R/word2vec.R
h2o.word2vec.Rd
Trains a word2vec model on a String column of an H2O data frame
h2o.word2vec( training_frame = NULL, model_id = NULL, min_word_freq = 5, word_model = c("SkipGram", "CBOW"), norm_model = c("HSM"), vec_size = 100, window_size = 5, sent_sample_rate = 0.001, init_learning_rate = 0.025, epochs = 5, pre_trained = NULL, max_runtime_secs = 0, export_checkpoints_dir = NULL )
training_frame | Id of the training data frame. |
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model_id | Destination id for this model; auto-generated if not specified. |
min_word_freq | This will discard words that appear less than <int> times Defaults to 5. |
word_model | The word model to use (SkipGram or CBOW) Must be one of: "SkipGram", "CBOW". Defaults to SkipGram. |
norm_model | Use Hierarchical Softmax Must be one of: "HSM". Defaults to HSM. |
vec_size | Set size of word vectors Defaults to 100. |
window_size | Set max skip length between words Defaults to 5. |
sent_sample_rate | Set threshold for occurrence of words. Those that appear with higher frequency in the training data will be randomly down-sampled; useful range is (0, 1e-5) Defaults to 0.001. |
init_learning_rate | Set the starting learning rate Defaults to 0.025. |
epochs | Number of training iterations to run Defaults to 5. |
pre_trained | Id of a data frame that contains a pre-trained (external) word2vec model |
max_runtime_secs | Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0. |
export_checkpoints_dir | Automatically export generated models to this directory. |
# NOT RUN { library(h2o) h2o.init() # Import the CraigslistJobTitles dataset job_titles <- h2o.importFile( "https://s3.amazonaws.com/h2o-public-test-data/smalldata/craigslistJobTitles.csv", col.names = c("category", "jobtitle"), col.types = c("String", "String"), header = TRUE ) # Build and train the Word2Vec model words <- h2o.tokenize(job_titles, " ") vec <- h2o.word2vec(training_frame = words) h2o.findSynonyms(vec, "teacher", count = 20) # }