├── .gitignore ├── LICENSE.md ├── README.md ├── data-raw └── talks.R ├── data ├── complaints.csv.gz └── talks.csv ├── figure ├── all-split-1.png ├── unnamed-chunk-22-1.png ├── unnamed-chunk-23-1.png ├── unnamed-chunk-49-1.png ├── unnamed-chunk-54-1.png ├── unnamed-chunk-60-1.png ├── unnamed-chunk-67-1.png ├── unnamed-chunk-8-1.png └── unnamed-chunk-9-1.png ├── images └── cross-validation │ ├── Slide10.png │ ├── Slide11.png │ ├── Slide2.png │ ├── Slide3.png │ ├── Slide4.png │ ├── Slide5.png │ ├── Slide6.png │ ├── Slide7.png │ ├── Slide8.png │ └── Slide9.png ├── index.Rmd ├── index.html ├── index_files └── figure-html │ ├── all-split-1.png │ ├── unnamed-chunk-18-1.png │ ├── unnamed-chunk-19-1.png │ ├── unnamed-chunk-20-1.png │ ├── unnamed-chunk-21-1.png │ ├── unnamed-chunk-22-1.png │ ├── unnamed-chunk-23-1.png │ ├── unnamed-chunk-24-1.png │ ├── unnamed-chunk-25-1.png │ ├── unnamed-chunk-26-1.png │ ├── unnamed-chunk-27-1.png │ ├── unnamed-chunk-31-1.png │ ├── unnamed-chunk-32-1.png │ ├── unnamed-chunk-33-1.png │ ├── unnamed-chunk-36-1.png │ ├── unnamed-chunk-37-1.png │ ├── unnamed-chunk-38-1.png │ ├── unnamed-chunk-48-1.png │ ├── unnamed-chunk-53-1.png │ ├── unnamed-chunk-59-1.png │ ├── unnamed-chunk-66-1.png │ └── unnamed-chunk-8-1.png ├── libs ├── font-awesome-5.3.1 │ ├── css │ │ ├── fontawesome-all.css │ │ └── fontawesome-all.min.css │ ├── less │ │ ├── _animated.less │ │ ├── _bordered-pulled.less │ │ ├── _core.less │ │ ├── _fixed-width.less │ │ ├── _icons.less │ │ ├── _larger.less │ │ ├── _list.less │ │ ├── _mixins.less │ │ ├── _rotated-flipped.less │ │ ├── _screen-reader.less │ │ ├── _shims.less │ │ ├── _stacked.less │ │ ├── _variables.less │ │ ├── brands.less │ │ ├── fontawesome.less │ │ ├── regular.less │ │ ├── solid.less │ │ └── v4-shims.less │ ├── scss │ │ ├── _animated.scss │ │ ├── _bordered-pulled.scss │ │ ├── _core.scss │ │ ├── _fixed-width.scss │ │ ├── _icons.scss │ │ ├── _larger.scss │ │ ├── _list.scss │ │ ├── _mixins.scss │ │ ├── _rotated-flipped.scss │ │ ├── _screen-reader.scss │ │ ├── _shims.scss │ │ ├── _stacked.scss │ │ ├── _variables.scss │ │ ├── brands.scss │ │ ├── fontawesome.scss │ │ ├── regular.scss │ │ ├── solid.scss │ │ └── v4-shims.scss │ └── webfonts │ │ ├── fa-brands-400.eot │ │ ├── fa-brands-400.svg │ │ ├── fa-brands-400.ttf │ │ ├── fa-brands-400.woff │ │ ├── fa-brands-400.woff2 │ │ ├── fa-regular-400.eot │ │ ├── fa-regular-400.svg │ │ ├── fa-regular-400.ttf │ │ ├── fa-regular-400.woff │ │ ├── fa-regular-400.woff2 │ │ ├── fa-solid-900.eot │ │ ├── fa-solid-900.svg │ │ ├── fa-solid-900.ttf │ │ ├── fa-solid-900.woff │ │ └── fa-solid-900.woff2 └── remark-css-0.0.1 │ └── default.css ├── macros.js ├── text_modeling.Rmd ├── theme.css ├── theme.sass └── useR2020-text-modeling-tutorial.Rproj /.gitignore: -------------------------------------------------------------------------------- 1 | .Rproj.user 2 | .Rhistory 3 | .RData 4 | -------------------------------------------------------------------------------- /LICENSE.md: -------------------------------------------------------------------------------- 1 | ## creative commons 2 | 3 | # Attribution-ShareAlike 4.0 International 4 | 5 | Creative Commons Corporation (“Creative Commons”) is not a law firm and does not provide legal services or legal advice. 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For the avoidance of doubt, this paragraph does not form part of the public licenses. 172 | > 173 | > Creative Commons may be contacted at creativecommons.org 174 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Predictive modeling with text using tidy data principles 2 | 3 | **Authors:** [Emil Hvitfeldt](https://www.hvitfeldt.me/), [Julia Silge](https://juliasilge.com/) 4 | 5 | **Materials for our [useR! 2020](https://user2020.r-project.org/) online tutorial on 24 July 2020** 6 | 7 | This tutorial was hosted by [R-Ladies en Argentina](https://github.com/RLadiesEnArgentina/user2020tutorial). 8 | 9 | - Have you ever encountered text data and suspected there was useful insight latent within it but felt frustrated about how to find that insight? 10 | - Are you familiar with the basics of predictive modeling, and ready to learn how unstructured text data can be used for prediction within the tidyverse and tidymodels ecosystems? 11 | - Do you need a flexible framework for handling text data that empowers you to build supervised predictive models? 12 | 13 | Text data is increasingly important in many domains, and tidy modeling principles can be applied to natural language processing tasks. This presentation is designed to provide practical guidance and directly applicable knowledge for data scientists and analysts who want to integrate text into their modeling pipelines. 14 | 15 | In [this 90-minute tutorial](https://youtu.be/Sz8RB_fPYOk), learn how to preprocess text data for modeling, train models, and evaluate model performance. We use slides and live coding during the tutorial to walk through a realistic case study. The tutorial [was streamed, recorded, and captioned](https://youtu.be/Sz8RB_fPYOk), and there will be supporting materials and code on GitHub for you to work through afterward. 16 | 17 | ## Expected level of audience's R background 18 | 19 | Intermediate familiarity with R, RStudio, basics of regression and classification modeling, and tidyverse packages such as dplyr and ggplot2. 20 | 21 | ## What is in this repo 22 | 23 | There are two main resources in this repo: 24 | 25 | - Slides, which you can [see rendered here](https://emilhvitfeldt.github.io/useR2020-text-modeling-tutorial/) and the [source for here](https://github.com/EmilHvitfeldt/useR2020-text-modeling-tutorial/blob/master/index.Rmd) 26 | - An [R Markdown file to work through](https://github.com/EmilHvitfeldt/useR2020-text-modeling-tutorial/blob/master/text_modeling.Rmd) 27 | 28 | If you get stuck, you can [post a question as an issue on this repo](https://github.com/EmilHvitfeldt/useR2020-text-modeling-tutorial/issues) or [post on RStudio Community](https://rstd.io/tidymodels-community). 29 | -------------------------------------------------------------------------------- /data-raw/talks.R: -------------------------------------------------------------------------------- 1 | library(tidyverse) 2 | library(rvest) 3 | library(here) 4 | 5 | pycon2018 <- read_html("https://pycon-archive.python.org/2018/schedule/talks/list/") %>% 6 | html_nodes('div[class="presentation-description"]') %>% 7 | html_text() %>% 8 | tibble(description = ., 9 | language = "python", 10 | year = 2018) 11 | 12 | pycon2019 <- read_html("https://us.pycon.org/2019/schedule/talks/list/") %>% 13 | html_nodes('div[class="presentation-description"]') %>% 14 | html_text() %>% 15 | tibble(description = ., 16 | language = "python", 17 | year = 2019) 18 | 19 | 20 | erum2020 <- read_html("https://milano-r.github.io/erum2020program/regular-talks.html") %>% 21 | html_nodes("div[class='section level3']") %>% 22 | html_text() %>% 23 | map_chr(~ str_split(.x, "Abstract:\n")[[1]][2]) %>% 24 | tibble(description = ., 25 | language = "r", 26 | year = 2020) 27 | 28 | user2019 <- read_html("https://www.user2019.fr/talk_schedule/") %>% 29 | html_nodes('table tbody') %>% 30 | html_nodes('tr[class!="filtered"]') %>% 31 | html_text() %>% 32 | tibble(description = ., 33 | language = "r", 34 | year = 2019) 35 | 36 | talks <- bind_rows( 37 | pycon2019, 38 | pycon2018, 39 | user2019, 40 | erum2020 41 | ) 42 | 43 | write_csv(talks, here("data/talks.csv")) 44 | 45 | -------------------------------------------------------------------------------- /data/complaints.csv.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/EmilHvitfeldt/useR2020-text-modeling-tutorial/b0e9afa5a71d48b1f740403ac8bfe362f754734d/data/complaints.csv.gz -------------------------------------------------------------------------------- /figure/all-split-1.png: 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-------------------------------------------------------------------------------- 1 | --- 2 | title: "Predictive modeling with text using tidy data principles" 3 | subtitle: "useR2020" 4 | author: "Julia Silge & Emil Hvitfeldt" 5 | date: "2019-7-24" 6 | output: 7 | xaringan::moon_reader: 8 | css: ["default", "theme.css"] 9 | lib_dir: libs 10 | nature: 11 | highlightLanguage: "r" 12 | beforeInit: "macros.js" 13 | ratio: "16:9" 14 | highlightStyle: github 15 | highlightLines: true 16 | countIncrementalSlides: false 17 | titleSlideClass: [center, middle] 18 | --- 19 | 20 | ```{r, eval = FALSE, echo = FALSE} 21 | The general outline of the talk is: 22 | 23 | Introduction 24 | Motivation 25 | talk about data 26 | Start modeling 27 | side tangent - talk about one of chapter 1-5 28 | more modeling 29 | side tangent - talk about one of chapter 1-5 30 | finish modeling 31 | evaluate 32 | 33 | We will be doing 1 model workflow from start to finish, with sidestepping to talk about chapter 1-5 things 34 | 35 | ``` 36 | 37 | 38 | ```{r include=FALSE} 39 | library(knitr) 40 | hook_output <- knit_hooks$get("output") 41 | knit_hooks$set(output = function(x, options) { 42 | lines <- options$output.lines 43 | if (is.null(lines)) { 44 | return(hook_output(x, options)) # pass to default hook 45 | } 46 | x <- unlist(strsplit(x, "\n")) 47 | more <- "..." 48 | if (length(lines) == 1) { # first n lines 49 | if (length(x) > lines) { 50 | # truncate the output, but add .... 51 | x <- c(head(x, lines), more) 52 | } 53 | } else { 54 | x <- c(more, x[lines], more) 55 | } 56 | # paste these lines together 57 | x <- paste(c(x, ""), collapse = "\n") 58 | hook_output(x, options) 59 | }) 60 | knit_hooks$set(output = function(x, options) { 61 | # this hook is used only when the linewidth option is not NULL 62 | if (!is.null(n <- options$linewidth)) { 63 | x = knitr:::split_lines(x) 64 | # any lines wider than n should be wrapped 65 | if (any(nchar(x) > n)) x = strwrap(x, width = n) 66 | x = paste(x, collapse = '\n') 67 | } 68 | hook_output(x, options) 69 | }) 70 | 71 | opts_chunk$set( 72 | echo = TRUE, 73 | cache = TRUE, 74 | fig.width = 7, 75 | fig.align = 'center', 76 | fig.asp = 0.618, # 1 / phi 77 | out.width = "700px", 78 | fig.retina = 3) 79 | ``` 80 | 81 | ```{r, echo = FALSE} 82 | library(sass) 83 | sass(sass_file("theme.sass"), output = "theme.css") 84 | set.seed(1234) 85 | library(ggplot2) 86 | theme_set(theme_minimal()) 87 | ``` 88 | 89 | class: center, middle 90 | 91 | # Hello! 92 | 93 | .pull-left[ 94 | 95 | [`r icon::fa("github")` @EmilHvitfeldt](https://github.com/EmilHvitfeldt) 96 | [`r icon::fa("twitter")` @Emil_Hvitfeldt](https://twitter.com/Emil_Hvitfeldt) 97 | [`r icon::fa("link")` hvitfeldt.me](https://www.hvitfeldt.me/) 98 | ] 99 | 100 | .pull-right[ 101 | 102 | [`r icon::fa("github")` @juliasilge](https://github.com/juliasilge) 103 | [`r icon::fa("twitter")` @juliasilge](https://twitter.com/juliasilge) 104 | [`r icon::fa("link")` juliasilge.com](https://juliasilge.com) 105 | ] 106 | 107 | --- 108 | 109 | # Plan for today 110 | 111 | -- 112 | 113 | - We will walk through our case study using slides and live coding 114 | 115 | 116 | 117 | -- 118 | 119 | 120 | - After the tutorial, use the [materials on GitHub](https://github.com/EmilHvitfeldt/useR2020-text-modeling-tutorial) and YouTube recording to run the code yourself `r emo::ji("muscle")` 121 | 122 | --- 123 | 124 | # Text as data 125 | 126 | Let's look at complaints submitted to the [United States Consumer Financial Protection Bureau (CFPB)](https://www.consumerfinance.gov/data-research/consumer-complaints/). 127 | 128 | -- 129 | 130 | 131 | - An individual experiences a problem `r emo::ji("weary")` with a consumer financial product or service, like a bank, loan, or credit card `r emo::ji("moneybag")` 132 | 133 | 134 | -- 135 | 136 | 137 | - They submit a **complaint** to the CFPB explaining what happened `r emo::ji("rage")` 138 | 139 | 140 | -- 141 | 142 | 143 | - This complaint is sent to the company, which can respond or dispute 144 | 145 | 146 | --- 147 | 148 | # Text as data 149 | 150 | Let's look at complaints submitted to the [United States Consumer Financial Protection Bureau (CFPB)](https://www.consumerfinance.gov/data-research/consumer-complaints/). 