  
 87 | 		for cc = 2, ngram do		    
 88 | 			local incepMin = nn.Sequential()
 89 | 		    	if not noExtra then
 90 | 				incepMin:add(nn.TemporalConvolution(D,D,1,dw)) --set
 91 | 				if pR == 1 then
 92 | 					incepMin:add(nn.PReLU())
 93 | 				else 
 94 | 					incepMin:add(nn.Tanh())
 95 | 				end
 96 | 		    	end		  
 97 | 		    	incepMin:add(nn.TemporalConvolution(D,NumFilter,cc,dw))
 98 | 		    	if pR == 1 then
 99 | 			  	incepMin:add(nn.PReLU())
100 | 			else 
101 | 			  	incepMin:add(nn.Tanh())
102 | 			end
103 | 		    	incepMin:add(nn.Min(1))
104 | 		    	incepMin:add(nn.Reshape(NumFilter,1))		    		  	
105 | 		  	combineDepth:add(incepMin)		      		    
106 | 		end  
107 | 		  
108 | 		local incep1mean = nn.Sequential()
109 | 		incep1mean:add(nn.TemporalConvolution(D,NumFilter,1,dw))
110 | 		if pR == 1 then
111 | 			incep1mean:add(nn.PReLU())
112 | 		else 
113 | 			incep1mean:add(nn.Tanh())
114 | 		end
115 | 		incep1mean:add(nn.Mean(1))
116 | 		incep1mean:add(nn.Reshape(NumFilter,1))		    		  		  
117 | 		local incep2mean = nn.Sequential()
118 | 		incep2mean:add(nn.Mean(1))
119 | 		incep2mean:add(nn.Reshape(NumFilter,1))		  
120 | 		combineDepth:add(incep1mean)
121 | 		combineDepth:add(incep2mean)		  
122 | 		for cc = 2, ngram do
123 | 		    local incepMean = nn.Sequential()
124 | 		    if not noExtra then
125 | 		    	incepMean:add(nn.TemporalConvolution(D,D,1,dw)) --set
126 | 		    	if pR == 1 then
127 | 				incepMean:add(nn.PReLU())
128 | 			else 
129 | 				incepMean:add(nn.Tanh())
130 | 			end
131 | 		    end
132 | 		    incepMean:add(nn.TemporalConvolution(D,NumFilter,cc,dw))
133 | 		    if pR == 1 then
134 | 		    	incepMean:add(nn.PReLU())
135 | 		    else 
136 | 			incepMean:add(nn.Tanh())
137 | 		    end
138 | 		    incepMean:add(nn.Mean(1))
139 | 		    incepMean:add(nn.Reshape(NumFilter,1))			    
140 | 		    combineDepth:add(incepMean)	
141 | 		end  
142 | 		  
143 | 		local conceptFNum = 20
144 | 		for cc = 1, ngram do
145 | 			local perConcept = nn.Sequential()
146 | 			perConcept:add(nn.PaddingReshape(2,2)) --set
147 | 		    	perConcept:add(nn.SpatialConvolutionMM(1,conceptFNum,1,cc)) --set
148 | 		    	perConcept:add(nn.Max(2)) --set
149 | 		    	if pR == 1 then
150 | 			 	perConcept:add(nn.PReLU())
151 | 			else 
152 | 			 	perConcept:add(nn.Tanh())
153 | 			end
154 | 			perConcept:add(nn.Transpose({1,2}))
155 | 		    	combineDepth:add(perConcept)	
156 | 		end
157 | 		
158 | 		for cc = 1, ngram do
159 | 			local perConcept = nn.Sequential()
160 | 		    	perConcept:add(nn.PaddingReshape(2,2)) --set
161 | 		    	perConcept:add(nn.SpatialConvolutionMM(1,conceptFNum,1,cc)) --set
162 | 		    	perConcept:add(nn.Min(2)) --set
163 | 		    	if pR == 1 then
164 | 			 	perConcept:add(nn.PReLU())
165 | 			else 
166 | 			 	perConcept:add(nn.Tanh())
167 | 			end
168 | 			perConcept:add(nn.Transpose({1,2}))
169 | 		    	combineDepth:add(perConcept)	
170 | 		end
171 | 		  
172 | 		featext:add(combineDepth)		
173 | 		local items = (ngram+1)*3  		
174 | 		local separator = items+2*conceptFNum*ngram
175 | 		local sepModel = 0 
176 | 		if sepModel == 1 then  
177 | 			modelQ= featext:clone()
178 | 		else
179 | 			modelQ= featext:clone('weight','bias','gradWeight','gradBias')
180 | 		end
181 | 		paraQuery=nn.ParallelTable()
182 | 		paraQuery:add(modelQ)
183 |           	paraQuery:add(featext)			
184 |           	deepQuery:add(paraQuery) 
185 | 		deepQuery:add(nn.JoinTable(2)) 
186 | 			
187 | 		d=nn.Concat(1) 
188 | 		for i=1,items do
189 |   			if i <= items/3 then 					
190 | 	  			for j=1,items/3 do
191 | 	  				local connection = nn.Sequential()
192 | 					local minus=nn.Concat(2)
193 | 					local c1=nn.Sequential()
194 | 					local c2=nn.Sequential()
195 | 					c1:add(nn.Select(2,i)) -- == D, not D*1
196 | 					c1:add(nn.Reshape(NumFilter,1)) --D*1 here					
197 | 					c2:add(nn.Select(2,separator+j))					
198 | 					c2:add(nn.Reshape(NumFilter,1))
199 | 					minus:add(c1)
200 | 					minus:add(c2)
201 | 					connection:add(minus) -- D*2						
202 | 					local similarityC=nn.Concat(1) -- multi similarity criteria			
203 | 					local s1=nn.Sequential()
204 | 					s1:add(nn.SplitTable(2))
205 | 					s1:add(nn.PairwiseDistance(2)) -- scalar
206 | 					local s2=nn.Sequential()
207 | 					if 1 < 3 then
208 | 						s2:add(nn.SplitTable(2))
209 | 					else
210 | 						s2:add(nn.Transpose({1,2})) 
211 | 						s2:add(nn.SoftMax())
212 | 						s2:add(nn.SplitTable(1))										
213 | 					end						
214 | 					s2:add(nn.CsDis()) -- scalar
215 | 					local s3=nn.Sequential()
216 | 					s3:add(nn.SplitTable(2))
217 | 					s3:add(nn.CSubTable()) -- linear
218 | 					s3:add(nn.Abs()) -- linear						
219 | 					similarityC:add(s1)
220 | 					similarityC:add(s2)					
221 | 					similarityC:add(s3)
222 | 					connection:add(similarityC) -- scalar											
223 | 					d:add(connection)
224 | 					end
225 | 				elseif i <= 2*items/3 then				
226 | 					for j=1+items/3, 2*items/3 do
227 | 						local connection = nn.Sequential()
228 | 						local minus=nn.Concat(2)
229 | 						local c1=nn.Sequential()
230 | 						local c2=nn.Sequential()
231 | 						c1:add(nn.Select(2,i)) -- == NumFilter, not NumFilter*1
232 | 						c1:add(nn.Reshape(NumFilter,1)) --NumFilter*1 here
233 | 						c2:add(nn.Select(2,separator+j))
234 | 						c2:add(nn.Reshape(NumFilter,1))
235 | 						minus:add(c1)
236 | 						minus:add(c2)
237 | 						connection:add(minus) -- D*2						
238 | 						local similarityC=nn.Concat(1) -- multi similarity criteria			
