View source: R/chunk_entity_resolver.R
nlp_chunk_entity_resolver_pretrained | R Documentation |
Create a pretrained Spark NLP ChunkEntityResolverModel
model
nlp_chunk_entity_resolver_pretrained( sc, input_cols, output_col, all_distances_metadata = NULL, alternatives = NULL, case_sensitive = NULL, confidence_function = NULL, distance_function = NULL, distance_weights = NULL, enable_jaccard = NULL, enable_jaro_winkler = NULL, enable_levenshtein = NULL, enable_sorensen_dice = NULL, enable_tfidf = NULL, enable_wmd = NULL, extra_mass_penalty = NULL, miss_as_empty = NULL, neighbors = NULL, pooling_strategy = NULL, threshold = NULL, name, lang = NULL, remote_loc = NULL )
sc |
A Spark connection |
input_cols |
Input columns. String array. |
output_col |
Output column. String. |
all_distances_metadata |
whether or not to return an all distance values in the metadata. |
alternatives |
number of results to return in the metadata after sorting by last distance calculated |
case_sensitive |
whether to treat the entities as case sensitive |
confidence_function |
what function to use to calculate confidence: INVERSE or SOFTMAX |
distance_function |
what distance function to use for KNN: 'EUCLIDEAN' or 'COSINE' |
distance_weights |
distance weights to apply before pooling: (WMD, TFIDF, Jaccard, SorensenDice, JaroWinkler, Levenshtein) |
enable_jaccard |
whether or not to use Jaccard token distance. |
enable_jaro_winkler |
whether or not to use Jaro-Winkler character distance. |
enable_levenshtein |
whether or not to use Levenshtein character distance. |
enable_sorensen_dice |
whether or not to use Sorensen-Dice token distance. |
enable_tfidf |
whether or not to use TFIDF token distance. |
enable_wmd |
whether or not to use WMD token distance. |
extra_mass_penalty |
penalty for extra words in the knowledge base match during WMD calculation |
miss_as_empty |
whether or not to return an empty annotation on unmatched chunks |
neighbors |
number of neighbours to consider in the KNN query to calculate WMD |
pooling_strategy |
pooling strategy to aggregate distances: AVERAGE or SUM |
threshold |
threshold value for the aggregated distance#' |
name |
the name of the model to load. If NULL will use the default value |
lang |
the language of the model to be loaded. If NULL will use the default value |
remote_loc |
the remote location of the model. If NULL will use the default value |
The Spark NLP model with the pretrained model loaded
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