자연어 처리 - 텍스트

기계독해(MRC): RBERT

Tidyverse Korea

1 의존성 문제 해결¹

RBERT를 실행시키기 위해서 tensorflow가 필요한데 이에 대한 선행 작업을 다음과 같이 수행한다.

• 파이썬 3 설치
  • brew install python3

python --version

Python 3.6.10

• virtualenv 설치
  • brew install virtualenv

virtualenv --version

1.1 reticulate 설정

reticulate::py_config()

python:         /Users/kwangchunlee/Library/r-miniconda/envs/r-reticulate/bin/python
libpython:      /Users/kwangchunlee/Library/r-miniconda/envs/r-reticulate/lib/libpython3.6m.dylib
pythonhome:     /Users/kwangchunlee/Library/r-miniconda/envs/r-reticulate:/Users/kwangchunlee/Library/r-miniconda/envs/r-reticulate
version:        3.6.10 | packaged by conda-forge | (default, Apr 24 2020, 16:27:41)  [GCC Clang 9.0.1 ]
numpy:          /Users/kwangchunlee/Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/numpy
numpy_version:  1.18.5

NOTE: Python version was forced by RETICULATE_PYTHON

Sys.setenv(RETICULATE_PYTHON = "/Users/kwangchunlee/Library/r-miniconda/envs/r-reticulate/bin/python")

library(tensorflow)
library(keras)

tf$constant("Hellow Tensorflow")

Tensor("Const:0", shape=(), dtype=string)

2 RBERT 설치

# tensorflow::install_tensorflow(version = "1.13.1")
# devtools::install_github("jonathanbratt/RBERT", build_vignettes = TRUE)

library(RBERT)
library(tidyverse)

BERT_PRETRAINED_DIR <- RBERT::download_BERT_checkpoint(
  model = "bert_base_uncased"
)

text_to_process <- c("Impulse is equal to the change in momentum.",
                     "Changing momentum requires an impulse.",
                     "An impulse is like a push.",
                     "Impulse is force times time.")

BERT_feats <- extract_features(
  examples = text_to_process,
  ckpt_dir = BERT_PRETRAINED_DIR
)

BERT_feats

$output
# A tibble: 144 x 773
   sequence_index segment_index token_index token layer_index     V1
            <int>         <int>       <int> <chr>       <int>  <dbl>
 1              1             1           1 [CLS]           9 -0.226
 2              1             1           2 impu…           9  0.734
 3              1             1           3 is              9 -0.426
 4              1             1           4 equal           9  1.30 
 5              1             1           5 to              9 -1.13 
 6              1             1           6 the             9 -1.42 
 7              1             1           7 chan…           9 -0.219
 8              1             1           8 in              9 -1.06 
 9              1             1           9 mome…           9  0.844
10              1             1          10 .               9 -1.23 
# … with 134 more rows, and 767 more variables: V2 <dbl>, V3 <dbl>,
#   V4 <dbl>, V5 <dbl>, V6 <dbl>, V7 <dbl>, V8 <dbl>, V9 <dbl>, V10 <dbl>,
#   V11 <dbl>, V12 <dbl>, V13 <dbl>, V14 <dbl>, V15 <dbl>, V16 <dbl>,
#   V17 <dbl>, V18 <dbl>, V19 <dbl>, V20 <dbl>, V21 <dbl>, V22 <dbl>,
#   V23 <dbl>, V24 <dbl>, V25 <dbl>, V26 <dbl>, V27 <dbl>, V28 <dbl>,
#   V29 <dbl>, V30 <dbl>, V31 <dbl>, V32 <dbl>, V33 <dbl>, V34 <dbl>,
#   V35 <dbl>, V36 <dbl>, V37 <dbl>, V38 <dbl>, V39 <dbl>, V40 <dbl>,
#   V41 <dbl>, V42 <dbl>, V43 <dbl>, V44 <dbl>, V45 <dbl>, V46 <dbl>,
#   V47 <dbl>, V48 <dbl>, V49 <dbl>, V50 <dbl>, V51 <dbl>, V52 <dbl>,
#   V53 <dbl>, V54 <dbl>, V55 <dbl>, V56 <dbl>, V57 <dbl>, V58 <dbl>,
#   V59 <dbl>, V60 <dbl>, V61 <dbl>, V62 <dbl>, V63 <dbl>, V64 <dbl>,
#   V65 <dbl>, V66 <dbl>, V67 <dbl>, V68 <dbl>, V69 <dbl>, V70 <dbl>,
#   V71 <dbl>, V72 <dbl>, V73 <dbl>, V74 <dbl>, V75 <dbl>, V76 <dbl>,
#   V77 <dbl>, V78 <dbl>, V79 <dbl>, V80 <dbl>, V81 <dbl>, V82 <dbl>,
#   V83 <dbl>, V84 <dbl>, V85 <dbl>, V86 <dbl>, V87 <dbl>, V88 <dbl>,
#   V89 <dbl>, V90 <dbl>, V91 <dbl>, V92 <dbl>, V93 <dbl>, V94 <dbl>,
#   V95 <dbl>, V96 <dbl>, V97 <dbl>, V98 <dbl>, V99 <dbl>, V100 <dbl>,
#   V101 <dbl>, …

output_vector1 <- BERT_feats$output %>%
  dplyr::filter(
    sequence_index == 1, 
    token == "[CLS]", 
    layer_index == 12
  ) %>% 
  dplyr::select(dplyr::starts_with("V")) %>% 
  unlist()
output_vector1