239 | 						local s1=nn.Sequential()
240 | 						s1:add(nn.SplitTable(2))
241 | 						s1:add(nn.PairwiseDistance(2)) -- scalar
242 | 						local s2=nn.Sequential()			
243 | 						if 1 < 3 then
244 | 							s2:add(nn.SplitTable(2))
245 | 						else
246 | 							s2:add(nn.Transpose({1,2})) -- D*2 -> 2*D
247 | 							s2:add(nn.SoftMax())
248 | 							s2:add(nn.SplitTable(1))										
249 | 						end									
250 | 						s2:add(nn.CsDis()) -- scalar						
251 | 						local s3=nn.Sequential()
252 | 						s3:add(nn.SplitTable(2))
253 | 						s3:add(nn.CSubTable()) -- linear
254 | 						s3:add(nn.Abs()) -- linear						
255 | 						similarityC:add(s1)
256 | 						similarityC:add(s2)					
257 | 						similarityC:add(s3)
258 | 						connection:add(similarityC) -- scalar												
259 | 						d:add(connection)						
260 | 					end
261 | 				else 
262 | 					for j=1+2*items/3, items do
263 | 						local connection = nn.Sequential()
264 | 						local minus=nn.Concat(2)
265 | 						local c1=nn.Sequential()
266 | 						local c2=nn.Sequential()
267 | 						c1:add(nn.Select(2,i)) -- == D, not D*1
268 | 						c1:add(nn.Reshape(NumFilter,1)) --D*1 here
269 | 						c2:add(nn.Select(2,separator+j))
270 | 						c2:add(nn.Reshape(NumFilter,1))
271 | 						minus:add(c1)
272 | 						minus:add(c2)
273 | 						connection:add(minus) -- D*2						
274 | 						local similarityC=nn.Concat(1) -- multi similarity criteria			
275 | 						local s1=nn.Sequential()
276 | 						s1:add(nn.SplitTable(2))
277 | 						s1:add(nn.PairwiseDistance(2)) -- scalar
278 | 						local s2=nn.Sequential()					
279 | 						if 1 < 3 then
280 | 							s2:add(nn.SplitTable(2))
281 | 						else
282 | 							s2:add(nn.Transpose({1,2})) -- D*2 -> 2*D
283 | 							s2:add(nn.SoftMax())
284 | 							s2:add(nn.SplitTable(1))										
285 | 						end							
286 | 						s2:add(nn.CsDis()) -- scalar
287 | 						local s3=nn.Sequential()
288 | 						s3:add(nn.SplitTable(2))
289 | 						s3:add(nn.CSubTable()) -- linear
290 | 						s3:add(nn.Abs()) -- linear						
291 | 						similarityC:add(s1)
292 | 						similarityC:add(s2)					
293 | 						similarityC:add(s3)					
294 | 						connection:add(similarityC) -- scalar											
295 | 						d:add(connection)						
296 | 					end		
297 | 				end
298 | 			end	
299 | 				  				
300 | 			for i=1,NumFilter do
301 | 				for j=1,3 do 
302 | 					local connection = nn.Sequential()
303 | 					connection:add(nn.Select(1,i)) -- == 2items
304 | 					connection:add(nn.Reshape(2*separator,1)) --2items*1 here					
305 | 					local minus=nn.Concat(2)
306 | 					local c1=nn.Sequential()
307 | 					local c2=nn.Sequential()
308 | 					if j == 1 then 
309 | 						c1:add(nn.Narrow(1,1,ngram+1)) -- first half (items/3)*1
310 | 						c2:add(nn.Narrow(1,separator+1,ngram+1)) -- first half (items/3)*1
311 | 					elseif j == 2 then
312 | 						c1:add(nn.Narrow(1,ngram+2,ngram+1)) -- 
313 | 						c2:add(nn.Narrow(1,separator+ngram+2,ngram+1)) 
314 | 					else
315 | 						c1:add(nn.Narrow(1,2*(ngram+1)+1,ngram+1)) 
316 | 						c2:add(nn.Narrow(1,separator+2*(ngram+1)+1,ngram+1)) --each is ngram+1 portion (max or min or mean)
317 | 					end						
318 | 					
319 | 					minus:add(c1)
320 | 					minus:add(c2)
321 | 					connection:add(minus) -- (items/3)*2					
322 | 					local similarityC=nn.Concat(1) 	
323 | 					local s1=nn.Sequential()
324 | 					s1:add(nn.SplitTable(2))
325 | 					s1:add(nn.PairwiseDistance(2)) -- scalar
326 | 					local s2=nn.Sequential()					
327 | 					if 1 >= 2 then
328 | 						s2:add(nn.Transpose({1,2})) -- (items/3)*2 -> 2*(items/3)
329 | 						s2:add(nn.SoftMax()) --for softmax have to do transpose from (item/3)*2 -> 2*(item/3)
330 | 						s2:add(nn.SplitTable(1)) --softmax only works on row						
331 | 					else 																				
332 | 						s2:add(nn.SplitTable(2)) --(items/3)*2
333 | 					end
334 | 					s2:add(nn.CsDis()) -- scalar
335 | 					similarityC:add(s1)
336 | 					similarityC:add(s2)					
337 | 					connection:add(similarityC) -- scalar											
338 | 					d:add(connection)				
339 | 				end
340 | 			end			
341 | 			
342 | 			for i=items+1,separator do
343 | 	  			local connection = nn.Sequential()
344 | 				local minus=nn.Concat(2)
345 | 				local c1=nn.Sequential()
346 | 				local c2=nn.Sequential()
347 | 				c1:add(nn.Select(2,i)) -- == D, not D*1
348 | 				c1:add(nn.Reshape(NumFilter,1)) --D*1 here
349 | 				c2:add(nn.Select(2,separator+i))
350 | 				c2:add(nn.Reshape(NumFilter,1))
351 | 				minus:add(c1)
352 | 				minus:add(c2)
353 | 				connection:add(minus) -- D*2						
354 | 				local similarityC=nn.Concat(1) 			
355 | 				local s1=nn.Sequential()
356 | 				s1:add(nn.SplitTable(2))
357 | 				s1:add(nn.PairwiseDistance(2)) -- scalar
358 | 				local s2=nn.Sequential()					
359 | 				if 1 < 3 then
360 | 					s2:add(nn.SplitTable(2))
361 | 				else
362 | 					s2:add(nn.Transpose({1,2})) 
363 | 					s2:add(nn.SoftMax())
364 | 					s2:add(nn.SplitTable(1))										
365 | 				end							
366 | 				s2:add(nn.CsDis()) -- scalar
367 | 				local s3=nn.Sequential()
368 | 				s3:add(nn.SplitTable(2))
369 | 				s3:add(nn.CSubTable()) -- linear
370 | 				s3:add(nn.Abs()) -- linear						
371 | 				similarityC:add(s1)
372 | 				similarityC:add(s2)					
373 | 				similarityC:add(s3)					
374 | 				connection:add(similarityC) -- scalar											
375 | 				d:add(connection)		
376 | 			end
377 | 	  	
378 | 			deepQuery:add(d)	    
379 | 			return deepQuery	
380 | 		end
381 | end
382 | 
383 | 


--------------------------------------------------------------------------------