151 | 152 | ```{r R.options = list(width = 80), message=FALSE} 153 | library(tidyverse) 154 | 155 | complaints <- read_csv("data/complaints.csv.gz") %>% sample_frac(0.1) 156 | names(complaints) 157 | ``` 158 | 159 | --- 160 | 161 | # Text as data 162 | 163 | ```{r} 164 | complaints %>% 165 | sample_n(10) %>% 166 | pull(consumer_complaint_narrative) 167 | ``` 168 | 169 | --- 170 | 171 | # Text as data 172 | 173 | 174 | -- 175 | 176 | - Text like this can be used for **supervised** or **predictive** modeling 177 | 178 | 179 | -- 180 | 181 | 182 | - We can build both regression and classification models with text data 183 | 184 | 185 | -- 186 | 187 | 188 | - We can use the ways language exhibits organization to create features for modeling 189 | 190 | 191 | --- 192 | 193 | # Modeling Packages 194 | 195 | ```{r, message=FALSE} 196 | library(tidymodels) 197 | library(textrecipes) 198 | ``` 199 | 200 | - [tidymodels](https://www.tidymodels.org/) is a collection of packages for modeling and machine learning using tidyverse principles 201 | - [textrecipes](https://textrecipes.tidymodels.org/) extends the recipes package to handle text preprocessing 202 | 203 | --- 204 | 205 | # Modeling workflow 206 | 207 | ![](https://rviews.rstudio.com/post/2019-06-14-a-gentle-intro-to-tidymodels_files/figure-html/tidymodels.png) 208 | 209 | --- 210 | 211 | # smltar.com 212 | 213 | ```{r echo=FALSE, out.width="100%"} 214 | knitr::include_url("https://smltar.com/") 215 | ``` 216 | 217 | --- 218 | 219 | # Class imbalance 220 | 221 | ```{r, echo=FALSE} 222 | complaints %>% 223 | mutate(product = str_wrap(product, 50), 224 | product = fct_rev(fct_infreq(factor(product)))) %>% 225 | ggplot(aes(y = product)) + 226 | geom_bar() + 227 | labs(x = NULL, y = NULL) 228 | ``` 229 | 230 | --- 231 | 232 | class: inverse, right, middle 233 | 234 | # Let's approach this as a **binary classification task** 235 | 236 | --- 237 | 238 | # Credit or not? 239 | 240 | ```{r} 241 | credit <- "Credit reporting, credit repair services, or other personal consumer reports" 242 | 243 | complaints2class <- complaints %>% 244 | mutate(product = factor(if_else( 245 | condition = product == credit, 246 | true = "Credit", 247 | false = "Other"))) %>% 248 | rename(text = consumer_complaint_narrative) 249 | ``` 250 | 251 | --- 252 | 253 | # Data splitting 254 | 255 | The testing set is a precious resource which can be used only once 256 | 257 | ```{r all-split, echo = FALSE, fig.width=10} 258 | set.seed(16) 259 | one_split <- slice(complaints2class, 1:30) %>% 260 | initial_split() %>% 261 | tidy() %>% 262 | add_row(Row = 1:30, Data = "Original") %>% 263 | mutate(Data = case_when( 264 | Data == "Analysis" ~ "Training", 265 | Data == "Assessment" ~ "Testing", 266 | TRUE ~ Data 267 | )) %>% 268 | mutate(Data = factor(Data, levels = c("Original", "Training", "Testing"))) 269 | 270 | all_split <- 271 | ggplot(one_split, aes(x = Row, y = fct_rev(Data), fill = Data)) + 272 | geom_tile(color = "white", 273 | size = 1) + 274 | #scale_fill_manual(values = splits_pal, guide = FALSE) + 275 | theme_minimal() + 276 | theme(axis.text.y = element_text(size = rel(2)), 277 | axis.text.x = element_blank(), 278 | legend.position = "top", 279 | panel.grid = element_blank()) + 280 | coord_equal(ratio = 1) + 281 | labs(x = NULL, y = NULL) 282 | 283 | all_split 284 | ``` 285 | 286 | --- 287 | 288 | # Data splitting 289 | 290 | ```{r} 291 | set.seed(1234) 292 | 293 | complaints_split <- initial_split(complaints2class, strata = product) 294 | 295 | complaints_train <- training(complaints_split) 296 | complaints_test <- testing(complaints_split) 297 | ``` 298 | 299 | --- 300 | 301 | # Which of these variables can we use? 302 | 303 | ```{r} 304 | names(complaints_train) 305 | ``` 306 | 307 | --- 308 | 309 | # Feature selection checklist 310 | 311 | -- 312 | 313 | 314 | - Is it ethical to use this variable? (or even legal?) 315 | 316 | 317 | -- 318 | 319 | 320 | - Will this variable be available at prediction time? 321 | 322 | 323 | -- 324 | 325 | 326 | - Does this variable contribute to explainability? 327 | 328 | --- 329 | 330 | # Which of these variables can we use? 331 | 332 | ```{r} 333 | names(complaints_train) 334 | ``` 335 | 336 | --- 337 | 338 | # Which of these variables can we use? 339 | 340 | - `date_received` 341 | - `tags` 342 | - `consumer_complaint_narrative` == `r emo::ji("page_with_curl")` 343 | 344 | --- 345 | 346 | # Preprocessing specification 347 | 348 | ```{r} 349 | complaints_rec <- 350 | recipe(product ~ date_received + tags + text, 351 | data = complaints_train 352 | ) %>% 353 | step_date(date_received, features = c("month", "dow"), role = "dates") %>% 354 | step_rm(date_received) %>% 355 | step_dummy(has_role("dates")) %>% 356 | step_unknown(tags) %>% 357 | step_dummy(tags) %>% 358 | step_tokenize(text) %>% 359 | step_stopwords(text) %>% 360 | step_ngram(text, num_tokens = 3, min_num_tokens = 1) %>% 361 | step_tokenfilter(text, max_tokens = tune(), min_times = 5) %>% 362 | step_tfidf(text) 363 | ``` 364 | 365 | --- 366 | 367 | # Feature engineering 368 | 369 | ```{r} 370 | complaints_rec <- 371 | recipe(product ~ date_received + tags + text, 372 | data = complaints_train 373 | ) %>% 374 | step_date(date_received, features = c("month", "dow"), role = "dates") %>% 375 | step_rm(date_received) %>% 376 | step_dummy(has_role("dates")) %>% 377 | step_unknown(tags) %>% 378 | step_dummy(tags) %>% 379 | step_tokenize(text) %>% 380 | step_stopwords(text) %>% 381 | step_ngram(text, num_tokens = 3, min_num_tokens = 1) %>% 382 | step_tokenfilter(text, max_tokens = tune(), min_times = 5) %>% 383 | step_tfidf(text) 384 | ``` 385 | 386 | -- 387 | 388 | Also, what does `tune()` mean here? `r emo::ji("thinking")` 389 | 390 | --- 391 | 392 | class: inverse, right, middle 393 | 394 | # You can **combine** text and non-text features in your model 395 | 396 | --- 397 | 398 | # Feature engineering: handling dates 399 | 400 | ```{r} 401 | recipe(product ~ date_received + tags + text, 402 | data = complaints_train 403 | ) %>% 404 | step_date(date_received, features = c("month", "dow"), role = "dates") %>% 405 | step_rm(date_received) 406 | ``` 407 | 408 | --- 409 | 410 | # Feature engineering: categorical data 411 | 412 | ```{r} 413 | recipe(product ~ date_received + tags + text, 414 | data = complaints_train 415 | ) %>% 416 | step_unknown(tags) %>% 417 | step_dummy(tags) 418 | ``` 419 | 420 | --- 421 | 422 | class: inverse, right, middle 423 | 424 | # You can **combine** text and non-text features in your model 425 | 426 | 427 | --- 428 | 429 | # Feature engineering: text 430 | 431 | ```{r eval=FALSE} 432 | recipe(product ~ date_received + tags + text, 433 | data = complaints_train 434 | ) %>% 435 | step_tokenize(text) %>% 436 | step_stopwords(text) %>% 437 | step_ngram(text, num_tokens = 3, min_num_tokens = 1) %>% 438 | step_tokenfilter(text, max_tokens = tune(), min_times = 5) %>% 439 | step_tfidf(text) 440 | ``` 441 | 442 | --- 443 | 444 | # Feature engineering: text 445 | 446 | ```{r echo=FALSE} 447 | recipe(product ~ date_received + tags + text, 448 | data = complaints_train 449 | ) %>% 450 | step_tokenize(text) %>% 451 | step_stopwords(text) %>% 452 | step_ngram(text, num_tokens = 3, min_num_tokens = 1) %>% 453 | step_tokenfilter(text, max_tokens = tune(), min_times = 5) %>% 454 | step_tfidf(text) 455 | ``` 456 | 457 | 458 | --- 459 | 460 | class: inverse, right, middle 461 | 462 | # How do we create **features** from **natural language**? 463 | 464 | 465 | --- 466 | 467 | # From natural language to ML features 468 | 469 | ```{r R.options=list(quanteda_print_dfm_max_ndoc = 0, quanteda_print_dfm_max_nfeat = 0)} 470 | library(tidytext) 471 | 472 | complaints_train %>% 473 | unnest_tokens(word, text) %>% 474 | anti_join(get_stopwords(), by = "word") %>% 475 | count(complaint_id, word) %>% 476 | bind_tf_idf(word, complaint_id, n) %>% 477 | cast_dfm(complaint_id, word, tf_idf) 478 | ``` 479 | 480 | 481 | --- 482 | 483 | class: inverse, center, middle 484 | 485 | # `r emo::ji("stop_sign")` STOP WORDS `r emo::ji("stop_sign")` 486 | 487 | --- 488 | 489 | # Stop words 490 | 491 | ```{r} 492 | library(stopwords) 493 | stopwords() 494 | ``` 495 | 496 | 497 | --- 498 | 499 | class: center, middle 500 | 501 | ```{r echo=FALSE} 502 | library(UpSetR) 503 | fromList(list(smart = stopwords(source = "smart"), 504 | snowball = stopwords(source = "snowball"), 505 | iso = stopwords(source = "stopwords-iso"))) %>% 506 | upset(empty.intersections = "on") 507 | ``` 508 | 509 | --- 510 | 511 | # Stop words 512 | 513 | 514 | -- 515 | 516 | 517 | - Stop words are context specific 518 | 519 | 520 | -- 521 | 522 | 523 | - Stop word lexicons can have bias 524 | 525 | 526 | -- 527 | 528 | 529 | - You can create your own stop word list 530 | 531 | 532 | -- 533 | 534 | 535 | - See [Chapter 3](https://smltar.com/stopwords.html) for more! `r emo::ji("stop_sign")` 536 | 537 | --- 538 | 539 | class: inverse, right, middle 540 | 541 | ## What kind of **models** work well for text? 542 | 543 | --- 544 | 545 | # Text models 546 | 547 | Remember that text data is sparse! `r emo::ji("open_mouth")` 548 | 549 | -- 550 | 551 | 552 | - Regularized linear models (glmnet) 553 | - Support vector machines 554 | - naive Bayes 555 | - Tree-based models like random forest? 556 | 557 | --- 558 | 559 | # Text models 560 | 561 | Remember that text data is sparse! `r emo::ji("open_mouth")` 562 | 563 | - Regularized linear models (glmnet) 564 | - Support vector machines 565 | - naive Bayes 566 | - Tree-based models like random forest? `r emo::ji("no_good")` 567 | 568 | --- 569 | 570 | class: inverse, right, middle 571 | 572 | # Does text data have to be **sparse**? 573 | 574 | --- 575 | 576 | 577 | >### You shall know a word by the company it keeps. 578 | #### [`r emo::ji("speech_balloon")` John Rupert Firth](https://en.wikiquote.org/wiki/John_Rupert_Firth) 579 | 580 | 581 | 582 | -- 583 | 584 | Learn more about word embeddings: 585 | 586 | - in [Chapter 5](https://smltar.com/embeddings.html) 587 | - at [juliasilge.github.io/why-r-webinar/](https://juliasilge.github.io/why-r-webinar/) 588 | 589 | 590 | --- 591 | 592 | # To specify a model in tidymodels 593 | 594 | 1\. Pick a **model** 595 | 596 | 2\. Set the **mode** (if needed) 597 | 598 | 3\. Set the **engine** 599 | 600 | --- 601 | 602 | background-image: url(https://github.com/allisonhorst/stats-illustrations/raw/master/rstats-artwork/parsnip.png) 603 | background-size: cover 604 | 605 | .footnote[ 606 | Art by [Allison Horst](https://github.com/allisonhorst/stats-illustrations) 607 | ] 608 | 609 | --- 610 | 611 | # To specify a model in tidymodels 612 | 613 | All available models are listed at 614 | 615 | ```{r echo=FALSE, out.width="100%"} 616 | knitr::include_url("https://tidymodels.org/find/parsnip") 617 | ``` 618 | 619 | --- 620 | 621 | class: middle 622 | 623 | 624 | # `set_mode()` 625 | 626 | Some models can solve multiple types of problems 627 | 628 | 629 | ```{r} 630 | svm_rbf() %>% set_mode(mode = "regression") 631 | ``` 632 | 633 | --- 634 | 635 | class: middle 636 | 637 | 638 | # `set_mode()` 639 | 640 | Some models can solve multiple types of problems 641 | 642 | 643 | ```{r} 644 | svm_rbf() %>% set_mode(mode = "classification") 645 | ``` 646 | 647 | --- 648 | 649 | class: middle 650 | 651 | 652 | # `set_engine()` 653 | 654 | The same model can be implemented by multiple computational engines 655 | 656 | 657 | ```{r eval} 658 | svm_rbf() %>% set_engine("kernlab") 659 | ``` 660 | 661 | --- 662 | 663 | class: middle 664 | 665 | 666 | # `set_engine()` 667 | 668 | The same model can be implemented by multiple computational engines 669 | 670 | 671 | ```{r} 672 | svm_rbf() %>% set_engine("liquidSVM") 673 | ``` 674 | 675 | --- 676 | 677 | # What makes a model? 