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         V11          V12          V13          V14          V15 
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         V16          V17          V18          V19          V20 
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         V21          V22          V23          V24          V25 
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         V26          V27          V28          V29          V30 
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-0.237946346  1.339154959  0.369980365  0.511419296  0.100916341 
        V606         V607         V608         V609         V610 
-0.269511044 -0.774623752 -0.466983467 -0.494631529  0.358213127 
        V611         V612         V613         V614         V615 
 0.488821507  0.026737325  0.008949973  0.717032731  0.115034871 
        V616         V617         V618         V619         V620 
-0.263363361  0.075989030  0.445580363  0.219485685  0.593416631 
        V621         V622         V623         V624         V625 
-0.436996996 -0.078865215 -1.005222201 -0.583927751  0.256022096 
        V626         V627         V628         V629         V630 
-0.332528770 -0.689943612  0.426239192 -0.225680247 -0.787585497 
        V631         V632         V633         V634         V635 
 1.170485377 -0.045915484 -0.336460531  0.957744479 -0.461872697 
        V636         V637         V638         V639         V640 
-0.048079215 -0.340103686  0.618069768  1.172930121  0.253866255 
        V641         V642         V643         V644         V645 
-0.984215319  0.862671912 -0.352393031 -0.111014701  0.597480237 
        V646         V647         V648         V649         V650 
 0.557858348 -0.600097895  0.543230772  0.468553185  0.857713401 
        V651         V652         V653         V654         V655 
-0.188041151  0.269941419 -0.221002445 -0.159422100 -0.161348343 
        V656         V657         V658         V659         V660 
 0.833555698  0.033192411 -0.179883629 -0.003060237  0.569723427 
        V661         V662         V663         V664         V665 
 0.357718796  0.699060321  0.313856721  0.302993596  0.575642765 
        V666         V667         V668         V669         V670 
-0.051228836 -0.453384012 -1.118219852  0.391545355  0.666636288 
        V671         V672         V673         V674         V675 
 1.150002599 -0.664185882  0.553151071 -0.143985957  0.328802764 
        V676         V677         V678         V679         V680 
 0.331800103 -0.238317400  0.346388906  0.377521276  0.777694643 
        V681         V682         V683         V684         V685 
-0.277723044  0.179897517  0.721463025  0.511422038  0.011029847 
        V686         V687         V688         V689         V690 
-0.145932510 -0.377478480 -0.303765774 -0.443033367 -0.039036982 
        V691         V692         V693         V694         V695 
-0.188079491 -1.398189783 -0.637902498  0.334215343  0.307030767 
        V696         V697         V698         V699         V700 
 0.016914867  0.416124642 -0.749973655  1.076300502  0.125640839 
        V701         V702         V703         V704         V705 
 0.332057804 -0.023831360  0.614924729  0.295576215  0.211165845 
        V706         V707         V708         V709         V710 
 0.518762171  0.269092202 -1.126153231  0.569447637 -0.703825355 
        V711         V712         V713         V714         V715 
-0.192900598  0.251399368  0.027419411  0.970783651 -0.597183406 
        V716         V717         V718         V719         V720 
 1.385594249 -0.250855833  0.030310787  0.050497908 -0.118641272 
        V721         V722         V723         V724         V725 
 0.303394258  1.110183835 -0.193579331  0.641880512 -0.128473178 
        V726         V727         V728         V729         V730 
 0.247657984 -0.149536073 -0.336854339 -0.224512428  0.037582412 
        V731         V732         V733         V734         V735 
-0.230640069  1.041052818  0.016714470 -0.245454282  0.601938188 
        V736         V737         V738         V739         V740 
 0.536032319 -0.834958553 -0.807084799 -0.749644458  1.272573113 
        V741         V742         V743         V744         V745 
 0.580855012  0.098985396  0.358252764 -0.030859120  0.403456479 
        V746         V747         V748         V749         V750 
 0.691784263 -0.246995658 -0.137335494 -0.133693695  0.258291751 
        V751         V752         V753         V754         V755 
 0.167389870  0.784490407 -3.851931572 -0.326559991 -0.726175606 
        V756         V757         V758         V759         V760 
-0.120646395  0.095286146  0.043689609  0.237973928 -0.422520310 
        V761         V762         V763         V764         V765 
 0.215968937 -0.359701544 -0.377799779  0.599027932  0.049806491 
        V766         V767         V768 
-0.237085283 -0.384729862  0.739824414 

tokens <- tokenize_text(text = "Who doesn't like tacos?",
                        ckpt_dir = BERT_PRETRAINED_DIR)
tokens

[[1]]
 [1] "[CLS]" "who"   "doesn" "'"     "t"     "like"  "ta"    "##cos"
 [9] "?"     "[SEP]"

check_vocab(words = c("positron", "electron"), ckpt_dir = BERT_PRETRAINED_DIR)

[1] FALSE  TRUE

---

1. Dr. Bharatendra Rai (2020. 7. 12.), “Installing the Latest Tensorflow, Keras & Python for R & RStudio”↩︎

 

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데이터 과학자 이광춘 저작

kwangchun.lee.7@gmail.com