/scripts/build_vocab.py:
--------------------------------------------------------------------------------
 1 | import os
 2 | import glob
 3 | 
 4 | def build_vocab(filepaths, dst_path, lowercase=True):
 5 |     vocab = set()
 6 |     for filepath in filepaths:
 7 |         with open(filepath) as f:
 8 |             for line in f:
 9 |                 if lowercase:
10 |                     line = line.lower()
11 |                 vocab |= set(line.split())
12 |     with open(dst_path, 'w') as f:
13 |         for w in sorted(vocab):
14 |             f.write(w + '\n')
15 | 
16 | data_dir = 'data/sick/'
17 | build_vocab(
18 |         glob.glob(os.path.join(data_dir, '*/*.toks')),
19 |         os.path.join(data_dir, 'vocab-cased.txt'), False)
20 | 


--------------------------------------------------------------------------------
/scripts/convert-wordvecs.lua:
--------------------------------------------------------------------------------
 1 | -- We thank Kai Sheng Tai for providing this preprocessing codes
 2 | 
 3 | require('torch')
 4 | require('xlua')
 5 | 
 6 | local path = arg[1]
 7 | local vocabpath = arg[2]
 8 | local vecpath = arg[3]
 9 | local prefix_toks = stringx.split(path, '.')
10 | print('Converting ' .. path .. ' to Torch serialized format')
11 | 
12 | -- get dimension and number of lines
13 | local file = io.open(path, 'r')
14 | local line
15 | local count = 0
16 | local dim = 0
17 | while true do
18 |   line = file:read()
19 |   if not line then break end
20 |   if count == 0 then
21 |     dim = #stringx.split(line) - 1
22 |   end
23 |   count = count + 1
24 | end
25 | 
26 | print('count = ' .. count)
27 | print('dim = ' .. dim)
28 | 
29 | -- convert to torch-friendly format
30 | file:seek('set')
31 | local vocab = io.open(vocabpath, 'w')
32 | local vecs = torch.FloatTensor(count, dim)
33 | for i = 1, count do
34 |   xlua.progress(i, count)
35 |   local tokens = stringx.split(file:read())
36 |   local word = tokens[1]
37 |   vocab:write(word .. '\n')
38 |   for j = 1, dim do
39 |     vecs[{i, j}] = tonumber(tokens[j + 1])
40 |   end
41 | end
42 | file:close()
43 | vocab:close()
44 | torch.save(vecpath, vecs)
45 | 


--------------------------------------------------------------------------------
/scripts/download.py:
--------------------------------------------------------------------------------
  1 | """
  2 | Downloads the following:
  3 | - Glove vectors
  4 | 
  5 | We Thank Kai Sheng Tai for providing the preprocessing/basis codes. 
  6 | """
  7 | 
  8 | from __future__ import print_function
  9 | import urllib2
 10 | import sys
 11 | import os
 12 | import shutil
 13 | import zipfile
 14 | import gzip
 15 | 
 16 | def download(url, dirpath):
 17 |     filename = url.split('/')[-1]
 18 |     filepath = os.path.join(dirpath, filename)
 19 |     try:
 20 |         u = urllib2.urlopen(url)
 21 |     except:
 22 |         print("URL %s failed to open" %url)
 23 |         raise Exception
 24 |     try:
 25 |         f = open(filepath, 'wb')
 26 |     except:
 27 |         print("Cannot write %s" %filepath)
 28 |         raise Exception
 29 |     try:
 30 |         filesize = int(u.info().getheaders("Content-Length")[0])
 31 |     except:
 32 |         print("URL %s failed to report length" %url)
 33 |         raise Exception
 34 |     print("Downloading: %s Bytes: %s" % (filename, filesize))
 35 | 
 36 |     downloaded = 0
 37 |     block_sz = 8192
 38 |     status_width = 70
 39 |     while True:
 40 |         buf = u.read(block_sz)
 41 |         if not buf:
 42 |             print('')
 43 |             break
 44 |         else:
 45 |             print('', end='\r')
 46 |         downloaded += len(buf)
 47 |         f.write(buf)
 48 |         status = (("[%-" + str(status_width + 1) + "s] %3.2f%%") %
 49 |             ('=' * int(float(downloaded) / filesize * status_width) + '>', downloaded * 100. / filesize))
 50 |         print(status, end='')
 51 |         sys.stdout.flush()
 52 |     f.close()
 53 |     return filepath
 54 | 
 55 | def unzip(filepath):
 56 |     print("Extracting: " + filepath)
 57 |     dirpath = os.path.dirname(filepath)
 58 |     with zipfile.ZipFile(filepath) as zf:
 59 |         zf.extractall(dirpath)
 60 |     os.remove(filepath)
 61 | 
 62 | def download_tagger(dirpath):
 63 |     tagger_dir = 'stanford-tagger'
 64 |     if os.path.exists(os.path.join(dirpath, tagger_dir)):
 65 |         print('Found Stanford POS Tagger - skip')
 66 |         return
 67 |     url = 'http://nlp.stanford.edu/software/stanford-postagger-2015-01-29.zip'
 68 |     filepath = download(url, dirpath)
 69 |     zip_dir = ''
 70 |     with zipfile.ZipFile(filepath) as zf:
 71 |         zip_dir = zf.namelist()[0]
 72 |         zf.extractall(dirpath)
 73 |     os.remove(filepath)
 74 |     os.rename(os.path.join(dirpath, zip_dir), os.path.join(dirpath, tagger_dir))
 75 | 
 76 | def download_parser(dirpath):
 77 |     parser_dir = 'stanford-parser'
 78 |     if os.path.exists(os.path.join(dirpath, parser_dir)):
 79 |         print('Found Stanford Parser - skip')
 80 |         return
 81 |     url = 'http://nlp.stanford.edu/software/stanford-parser-full-2015-01-29.zip'
 82 |     filepath = download(url, dirpath)
 83 |     zip_dir = ''
 84 |     with zipfile.ZipFile(filepath) as zf:
 85 |         zip_dir = zf.namelist()[0]
 86 |         zf.extractall(dirpath)
 87 |     os.remove(filepath)
 88 |     os.rename(os.path.join(dirpath, zip_dir), os.path.join(dirpath, parser_dir))
 89 | 
