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config.ini
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config.ini
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[GPU]
DisableCuda = False
GPUs = 0,1
[Training]
; Which KGC model to use: ComplEx, TransE, TransR, DistMult
LinkPredictionModelType = ComplEx
Epochs = 1000
BatchSize = 256
; Dimensionality of Embedding file is used, if one is given
EmbeddingDimensionality = 300
LearningRate = 0.001
LearningRateSchedule = 70,150,300
LearningRateGammas = 0.1,0.1,0.1
; Type of OWE transformation to use: Linear, Affine, FCN
TransformationType = Affine
; Type of OWE encoder to use: Average, LSTM
EncoderType = Average
; Which loss to use: Pairwise (Euclidean) or Cosine
Loss = Pairwise
; What to use as an UNK token: Zero, Average, TODO
UNKType = Zero
; How much word dropout to use
AverageWordDropout = 0
; Whether we use only heads or heads+tails during optimization (tail prediction)
UseTailsToOptimize = False
; What should be iterated during training: triplets or entities
IterTriplets = True
; Whether we want to initialize with embeddings obtained from OpenKE
; These are read from the embedding subdir
; InitializeWithPretrainedKGCEmbedding = True # FIXME not needed as we always try to initialize with these embeddings
[FCN]
FCNUseSigmoid = False
FCNLayers = 1
FCNDropout = 0.5
FCNHiddenDim = 300
[LSTM]
LSTMOutputDim = 300
LSTMBidirectional = False
[Evaluation]
; Note: most of the options below will slow down the training if true
ValidateEvery = 1
; Whether we should use Target Filtering (from ConMask)
UseTargetFilteringShi = False
; Prints nearest neighbour entities to the test entities
PrintTestNN = False
; Prints nearest neighbour entities to the training entities
PrintTrainNN = False
; Baseline where evaluation is done by randomly corrupting heads; Warning: normal evaluation doesn't work when True.
EvalRandomHeads = False
; Calculate mean nearest neighbour rank
CalculateNNMeanRank = False
; Target filtering baseline from ConMask; Warning: normal evaluation doesn't work when True.
ShiTargetFilteringBaseline = False
; Generate embeddings to view using tensorboard projector
GetTensorboardEmbeddings = False
[EarlyStopping]
EarlyStopping = True
EarlyStoppingThreshold = 0.001
EarlyStoppingLastX = 10
EarlyStoppingMinEpochs = 100
[Entity2Text]
; Path to the pretrained word embeddings
PretrainedEmbeddingFile = /mnt/data/pretrained_embeddings/glove_common_crawl42B/glove.42B.300d.w2vformat.bin
;PretrainedEmbeddingFile = /mnt/data/pretrained_embeddings/wikipedia2vec/enwiki_20180420_300d.bin
; Whether we should read the entity data from the entitiy2wikidata file
ConvertEntities = True
ConvertEntitiesWithMultiprocessing = True
; Tries to convert entity to a single token and match that token in embedding.
; Uses wikipedia link suffix as token.
; Fallback is to avg all lemmas.
MatchTokenInEmbedding = False
; Tries to convert entity into single token and match that token in embedding.
; This one uses label of the entity where spaces are replaced by underscores.
MatchLabelInEmbedding = False
[Dataset]
TrainFile = train.txt
ValidationFile = valid_zero.txt
TestFile = test_zero.txt
SkipHeader = False
; TAB or SPACE
SplitSymbol = TAB