jaccard_sims: Compute jaccard index of two embeddings models for N nearest...
In prodriguezsosa/weeval: Compare Word Embedding Models
Description
Usage
Arguments
Value
Description
Compute jaccard index of two embeddings models for N nearest neighbors
Usage
1
jaccard_sims(sims1, sims2, N = 10, common_vocab = FALSE)
Arguments
sims1
matrix of similarities (e.g. cosine) between a set of cues (rows) and the full vocab (columns)
sims2
matrix of similarities (e.g. cosine) between a set of cues (rows) and the full vocab (columns)
N
number of nearest neighbors with which to compute jaccard index
common_vocab
whether to use a common vocabulary to compute jaccard index
Value
a tibble with mean and standard error of jaccard index overt set of cues
prodriguezsosa/weeval documentation built on Aug. 15, 2020, 7:01 a.m.
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Description Usage Arguments Value
Description
Compute jaccard index of two embeddings models for N nearest neighbors
Usage
1 | jaccard_sims(sims1, sims2, N = 10, common_vocab = FALSE)
|
Arguments
sims1 |
matrix of similarities (e.g. cosine) between a set of cues (rows) and the full vocab (columns) |
sims2 |
matrix of similarities (e.g. cosine) between a set of cues (rows) and the full vocab (columns) |
N |
number of nearest neighbors with which to compute jaccard index |
common_vocab |
whether to use a common vocabulary to compute jaccard index |
Value
a tibble with mean and standard error of jaccard index overt set of cues
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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