 90 | def download_wordvecs(dirpath):
 91 |     if os.path.exists(dirpath):
 92 |         print('Found Glove vectors - skip')
 93 |         return
 94 |     else:
 95 |         os.makedirs(dirpath)
 96 |     url = 'http://www-nlp.stanford.edu/data/glove.840B.300d.zip'
 97 |     unzip(download(url, dirpath))
 98 | 
 99 | def download_sick(dirpath):
100 |     if os.path.exists(dirpath):
101 |         print('Found SICK dataset - skip')
102 |         return
103 |     else:
104 |         os.makedirs(dirpath)
105 |     train_url = 'http://alt.qcri.org/semeval2014/task1/data/uploads/sick_train.zip'
106 |     trial_url = 'http://alt.qcri.org/semeval2014/task1/data/uploads/sick_trial.zip'
107 |     test_url = 'http://alt.qcri.org/semeval2014/task1/data/uploads/sick_test_annotated.zip'
108 |     unzip(download(train_url, dirpath))
109 |     unzip(download(trial_url, dirpath))
110 |     unzip(download(test_url, dirpath))
111 | 
112 | def download_sst(dirpath):
113 |     if os.path.exists(dirpath):
114 |         print('Found SST dataset - skip')
115 |         return
116 |     url = 'http://nlp.stanford.edu/~socherr/stanfordSentimentTreebank.zip'
117 |     parent_dir = os.path.dirname(dirpath)
118 |     unzip(download(url, parent_dir))
119 |     os.rename(
120 |         os.path.join(parent_dir, 'stanfordSentimentTreebank'),
121 |         os.path.join(parent_dir, 'sst'))
122 |     shutil.rmtree(os.path.join(parent_dir, '__MACOSX')) # remove extraneous dir
123 | 
124 | if __name__ == '__main__':
125 |     base_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
126 | 
127 |     # data
128 |     data_dir = os.path.join(base_dir, 'data')
129 |     wordvec_dir = os.path.join(data_dir, 'glove')
130 |     sick_dir = os.path.join(data_dir, 'sick')
131 |     sst_dir = os.path.join(data_dir, 'sst')
132 | 
133 |     # libraries
134 |     lib_dir = os.path.join(base_dir, 'lib')
135 | 
136 |     # download dependencies
137 |     #download_tagger(lib_dir)
138 |     #download_parser(lib_dir)
139 |     download_wordvecs(wordvec_dir)
140 |     #download_sick(sick_dir)
141 |     #download_sst(sst_dir)
142 | 


--------------------------------------------------------------------------------
/scripts/preprocess-sick.py:
--------------------------------------------------------------------------------
  1 | """
  2 | This file is not required to run our codes, as we have provided the preprocessed data.
  3 | We Thank Kai Sheng Tai for the preprocessing script of SICK data.
  4 | """
  5 | 
  6 | import os
  7 | import glob
  8 | 
  9 | def make_dirs(dirs):
 10 |     for d in dirs:
 11 |         if not os.path.exists(d):
 12 |             os.makedirs(d)
 13 | 
 14 | def javac(filepath, cp):
 15 |     cmd = 'javac -cp %s %s' % (cp, filepath)
 16 |     print(cmd)
 17 |     os.system(cmd)
 18 | 
 19 | def dependency_parse(filepath, cp='', tokenize=True):
 20 |     print('\nDependency parsing ' + filepath)
 21 |     dirpath = os.path.dirname(filepath)
 22 |     filepre = os.path.splitext(os.path.basename(filepath))[0]
 23 |     tokpath = os.path.join(dirpath, filepre + '.toks')
 24 |     parentpath = os.path.join(dirpath, filepre + '.parents')
 25 |     relpath =  os.path.join(dirpath, filepre + '.rels')
 26 |     tokenize_flag = '-tokenize - ' if tokenize else ''
 27 |     cmd = ('java -cp %s DependencyParse -tokpath %s -parentpath %s -relpath %s %s < %s'
 28 |         % (cp, tokpath, parentpath, relpath, tokenize_flag, filepath))
 29 |     os.system(cmd)
 30 | 
 31 | def constituency_parse(filepath, cp='', tokenize=True):
 32 |     dirpath = os.path.dirname(filepath)
 33 |     filepre = os.path.splitext(os.path.basename(filepath))[0]
 34 |     tokpath = os.path.join(dirpath, filepre + '.toks')
 35 |     parentpath = os.path.join(dirpath, filepre + '.cparents')
 36 |     tokenize_flag = '-tokenize - ' if tokenize else ''
 37 |     cmd = ('java -cp %s ConstituencyParse -tokpath %s -parentpath %s %s < %s'
 38 |         % (cp, tokpath, parentpath, tokenize_flag, filepath))
 39 |     os.system(cmd)
 40 | 
 41 | def build_vocab(filepaths, dst_path, lowercase=True):
 42 |     vocab = set()
 43 |     for filepath in filepaths:
 44 |         with open(filepath) as f:
 45 |             for line in f:
 46 |                 if lowercase:
 47 |                     line = line.lower()
 48 |                 vocab |= set(line.split())
 49 |     with open(dst_path, 'w') as f:
 50 |         for w in sorted(vocab):
 51 |             f.write(w + '\n')
 52 | 
 53 | def split(filepath, dst_dir):
 54 |     with open(filepath) as datafile, \
 55 |          open(os.path.join(dst_dir, 'a.txt'), 'w') as afile, \
 56 |          open(os.path.join(dst_dir, 'b.txt'), 'w') as bfile,  \
 57 |          open(os.path.join(dst_dir, 'id.txt'), 'w') as idfile, \
 58 |          open(os.path.join(dst_dir, 'sim.txt'), 'w') as simfile:
 59 |             datafile.readline()
 60 |             for line in datafile:
 61 |                 i, a, b, sim, ent = line.strip().split('\t')
 62 |                 idfile.write(i + '\n')
 63 |                 afile.write(a + '\n')
 64 |                 bfile.write(b + '\n')
 65 |                 simfile.write(sim + '\n')
 66 | 
 67 | def parse(dirpath, cp=''):
 68 |     dependency_parse(os.path.join(dirpath, 'a.txt'), cp=cp, tokenize=True)
 69 |     dependency_parse(os.path.join(dirpath, 'b.txt'), cp=cp, tokenize=True)
 70 | 
 71 | if __name__ == '__main__':
 72 |     print('=' * 80)
 73 |     print('Preprocessing SICK dataset')
 74 |     print('=' * 80)
 75 | 