678 | 679 | ```{r} 680 | lasso_spec <- logistic_reg(penalty = tune(), mixture = 1) %>% 681 | set_mode("classification") %>% 682 | set_engine("glmnet") 683 | 684 | 685 | lasso_spec 686 | ``` 687 | 688 | 689 | -- 690 | 691 | It's `tune()` again! `r emo::ji("worried")` 692 | 693 | --- 694 | 695 | ## Parameters and... hyperparameters? 696 | 697 | - Some model parameters can be learned from data during fitting/training 698 | 699 | 700 | -- 701 | 702 | 703 | - Some CANNOT `r emo::ji("scream")` 704 | 705 | 706 | -- 707 | 708 | 709 | - These are **hyperparameters** of a model, and we estimate them by training lots of models with different hyperparameters and comparing them 710 | 711 | 712 | --- 713 | 714 | # A grid of possible hyperparameters 715 | 716 | ```{r} 717 | param_grid <- grid_regular( 718 | penalty(range = c(-4, 0)), 719 | max_tokens(range = c(500, 2000)), 720 | levels = 6 721 | ) 722 | ``` 723 | 724 | 725 | --- 726 | 727 | # A grid of possible hyperparameters 728 | 729 | ```{r echo=FALSE} 730 | param_grid 731 | ``` 732 | 733 | --- 734 | 735 | class: inverse, right, middle 736 | 737 | # How can we **compare** and **evaluate** these different models? 738 | 739 | --- 740 | 741 | background-image: url(https://www.tidymodels.org/start/resampling/img/resampling.svg) 742 | background-size: 60% 743 | 744 | --- 745 | 746 | # Spend your data budget 747 | 748 | ```{r} 749 | set.seed(123) 750 | complaints_folds <- vfold_cv(complaints_train, v = 10, strata = product) 751 | 752 | complaints_folds 753 | ``` 754 | 755 | --- 756 | class: middle, center, inverse 757 | 758 | # `r emo::ji("sparkles")` CROSS-VALIDATION `r emo::ji("sparkles")` 759 | 760 | --- 761 | background-image: url(images/cross-validation/Slide2.png) 762 | background-size: contain 763 | 764 | .footnote[ 765 | Art by [Alison Hill](https://alison.rbind.io/) 766 | ] 767 | --- 768 | background-image: url(images/cross-validation/Slide3.png) 769 | background-size: contain 770 | 771 | .footnote[ 772 | Art by [Alison Hill](https://alison.rbind.io/) 773 | ] 774 | --- 775 | background-image: url(images/cross-validation/Slide4.png) 776 | background-size: contain 777 | 778 | .footnote[ 779 | Art by [Alison Hill](https://alison.rbind.io/) 780 | ] 781 | --- 782 | background-image: url(images/cross-validation/Slide5.png) 783 | background-size: contain 784 | 785 | .footnote[ 786 | Art by [Alison Hill](https://alison.rbind.io/) 787 | ] 788 | --- 789 | background-image: url(images/cross-validation/Slide6.png) 790 | background-size: contain 791 | 792 | .footnote[ 793 | Art by [Alison Hill](https://alison.rbind.io/) 794 | ] 795 | --- 796 | background-image: url(images/cross-validation/Slide7.png) 797 | background-size: contain 798 | 799 | .footnote[ 800 | Art by [Alison Hill](https://alison.rbind.io/) 801 | ] 802 | --- 803 | background-image: url(images/cross-validation/Slide8.png) 804 | background-size: contain 805 | 806 | .footnote[ 807 | Art by [Alison Hill](https://alison.rbind.io/) 808 | ] 809 | --- 810 | background-image: url(images/cross-validation/Slide9.png) 811 | background-size: contain 812 | 813 | .footnote[ 814 | Art by [Alison Hill](https://alison.rbind.io/) 815 | ] 816 | --- 817 | background-image: url(images/cross-validation/Slide10.png) 818 | background-size: contain 819 | 820 | .footnote[ 821 | Art by [Alison Hill](https://alison.rbind.io/) 822 | ] 823 | --- 824 | background-image: url(images/cross-validation/Slide11.png) 825 | background-size: contain 826 | 827 | .footnote[ 828 | Art by [Alison Hill](https://alison.rbind.io/) 829 | ] 830 | --- 831 | 832 | class: inverse, right, middle 833 | 834 | # Spend your data wisely to create **simulated** validation sets 835 | 836 | --- 837 | 838 | class: inverse, right, middle 839 | 840 | # Now we have **resamples**, **features**, plus a **model** 841 | 842 | --- 843 | 844 | # Create a workflow 845 | 846 | ```{r} 847 | complaints_wf <- workflow() %>% 848 | add_recipe(complaints_rec) %>% 849 | add_model(lasso_spec) 850 | ``` 851 | 852 | --- 853 | 854 | class: inverse, right, middle 855 | 856 | # What is a `workflow()`? 857 | 858 | --- 859 | 860 | ## Time to tune! `r emo::ji("zap")` 861 | 862 | ```{r} 863 | set.seed(42) 864 | lasso_rs <- tune_grid( 865 | complaints_wf, 866 | resamples = complaints_folds, 867 | grid = param_grid, 868 | control = control_grid(save_pred = TRUE) 869 | ) 870 | ``` 871 | 872 | --- 873 | 874 | ## Time to tune! `r emo::ji("zap")` 875 | 876 | ```{r echo=FALSE} 877 | lasso_rs 878 | ``` 879 | 880 | --- 881 | class: middle, center, inverse 882 | 883 | # `r emo::ji("dizzy")` TOKENIZATION `r emo::ji("dizzy")` 884 | 885 | --- 886 | 887 | # Tokenization 888 | 889 | - The process of splitting text in smaller pieces of text (_tokens_) 890 | 891 | 892 | -- 893 | 894 | 895 | - Most common token == word, but sometimes we tokenize in a different way 896 | 897 | 898 | -- 899 | 900 | 901 | - An essential part of most text analyses 902 | 903 | 904 | -- 905 | 906 | 907 | - Many options to take into consideration 908 | 909 | --- 910 | 911 | # Tokenization: whitespace 912 | 913 | ```{r, echo=FALSE} 914 | token_example <- "I am a long-time victim of identity theft. This debt doesn't belong to me." 915 | ``` 916 | 917 | ```{r} 918 | token_example 919 | ``` 920 | 921 | -- 922 | 923 | ```{r} 924 | strsplit(token_example, "\\s") 925 | ``` 926 | 927 | --- 928 | 929 | # Tokenization: [tokenizers](https://docs.ropensci.org/tokenizers/) package 930 | 931 | 932 | ```{r} 933 | token_example 934 | ``` 935 | 936 | ```{r} 937 | library(tokenizers) 938 | 939 | tokenize_words(token_example) 940 | ``` 941 | 942 | --- 943 | 944 | # Tokenization: [spaCy](https://spacy.io/) library 945 | 946 | 947 | ```{r} 948 | token_example 949 | ``` 950 | 951 | ```{r, eval=FALSE} 952 | library(spacyr) 953 | 954 | spacy_tokenize(token_example) 955 | ``` 956 | 957 | ```{r echo=FALSE} 958 | list(c("I", "am", "a", "long", "-", "time", "victim", 959 | "of", "identity", "theft", ".", "This", "debt", "does", "n't", 960 | "belong", "to", "me", ".")) 961 | ``` 962 | 963 | --- 964 | 965 | whitespace 966 | 967 | ```{r, echo=FALSE} 968 | strsplit(token_example, "\\s") 969 | ``` 970 | 971 | tokenizers package 972 | 973 | ```{r, echo=FALSE} 974 | tokenize_words(token_example) 975 | ``` 976 | 977 | spaCy library 978 | 979 | ```{r echo=FALSE} 980 | list(c("I", "am", "a", "long", "-", "time", "victim", 981 | "of", "identity", "theft", ".", "This", "debt", "does", "n't", 982 | "belong", "to", "me", ".")) 983 | ``` 984 | 985 | --- 986 | 987 | # Tokenization considerations 988 | 989 | - Should we turn UPPERCASE letters to lowercase? 990 | 991 | 992 | -- 993 | 994 | 995 | - How should we handle punctuation`r emo::ji("interrobang")` 996 | 997 | 998 | -- 999 | 1000 | 1001 | - What about non-word characters _inside_ words? 1002 | 1003 | 1004 | -- 1005 | 1006 | 1007 | - Should compound words be split or multi-word ideas be kept together? 1008 | 1009 | --- 1010 | 1011 | class: inverse, right, middle 1012 | 1013 | ## Tokenization for English text is typically **much easier** than other languages. 1014 | 1015 | --- 1016 | 1017 | # N-grams 1018 | 1019 | ## A sequence of `n` sequential tokens 1020 | 1021 | -- 1022 | 1023 | 1024 | - Captures words that appear together often 1025 | 1026 | 1027 | -- 1028 | 1029 | 1030 | - Can detect negations ("not happy") 1031 | 1032 | 1033 | -- 1034 | 1035 | 1036 | - Larger cardinality 1037 | 1038 | 1039 | --- 1040 | 1041 | ```{r} 1042 | tokenize_ngrams(token_example, n = 1) 1043 | ``` 1044 | 1045 | 1046 | ```{r} 1047 | tokenize_ngrams(token_example, n = 2) 1048 | ``` 1049 | 1050 | 1051 | ```{r} 1052 | tokenize_ngrams(token_example, n = 3) 1053 | ``` 1054 | 1055 | --- 1056 | 1057 | # Tokenization 1058 | 1059 | See [Chapter 2](https://smltar.com/tokenization.html) for more! 1060 | 1061 | --- 1062 | 1063 | # Look at the tuning results `r emo::ji("eyes")` 1064 | 1065 | 1066 | ```{r} 1067 | collect_metrics(lasso_rs) 1068 | ``` 1069 | 1070 | --- 1071 | 1072 | ```{r} 1073 | autoplot(lasso_rs) 1074 | ``` 1075 | 1076 | --- 1077 | 1078 | # Look at the tuning results `r emo::ji("eyes")` 1079 | 1080 | ```{r} 1081 | lasso_rs %>% 1082 | show_best("roc_auc") 1083 | ``` 1084 | 1085 | --- 1086 | 1087 | # The **best** `r emo::ji("first")` hyperparameters 1088 | 1089 | ```{r} 1090 | best_roc_auc <- select_best(lasso_rs, "roc_auc") 1091 | 1092 | best_roc_auc 1093 | ``` 1094 | 1095 | --- 1096 | 1097 | # Evaluate the best model `r emo::ji("ruler")` 1098 | 1099 | ```{r} 1100 | collect_predictions(lasso_rs, parameters = best_roc_auc) 1101 | ``` 1102 | 1103 | --- 1104 | 1105 | # Evaluate the best model `r emo::ji("ruler")` 1106 | 1107 | ```{r, eval=FALSE} 1108 | collect_predictions(lasso_rs, parameters = best_roc_auc) %>% 1109 | group_by(id) %>% 1110 | roc_curve(truth = product, .pred_Credit) %>% 1111 | autoplot() 1112 | ``` 1113 | 1114 | --- 1115 | 1116 | # Evaluate the best model `r emo::ji("ruler")` 1117 | 1118 | ```{r, echo=FALSE} 1119 | collect_predictions(lasso_rs, parameters = best_roc_auc) %>% 1120 | group_by(id) %>% 1121 | roc_curve(truth = product, .pred_Credit) %>% 1122 | autoplot() + 1123 | labs( 1124 | color = NULL, 1125 | title = "Receiver operator curve for US Consumer Finance Complaints", 1126 | subtitle = "Each resample fold is shown in a different color" 1127 | ) 1128 | ``` 1129 | 1130 | --- 1131 | 1132 | # Update the workflow 1133 | 1134 | We can update our workflow with the best performing hyperparameters. 1135 | 1136 | ```{r} 1137 | wf_spec_final <- finalize_workflow(complaints_wf, best_roc_auc) 1138 | ``` 1139 | 1140 | This workflow is ready to go! It can now be applied to new data. 1141 | 1142 | --- 1143 | 1144 | class: inverse, right, middle 1145 | 1146 | # How is our model **thinking**? 1147 | 1148 | --- 1149 | 1150 | ## Variable importance 1151 | 1152 | ```{r message=FALSE} 1153 | library(vip) 1154 | 1155 | wf_spec_final %>% 1156 | fit(complaints_train) %>% 1157 | pull_workflow_fit() %>% 1158 | vi(lambda = best_roc_auc$penalty) %>% 1159 | filter(!str_detect(Variable, "tfidf")) %>% 1160 | filter(Importance != 0) 1161 | ``` 1162 | 1163 | --- 1164 | 1165 | ## Variable importance 1166 | 1167 | ```{r} 1168 | vi_data <- wf_spec_final %>% 1169 | fit(complaints_train) %>% 1170 | pull_workflow_fit() %>% 1171 | vi(lambda = best_roc_auc$penalty) %>% 1172 | mutate(Variable = str_remove_all(Variable, "tfidf_text_")) %>% 1173 | filter(Importance != 0) 1174 | ``` 1175 | 1176 | --- 1177 | 1178 | ## Variable importance 1179 | 1180 | ```{r} 1181 | vi_data 1182 | ``` 1183 | 1184 | --- 1185 | 1186 | class: center, middle 1187 | 1188 | ```{r, echo=FALSE} 1189 | max_imp <- log(max(abs(vi_data$Importance))) 1190 | 1191 | log_neg <- function(x) { 1192 | sign(x) * log(abs(x)) 1193 | } 1194 | 1195 | range01 <- function(x) { 1196 | (log_neg(x) + (max_imp)) / (max_imp + max_imp) 1197 | } 1198 | 1199 | color_fun <- scales::colour_ramp(rev(scico::scico(256, palette = "tofino", begin = 0, end = 1))) 1200 | 1201 | highlighter <- function(x, sign) { 1202 | if(is.na(sign)) { 1203 | htmltools::span(x) 1204 | } else { 1205 | htmltools::span(htmltools::tags$em(x), style = glue::glue('color:{color_fun(range01(sign))};')) 1206 | } 1207 | } 1208 | ``` 1209 | 1210 | ```{r, echo=FALSE} 1211 | vi_data %>% 1212 | mutate( 1213 | Importance = abs(Importance) 1214 | ) %>% 1215 | filter(Importance != 0) %>% 1216 | group_by(Sign) %>% 1217 | top_n(20, Importance) %>% 1218 | ungroup() %>% 1219 | mutate(Sign = factor(Sign, c("POS", "NEG"), c("Other", "Credit"))) %>% 1220 | ggplot(aes( 1221 | x = Importance, 1222 | y = fct_reorder(Variable, Importance), 1223 | fill = Sign 1224 | )) + 1225 | geom_col(show.legend = FALSE) + 1226 | scale_x_continuous(expand = c(0, 0)) + 1227 | scale_fill_manual(values = color_fun(c(0.8, 0.2))) + 1228 | facet_wrap(~Sign, scales = "free") + 1229 | labs( 1230 | y = NULL 1231 | ) 1232 | ``` 1233 | 1234 | --- 1235 | 1236 | ## Credit Complaint #1 1237 | 1238 | Credit 1239 | And 1240 | Other 1241 | 1242 | ```{r, echo=FALSE} 1243 | complaints_train %>% 1244 | filter(product == "Credit", nchar(text) < 800) %>% 1245 | slice(1) %>% 1246 | tidytext::unnest_tokens(words, text) %>% 1247 | left_join(vi_data, by = c("words" = "Variable")) %>% 1248 | mutate(words = map2(words, Importance, highlighter)) %>% 1249 | pull(words) %>% 1250 | htmltools::div() 1251 | ``` 1252 | 1253 | --- 1254 | 1255 | ## Credit Complaint #2 1256 | 1257 | Credit 1258 | And 1259 | Other 1260 | 1261 | ```{r, echo=FALSE} 1262 | complaints_train %>% 1263 | filter(product == "Credit", nchar(text) < 800) %>% 1264 | slice(2) %>% 1265 | tidytext::unnest_tokens(words, text) %>% 1266 | left_join(vi_data, by = c("words" = "Variable")) %>% 1267 | mutate(words = map2(words, Importance, highlighter)) %>% 1268 | pull(words) %>% 1269 | htmltools::div() 1270 | ``` 1271 | 1272 | --- 1273 | 1274 | ## Other Complaint #1 1275 | 1276 | Credit 1277 | And 1278 | Other 1279 | 1280 | ```{r, echo=FALSE} 1281 | complaints_train %>% 1282 | filter(product == "Other", nchar(text) < 800) %>% 1283 | slice(1) %>% 1284 | tidytext::unnest_tokens(words, text) %>% 1285 | left_join(vi_data, by = c("words" = "Variable")) %>% 1286 | mutate(words = map2(words, Importance, highlighter)) %>% 1287 | pull(words) %>% 1288 | htmltools::div() 1289 | ``` 1290 | 1291 | --- 1292 | 1293 | ## Other Complaint #2 1294 | 1295 | Credit 1296 | And 1297 | Other 1298 | 1299 | ```{r, echo=FALSE} 1300 | complaints_train %>% 1301 | filter(product == "Other", nchar(text) < 800) %>% 1302 | slice(2) %>% 1303 | tidytext::unnest_tokens(words, text) %>% 1304 | left_join(vi_data, by = c("words" = "Variable")) %>% 1305 | mutate(words = map2(words, Importance, highlighter)) %>% 1306 | pull(words) %>% 1307 | htmltools::div() 1308 | ``` 1309 | 1310 | --- 1311 | 1312 | # Final fit 1313 | 1314 | We will now use `last_fit()` to **fit** our model one last time on our training data and **evaluate** it on our testing data. 1315 | 1316 | ```{r} 1317 | final_fit <- last_fit( 1318 | wf_spec_final, 1319 | complaints_split 1320 | ) 1321 | ``` 1322 | 1323 | --- 1324 | 1325 | class: inverse, right, middle 1326 | 1327 | # Notice that this is the **first** and **only** time we have used our **testing data** 1328 | 1329 | --- 1330 | 1331 | # Evaluate on the **test** data `r emo::ji("ruler")` 1332 | 1333 | ```{r} 1334 | final_fit %>% 1335 | collect_metrics() 1336 | ``` 1337 | 1338 | --- 1339 | 1340 | ```{r} 1341 | final_fit %>% 1342 | collect_predictions() %>% 1343 | roc_curve(truth = product, .pred_Credit) %>% 1344 | autoplot() 1345 | ``` 1346 | 1347 | --- 1348 | 1349 | class: center, middle 1350 | 1351 | # Thanks! 1352 | 1353 | ##[smltar.com](https://smltar.com/) 1354 | 1355 | .pull-left[ 1356 | 1357 | [`r icon::fa("github")` @EmilHvitfeldt](https://github.com/EmilHvitfeldt) 1358 | [`r icon::fa("twitter")` @Emil_Hvitfeldt](https://twitter.com/Emil_Hvitfeldt) 1359 | [`r icon::fa("link")` hvitfeldt.me](https://www.hvitfeldt.me/) 1360 | ] 1361 | 1362 | .pull-right[ 1363 | 1364 | [`r icon::fa("github")` @juliasilge](https://github.com/juliasilge) 1365 | [`r icon::fa("twitter")` @juliasilge](https://twitter.com/juliasilge) 1366 | [`r icon::fa("link")` juliasilge.com](https://juliasilge.com) 1367 | ] 1368 | -------------------------------------------------------------------------------- /index_files/figure-html/all-split-1.png: -------------------------------------------------------------------------------- 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fa-spin { 13 | 0% { 14 | transform: rotate(0deg); 15 | } 16 | 100% { 17 | transform: rotate(360deg); 18 | } 19 | } 20 | -------------------------------------------------------------------------------- /libs/font-awesome-5.3.1/less/_bordered-pulled.less: -------------------------------------------------------------------------------- 1 | // Bordered & Pulled 2 | // ------------------------- 3 | 4 | .@{fa-css-prefix}-border { 5 | border-radius: .1em; 6 | border: solid .08em @fa-border-color; 7 | padding: .2em .25em .15em; 8 | } 9 | 10 | .@{fa-css-prefix}-pull-left { float: left; } 11 | .@{fa-css-prefix}-pull-right { float: right; } 12 | 13 | .@{fa-css-prefix}, .fas, .far, .fal, .fab { 14 | &.@{fa-css-prefix}-pull-left { margin-right: .3em; } 15 | &.@{fa-css-prefix}-pull-right { margin-left: .3em; } 16 | } 17 | -------------------------------------------------------------------------------- /libs/font-awesome-5.3.1/less/_core.less: -------------------------------------------------------------------------------- 1 | // Base Class Definition 2 | // ------------------------- 3 | 4 | .@{fa-css-prefix}, .fas, .far, .fal, .fab { 5 | -moz-osx-font-smoothing: grayscale; 6 | -webkit-font-smoothing: antialiased; 7 | display: inline-block; 8 | font-style: normal; 9 | font-variant: normal; 10 | text-rendering: auto; 11 | line-height: 1; 12 | } 13 | -------------------------------------------------------------------------------- /libs/font-awesome-5.3.1/less/_fixed-width.less: -------------------------------------------------------------------------------- 1 | // Fixed Width Icons 2 | // ------------------------- 3 | .@{fa-css-prefix}-fw { 4 | text-align: center; 5 | width: (20em / 16); 6 | } 7 | -------------------------------------------------------------------------------- /libs/font-awesome-5.3.1/less/_larger.less: -------------------------------------------------------------------------------- 1 | // Icon Sizes 2 | // ------------------------- 3 | 4 | .larger(@factor) when (@factor > 0) { 5 | .larger((@factor - 1)); 6 | 7 | .@{fa-css-prefix}-@{factor}x { 8 | font-size: (@factor * 1em); 9 | } 10 | } 11 | 12 | /* makes the font 33% larger relative to the icon container */ 13 | .@{fa-css-prefix}-lg { 14 | font-size: (4em / 3); 15 | line-height: (3em / 4); 16 | vertical-align: -.0667em; 17 | } 18 | 19 | .@{fa-css-prefix}-xs { 20 | font-size: .75em; 21 | } 22 | 23 | .@{fa-css-prefix}-sm { 24 | font-size: .875em; 25 | } 26 | 27 | .larger(10); 28 | -------------------------------------------------------------------------------- /libs/font-awesome-5.3.1/less/_list.less: -------------------------------------------------------------------------------- 1 | // List Icons 2 | // ------------------------- 3 | 4 | .@{fa-css-prefix}-ul { 5 | list-style-type: none; 6 | margin-left: @fa-li-width * 5/4; 7 | padding-left: 0; 8 | 9 | > li { position: relative; } 10 | } 11 | 12 | .@{fa-css-prefix}-li { 13 | left: -@fa-li-width; 14 | position: absolute; 15 | text-align: center; 16 | width: @fa-li-width; 17 | line-height: inherit; 18 | } 19 | -------------------------------------------------------------------------------- /libs/font-awesome-5.3.1/less/_mixins.less: -------------------------------------------------------------------------------- 1 | // Mixins 2 | // -------------------------- 3 | 4 | .fa-icon() { 5 | -moz-osx-font-smoothing: grayscale; 6 | -webkit-font-smoothing: antialiased; 7 | display: inline-block; 8 | font-style: normal; 9 | font-variant: normal; 10 | font-weight: normal; 11 | line-height: 1; 12 | vertical-align: -.125em; 13 | } 14 | 15 | .fa-icon-rotate(@degrees, @rotation) { 16 | -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=@{rotation})"; 17 | transform: rotate(@degrees); 18 | } 19 | 20 | .fa-icon-flip(@horiz, @vert, @rotation) { 21 | -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=@{rotation}, mirror=1)"; 22 | transform: scale(@horiz, @vert); 23 | } 24 | 25 | 26 | // Only display content to screen readers. A la Bootstrap 4. 27 | // 28 | // See: http://a11yproject.com/posts/how-to-hide-content/ 29 | 30 | .sr-only() { 31 | border: 0; 32 | clip: rect(0,0,0,0); 33 | height: 1px; 34 | margin: -1px; 35 | overflow: hidden; 36 | padding: 0; 37 | position: absolute; 38 | width: 1px; 39 | } 40 | 41 | // Use in conjunction with .sr-only to only display content when it's focused. 42 | // 43 | // Useful for "Skip to main content" links; see http://www.w3.org/TR/2013/NOTE-WCAG20-TECHS-20130905/G1 44 | // 45 | // Credit: HTML5 Boilerplate 46 | 47 | .sr-only-focusable() { 48 | &:active, 49 | &:focus { 50 | clip: auto; 51 | height: auto; 52 | margin: 0; 53 | overflow: visible; 54 | position: static; 55 | width: auto; 56 | } 57 | } 58 | -------------------------------------------------------------------------------- /libs/font-awesome-5.3.1/less/_rotated-flipped.less: -------------------------------------------------------------------------------- 1 | // Rotated & Flipped Icons 2 | // ------------------------- 3 | 4 | .@{fa-css-prefix}-rotate-90 { .fa-icon-rotate(90deg, 1); } 5 | .@{fa-css-prefix}-rotate-180 { .fa-icon-rotate(180deg, 2); } 6 | .@{fa-css-prefix}-rotate-270 { .fa-icon-rotate(270deg, 3); } 7 | 8 | .@{fa-css-prefix}-flip-horizontal { .fa-icon-flip(-1, 1, 0); } 9 | .@{fa-css-prefix}-flip-vertical { .fa-icon-flip(1, -1, 2); } 10 | .@{fa-css-prefix}-flip-horizontal.@{fa-css-prefix}-flip-vertical { .fa-icon-flip(-1, -1, 2); } 11 | 12 | // Hook for IE8-9 13 | // ------------------------- 14 | 15 | :root { 16 | .@{fa-css-prefix}-rotate-90, 17 | .@{fa-css-prefix}-rotate-180, 18 | .@{fa-css-prefix}-rotate-270, 19 | .@{fa-css-prefix}-flip-horizontal, 20 | .@{fa-css-prefix}-flip-vertical { 21 | filter: none; 22 | } 23 | } 24 | -------------------------------------------------------------------------------- /libs/font-awesome-5.3.1/less/_screen-reader.less: -------------------------------------------------------------------------------- 1 | // Screen Readers 2 | // ------------------------- 3 | 4 | .sr-only { .sr-only(); } 5 | .sr-only-focusable { .sr-only-focusable(); } 6 | -------------------------------------------------------------------------------- /libs/font-awesome-5.3.1/less/_stacked.less: -------------------------------------------------------------------------------- 1 | // Stacked Icons 2 | // ------------------------- 3 | 4 | .@{fa-css-prefix}-stack { 5 | display: inline-block; 6 | height: 2em; 7 | line-height: 2em; 8 | position: relative; 9 | vertical-align: middle; 10 | width: 2em; 11 | } 12 | 13 | .@{fa-css-prefix}-stack-1x, .@{fa-css-prefix}-stack-2x { 14 | left: 0; 15 | position: absolute; 16 | text-align: center; 17 | width: 100%; 18 | } 19 | 20 | .@{fa-css-prefix}-stack-1x { line-height: inherit; } 21 | .@{fa-css-prefix}-stack-2x { font-size: 2em; } 22 | .@{fa-css-prefix}-inverse { color: @fa-inverse; } 23 | -------------------------------------------------------------------------------- /libs/font-awesome-5.3.1/less/_variables.less: -------------------------------------------------------------------------------- 1 | // Variables 2 | // -------------------------- 3 | 4 | @fa-font-path: "../webfonts"; 5 | @fa-font-size-base: 16px; 6 | @fa-line-height-base: 1; 7 | @fa-css-prefix: fa; 8 | @fa-version: "5.3.1"; 9 | @fa-border-color: #eee; 10 | @fa-inverse: #fff; 11 | @fa-li-width: 2em; 12 | 13 | @fa-var-500px: "\f26e"; 14 | @fa-var-accessible-icon: "\f368"; 15 | @fa-var-accusoft: "\f369"; 16 | @fa-var-ad: "\f641"; 17 | @fa-var-address-book: "\f2b9"; 18 | @fa-var-address-card: "\f2bb"; 19 | @fa-var-adjust: "\f042"; 20 | @fa-var-adn: "\f170"; 21 | @fa-var-adversal: "\f36a"; 22 | @fa-var-affiliatetheme: "\f36b"; 23 | @fa-var-air-freshener: "\f5d0"; 24 | @fa-var-algolia: "\f36c"; 25 | @fa-var-align-center: "\f037"; 26 | @fa-var-align-justify: "\f039"; 27 | @fa-var-align-left: "\f036"; 28 | @fa-var-align-right: "\f038"; 29 | @fa-var-alipay: "\f642"; 30 | @fa-var-allergies: "\f461"; 31 | @fa-var-amazon: "\f270"; 32 | @fa-var-amazon-pay: "\f42c"; 33 | @fa-var-ambulance: "\f0f9"; 34 | @fa-var-american-sign-language-interpreting: "\f2a3"; 35 | @fa-var-amilia: "\f36d"; 36 | @fa-var-anchor: "\f13d"; 37 | @fa-var-android: "\f17b"; 38 | @fa-var-angellist: "\f209"; 39 | @fa-var-angle-double-down: "\f103"; 40 | @fa-var-angle-double-left: "\f100"; 41 | @fa-var-angle-double-right: "\f101"; 42 | @fa-var-angle-double-up: "\f102"; 43 | @fa-var-angle-down: "\f107"; 44 | @fa-var-angle-left: "\f104"; 45 | @fa-var-angle-right: "\f105"; 46 | @fa-var-angle-up: "\f106"; 47 | @fa-var-angry: "\f556"; 48 | @fa-var-angrycreative: "\f36e"; 49 | @fa-var-angular: "\f420"; 50 | @fa-var-ankh: "\f644"; 51 | @fa-var-app-store: "\f36f"; 52 | @fa-var-app-store-ios: "\f370"; 53 | @fa-var-apper: "\f371"; 54 | @fa-var-apple: "\f179"; 55 | @fa-var-apple-alt: "\f5d1"; 56 | @fa-var-apple-pay: "\f415"; 57 | @fa-var-archive: "\f187"; 58 | @fa-var-archway: "\f557"; 59 | @fa-var-arrow-alt-circle-down: "\f358"; 60 | @fa-var-arrow-alt-circle-left: "\f359"; 61 | @fa-var-arrow-alt-circle-right: "\f35a"; 62 | @fa-var-arrow-alt-circle-up: "\f35b"; 63 | @fa-var-arrow-circle-down: "\f0ab"; 64 | @fa-var-arrow-circle-left: "\f0a8"; 65 | @fa-var-arrow-circle-right: "\f0a9"; 66 | @fa-var-arrow-circle-up: "\f0aa"; 67 | @fa-var-arrow-down: "\f063"; 68 | @fa-var-arrow-left: "\f060"; 69 | @fa-var-arrow-right: "\f061"; 70 | @fa-var-arrow-up: "\f062"; 71 | @fa-var-arrows-alt: "\f0b2"; 72 | @fa-var-arrows-alt-h: "\f337"; 73 | @fa-var-arrows-alt-v: "\f338"; 74 | @fa-var-assistive-listening-systems: "\f2a2"; 75 | @fa-var-asterisk: "\f069"; 76 | @fa-var-asymmetrik: "\f372"; 77 | @fa-var-at: "\f1fa"; 78 | @fa-var-atlas: "\f558"; 79 | @fa-var-atom: "\f5d2"; 80 | @fa-var-audible: "\f373"; 81 | @fa-var-audio-description: "\f29e"; 82 | @fa-var-autoprefixer: "\f41c"; 83 | @fa-var-avianex: "\f374"; 84 | @fa-var-aviato: "\f421"; 85 | @fa-var-award: "\f559"; 86 | @fa-var-aws: "\f375"; 87 | @fa-var-backspace: "\f55a"; 88 | @fa-var-backward: "\f04a"; 89 | @fa-var-balance-scale: "\f24e"; 90 | @fa-var-ban: "\f05e"; 91 | @fa-var-band-aid: "\f462"; 92 | @fa-var-bandcamp: "\f2d5"; 93 | @fa-var-barcode: "\f02a"; 94 | @fa-var-bars: "\f0c9"; 95 | @fa-var-baseball-ball: "\f433"; 96 | @fa-var-basketball-ball: "\f434"; 97 | @fa-var-bath: "\f2cd"; 98 | @fa-var-battery-empty: "\f244"; 99 | @fa-var-battery-full: "\f240"; 100 | @fa-var-battery-half: "\f242"; 101 | @fa-var-battery-quarter: "\f243"; 102 | @fa-var-battery-three-quarters: "\f241"; 103 | @fa-var-bed: "\f236"; 104 | @fa-var-beer: "\f0fc"; 105 | @fa-var-behance: "\f1b4"; 106 | @fa-var-behance-square: "\f1b5"; 107 | @fa-var-bell: "\f0f3"; 108 | @fa-var-bell-slash: "\f1f6"; 109 | @fa-var-bezier-curve: "\f55b"; 110 | @fa-var-bible: "\f647"; 111 | @fa-var-bicycle: "\f206"; 112 | @fa-var-bimobject: "\f378"; 113 | @fa-var-binoculars: "\f1e5"; 114 | @fa-var-birthday-cake: "\f1fd"; 115 | @fa-var-bitbucket: "\f171"; 116 | @fa-var-bitcoin: "\f379"; 117 | @fa-var-bity: "\f37a"; 118 | @fa-var-black-tie: "\f27e"; 119 | @fa-var-blackberry: "\f37b"; 120 | @fa-var-blender: "\f517"; 121 | @fa-var-blind: "\f29d"; 122 | @fa-var-blogger: "\f37c"; 123 | @fa-var-blogger-b: "\f37d"; 124 | @fa-var-bluetooth: "\f293"; 125 | @fa-var-bluetooth-b: "\f294"; 126 | @fa-var-bold: "\f032"; 127 | @fa-var-bolt: "\f0e7"; 128 | @fa-var-bomb: "\f1e2"; 129 | @fa-var-bone: "\f5d7"; 130 | @fa-var-bong: "\f55c"; 131 | @fa-var-book: "\f02d"; 132 | @fa-var-book-open: "\f518"; 133 | @fa-var-book-reader: "\f5da"; 134 | @fa-var-bookmark: "\f02e"; 135 | @fa-var-bowling-ball: "\f436"; 136 | @fa-var-box: "\f466"; 137 | @fa-var-box-open: "\f49e"; 138 | @fa-var-boxes: "\f468"; 139 | @fa-var-braille: "\f2a1"; 140 | @fa-var-brain: "\f5dc"; 141 | @fa-var-briefcase: "\f0b1"; 142 | @fa-var-briefcase-medical: "\f469"; 143 | @fa-var-broadcast-tower: "\f519"; 144 | @fa-var-broom: "\f51a"; 145 | @fa-var-brush: "\f55d"; 146 | @fa-var-btc: "\f15a"; 147 | @fa-var-bug: "\f188"; 148 | @fa-var-building: "\f1ad"; 149 | @fa-var-bullhorn: "\f0a1"; 150 | @fa-var-bullseye: "\f140"; 151 | @fa-var-burn: "\f46a"; 152 | @fa-var-buromobelexperte: "\f37f"; 153 | @fa-var-bus: "\f207"; 154 | @fa-var-bus-alt: "\f55e"; 155 | @fa-var-business-time: "\f64a"; 156 | @fa-var-buysellads: "\f20d"; 157 | @fa-var-calculator: "\f1ec"; 158 | @fa-var-calendar: "\f133"; 159 | @fa-var-calendar-alt: "\f073"; 160 | @fa-var-calendar-check: "\f274"; 161 | @fa-var-calendar-minus: "\f272"; 162 | @fa-var-calendar-plus: "\f271"; 163 | @fa-var-calendar-times: "\f273"; 164 | @fa-var-camera: "\f030"; 165 | @fa-var-camera-retro: "\f083"; 166 | @fa-var-cannabis: "\f55f"; 167 | @fa-var-capsules: "\f46b"; 168 | @fa-var-car: "\f1b9"; 169 | @fa-var-car-alt: "\f5de"; 170 | @fa-var-car-battery: "\f5df"; 171 | @fa-var-car-crash: "\f5e1"; 172 | @fa-var-car-side: "\f5e4"; 173 | @fa-var-caret-down: "\f0d7"; 174 | @fa-var-caret-left: "\f0d9"; 175 | @fa-var-caret-right: "\f0da"; 176 | @fa-var-caret-square-down: "\f150"; 177 | @fa-var-caret-square-left: "\f191"; 178 | @fa-var-caret-square-right: "\f152"; 179 | @fa-var-caret-square-up: "\f151"; 180 | @fa-var-caret-up: "\f0d8"; 181 | @fa-var-cart-arrow-down: "\f218"; 182 | @fa-var-cart-plus: "\f217"; 183 | @fa-var-cc-amazon-pay: "\f42d"; 184 | @fa-var-cc-amex: "\f1f3"; 185 | @fa-var-cc-apple-pay: "\f416"; 186 | @fa-var-cc-diners-club: "\f24c"; 187 | @fa-var-cc-discover: "\f1f2"; 188 | @fa-var-cc-jcb: "\f24b"; 189 | @fa-var-cc-mastercard: "\f1f1"; 190 | @fa-var-cc-paypal: "\f1f4"; 191 | @fa-var-cc-stripe: "\f1f5"; 192 | @fa-var-cc-visa: "\f1f0"; 193 | @fa-var-centercode: "\f380"; 194 | @fa-var-certificate: "\f0a3"; 195 | @fa-var-chalkboard: "\f51b"; 196 | @fa-var-chalkboard-teacher: "\f51c"; 197 | @fa-var-charging-station: "\f5e7"; 198 | @fa-var-chart-area: "\f1fe"; 199 | @fa-var-chart-bar: "\f080"; 200 | @fa-var-chart-line: "\f201"; 201 | @fa-var-chart-pie: "\f200"; 202 | @fa-var-check: "\f00c"; 203 | @fa-var-check-circle: "\f058"; 204 | @fa-var-check-double: "\f560"; 205 | @fa-var-check-square: "\f14a"; 206 | @fa-var-chess: "\f439"; 207 | @fa-var-chess-bishop: "\f43a"; 208 | @fa-var-chess-board: "\f43c"; 209 | @fa-var-chess-king: "\f43f"; 210 | @fa-var-chess-knight: "\f441"; 211 | @fa-var-chess-pawn: "\f443"; 212 | @fa-var-chess-queen: "\f445"; 213 | @fa-var-chess-rook: "\f447"; 214 | @fa-var-chevron-circle-down: "\f13a"; 215 | @fa-var-chevron-circle-left: "\f137"; 216 | @fa-var-chevron-circle-right: "\f138"; 217 | @fa-var-chevron-circle-up: "\f139"; 218 | @fa-var-chevron-down: "\f078"; 219 | @fa-var-chevron-left: "\f053"; 220 | @fa-var-chevron-right: "\f054"; 221 | @fa-var-chevron-up: "\f077"; 222 | @fa-var-child: "\f1ae"; 223 | @fa-var-chrome: "\f268"; 224 | @fa-var-church: "\f51d"; 225 | @fa-var-circle: "\f111"; 226 | @fa-var-circle-notch: "\f1ce"; 227 | @fa-var-city: "\f64f"; 228 | @fa-var-clipboard: "\f328"; 229 | @fa-var-clipboard-check: "\f46c"; 230 | @fa-var-clipboard-list: "\f46d"; 231 | @fa-var-clock: "\f017"; 232 | @fa-var-clone: "\f24d"; 233 | @fa-var-closed-captioning: "\f20a"; 234 | @fa-var-cloud: "\f0c2"; 235 | @fa-var-cloud-download-alt: "\f381"; 236 | @fa-var-cloud-upload-alt: "\f382"; 237 | @fa-var-cloudscale: "\f383"; 238 | @fa-var-cloudsmith: "\f384"; 239 | @fa-var-cloudversify: "\f385"; 240 | @fa-var-cocktail: "\f561"; 241 | @fa-var-code: "\f121"; 242 | @fa-var-code-branch: "\f126"; 243 | @fa-var-codepen: "\f1cb"; 244 | @fa-var-codiepie: "\f284"; 245 | @fa-var-coffee: "\f0f4"; 246 | @fa-var-cog: "\f013"; 247 | @fa-var-cogs: "\f085"; 248 | @fa-var-coins: "\f51e"; 249 | @fa-var-columns: "\f0db"; 250 | @fa-var-comment: "\f075"; 251 | @fa-var-comment-alt: "\f27a"; 252 | @fa-var-comment-dollar: "\f651"; 253 | @fa-var-comment-dots: "\f4ad"; 254 | @fa-var-comment-slash: "\f4b3"; 255 | @fa-var-comments: "\f086"; 256 | @fa-var-comments-dollar: "\f653"; 257 | @fa-var-compact-disc: "\f51f"; 258 | @fa-var-compass: "\f14e"; 259 | @fa-var-compress: "\f066"; 260 | @fa-var-concierge-bell: "\f562"; 261 | @fa-var-connectdevelop: "\f20e"; 262 | @fa-var-contao: "\f26d"; 263 | @fa-var-cookie: "\f563"; 264 | @fa-var-cookie-bite: "\f564"; 265 | @fa-var-copy: "\f0c5"; 266 | @fa-var-copyright: "\f1f9"; 267 | @fa-var-couch: "\f4b8"; 268 | @fa-var-cpanel: "\f388"; 269 | @fa-var-creative-commons: "\f25e"; 270 | @fa-var-creative-commons-by: "\f4e7"; 271 | @fa-var-creative-commons-nc: "\f4e8"; 272 | @fa-var-creative-commons-nc-eu: "\f4e9"; 273 | @fa-var-creative-commons-nc-jp: "\f4ea"; 274 | @fa-var-creative-commons-nd: "\f4eb"; 275 | @fa-var-creative-commons-pd: "\f4ec"; 276 | @fa-var-creative-commons-pd-alt: "\f4ed"; 277 | @fa-var-creative-commons-remix: "\f4ee"; 278 | @fa-var-creative-commons-sa: "\f4ef"; 279 | @fa-var-creative-commons-sampling: "\f4f0"; 280 | @fa-var-creative-commons-sampling-plus: "\f4f1"; 281 | @fa-var-creative-commons-share: "\f4f2"; 282 | @fa-var-credit-card: "\f09d"; 283 | @fa-var-crop: "\f125"; 284 | @fa-var-crop-alt: "\f565"; 285 | @fa-var-cross: "\f654"; 286 | @fa-var-crosshairs: "\f05b"; 287 | @fa-var-crow: "\f520"; 288 | @fa-var-crown: "\f521"; 289 | @fa-var-css3: "\f13c"; 290 | @fa-var-css3-alt: "\f38b"; 291 | @fa-var-cube: "\f1b2"; 292 | @fa-var-cubes: "\f1b3"; 293 | @fa-var-cut: "\f0c4"; 294 | @fa-var-cuttlefish: "\f38c"; 295 | @fa-var-d-and-d: "\f38d"; 296 | @fa-var-dashcube: "\f210"; 297 | @fa-var-database: "\f1c0"; 298 | @fa-var-deaf: "\f2a4"; 299 | @fa-var-delicious: "\f1a5"; 300 | @fa-var-deploydog: "\f38e"; 301 | @fa-var-deskpro: "\f38f"; 302 | @fa-var-desktop: "\f108"; 303 | @fa-var-deviantart: "\f1bd"; 304 | @fa-var-dharmachakra: "\f655"; 305 | @fa-var-diagnoses: "\f470"; 306 | @fa-var-dice: "\f522"; 307 | @fa-var-dice-five: "\f523"; 308 | @fa-var-dice-four: "\f524"; 309 | @fa-var-dice-one: "\f525"; 310 | @fa-var-dice-six: "\f526"; 311 | @fa-var-dice-three: "\f527"; 312 | @fa-var-dice-two: "\f528"; 313 | @fa-var-digg: "\f1a6"; 314 | @fa-var-digital-ocean: "\f391"; 315 | @fa-var-digital-tachograph: "\f566"; 316 | @fa-var-directions: "\f5eb"; 317 | @fa-var-discord: "\f392"; 318 | @fa-var-discourse: "\f393"; 319 | @fa-var-divide: "\f529"; 320 | @fa-var-dizzy: "\f567"; 321 | @fa-var-dna: "\f471"; 322 | @fa-var-dochub: "\f394"; 323 | @fa-var-docker: "\f395"; 324 | @fa-var-dollar-sign: "\f155"; 325 | @fa-var-dolly: "\f472"; 326 | @fa-var-dolly-flatbed: "\f474"; 327 | @fa-var-donate: "\f4b9"; 328 | @fa-var-door-closed: "\f52a"; 329 | @fa-var-door-open: "\f52b"; 330 | @fa-var-dot-circle: "\f192"; 331 | @fa-var-dove: "\f4ba"; 332 | @fa-var-download: "\f019"; 333 | @fa-var-draft2digital: "\f396"; 334 | @fa-var-drafting-compass: "\f568"; 335 | @fa-var-draw-polygon: "\f5ee"; 336 | @fa-var-dribbble: "\f17d"; 337 | @fa-var-dribbble-square: "\f397"; 338 | @fa-var-dropbox: "\f16b"; 339 | @fa-var-drum: "\f569"; 340 | @fa-var-drum-steelpan: "\f56a"; 341 | @fa-var-drupal: "\f1a9"; 342 | @fa-var-dumbbell: "\f44b"; 343 | @fa-var-dyalog: "\f399"; 344 | @fa-var-earlybirds: "\f39a"; 345 | @fa-var-ebay: "\f4f4"; 346 | @fa-var-edge: "\f282"; 347 | @fa-var-edit: "\f044"; 348 | @fa-var-eject: "\f052"; 349 | @fa-var-elementor: "\f430"; 350 | @fa-var-ellipsis-h: "\f141"; 351 | @fa-var-ellipsis-v: "\f142"; 352 | @fa-var-ello: "\f5f1"; 353 | @fa-var-ember: "\f423"; 354 | @fa-var-empire: "\f1d1"; 355 | @fa-var-envelope: "\f0e0"; 356 | @fa-var-envelope-open: "\f2b6"; 357 | @fa-var-envelope-open-text: "\f658"; 358 | @fa-var-envelope-square: "\f199"; 359 | @fa-var-envira: "\f299"; 360 | @fa-var-equals: "\f52c"; 361 | @fa-var-eraser: "\f12d"; 362 | @fa-var-erlang: "\f39d"; 363 | @fa-var-ethereum: "\f42e"; 364 | @fa-var-etsy: "\f2d7"; 365 | @fa-var-euro-sign: "\f153"; 366 | @fa-var-exchange-alt: "\f362"; 367 | @fa-var-exclamation: "\f12a"; 368 | @fa-var-exclamation-circle: "\f06a"; 369 | @fa-var-exclamation-triangle: "\f071"; 370 | @fa-var-expand: "\f065"; 371 | @fa-var-expand-arrows-alt: "\f31e"; 372 | @fa-var-expeditedssl: "\f23e"; 373 | @fa-var-external-link-alt: "\f35d"; 374 | @fa-var-external-link-square-alt: "\f360"; 375 | @fa-var-eye: "\f06e"; 376 | @fa-var-eye-dropper: "\f1fb"; 377 | @fa-var-eye-slash: "\f070"; 378 | @fa-var-facebook: "\f09a"; 379 | @fa-var-facebook-f: "\f39e"; 380 | @fa-var-facebook-messenger: "\f39f"; 381 | @fa-var-facebook-square: "\f082"; 382 | @fa-var-fast-backward: "\f049"; 383 | @fa-var-fast-forward: "\f050"; 384 | @fa-var-fax: "\f1ac"; 385 | @fa-var-feather: "\f52d"; 386 | @fa-var-feather-alt: "\f56b"; 387 | @fa-var-female: "\f182"; 388 | @fa-var-fighter-jet: "\f0fb"; 389 | @fa-var-file: "\f15b"; 390 | @fa-var-file-alt: "\f15c"; 391 | @fa-var-file-archive: "\f1c6"; 392 | @fa-var-file-audio: "\f1c7"; 393 | @fa-var-file-code: "\f1c9"; 394 | @fa-var-file-contract: "\f56c"; 395 | @fa-var-file-download: "\f56d"; 396 | @fa-var-file-excel: "\f1c3"; 397 | @fa-var-file-export: "\f56e"; 398 | @fa-var-file-image: "\f1c5"; 399 | @fa-var-file-import: "\f56f"; 400 | @fa-var-file-invoice: "\f570"; 401 | @fa-var-file-invoice-dollar: "\f571"; 402 | @fa-var-file-medical: "\f477"; 403 | @fa-var-file-medical-alt: "\f478"; 404 | @fa-var-file-pdf: "\f1c1"; 405 | @fa-var-file-powerpoint: "\f1c4"; 406 | @fa-var-file-prescription: "\f572"; 407 | @fa-var-file-signature: "\f573"; 408 | @fa-var-file-upload: "\f574"; 409 | @fa-var-file-video: "\f1c8"; 410 | @fa-var-file-word: "\f1c2"; 411 | @fa-var-fill: "\f575"; 412 | @fa-var-fill-drip: "\f576"; 413 | @fa-var-film: "\f008"; 414 | @fa-var-filter: "\f0b0"; 415 | @fa-var-fingerprint: "\f577"; 416 | @fa-var-fire: "\f06d"; 417 | @fa-var-fire-extinguisher: "\f134"; 418 | @fa-var-firefox: "\f269"; 419 | @fa-var-first-aid: "\f479"; 420 | @fa-var-first-order: "\f2b0"; 421 | @fa-var-first-order-alt: "\f50a"; 422 | @fa-var-firstdraft: "\f3a1"; 423 | @fa-var-fish: "\f578"; 424 | @fa-var-flag: "\f024"; 425 | @fa-var-flag-checkered: "\f11e"; 426 | @fa-var-flask: "\f0c3"; 427 | @fa-var-flickr: "\f16e"; 428 | @fa-var-flipboard: "\f44d"; 429 | @fa-var-flushed: "\f579"; 430 | @fa-var-fly: "\f417"; 431 | @fa-var-folder: "\f07b"; 432 | @fa-var-folder-minus: "\f65d"; 433 | @fa-var-folder-open: "\f07c"; 434 | @fa-var-folder-plus: "\f65e"; 435 | @fa-var-font: "\f031"; 436 | @fa-var-font-awesome: "\f2b4"; 437 | @fa-var-font-awesome-alt: "\f35c"; 438 | @fa-var-font-awesome-flag: "\f425"; 439 | @fa-var-font-awesome-logo-full: "\f4e6"; 440 | @fa-var-fonticons: "\f280"; 441 | @fa-var-fonticons-fi: "\f3a2"; 442 | @fa-var-football-ball: "\f44e"; 443 | @fa-var-fort-awesome: "\f286"; 444 | @fa-var-fort-awesome-alt: "\f3a3"; 445 | @fa-var-forumbee: "\f211"; 446 | @fa-var-forward: "\f04e"; 447 | @fa-var-foursquare: "\f180"; 448 | @fa-var-free-code-camp: "\f2c5"; 449 | @fa-var-freebsd: "\f3a4"; 450 | @fa-var-frog: "\f52e"; 451 | @fa-var-frown: "\f119"; 452 | @fa-var-frown-open: "\f57a"; 453 | @fa-var-fulcrum: "\f50b"; 454 | @fa-var-funnel-dollar: "\f662"; 455 | @fa-var-futbol: "\f1e3"; 456 | @fa-var-galactic-republic: "\f50c"; 457 | @fa-var-galactic-senate: "\f50d"; 458 | @fa-var-gamepad: "\f11b"; 459 | @fa-var-gas-pump: "\f52f"; 460 | @fa-var-gavel: "\f0e3"; 461 | @fa-var-gem: "\f3a5"; 462 | @fa-var-genderless: "\f22d"; 463 | @fa-var-get-pocket: "\f265"; 464 | @fa-var-gg: "\f260"; 465 | @fa-var-gg-circle: "\f261"; 466 | @fa-var-gift: "\f06b"; 467 | @fa-var-git: "\f1d3"; 468 | @fa-var-git-square: "\f1d2"; 469 | @fa-var-github: "\f09b"; 470 | @fa-var-github-alt: "\f113"; 471 | @fa-var-github-square: "\f092"; 472 | @fa-var-gitkraken: "\f3a6"; 473 | @fa-var-gitlab: "\f296"; 474 | @fa-var-gitter: "\f426"; 475 | @fa-var-glass-martini: "\f000"; 476 | @fa-var-glass-martini-alt: "\f57b"; 477 | @fa-var-glasses: "\f530"; 478 | @fa-var-glide: "\f2a5"; 479 | @fa-var-glide-g: "\f2a6"; 480 | @fa-var-globe: "\f0ac"; 481 | @fa-var-globe-africa: "\f57c"; 482 | @fa-var-globe-americas: "\f57d"; 483 | @fa-var-globe-asia: "\f57e"; 484 | @fa-var-gofore: "\f3a7"; 485 | @fa-var-golf-ball: "\f450"; 486 | @fa-var-goodreads: "\f3a8"; 487 | @fa-var-goodreads-g: "\f3a9"; 488 | @fa-var-google: "\f1a0"; 489 | @fa-var-google-drive: "\f3aa"; 490 | @fa-var-google-play: "\f3ab"; 491 | @fa-var-google-plus: "\f2b3"; 492 | @fa-var-google-plus-g: "\f0d5"; 493 | @fa-var-google-plus-square: "\f0d4"; 494 | @fa-var-google-wallet: "\f1ee"; 495 | @fa-var-gopuram: "\f664"; 496 | @fa-var-graduation-cap: "\f19d"; 497 | @fa-var-gratipay: "\f184"; 498 | @fa-var-grav: "\f2d6"; 499 | @fa-var-greater-than: "\f531"; 500 | @fa-var-greater-than-equal: "\f532"; 501 | @fa-var-grimace: "\f57f"; 502 | @fa-var-grin: "\f580"; 503 | @fa-var-grin-alt: "\f581"; 504 | @fa-var-grin-beam: "\f582"; 505 | @fa-var-grin-beam-sweat: "\f583"; 506 | @fa-var-grin-hearts: "\f584"; 507 | @fa-var-grin-squint: "\f585"; 508 | @fa-var-grin-squint-tears: "\f586"; 509 | @fa-var-grin-stars: "\f587"; 510 | @fa-var-grin-tears: "\f588"; 511 | @fa-var-grin-tongue: "\f589"; 512 | @fa-var-grin-tongue-squint: "\f58a"; 513 | @fa-var-grin-tongue-wink: "\f58b"; 514 | @fa-var-grin-wink: "\f58c"; 515 | @fa-var-grip-horizontal: "\f58d"; 516 | @fa-var-grip-vertical: "\f58e"; 517 | @fa-var-gripfire: "\f3ac"; 518 | @fa-var-grunt: "\f3ad"; 519 | @fa-var-gulp: "\f3ae"; 520 | @fa-var-h-square: "\f0fd"; 521 | @fa-var-hacker-news: "\f1d4"; 522 | @fa-var-hacker-news-square: "\f3af"; 523 | @fa-var-hackerrank: "\f5f7"; 524 | @fa-var-hamsa: "\f665"; 525 | @fa-var-hand-holding: "\f4bd"; 526 | @fa-var-hand-holding-heart: "\f4be"; 527 | @fa-var-hand-holding-usd: "\f4c0"; 528 | @fa-var-hand-lizard: "\f258"; 529 | @fa-var-hand-paper: "\f256"; 530 | @fa-var-hand-peace: "\f25b"; 531 | @fa-var-hand-point-down: "\f0a7"; 532 | @fa-var-hand-point-left: "\f0a5"; 533 | @fa-var-hand-point-right: "\f0a4"; 534 | @fa-var-hand-point-up: "\f0a6"; 535 | @fa-var-hand-pointer: "\f25a"; 536 | @fa-var-hand-rock: "\f255"; 537 | @fa-var-hand-scissors: "\f257"; 538 | @fa-var-hand-spock: "\f259"; 539 | @fa-var-hands: "\f4c2"; 540 | @fa-var-hands-helping: "\f4c4"; 541 | @fa-var-handshake: "\f2b5"; 542 | @fa-var-hashtag: "\f292"; 543 | @fa-var-haykal: "\f666"; 544 | @fa-var-hdd: "\f0a0"; 545 | @fa-var-heading: "\f1dc"; 546 | @fa-var-headphones: "\f025"; 547 | @fa-var-headphones-alt: "\f58f"; 548 | @fa-var-headset: "\f590"; 549 | @fa-var-heart: "\f004"; 550 | @fa-var-heartbeat: "\f21e"; 551 | @fa-var-helicopter: "\f533"; 552 | @fa-var-highlighter: "\f591"; 553 | @fa-var-hips: "\f452"; 554 | @fa-var-hire-a-helper: "\f3b0"; 555 | @fa-var-history: "\f1da"; 556 | @fa-var-hockey-puck: "\f453"; 557 | @fa-var-home: "\f015"; 558 | @fa-var-hooli: "\f427"; 559 | @fa-var-hornbill: "\f592"; 560 | @fa-var-hospital: "\f0f8"; 561 | @fa-var-hospital-alt: "\f47d"; 562 | @fa-var-hospital-symbol: "\f47e"; 563 | @fa-var-hot-tub: "\f593"; 564 | @fa-var-hotel: "\f594"; 565 | @fa-var-hotjar: "\f3b1"; 566 | @fa-var-hourglass: "\f254"; 567 | @fa-var-hourglass-end: "\f253"; 568 | @fa-var-hourglass-half: "\f252"; 569 | @fa-var-hourglass-start: "\f251"; 570 | @fa-var-houzz: "\f27c"; 571 | @fa-var-html5: "\f13b"; 572 | @fa-var-hubspot: "\f3b2"; 573 | @fa-var-i-cursor: "\f246"; 574 | @fa-var-id-badge: "\f2c1"; 575 | @fa-var-id-card: "\f2c2"; 576 | @fa-var-id-card-alt: "\f47f"; 577 | @fa-var-image: "\f03e"; 578 | @fa-var-images: "\f302"; 579 | @fa-var-imdb: "\f2d8"; 580 | @fa-var-inbox: "\f01c"; 581 | @fa-var-indent: "\f03c"; 582 | @fa-var-industry: "\f275"; 583 | @fa-var-infinity: "\f534"; 584 | @fa-var-info: "\f129"; 585 | @fa-var-info-circle: "\f05a"; 586 | @fa-var-instagram: "\f16d"; 587 | @fa-var-internet-explorer: "\f26b"; 588 | @fa-var-ioxhost: "\f208"; 589 | @fa-var-italic: "\f033"; 590 | @fa-var-itunes: "\f3b4"; 591 | @fa-var-itunes-note: "\f3b5"; 592 | @fa-var-java: "\f4e4"; 593 | @fa-var-jedi: "\f669"; 594 | @fa-var-jedi-order: "\f50e"; 595 | @fa-var-jenkins: "\f3b6"; 596 | @fa-var-joget: "\f3b7"; 597 | @fa-var-joint: "\f595"; 598 | @fa-var-joomla: "\f1aa"; 599 | @fa-var-journal-whills: "\f66a"; 600 | @fa-var-js: "\f3b8"; 601 | @fa-var-js-square: "\f3b9"; 602 | @fa-var-jsfiddle: "\f1cc"; 603 | @fa-var-kaaba: "\f66b"; 604 | @fa-var-kaggle: "\f5fa"; 605 | @fa-var-key: "\f084"; 606 | @fa-var-keybase: "\f4f5"; 607 | @fa-var-keyboard: "\f11c"; 608 | @fa-var-keycdn: "\f3ba"; 609 | @fa-var-khanda: "\f66d"; 610 | @fa-var-kickstarter: "\f3bb"; 611 | @fa-var-kickstarter-k: "\f3bc"; 612 | @fa-var-kiss: "\f596"; 613 | @fa-var-kiss-beam: "\f597"; 614 | @fa-var-kiss-wink-heart: "\f598"; 615 | @fa-var-kiwi-bird: "\f535"; 616 | @fa-var-korvue: "\f42f"; 617 | @fa-var-landmark: "\f66f"; 618 | @fa-var-language: "\f1ab"; 619 | @fa-var-laptop: "\f109"; 620 | @fa-var-laptop-code: "\f5fc"; 621 | @fa-var-laravel: "\f3bd"; 622 | @fa-var-lastfm: "\f202"; 623 | @fa-var-lastfm-square: "\f203"; 624 | @fa-var-laugh: "\f599"; 625 | @fa-var-laugh-beam: "\f59a"; 626 | @fa-var-laugh-squint: "\f59b"; 627 | @fa-var-laugh-wink: "\f59c"; 628 | @fa-var-layer-group: "\f5fd"; 629 | @fa-var-leaf: "\f06c"; 630 | @fa-var-leanpub: "\f212"; 631 | @fa-var-lemon: "\f094"; 632 | @fa-var-less: "\f41d"; 633 | @fa-var-less-than: "\f536"; 634 | @fa-var-less-than-equal: "\f537"; 635 | @fa-var-level-down-alt: "\f3be"; 636 | @fa-var-level-up-alt: "\f3bf"; 637 | @fa-var-life-ring: "\f1cd"; 638 | @fa-var-lightbulb: "\f0eb"; 639 | @fa-var-line: "\f3c0"; 640 | @fa-var-link: "\f0c1"; 641 | @fa-var-linkedin: "\f08c"; 642 | @fa-var-linkedin-in: "\f0e1"; 643 | @fa-var-linode: "\f2b8"; 644 | @fa-var-linux: "\f17c"; 645 | @fa-var-lira-sign: "\f195"; 646 | @fa-var-list: "\f03a"; 647 | @fa-var-list-alt: "\f022"; 648 | @fa-var-list-ol: "\f0cb"; 649 | @fa-var-list-ul: "\f0ca"; 650 | @fa-var-location-arrow: "\f124"; 651 | @fa-var-lock: "\f023"; 652 | @fa-var-lock-open: "\f3c1"; 653 | @fa-var-long-arrow-alt-down: "\f309"; 654 | @fa-var-long-arrow-alt-left: "\f30a"; 655 | @fa-var-long-arrow-alt-right: "\f30b"; 656 | @fa-var-long-arrow-alt-up: "\f30c"; 657 | @fa-var-low-vision: "\f2a8"; 658 | @fa-var-luggage-cart: "\f59d"; 659 | @fa-var-lyft: "\f3c3"; 660 | @fa-var-magento: "\f3c4"; 661 | @fa-var-magic: "\f0d0"; 662 | @fa-var-magnet: "\f076"; 663 | @fa-var-mail-bulk: "\f674"; 664 | @fa-var-mailchimp: "\f59e"; 665 | @fa-var-male: "\f183"; 666 | @fa-var-mandalorian: "\f50f"; 667 | @fa-var-map: "\f279"; 668 | @fa-var-map-marked: "\f59f"; 669 | @fa-var-map-marked-alt: "\f5a0"; 670 | @fa-var-map-marker: "\f041"; 671 | @fa-var-map-marker-alt: "\f3c5"; 672 | @fa-var-map-pin: "\f276"; 673 | @fa-var-map-signs: "\f277"; 674 | @fa-var-markdown: "\f60f"; 675 | @fa-var-marker: "\f5a1"; 676 | @fa-var-mars: "\f222"; 677 | @fa-var-mars-double: "\f227"; 678 | @fa-var-mars-stroke: "\f229"; 679 | @fa-var-mars-stroke-h: "\f22b"; 680 | @fa-var-mars-stroke-v: "\f22a"; 681 | @fa-var-mastodon: "\f4f6"; 682 | @fa-var-maxcdn: "\f136"; 683 | @fa-var-medal: "\f5a2"; 684 | @fa-var-medapps: "\f3c6"; 685 | @fa-var-medium: "\f23a"; 686 | @fa-var-medium-m: "\f3c7"; 687 | @fa-var-medkit: "\f0fa"; 688 | @fa-var-medrt: "\f3c8"; 689 | @fa-var-meetup: "\f2e0"; 690 | @fa-var-megaport: "\f5a3"; 691 | @fa-var-meh: "\f11a"; 692 | @fa-var-meh-blank: "\f5a4"; 693 | @fa-var-meh-rolling-eyes: "\f5a5"; 694 | @fa-var-memory: "\f538"; 695 | @fa-var-menorah: "\f676"; 696 | @fa-var-mercury: "\f223"; 697 | @fa-var-microchip: "\f2db"; 698 | @fa-var-microphone: "\f130"; 699 | @fa-var-microphone-alt: "\f3c9"; 700 | @fa-var-microphone-alt-slash: "\f539"; 701 | @fa-var-microphone-slash: "\f131"; 702 | @fa-var-microscope: "\f610"; 703 | @fa-var-microsoft: "\f3ca"; 704 | @fa-var-minus: "\f068"; 705 | @fa-var-minus-circle: "\f056"; 706 | @fa-var-minus-square: "\f146"; 707 | @fa-var-mix: "\f3cb"; 708 | @fa-var-mixcloud: "\f289"; 709 | @fa-var-mizuni: "\f3cc"; 710 | @fa-var-mobile: "\f10b"; 711 | @fa-var-mobile-alt: "\f3cd"; 712 | @fa-var-modx: "\f285"; 713 | @fa-var-monero: "\f3d0"; 714 | @fa-var-money-bill: "\f0d6"; 715 | @fa-var-money-bill-alt: "\f3d1"; 716 | @fa-var-money-bill-wave: "\f53a"; 717 | @fa-var-money-bill-wave-alt: "\f53b"; 718 | @fa-var-money-check: "\f53c"; 719 | @fa-var-money-check-alt: "\f53d"; 720 | @fa-var-monument: "\f5a6"; 721 | @fa-var-moon: "\f186"; 722 | @fa-var-mortar-pestle: "\f5a7"; 723 | @fa-var-mosque: "\f678"; 724 | @fa-var-motorcycle: "\f21c"; 725 | @fa-var-mouse-pointer: "\f245"; 726 | @fa-var-music: "\f001"; 727 | @fa-var-napster: "\f3d2"; 728 | @fa-var-neos: "\f612"; 729 | @fa-var-neuter: "\f22c"; 730 | @fa-var-newspaper: "\f1ea"; 731 | @fa-var-nimblr: "\f5a8"; 732 | @fa-var-nintendo-switch: "\f418"; 733 | @fa-var-node: "\f419"; 734 | @fa-var-node-js: "\f3d3"; 735 | @fa-var-not-equal: "\f53e"; 736 | @fa-var-notes-medical: "\f481"; 737 | @fa-var-npm: "\f3d4"; 738 | @fa-var-ns8: "\f3d5"; 739 | @fa-var-nutritionix: "\f3d6"; 740 | @fa-var-object-group: "\f247"; 741 | @fa-var-object-ungroup: "\f248"; 742 | @fa-var-odnoklassniki: "\f263"; 743 | @fa-var-odnoklassniki-square: "\f264"; 744 | @fa-var-oil-can: "\f613"; 745 | @fa-var-old-republic: "\f510"; 746 | @fa-var-om: "\f679"; 747 | @fa-var-opencart: "\f23d"; 748 | @fa-var-openid: "\f19b"; 749 | @fa-var-opera: "\f26a"; 750 | @fa-var-optin-monster: "\f23c"; 751 | @fa-var-osi: "\f41a"; 752 | @fa-var-outdent: "\f03b"; 753 | @fa-var-page4: "\f3d7"; 754 | @fa-var-pagelines: "\f18c"; 755 | @fa-var-paint-brush: "\f1fc"; 