R: Evaluate the accuracy of your UDPipe model on holdout data

udpipe_accuracy {udpipe} | R Documentation

## Evaluate the accuracy of your UDPipe model on holdout data

### Description

Get precision, recall and F1 measures on finding words / sentences / upos / xpos / features annotation as well as UAS and LAS dependency scores on holdout data in conllu format.

### Usage

    udpipe_accuracy(
      object,
      file_conllu,
      tokenizer = c("default", "none"),
      tagger = c("default", "none"),
      parser = c("default", "none")
    )

### Arguments

`object` | an object of class `udpipe_model` as returned by `udpipe_load_model`
`file_conllu` | the full path to a file on disk containing holdout data in conllu format
`tokenizer` | a character string of length 1, which is either 'default' or 'none'
`tagger` | a character string of length 1, which is either 'default' or 'none'
`parser` | a character string of length 1, which is either 'default' or 'none'

### Value

a list with 3 elements

- accuracy: A character vector with accuracy metrics.

- error: A character string with possible errors when calculating the accuracy metrics

### References

https://ufal.mff.cuni.cz/udpipe, https://universaldependencies.org/format.html

### See Also

`udpipe_load_model`

### Examples

    model <- udpipe_download_model(language = "dutch-lassysmall")
    if(!model$download_failed){
    ud_dutch <- udpipe_load_model(model$file_model)

    file_conllu <- system.file(package = "udpipe", "dummydata", "traindata.conllu")
    metrics <- udpipe_accuracy(ud_dutch, file_conllu)
    metrics$accuracy
    metrics <- udpipe_accuracy(ud_dutch, file_conllu,
                               tokenizer = "none", tagger = "default", parser = "default")
    metrics$accuracy
    metrics <- udpipe_accuracy(ud_dutch, file_conllu,
                               tokenizer = "none", tagger = "none", parser = "default")
    metrics$accuracy
    metrics <- udpipe_accuracy(ud_dutch, file_conllu,
                               tokenizer = "default", tagger = "none", parser = "none")
    metrics$accuracy
    }

    ## cleanup for CRAN only - you probably want to keep your model if you have downloaded it
    if(file.exists(model$file_model)) file.remove(model$file_model)

[Package udpipe version 0.8.6 Index]