 76 |     base_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
 77 |     data_dir = os.path.join(base_dir, 'data')
 78 |     sick_dir = os.path.join(data_dir, 'sick')
 79 |     lib_dir = os.path.join(base_dir, 'lib')
 80 |     train_dir = os.path.join(sick_dir, 'train')
 81 |     dev_dir = os.path.join(sick_dir, 'dev')
 82 |     test_dir = os.path.join(sick_dir, 'test')
 83 |     make_dirs([train_dir, dev_dir, test_dir])
 84 | 
 85 |     # java classpath for calling Stanford parser
 86 |     classpath = ':'.join([
 87 |         lib_dir,
 88 |         os.path.join(lib_dir, 'stanford-parser/stanford-parser.jar'),
 89 |         os.path.join(lib_dir, 'stanford-parser/stanford-parser-3.5.1-models.jar')])
 90 |     javac(os.path.join(lib_dir, 'DependencyParse.java'), cp=classpath)
 91 |     javac(os.path.join(lib_dir, 'CollapseUnaryTransformer.java'), cp=classpath)
 92 |     javac(os.path.join(lib_dir, 'ConstituencyParse.java'), cp=classpath)
 93 | 
 94 |     # split into separate files
 95 |     split(os.path.join(sick_dir, 'SICK_train.txt'), train_dir)
 96 |     split(os.path.join(sick_dir, 'SICK_trial.txt'), dev_dir)
 97 |     split(os.path.join(sick_dir, 'SICK_test_annotated.txt'), test_dir)
 98 | 
 99 |     # parse sentences
100 |     parse(train_dir, cp=classpath)
101 |     parse(dev_dir, cp=classpath)
102 |     parse(test_dir, cp=classpath)
103 | 
104 |     # get vocabulary
105 |     build_vocab(
106 |         glob.glob(os.path.join(sick_dir, '*/*.toks')),
107 |         os.path.join(sick_dir, 'vocab.txt'))
108 |     build_vocab(
109 |         glob.glob(os.path.join(sick_dir, '*/*.toks')),
110 |         os.path.join(sick_dir, 'vocab-cased.txt'),
111 |         lowercase=False)
112 | 


--------------------------------------------------------------------------------
/testDeployTrainedModel.lua:
--------------------------------------------------------------------------------
  1 | --[[
  2 |   Author: Hua He
  3 |   Usage： th testRun.lua
  4 |   Training script for semantic relatedness prediction on the SICK dataset. 
  5 | --]]
  6 | 
  7 | require('torch')
  8 | require('nn')
  9 | require('nngraph')
 10 | require('optim')
 11 | require('xlua')
 12 | require('sys')
 13 | require('lfs')
 14 | 
 15 | similarityMeasure = {}
 16 | 
 17 | include('util/read_data.lua')
 18 | include('util/Vocab.lua')
 19 | include('Conv.lua')
 20 | include('CsDis.lua')
 21 | --include('PaddingReshape.lua')
 22 | printf = utils.printf
 23 | 
 24 | -- global paths (modify if desired)
 25 | similarityMeasure.data_dir        = 'data'
 26 | similarityMeasure.models_dir      = 'trained_models'
 27 | similarityMeasure.predictions_dir = 'predictions'
 28 | 
 29 | function header(s)
 30 |   print(string.rep('-', 80))
 31 |   print(s)
 32 |   print(string.rep('-', 80))
 33 | end
 34 | 
 35 | -- Pearson correlation
 36 | function pearson(x, y)
 37 |   x = x - x:mean()
 38 |   y = y - y:mean()
 39 |   return x:dot(y) / (x:norm() * y:norm())
 40 | end
 41 | 
 42 | -- read command line arguments
 43 | local args = lapp [[
 44 | Training script for semantic relatedness prediction on the SICK dataset.
 45 |   -m,--model  (default dependency) Model architecture: [dependency, lstm, bilstm]
 46 |   -l,--layers (default 1)          Number of layers (ignored for Tree-LSTM)
 47 |   -d,--dim    (default 150)        LSTM memory dimension
 48 | ]]
 49 | 
 50 | local model_name, model_class, model_structure
 51 | model_name = 'convOnly'
 52 | model_class = similarityMeasure.Conv
 53 | model_structure = model_name
 54 | 
 55 | --torch.seed()
 56 | torch.manualSeed(-3.0753778015266e+18)
 57 | --print('<torch> using the automatic seed: ' .. torch.initialSeed())
 58 | 
 59 | -- directory containing dataset files
 60 | local data_dir = 'data/sick/'
 61 | 
 62 | -- load vocab
 63 | local vocab = similarityMeasure.Vocab(data_dir .. 'vocab-cased.txt')
 64 | 
 65 | -- load embeddings
 66 | print('loading word embeddings')
 67 | 
 68 | local emb_dir = 'data/glove/'
 69 | local emb_prefix = emb_dir .. 'glove.840B'
 70 | local emb_vocab, emb_vecs = similarityMeasure.read_embedding(emb_prefix .. '.vocab', emb_prefix .. '.300d.th')
 71 | 
 72 | local emb_dim = emb_vecs:size(2)
 73 | 
 74 | collectgarbage()
 75 | 
 76 | local taskD = 'sic'
 77 | -- load datasets
 78 | local test_dir = data_dir .. 'test/'
 79 | local test_dataset = similarityMeasure.read_relatedness_dataset(test_dir, vocab, taskD)
 80 | printf('num test  = %d\n', test_dataset.size)
 81 | 
 82 | -- initialize model
 83 | modelTrained = torch.load("modelSTS.trained.th", 'ascii')
 84 | modelTrained.convModel:evaluate()
 85 | modelTrained.softMaxC:evaluate()
 86 | 
 87 | -- Predict dataset with existing models
 88 | for i = 1, test_dataset.size do
 89 |     local lsent, rsent = test_dataset.lsents[i], test_dataset.rsents[i]
 90 |     local linputs = emb_vecs:index(1, lsent:long()):double()
 91 |     local rinputs = emb_vecs:index(1, rsent:long()):double()
 92 |     local part2 = modelTrained.convModel:forward({linputs, rinputs})
 93 |     local output = modelTrained.softMaxC:forward(part2)
 94 |     local val = torch.range(0, 5, 1):dot(output:exp()) 
 95 |     print(val)
 96 | end
 97 | 
 98 | 
 99 | 
100 | 


--------------------------------------------------------------------------------
/trainMSRVID.lua:
--------------------------------------------------------------------------------
  1 | --[[
  2 |   Author: Hua He
  3 |   Usage: th trainMSRVID.lua
  4 |   Training script for semantic relatedness prediction on the MSRVID dataset.
