Approximating the Softmax (a blog post), S.Notes on Noise Contrastive Estimation and Negative Sampling, C.Learning word embeddings efficiently with noise-contrastive estimation, A.Distributed Representations of Words and Phrases and their Compositionality, T.Dataset was comprised of 74219 documents and 91417 unique words. Shown are two subcategories from Computer Science. Name of a file in the models directory (a model trained on the data_file_name dataset).įirst two principal components (1% cumulative variance explained) of 300-dimensional document vectors trained on arXiv abstracts. Name of a file in the data directory that was used during training. Python export_vectors.py start -data_file_name 'example.csv ' -model_file_name 'example_model.dbow_numnoisewords.2_vecdim.100_batchsize.32_lr.0.001000_epoch.25_loss.0. ' Parameters Export trained paragraph vectors to a csv file (vectors are saved in the data directory).Note that order of batches is not guaranteed when num_workers > 1. If value is set to -1, total number of machine CPUs is used.
#PARAGRAPH VECTOR CODE GENERATOR#
Number of batch generator jobs to run in parallel. Indicates whether a diagnostic plot displaying loss value over epochs is generated after each epoch. If false, only the best performing model is saved. Indicates whether a checkpoint is saved after each epoch. Number of examples per single gradient update. number of times every example is seen during training). Number of iterations to train the model (i.e. Number of noise words to sample from the noise distribution.ĭimensionality of vectors to be learned (for paragraphs and words). When model_ver='dm' context_size has to greater than 0, when model_ver='dbow' context_size has to be 0. how many words left and right are regarded as context). Half the size of a neighbourhood of target words when model_ver='dm' (i.e. Currently only the 'sum' operation is implemented. Method for combining paragraph and word vectors when model_ver='dm'. vec_combine_method: str, one of ('sum', 'concat'), default='sum'.'dbow' stands for Distributed Bag Of Words, 'dm' stands for Distributed Memory. , Distributed Representations of Sentences and Documents. model_ver: str, one of ('dm', 'dbow'), default='dbow'.CRUSO-P achieves the highest mean accuracy score of 99.6% when tested with the C programming language, thus achieving an improvement of 5.6% over the existing method.Python train.py start -data_file_name 'example.csv ' -num_epochs 100 -batch_size 32 -num_noise_words 2 -vec_dim 100 -lr 1e-3 Parameters CRUSO-P outperforms CRUSO with an improvement of 97.82% in response time and a storage reduction of 99.15%.
#PARAGRAPH VECTOR CODE CODE#
The significant contributions of our paper are i) SOpostsDB: a dataset containing the PVA vectors and the SO posts information, ii) CRUSO-P: a code review assisting system based on PVA models trained on \emph. The central idea of the approach is to estimate the defectiveness for an input source code by using the defectiveness score of similar code fragments present in various StackOverflow (SO) posts. In this paper, we improve the performance (in terms of speed and memory usage) of our existing code review assisting tool-CRUSO. However, the existing methods are dependent on experts or inefficient. Code reviews are one of the effective methods to estimate defectiveness in source code.