  5 | --]]
  6 | 
  7 | require('torch')
  8 | require('nn')
  9 | require('nngraph')
 10 | require('optim')
 11 | require('xlua')
 12 | require('sys')
 13 | require('lfs')
 14 | 
 15 | similarityMeasure = {}
 16 | 
 17 | include('util/read_data.lua')
 18 | include('util/Vocab.lua')
 19 | include('Conv.lua')
 20 | include('CsDis.lua')
 21 | --include('PaddingReshape.lua')
 22 | printf = utils.printf
 23 | 
 24 | -- global paths (modify if desired)
 25 | similarityMeasure.data_dir        = 'data'
 26 | similarityMeasure.models_dir      = 'trained_models'
 27 | similarityMeasure.predictions_dir = 'predictions'
 28 | 
 29 | function header(s)
 30 |   print(string.rep('-', 80))
 31 |   print(s)
 32 |   print(string.rep('-', 80))
 33 | end
 34 | 
 35 | -- Pearson correlation
 36 | function pearson(x, y)
 37 |   x = x - x:mean()
 38 |   y = y - y:mean()
 39 |   return x:dot(y) / (x:norm() * y:norm())
 40 | end
 41 | 
 42 | -- read command line arguments
 43 | local args = lapp [[
 44 | Training script for semantic relatedness prediction on the SICK dataset.
 45 |   -m,--model  (default dependency) Model architecture: [dependency, lstm, bilstm]
 46 |   -l,--layers (default 1)          Number of layers (ignored for Tree-LSTM)
 47 |   -d,--dim    (default 150)        LSTM memory dimension
 48 | ]]
 49 | 
 50 | local model_name, model_class, model_structure
 51 | model_name = 'convOnly'
 52 | model_class = similarityMeasure.Conv
 53 | model_structure = model_name
 54 | 
 55 | torch.seed()
 56 | --torch.manualSeed(123)
 57 | print('<torch> using the specified seed: ' .. torch.initialSeed())
 58 | 
 59 | -- directory containing dataset files
 60 | local data_dir = 'data/msrvid/'
 61 | 
 62 | -- load vocab
 63 | local vocab = similarityMeasure.Vocab(data_dir .. 'vocab-cased.txt')
 64 | 
 65 | -- load embeddings
 66 | print('loading word embeddings')
 67 | 
 68 | local emb_dir = 'data/glove/'
 69 | local emb_prefix = emb_dir .. 'glove.840B'
 70 | local emb_vocab, emb_vecs = similarityMeasure.read_embedding(emb_prefix .. '.vocab', emb_prefix .. '.300d.th')
 71 | 
 72 | local emb_dim = emb_vecs:size(2)
 73 | 
 74 | -- use only vectors in vocabulary (not necessary, but gives faster training)
 75 | local num_unk = 0
 76 | local vecs = torch.Tensor(vocab.size, emb_dim)
 77 | for i = 1, vocab.size do
 78 |   local w = vocab:token(i)
 79 |   if emb_vocab:contains(w) then
 80 |     vecs[i] = emb_vecs[emb_vocab:index(w)]
 81 |   else
 82 |     num_unk = num_unk + 1
 83 |     vecs[i]:uniform(-0.05, 0.05)
 84 |   end
 85 | end
 86 | print('unk count = ' .. num_unk)
 87 | emb_vocab = nil
 88 | emb_vecs = nil
 89 | collectgarbage()
 90 | local taskD = 'vid'
 91 | -- load datasets
 92 | print('loading datasets')
 93 | local train_dir = data_dir .. 'train/'
 94 | local dev_dir = data_dir .. 'dev/'
 95 | local test_dir = data_dir .. 'test/'
 96 | local train_dataset = similarityMeasure.read_relatedness_dataset(train_dir, vocab, taskD)
 97 | local dev_dataset = similarityMeasure.read_relatedness_dataset(dev_dir, vocab, taskD)
 98 | printf('num train = %d\n', train_dataset.size)
 99 | printf('num dev   = %d\n', dev_dataset.size)
100 | 
101 | -- initialize model
102 | local model = model_class{
103 |   emb_vecs   = vecs,
104 |   structure  = model_structure,
105 |   mem_dim    = 150,
106 |   task       = taskD,
107 | }
108 | 
109 | -- number of epochs to train
110 | local num_epochs = 35
111 | 
112 | -- print information
113 | header('model configuration')
114 | printf('max epochs = %d\n', num_epochs)
115 | model:print_config()
116 | 
117 | 
118 | if lfs.attributes(similarityMeasure.predictions_dir) == nil then
119 |   lfs.mkdir(similarityMeasure.predictions_dir)
120 | end
121 | 
122 | -- train
123 | local train_start = sys.clock()
124 | local best_dev_score = -1.0
125 | local best_dev_model = model
126 | 
127 | -- threads
128 | --torch.setnumthreads(4)
129 | --print('<torch> number of threads in used: ' .. torch.getnumthreads())
130 | 
131 | header('Training model')
132 | 
133 | local id = 2007
134 | print("Id: " .. id)
135 | for i = 1, num_epochs do
136 |   local start = sys.clock()
137 |   print('--------------- EPOCH ' .. i .. '--- -------------')
138 |   model:trainCombineOnly(train_dataset)
139 |   print('Finished epoch in ' .. ( sys.clock() - start) )
140 | 
141 |   local dev_predictions = model:predict_dataset(dev_dataset)
142 |   local dev_score = pearson(dev_predictions, dev_dataset.labels)
143 |   printf('-- score: %.5f\n', dev_score)
144 | end
145 | print('finished training in ' .. (sys.clock() - train_start))
146 | 
147 | 


--------------------------------------------------------------------------------
/trainSIC.lua:
--------------------------------------------------------------------------------
  1 | --[[
  2 |   Author: Hua He
  3 |   Usage： th trainSIC.lua
  4 |   Training script for semantic relatedness prediction on the SICK dataset. 
  5 | --]]
  6 | 
  7 | require('torch')
  8 | require('nn')
  9 | require('nngraph')
 10 | require('optim')
 11 | require('xlua')
 12 | require('sys')
 13 | require('lfs')
 14 | 
 15 | similarityMeasure = {}
 16 | 
 17 | include('util/read_data.lua')
 18 | include('util/Vocab.lua')
 19 | include('Conv.lua')
 20 | include('CsDis.lua')
 21 | --include('PaddingReshape.lua')
 22 | printf = utils.printf
 23 | 
 24 | -- global paths (modify if desired)
 25 | similarityMeasure.data_dir        = 'data'
 26 | similarityMeasure.models_dir      = 'trained_models'
 27 | similarityMeasure.predictions_dir = 'predictions'
 28 | 
 29 | function header(s)
 30 |   print(string.rep('-', 80))
 31 |   print(s)
 32 |   print(string.rep('-', 80))
 33 | end
 34 | 
 35 | -- Pearson correlation
 36 | function pearson(x, y)
 37 |   x = x - x:mean()
 38 |   y = y - y:mean()
 39 |   return x:dot(y) / (x:norm() * y:norm())
 40 | end
 41 | 
 42 | -- read command line arguments
 43 | local args = lapp [[
 44 | Training script for semantic relatedness prediction on the SICK dataset.
 45 |   -m,--model  (default dependency) Model architecture: [dependency, lstm, bilstm]
 46 |   -l,--layers (default 1)          Number of layers (ignored for Tree-LSTM)
 47 |   -d,--dim    (default 150)        LSTM memory dimension
 48 | ]]
 49 | 
 50 | local model_name, model_class, model_structure
 51 | model_name = 'convOnly'
 52 | model_class = similarityMeasure.Conv
 53 | model_structure = model_name
 54 | 
 55 | torch.seed()
 56 | --torch.manualSeed(-3.0753778015266e+18)
 57 | print('<torch> using the automatic seed: ' .. torch.initialSeed())
 58 | 
 59 | -- directory containing dataset files
 60 | local data_dir = 'data/sick/'
 61 | 
 62 | -- load vocab
 63 | local vocab = similarityMeasure.Vocab(data_dir .. 'vocab-cased.txt')
 64 | 
 65 | -- load embeddings
 66 | print('loading word embeddings')
 67 | 
 68 | local emb_dir = 'data/glove/'
 69 | local emb_prefix = emb_dir .. 'glove.840B'
 70 | local emb_vocab, emb_vecs = similarityMeasure.read_embedding(emb_prefix .. '.vocab', emb_prefix .. '.300d.th')
 71 | 
 72 | local emb_dim = emb_vecs:size(2)
 73 | 
 74 | -- use only vectors in vocabulary (not necessary, but gives faster training)
 75 | local num_unk = 0
 76 | local vecs = torch.Tensor(vocab.size, emb_dim)
 77 | for i = 1, vocab.size do
 78 |   local w = vocab:token(i)
 79 |   if emb_vocab:contains(w) then
 80 |     vecs[i] = emb_vecs[emb_vocab:index(w)]
 81 |   else
 82 |     num_unk = num_unk + 1
 83 |     vecs[i]:uniform(-0.05, 0.05)
 84 |   end
 85 | end
 86 | print('unk count = ' .. num_unk)
 87 | emb_vocab = nil
 88 | emb_vecs = nil
 89 | collectgarbage()
 90 | local taskD = 'sic'
 91 | -- load datasets
 92 | print('loading datasets')
 93 | local train_dir = data_dir .. 'train/'
 94 | local dev_dir = data_dir .. 'dev/'
 95 | local test_dir = data_dir .. 'test/'
 96 | local train_dataset = similarityMeasure.read_relatedness_dataset(train_dir, vocab, taskD)
 97 | local dev_dataset = similarityMeasure.read_relatedness_dataset(dev_dir, vocab, taskD)
 98 | local test_dataset = similarityMeasure.read_relatedness_dataset(test_dir, vocab, taskD)
 99 | printf('num train = %d\n', train_dataset.size)
100 | printf('num dev   = %d\n', dev_dataset.size)
101 | printf('num test  = %d\n', test_dataset.size)
102 | 
103 | -- initialize model
104 | local model = model_class{
105 |   emb_vecs   = vecs,
106 |   structure  = model_structure,
107 |   num_layers = args.layers,
108 |   mem_dim    = args.dim,
109 |   task       = taskD,
110 | }
111 | 
112 | -- number of epochs to train
113 | local num_epochs = 30
114 | 
115 | -- print information
116 | header('model configuration')
117 | printf('max epochs = %d\n', num_epochs)
118 | model:print_config()
119 | 
120 | 
121 | if lfs.attributes(similarityMeasure.predictions_dir) == nil then
122 |   lfs.mkdir(similarityMeasure.predictions_dir)
123 | end
124 | 
125 | -- train
126 | local train_start = sys.clock()
127 | local best_dev_score = -1.0
128 | local best_dev_model = model
129 | 
130 | -- threads
131 | --torch.setnumthreads(4)
132 | --print('<torch> number of threads in used: ' .. torch.getnumthreads())
133 | 
134 | header('Training model')
135 | 
136 | local id = 10005
137 | print("Id: " .. id)
138 | for i = 1, num_epochs do
139 |   local start = sys.clock()
140 |   print('--------------- EPOCH ' .. i .. '--- -------------')
141 |   model:trainCombineOnly(train_dataset)
142 |   print('Finished epoch in ' .. ( sys.clock() - start) )
143 |   
144 |   local dev_predictions = model:predict_dataset(dev_dataset)
145 |   local dev_score = pearson(dev_predictions, dev_dataset.labels)
146 |   printf('-- dev score: %.5f\n', dev_score)
147 | 
148 |   if dev_score >= best_dev_score then
149 |     best_dev_score = dev_score
150 |     local test_predictions = model:predict_dataset(test_dataset)
151 |     local test_sco = pearson(test_predictions, test_dataset.labels)
152 |     printf('[[BEST DEV]]-- test score: %.4f\n', pearson(test_predictions, test_dataset.labels))
153 | 
154 |     local predictions_save_path = string.format(
155 | 	similarityMeasure.predictions_dir .. '/results-%s.%dl.%dd.epoch-%d.%.5f.%d.pred', args.model, args.layers, args.dim, i, test_sco, id)
156 |     local predictions_file = torch.DiskFile(predictions_save_path, 'w')
157 |     print('writing predictions to ' .. predictions_save_path)
158 |     for i = 1, test_predictions:size(1) do
159 |       predictions_file:writeFloat(test_predictions[i])
160 |     end
161 |     predictions_file:close()
162 |   end
163 | end
164 | print('finished training in ' .. (sys.clock() - train_start))
165 | 
166 | 


--------------------------------------------------------------------------------
/util/Vocab.lua:
--------------------------------------------------------------------------------
  1 | --[[
  2 | We Thank Kai Sheng Tai for providing the preprocessing codes. 
  3 | 
  4 | A vocabulary object. Initialized from a file with one vocabulary token per line.
  5 | Maps between vocabulary tokens and indices. If an UNK token is defined in the
  6 | vocabulary, returns the index to this token if queried for an out-of-vocabulary
  7 | token.
  8 | 
  9 | --]]
 10 | 
 11 | local Vocab = torch.class('similarityMeasure.Vocab')
 12 | 
 13 | function Vocab:__init(path)
 14 |   self.size = 0
 15 |   self._index = {}
 16 |   self._tokens = {}
 17 | 
 18 |   local file = io.open(path)
 19 |   while true do
 20 |     local line = file:read()
 21 |     if line == nil then break end
 22 |     self.size = self.size + 1
 23 |     self._tokens[self.size] = line
 24 |     self._index[line] = self.size
 25 |   end
 26 |   file:close()
 27 | 
 28 |   local unks = {'<unk>', '<UNK>', 'UUUNKKK'}
 29 |   for _, tok in pairs(unks) do
 30 |     self.unk_index = self.unk_index or self._index[tok]
 31 |     if self.unk_index ~= nil then
 32 |       self.unk_token = tok
 33 |       break
 34 |     end
 35 |   end
 36 | 
 37 |   local starts = {'<s>', '<S>'}
 38 |   for _, tok in pairs(starts) do
 39 |     self.start_index = self.start_index or self._index[tok]
 40 |     if self.start_index ~= nil then
 41 |       self.start_token = tok
 42 |       break
 43 |     end
 44 |   end
 45 | 
 46 |   local ends = {'</s>', '</S>'}
 47 |   for _, tok in pairs(ends) do
 48 |     self.end_index = self.end_index or self._index[tok]
 49 |     if self.end_index ~= nil then
 50 |       self.end_token = tok
 51 |       break
 52 |     end
 53 |   end
 54 | end
 55 | 
 56 | function Vocab:contains(w)
 57 |   if not self._index[w] then return false end
 58 |   return true
 59 | end
 60 | 
 61 | function Vocab:add(w)
 62 |   if self._index[w] ~= nil then
 63 |     return self._index[w]
 64 |   end
 65 |   self.size = self.size + 1
 66 |   self._tokens[self.size] = w
 67 |   self._index[w] = self.size
 68 |   return self.size
 69 | end
 70 | 
 71 | function Vocab:index(w)
 72 |   local index = self._index[w]
 73 |   if index == nil then
 74 |     if self.unk_index == nil then
 75 |       error('Token not in vocabulary and no UNK token defined: ' .. w)
 76 |     end
 77 |     return self.unk_index
 78 |   end
 79 |   return index
 80 | end
 81 | 
 82 | function Vocab:token(i)
 83 |   if i < 1 or i > self.size then
 84 |     error('Index ' .. i .. ' out of bounds')
 85 |   end
 86 |   return self._tokens[i]
 87 | end
 88 | 
 89 | function Vocab:map(tokens)
 90 |   local len = #tokens
 91 |   local output = torch.IntTensor(len)
 92 |   for i = 1, len do
 93 |     output[i] = self:index(tokens[i])
 94 |   end
 95 |   return output
 96 | end
 97 | 
 98 | function Vocab:add_unk_token()
 99 |   if self.unk_token ~= nil then return end
100 |   self.unk_index = self:add('<unk>')
101 | end
102 | 
103 | function Vocab:add_start_token()
104 |   if self.start_token ~= nil then return end
105 |   self.start_index = self:add('<s>')
106 | end
107 | 
108 | function Vocab:add_end_token()
109 |   if self.end_token ~= nil then return end
110 |   self.end_index = self:add('</s>')
111 | end
112 | 


--------------------------------------------------------------------------------
/util/read_data.lua:
--------------------------------------------------------------------------------
 1 | --[[
 2 | 
 3 |   Functions for loading data from disk.
 4 | 
 5 | --]]
 6 | 
 7 | function similarityMeasure.read_embedding(vocab_path, emb_path)
 8 |   local vocab = similarityMeasure.Vocab(vocab_path)
 9 |   local embedding = torch.load(emb_path)
10 |   return vocab, embedding
11 | end
12 | 
13 | function similarityMeasure.read_sentences(path, vocab)
14 |   local sentences = {}
15 |   local file = io.open(path, 'r')
16 |   local line
17 |   
18 |   local fixed = true
19 |   while true do
20 |     line = file:read()
21 |     if line == nil then break end
22 |     local tokens = stringx.split(line)
23 |     local len = #tokens
24 |     local padLen = len
25 |     if fixed and len < 3 then
26 |       padLen = 3
27 |     end
28 |     local sent = torch.IntTensor(padLen)
29 |     for i = 1, len do
30 |       local token = tokens[i]
31 |       sent[i] = vocab:index(token)
32 |     end
33 |     if fixed and len < 3 then
34 |       for i = len+1, padLen do
35 |         sent[i] = vocab:index("unk") -- sent[len]
36 |       end
37 |     end
38 |     sentences[#sentences + 1] = sent
39 |   end
40 |   
41 |   file:close()
42 |   return sentences
43 | end
44 | 
45 | function similarityMeasure.read_relatedness_dataset(dir, vocab, task)
46 |   local dataset = {}
47 |   dataset.vocab = vocab
48 |   dataset.lsents = similarityMeasure.read_sentences(dir .. 'a.toks', vocab)
49 |   dataset.rsents = similarityMeasure.read_sentences(dir .. 'b.toks', vocab)
50 |   dataset.size = #dataset.lsents
51 |   local id_file = torch.DiskFile(dir .. 'id.txt')
52 |   local sim_file = torch.DiskFile(dir .. 'sim.txt')
53 |   dataset.ids = torch.IntTensor(dataset.size)
54 |   dataset.labels = torch.Tensor(dataset.size)
55 |   for i = 1, dataset.size do
56 |     dataset.ids[i] = id_file:readInt()
57 |     if task == 'sic' then
58 |     	dataset.labels[i] = 0.25 * (sim_file:readDouble() - 1) -- sic data
59 |     elseif task == 'vid' then
60 | 	dataset.labels[i] = 0.2 * (sim_file:readDouble()) -- vid data
61 |     else
62 |     	dataset.labels[i] = (sim_file:readDouble()) -- twi and msp
63 |     end
64 |   end
65 |   id_file:close()
66 |   sim_file:close()
67 |   return dataset
68 | end
69 | 
70 | 


--------------------------------------------------------------------------------