756 | @fa-var-paint-roller: "\f5aa"; 757 | @fa-var-palette: "\f53f"; 758 | @fa-var-palfed: "\f3d8"; 759 | @fa-var-pallet: "\f482"; 760 | @fa-var-paper-plane: "\f1d8"; 761 | @fa-var-paperclip: "\f0c6"; 762 | @fa-var-parachute-box: "\f4cd"; 763 | @fa-var-paragraph: "\f1dd"; 764 | @fa-var-parking: "\f540"; 765 | @fa-var-passport: "\f5ab"; 766 | @fa-var-pastafarianism: "\f67b"; 767 | @fa-var-paste: "\f0ea"; 768 | @fa-var-patreon: "\f3d9"; 769 | @fa-var-pause: "\f04c"; 770 | @fa-var-pause-circle: "\f28b"; 771 | @fa-var-paw: "\f1b0"; 772 | @fa-var-paypal: "\f1ed"; 773 | @fa-var-peace: "\f67c"; 774 | @fa-var-pen: "\f304"; 775 | @fa-var-pen-alt: "\f305"; 776 | @fa-var-pen-fancy: "\f5ac"; 777 | @fa-var-pen-nib: "\f5ad"; 778 | @fa-var-pen-square: "\f14b"; 779 | @fa-var-pencil-alt: "\f303"; 780 | @fa-var-pencil-ruler: "\f5ae"; 781 | @fa-var-people-carry: "\f4ce"; 782 | @fa-var-percent: "\f295"; 783 | @fa-var-percentage: "\f541"; 784 | @fa-var-periscope: "\f3da"; 785 | @fa-var-phabricator: "\f3db"; 786 | @fa-var-phoenix-framework: "\f3dc"; 787 | @fa-var-phoenix-squadron: "\f511"; 788 | @fa-var-phone: "\f095"; 789 | @fa-var-phone-slash: "\f3dd"; 790 | @fa-var-phone-square: "\f098"; 791 | @fa-var-phone-volume: "\f2a0"; 792 | @fa-var-php: "\f457"; 793 | @fa-var-pied-piper: "\f2ae"; 794 | @fa-var-pied-piper-alt: "\f1a8"; 795 | @fa-var-pied-piper-hat: "\f4e5"; 796 | @fa-var-pied-piper-pp: "\f1a7"; 797 | @fa-var-piggy-bank: "\f4d3"; 798 | @fa-var-pills: "\f484"; 799 | @fa-var-pinterest: "\f0d2"; 800 | @fa-var-pinterest-p: "\f231"; 801 | @fa-var-pinterest-square: "\f0d3"; 802 | @fa-var-place-of-worship: "\f67f"; 803 | @fa-var-plane: "\f072"; 804 | @fa-var-plane-arrival: "\f5af"; 805 | @fa-var-plane-departure: "\f5b0"; 806 | @fa-var-play: "\f04b"; 807 | @fa-var-play-circle: "\f144"; 808 | @fa-var-playstation: "\f3df"; 809 | @fa-var-plug: "\f1e6"; 810 | @fa-var-plus: "\f067"; 811 | @fa-var-plus-circle: "\f055"; 812 | @fa-var-plus-square: "\f0fe"; 813 | @fa-var-podcast: "\f2ce"; 814 | @fa-var-poll: "\f681"; 815 | @fa-var-poll-h: "\f682"; 816 | @fa-var-poo: "\f2fe"; 817 | @fa-var-poop: "\f619"; 818 | @fa-var-portrait: "\f3e0"; 819 | @fa-var-pound-sign: "\f154"; 820 | @fa-var-power-off: "\f011"; 821 | @fa-var-pray: "\f683"; 822 | @fa-var-praying-hands: "\f684"; 823 | @fa-var-prescription: "\f5b1"; 824 | @fa-var-prescription-bottle: "\f485"; 825 | @fa-var-prescription-bottle-alt: "\f486"; 826 | @fa-var-print: "\f02f"; 827 | @fa-var-procedures: "\f487"; 828 | @fa-var-product-hunt: "\f288"; 829 | @fa-var-project-diagram: "\f542"; 830 | @fa-var-pushed: "\f3e1"; 831 | @fa-var-puzzle-piece: "\f12e"; 832 | @fa-var-python: "\f3e2"; 833 | @fa-var-qq: "\f1d6"; 834 | @fa-var-qrcode: "\f029"; 835 | @fa-var-question: "\f128"; 836 | @fa-var-question-circle: "\f059"; 837 | @fa-var-quidditch: "\f458"; 838 | @fa-var-quinscape: "\f459"; 839 | @fa-var-quora: "\f2c4"; 840 | @fa-var-quote-left: "\f10d"; 841 | @fa-var-quote-right: "\f10e"; 842 | @fa-var-quran: "\f687"; 843 | @fa-var-r-project: "\f4f7"; 844 | @fa-var-random: "\f074"; 845 | @fa-var-ravelry: "\f2d9"; 846 | @fa-var-react: "\f41b"; 847 | @fa-var-readme: "\f4d5"; 848 | @fa-var-rebel: "\f1d0"; 849 | @fa-var-receipt: "\f543"; 850 | @fa-var-recycle: "\f1b8"; 851 | @fa-var-red-river: "\f3e3"; 852 | @fa-var-reddit: "\f1a1"; 853 | @fa-var-reddit-alien: "\f281"; 854 | @fa-var-reddit-square: "\f1a2"; 855 | @fa-var-redo: "\f01e"; 856 | @fa-var-redo-alt: "\f2f9"; 857 | @fa-var-registered: "\f25d"; 858 | @fa-var-rendact: "\f3e4"; 859 | @fa-var-renren: "\f18b"; 860 | @fa-var-reply: "\f3e5"; 861 | @fa-var-reply-all: "\f122"; 862 | @fa-var-replyd: "\f3e6"; 863 | @fa-var-researchgate: "\f4f8"; 864 | @fa-var-resolving: "\f3e7"; 865 | @fa-var-retweet: "\f079"; 866 | @fa-var-rev: "\f5b2"; 867 | @fa-var-ribbon: "\f4d6"; 868 | @fa-var-road: "\f018"; 869 | @fa-var-robot: "\f544"; 870 | @fa-var-rocket: "\f135"; 871 | @fa-var-rocketchat: "\f3e8"; 872 | @fa-var-rockrms: "\f3e9"; 873 | @fa-var-route: "\f4d7"; 874 | @fa-var-rss: "\f09e"; 875 | @fa-var-rss-square: "\f143"; 876 | @fa-var-ruble-sign: "\f158"; 877 | @fa-var-ruler: "\f545"; 878 | @fa-var-ruler-combined: "\f546"; 879 | @fa-var-ruler-horizontal: "\f547"; 880 | @fa-var-ruler-vertical: "\f548"; 881 | @fa-var-rupee-sign: "\f156"; 882 | @fa-var-sad-cry: "\f5b3"; 883 | @fa-var-sad-tear: "\f5b4"; 884 | @fa-var-safari: "\f267"; 885 | @fa-var-sass: "\f41e"; 886 | @fa-var-save: "\f0c7"; 887 | @fa-var-schlix: "\f3ea"; 888 | @fa-var-school: "\f549"; 889 | @fa-var-screwdriver: "\f54a"; 890 | @fa-var-scribd: "\f28a"; 891 | @fa-var-search: "\f002"; 892 | @fa-var-search-dollar: "\f688"; 893 | @fa-var-search-location: "\f689"; 894 | @fa-var-search-minus: "\f010"; 895 | @fa-var-search-plus: "\f00e"; 896 | @fa-var-searchengin: "\f3eb"; 897 | @fa-var-seedling: "\f4d8"; 898 | @fa-var-sellcast: "\f2da"; 899 | @fa-var-sellsy: "\f213"; 900 | @fa-var-server: "\f233"; 901 | @fa-var-servicestack: "\f3ec"; 902 | @fa-var-shapes: "\f61f"; 903 | @fa-var-share: "\f064"; 904 | @fa-var-share-alt: "\f1e0"; 905 | @fa-var-share-alt-square: "\f1e1"; 906 | @fa-var-share-square: "\f14d"; 907 | @fa-var-shekel-sign: "\f20b"; 908 | @fa-var-shield-alt: "\f3ed"; 909 | @fa-var-ship: "\f21a"; 910 | @fa-var-shipping-fast: "\f48b"; 911 | @fa-var-shirtsinbulk: "\f214"; 912 | @fa-var-shoe-prints: "\f54b"; 913 | @fa-var-shopping-bag: "\f290"; 914 | @fa-var-shopping-basket: "\f291"; 915 | @fa-var-shopping-cart: "\f07a"; 916 | @fa-var-shopware: "\f5b5"; 917 | @fa-var-shower: "\f2cc"; 918 | @fa-var-shuttle-van: "\f5b6"; 919 | @fa-var-sign: "\f4d9"; 920 | @fa-var-sign-in-alt: "\f2f6"; 921 | @fa-var-sign-language: "\f2a7"; 922 | @fa-var-sign-out-alt: "\f2f5"; 923 | @fa-var-signal: "\f012"; 924 | @fa-var-signature: "\f5b7"; 925 | @fa-var-simplybuilt: "\f215"; 926 | @fa-var-sistrix: "\f3ee"; 927 | @fa-var-sitemap: "\f0e8"; 928 | @fa-var-sith: "\f512"; 929 | @fa-var-skull: "\f54c"; 930 | @fa-var-skyatlas: "\f216"; 931 | @fa-var-skype: "\f17e"; 932 | @fa-var-slack: "\f198"; 933 | @fa-var-slack-hash: "\f3ef"; 934 | @fa-var-sliders-h: "\f1de"; 935 | @fa-var-slideshare: "\f1e7"; 936 | @fa-var-smile: "\f118"; 937 | @fa-var-smile-beam: "\f5b8"; 938 | @fa-var-smile-wink: "\f4da"; 939 | @fa-var-smoking: "\f48d"; 940 | @fa-var-smoking-ban: "\f54d"; 941 | @fa-var-snapchat: "\f2ab"; 942 | @fa-var-snapchat-ghost: "\f2ac"; 943 | @fa-var-snapchat-square: "\f2ad"; 944 | @fa-var-snowflake: "\f2dc"; 945 | @fa-var-socks: "\f696"; 946 | @fa-var-solar-panel: "\f5ba"; 947 | @fa-var-sort: "\f0dc"; 948 | @fa-var-sort-alpha-down: "\f15d"; 949 | @fa-var-sort-alpha-up: "\f15e"; 950 | @fa-var-sort-amount-down: "\f160"; 951 | @fa-var-sort-amount-up: "\f161"; 952 | @fa-var-sort-down: "\f0dd"; 953 | @fa-var-sort-numeric-down: "\f162"; 954 | @fa-var-sort-numeric-up: "\f163"; 955 | @fa-var-sort-up: "\f0de"; 956 | @fa-var-soundcloud: "\f1be"; 957 | @fa-var-spa: "\f5bb"; 958 | @fa-var-space-shuttle: "\f197"; 959 | @fa-var-speakap: "\f3f3"; 960 | @fa-var-spinner: "\f110"; 961 | @fa-var-splotch: "\f5bc"; 962 | @fa-var-spotify: "\f1bc"; 963 | @fa-var-spray-can: "\f5bd"; 964 | @fa-var-square: "\f0c8"; 965 | @fa-var-square-full: "\f45c"; 966 | @fa-var-square-root-alt: "\f698"; 967 | @fa-var-squarespace: "\f5be"; 968 | @fa-var-stack-exchange: "\f18d"; 969 | @fa-var-stack-overflow: "\f16c"; 970 | @fa-var-stamp: "\f5bf"; 971 | @fa-var-star: "\f005"; 972 | @fa-var-star-and-crescent: "\f699"; 973 | @fa-var-star-half: "\f089"; 974 | @fa-var-star-half-alt: "\f5c0"; 975 | @fa-var-star-of-david: "\f69a"; 976 | @fa-var-star-of-life: "\f621"; 977 | @fa-var-staylinked: "\f3f5"; 978 | @fa-var-steam: "\f1b6"; 979 | @fa-var-steam-square: "\f1b7"; 980 | @fa-var-steam-symbol: "\f3f6"; 981 | @fa-var-step-backward: "\f048"; 982 | @fa-var-step-forward: "\f051"; 983 | @fa-var-stethoscope: "\f0f1"; 984 | @fa-var-sticker-mule: "\f3f7"; 985 | @fa-var-sticky-note: "\f249"; 986 | @fa-var-stop: "\f04d"; 987 | @fa-var-stop-circle: "\f28d"; 988 | @fa-var-stopwatch: "\f2f2"; 989 | @fa-var-store: "\f54e"; 990 | @fa-var-store-alt: "\f54f"; 991 | @fa-var-strava: "\f428"; 992 | @fa-var-stream: "\f550"; 993 | @fa-var-street-view: "\f21d"; 994 | @fa-var-strikethrough: "\f0cc"; 995 | @fa-var-stripe: "\f429"; 996 | @fa-var-stripe-s: "\f42a"; 997 | @fa-var-stroopwafel: "\f551"; 998 | @fa-var-studiovinari: "\f3f8"; 999 | @fa-var-stumbleupon: "\f1a4"; 1000 | @fa-var-stumbleupon-circle: "\f1a3"; 1001 | @fa-var-subscript: "\f12c"; 1002 | @fa-var-subway: "\f239"; 1003 | @fa-var-suitcase: "\f0f2"; 1004 | @fa-var-suitcase-rolling: "\f5c1"; 1005 | @fa-var-sun: "\f185"; 1006 | @fa-var-superpowers: "\f2dd"; 1007 | @fa-var-superscript: "\f12b"; 1008 | @fa-var-supple: "\f3f9"; 1009 | @fa-var-surprise: "\f5c2"; 1010 | @fa-var-swatchbook: "\f5c3"; 1011 | @fa-var-swimmer: "\f5c4"; 1012 | @fa-var-swimming-pool: "\f5c5"; 1013 | @fa-var-synagogue: "\f69b"; 1014 | @fa-var-sync: "\f021"; 1015 | @fa-var-sync-alt: "\f2f1"; 1016 | @fa-var-syringe: "\f48e"; 1017 | @fa-var-table: "\f0ce"; 1018 | @fa-var-table-tennis: "\f45d"; 1019 | @fa-var-tablet: "\f10a"; 1020 | @fa-var-tablet-alt: "\f3fa"; 1021 | @fa-var-tablets: "\f490"; 1022 | @fa-var-tachometer-alt: "\f3fd"; 1023 | @fa-var-tag: "\f02b"; 1024 | @fa-var-tags: "\f02c"; 1025 | @fa-var-tape: "\f4db"; 1026 | @fa-var-tasks: "\f0ae"; 1027 | @fa-var-taxi: "\f1ba"; 1028 | @fa-var-teamspeak: "\f4f9"; 1029 | @fa-var-teeth: "\f62e"; 1030 | @fa-var-teeth-open: "\f62f"; 1031 | @fa-var-telegram: "\f2c6"; 1032 | @fa-var-telegram-plane: "\f3fe"; 1033 | @fa-var-tencent-weibo: "\f1d5"; 1034 | @fa-var-terminal: "\f120"; 1035 | @fa-var-text-height: "\f034"; 1036 | @fa-var-text-width: "\f035"; 1037 | @fa-var-th: "\f00a"; 1038 | @fa-var-th-large: "\f009"; 1039 | @fa-var-th-list: "\f00b"; 1040 | @fa-var-the-red-yeti: "\f69d"; 1041 | @fa-var-theater-masks: "\f630"; 1042 | @fa-var-themeco: "\f5c6"; 1043 | @fa-var-themeisle: "\f2b2"; 1044 | @fa-var-thermometer: "\f491"; 1045 | @fa-var-thermometer-empty: "\f2cb"; 1046 | @fa-var-thermometer-full: "\f2c7"; 1047 | @fa-var-thermometer-half: "\f2c9"; 1048 | @fa-var-thermometer-quarter: "\f2ca"; 1049 | @fa-var-thermometer-three-quarters: "\f2c8"; 1050 | @fa-var-thumbs-down: "\f165"; 1051 | @fa-var-thumbs-up: "\f164"; 1052 | @fa-var-thumbtack: "\f08d"; 1053 | @fa-var-ticket-alt: "\f3ff"; 1054 | @fa-var-times: "\f00d"; 1055 | @fa-var-times-circle: "\f057"; 1056 | @fa-var-tint: "\f043"; 1057 | @fa-var-tint-slash: "\f5c7"; 1058 | @fa-var-tired: "\f5c8"; 1059 | @fa-var-toggle-off: "\f204"; 1060 | @fa-var-toggle-on: "\f205"; 1061 | @fa-var-toolbox: "\f552"; 1062 | @fa-var-tooth: "\f5c9"; 1063 | @fa-var-torah: "\f6a0"; 1064 | @fa-var-torii-gate: "\f6a1"; 1065 | @fa-var-trade-federation: "\f513"; 1066 | @fa-var-trademark: "\f25c"; 1067 | @fa-var-traffic-light: "\f637"; 1068 | @fa-var-train: "\f238"; 1069 | @fa-var-transgender: "\f224"; 1070 | @fa-var-transgender-alt: "\f225"; 1071 | @fa-var-trash: "\f1f8"; 1072 | @fa-var-trash-alt: "\f2ed"; 1073 | @fa-var-tree: "\f1bb"; 1074 | @fa-var-trello: "\f181"; 1075 | @fa-var-tripadvisor: "\f262"; 1076 | @fa-var-trophy: "\f091"; 1077 | @fa-var-truck: "\f0d1"; 1078 | @fa-var-truck-loading: "\f4de"; 1079 | @fa-var-truck-monster: "\f63b"; 1080 | @fa-var-truck-moving: "\f4df"; 1081 | @fa-var-truck-pickup: "\f63c"; 1082 | @fa-var-tshirt: "\f553"; 1083 | @fa-var-tty: "\f1e4"; 1084 | @fa-var-tumblr: "\f173"; 1085 | @fa-var-tumblr-square: "\f174"; 1086 | @fa-var-tv: "\f26c"; 1087 | @fa-var-twitch: "\f1e8"; 1088 | @fa-var-twitter: "\f099"; 1089 | @fa-var-twitter-square: "\f081"; 1090 | @fa-var-typo3: "\f42b"; 1091 | @fa-var-uber: "\f402"; 1092 | @fa-var-uikit: "\f403"; 1093 | @fa-var-umbrella: "\f0e9"; 1094 | @fa-var-umbrella-beach: "\f5ca"; 1095 | @fa-var-underline: "\f0cd"; 1096 | @fa-var-undo: "\f0e2"; 1097 | @fa-var-undo-alt: "\f2ea"; 1098 | @fa-var-uniregistry: "\f404"; 1099 | @fa-var-universal-access: "\f29a"; 1100 | @fa-var-university: "\f19c"; 1101 | @fa-var-unlink: "\f127"; 1102 | @fa-var-unlock: "\f09c"; 1103 | @fa-var-unlock-alt: "\f13e"; 1104 | @fa-var-untappd: "\f405"; 1105 | @fa-var-upload: "\f093"; 1106 | @fa-var-usb: "\f287"; 1107 | @fa-var-user: "\f007"; 1108 | @fa-var-user-alt: "\f406"; 1109 | @fa-var-user-alt-slash: "\f4fa"; 1110 | @fa-var-user-astronaut: "\f4fb"; 1111 | @fa-var-user-check: "\f4fc"; 1112 | @fa-var-user-circle: "\f2bd"; 1113 | @fa-var-user-clock: "\f4fd"; 1114 | @fa-var-user-cog: "\f4fe"; 1115 | @fa-var-user-edit: "\f4ff"; 1116 | @fa-var-user-friends: "\f500"; 1117 | @fa-var-user-graduate: "\f501"; 1118 | @fa-var-user-lock: "\f502"; 1119 | @fa-var-user-md: "\f0f0"; 1120 | @fa-var-user-minus: "\f503"; 1121 | @fa-var-user-ninja: "\f504"; 1122 | @fa-var-user-plus: "\f234"; 1123 | @fa-var-user-secret: "\f21b"; 1124 | @fa-var-user-shield: "\f505"; 1125 | @fa-var-user-slash: "\f506"; 1126 | @fa-var-user-tag: "\f507"; 1127 | @fa-var-user-tie: "\f508"; 1128 | @fa-var-user-times: "\f235"; 1129 | @fa-var-users: "\f0c0"; 1130 | @fa-var-users-cog: "\f509"; 1131 | @fa-var-ussunnah: "\f407"; 1132 | @fa-var-utensil-spoon: "\f2e5"; 1133 | @fa-var-utensils: "\f2e7"; 1134 | @fa-var-vaadin: "\f408"; 1135 | @fa-var-vector-square: "\f5cb"; 1136 | @fa-var-venus: "\f221"; 1137 | @fa-var-venus-double: "\f226"; 1138 | @fa-var-venus-mars: "\f228"; 1139 | @fa-var-viacoin: "\f237"; 1140 | @fa-var-viadeo: "\f2a9"; 1141 | @fa-var-viadeo-square: "\f2aa"; 1142 | @fa-var-vial: "\f492"; 1143 | @fa-var-vials: "\f493"; 1144 | @fa-var-viber: "\f409"; 1145 | @fa-var-video: "\f03d"; 1146 | @fa-var-video-slash: "\f4e2"; 1147 | @fa-var-vihara: "\f6a7"; 1148 | @fa-var-vimeo: "\f40a"; 1149 | @fa-var-vimeo-square: "\f194"; 1150 | @fa-var-vimeo-v: "\f27d"; 1151 | @fa-var-vine: "\f1ca"; 1152 | @fa-var-vk: "\f189"; 1153 | @fa-var-vnv: "\f40b"; 1154 | @fa-var-volleyball-ball: "\f45f"; 1155 | @fa-var-volume-down: "\f027"; 1156 | @fa-var-volume-off: "\f026"; 1157 | @fa-var-volume-up: "\f028"; 1158 | @fa-var-vuejs: "\f41f"; 1159 | @fa-var-walking: "\f554"; 1160 | @fa-var-wallet: "\f555"; 1161 | @fa-var-warehouse: "\f494"; 1162 | @fa-var-weebly: "\f5cc"; 1163 | @fa-var-weibo: "\f18a"; 1164 | @fa-var-weight: "\f496"; 1165 | @fa-var-weight-hanging: "\f5cd"; 1166 | @fa-var-weixin: "\f1d7"; 1167 | @fa-var-whatsapp: "\f232"; 1168 | @fa-var-whatsapp-square: "\f40c"; 1169 | @fa-var-wheelchair: "\f193"; 1170 | @fa-var-whmcs: "\f40d"; 1171 | @fa-var-wifi: "\f1eb"; 1172 | @fa-var-wikipedia-w: "\f266"; 1173 | @fa-var-window-close: "\f410"; 1174 | @fa-var-window-maximize: "\f2d0"; 1175 | @fa-var-window-minimize: "\f2d1"; 1176 | @fa-var-window-restore: "\f2d2"; 1177 | @fa-var-windows: "\f17a"; 1178 | @fa-var-wine-glass: "\f4e3"; 1179 | @fa-var-wine-glass-alt: "\f5ce"; 1180 | @fa-var-wix: "\f5cf"; 1181 | @fa-var-wolf-pack-battalion: "\f514"; 1182 | @fa-var-won-sign: "\f159"; 1183 | @fa-var-wordpress: "\f19a"; 1184 | @fa-var-wordpress-simple: "\f411"; 1185 | @fa-var-wpbeginner: "\f297"; 1186 | @fa-var-wpexplorer: "\f2de"; 1187 | @fa-var-wpforms: "\f298"; 1188 | @fa-var-wrench: "\f0ad"; 1189 | @fa-var-x-ray: "\f497"; 1190 | @fa-var-xbox: "\f412"; 1191 | @fa-var-xing: "\f168"; 1192 | @fa-var-xing-square: "\f169"; 1193 | @fa-var-y-combinator: "\f23b"; 1194 | @fa-var-yahoo: "\f19e"; 1195 | @fa-var-yandex: "\f413"; 1196 | @fa-var-yandex-international: "\f414"; 1197 | @fa-var-yelp: "\f1e9"; 1198 | @fa-var-yen-sign: "\f157"; 1199 | @fa-var-yin-yang: "\f6ad"; 1200 | @fa-var-yoast: "\f2b1"; 1201 | @fa-var-youtube: "\f167"; 1202 | @fa-var-youtube-square: "\f431"; 1203 | @fa-var-zhihu: "\f63f"; 1204 | -------------------------------------------------------------------------------- /libs/font-awesome-5.3.1/less/brands.less: -------------------------------------------------------------------------------- 1 | /*! 2 | * Font Awesome Free 5.3.1 by @fontawesome - https://fontawesome.com 3 | * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) 4 | */ 5 | @import "_variables.less"; 6 | 7 | @font-face { 8 | font-family: 'Font Awesome 5 Brands'; 9 | font-style: normal; 10 | font-weight: normal; 11 | src: url('@{fa-font-path}/fa-brands-400.eot'); 12 | src: url('@{fa-font-path}/fa-brands-400.eot?#iefix') format('embedded-opentype'), 13 | url('@{fa-font-path}/fa-brands-400.woff2') format('woff2'), 14 | url('@{fa-font-path}/fa-brands-400.woff') format('woff'), 15 | url('@{fa-font-path}/fa-brands-400.ttf') format('truetype'), 16 | url('@{fa-font-path}/fa-brands-400.svg#fontawesome') format('svg'); 17 | } 18 | 19 | .fab { 20 | font-family: 'Font Awesome 5 Brands'; 21 | } 22 | -------------------------------------------------------------------------------- /libs/font-awesome-5.3.1/less/fontawesome.less: -------------------------------------------------------------------------------- 1 | /*! 2 | * Font Awesome Free 5.3.1 by @fontawesome - https://fontawesome.com 3 | * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) 4 | */ 5 | @import "_variables.less"; 6 | @import "_mixins.less"; 7 | @import "_core.less"; 8 | @import "_larger.less"; 9 | @import "_fixed-width.less"; 10 | @import "_list.less"; 11 | @import "_bordered-pulled.less"; 12 | @import "_animated.less"; 13 | @import "_rotated-flipped.less"; 14 | @import "_stacked.less"; 15 | @import "_icons.less"; 16 | @import "_screen-reader.less"; 17 | -------------------------------------------------------------------------------- /libs/font-awesome-5.3.1/less/regular.less: -------------------------------------------------------------------------------- 1 | /*! 2 | * Font Awesome Free 5.3.1 by @fontawesome - https://fontawesome.com 3 | * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) 4 | */ 5 | @import "_variables.less"; 6 | 7 | @font-face { 8 | font-family: 'Font Awesome 5 Free'; 9 | font-style: normal; 10 | font-weight: 400; 11 | src: url('@{fa-font-path}/fa-regular-400.eot'); 12 | src: url('@{fa-font-path}/fa-regular-400.eot?#iefix') format('embedded-opentype'), 13 | url('@{fa-font-path}/fa-regular-400.woff2') format('woff2'), 14 | url('@{fa-font-path}/fa-regular-400.woff') format('woff'), 15 | url('@{fa-font-path}/fa-regular-400.ttf') format('truetype'), 16 | url('@{fa-font-path}/fa-regular-400.svg#fontawesome') format('svg'); 17 | } 18 | 19 | .far { 20 | font-family: 'Font Awesome 5 Free'; 21 | font-weight: 400; 22 | } 23 | -------------------------------------------------------------------------------- /libs/font-awesome-5.3.1/less/solid.less: -------------------------------------------------------------------------------- 1 | /*! 2 | * Font Awesome Free 5.3.1 by @fontawesome - https://fontawesome.com 3 | * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) 4 | */ 5 | @import "_variables.less"; 6 | 7 | @font-face { 8 | font-family: 'Font Awesome 5 Free'; 9 | font-style: normal; 10 | font-weight: 900; 11 | src: url('@{fa-font-path}/fa-solid-900.eot'); 12 | src: url('@{fa-font-path}/fa-solid-900.eot?#iefix') format('embedded-opentype'), 13 | url('@{fa-font-path}/fa-solid-900.woff2') format('woff2'), 14 | url('@{fa-font-path}/fa-solid-900.woff') format('woff'), 15 | url('@{fa-font-path}/fa-solid-900.ttf') format('truetype'), 16 | url('@{fa-font-path}/fa-solid-900.svg#fontawesome') format('svg'); 17 | } 18 | 19 | .fa, 20 | .fas { 21 | font-family: 'Font Awesome 5 Free'; 22 | font-weight: 900; 23 | } 24 | -------------------------------------------------------------------------------- /libs/font-awesome-5.3.1/less/v4-shims.less: -------------------------------------------------------------------------------- 1 | /*! 2 | * Font Awesome Free 5.3.1 by @fontawesome - https://fontawesome.com 3 | * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) 4 | */ 5 | @import '_variables.less'; 6 | @import '_shims.less'; 7 | -------------------------------------------------------------------------------- /libs/font-awesome-5.3.1/scss/_animated.scss: -------------------------------------------------------------------------------- 1 | // Animated Icons 2 | // -------------------------- 3 | 4 | .#{$fa-css-prefix}-spin { 5 | animation: fa-spin 2s infinite linear; 6 | } 7 | 8 | .#{$fa-css-prefix}-pulse { 9 | animation: fa-spin 1s infinite steps(8); 10 | } 11 | 12 | @keyframes fa-spin { 13 | 0% { 14 | transform: rotate(0deg); 15 | } 16 | 17 | 100% { 18 | transform: rotate(360deg); 19 | } 20 | } 21 | -------------------------------------------------------------------------------- /libs/font-awesome-5.3.1/scss/_bordered-pulled.scss: -------------------------------------------------------------------------------- 1 | // Bordered & Pulled 2 | // ------------------------- 3 | 4 | .#{$fa-css-prefix}-border { 5 | border: solid .08em $fa-border-color; 6 | border-radius: .1em; 7 | padding: .2em .25em .15em; 8 | } 9 | 10 | .#{$fa-css-prefix}-pull-left { float: left; } 11 | .#{$fa-css-prefix}-pull-right { float: right; } 12 | 13 | .#{$fa-css-prefix}, 14 | .fas, 15 | .far, 16 | .fal, 17 | .fab { 18 | &.#{$fa-css-prefix}-pull-left { margin-right: .3em; } 19 | &.#{$fa-css-prefix}-pull-right { margin-left: .3em; } 20 | } 21 | -------------------------------------------------------------------------------- /libs/font-awesome-5.3.1/scss/_core.scss: -------------------------------------------------------------------------------- 1 | // Base Class Definition 2 | // ------------------------- 3 | 4 | .#{$fa-css-prefix}, 5 | .fas, 6 | .far, 7 | .fal, 8 | .fab { 9 | -moz-osx-font-smoothing: grayscale; 10 | -webkit-font-smoothing: antialiased; 11 | display: inline-block; 12 | font-style: normal; 13 | font-variant: normal; 14 | text-rendering: auto; 15 | line-height: 1; 16 | } 17 | 18 | %fa-icon { 19 | @include fa-icon; 20 | } 21 | -------------------------------------------------------------------------------- /libs/font-awesome-5.3.1/scss/_fixed-width.scss: -------------------------------------------------------------------------------- 1 | // Fixed Width Icons 2 | // ------------------------- 3 | .#{$fa-css-prefix}-fw { 4 | text-align: center; 5 | width: (20em / 16); 6 | } 7 | -------------------------------------------------------------------------------- /libs/font-awesome-5.3.1/scss/_larger.scss: -------------------------------------------------------------------------------- 1 | // Icon Sizes 2 | // ------------------------- 3 | 4 | // makes the font 33% larger relative to the icon container 5 | .#{$fa-css-prefix}-lg { 6 | font-size: (4em / 3); 7 | line-height: (3em / 4); 8 | vertical-align: -.0667em; 9 | } 10 | 11 | .#{$fa-css-prefix}-xs { 12 | font-size: .75em; 13 | } 14 | 15 | .#{$fa-css-prefix}-sm { 16 | font-size: .875em; 17 | } 18 | 19 | @for $i from 1 through 10 { 20 | .#{$fa-css-prefix}-#{$i}x { 21 | font-size: $i * 1em; 22 | } 23 | } 24 | -------------------------------------------------------------------------------- /libs/font-awesome-5.3.1/scss/_list.scss: -------------------------------------------------------------------------------- 1 | // List Icons 2 | // ------------------------- 3 | 4 | .#{$fa-css-prefix}-ul { 5 | list-style-type: none; 6 | margin-left: $fa-li-width * 5/4; 7 | padding-left: 0; 8 | 9 | > li { position: relative; } 10 | } 11 | 12 | .#{$fa-css-prefix}-li { 13 | left: -$fa-li-width; 14 | position: absolute; 15 | text-align: center; 16 | width: $fa-li-width; 17 | line-height: inherit; 18 | } 19 | -------------------------------------------------------------------------------- /libs/font-awesome-5.3.1/scss/_mixins.scss: -------------------------------------------------------------------------------- 1 | // Mixins 2 | // -------------------------- 3 | 4 | @mixin fa-icon { 5 | -webkit-font-smoothing: antialiased; 6 | -moz-osx-font-smoothing: grayscale; 7 | display: inline-block; 8 | font-style: normal; 9 | font-variant: normal; 10 | font-weight: normal; 11 | line-height: 1; 12 | vertical-align: -.125em; 13 | } 14 | 15 | @mixin fa-icon-rotate($degrees, $rotation) { 16 | -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=#{$rotation})"; 17 | transform: rotate($degrees); 18 | } 19 | 20 | @mixin fa-icon-flip($horiz, $vert, $rotation) { 21 | -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=#{$rotation}, mirror=1)"; 22 | transform: scale($horiz, $vert); 23 | } 24 | 25 | 26 | // Only display content to screen readers. A la Bootstrap 4. 27 | // 28 | // See: http://a11yproject.com/posts/how-to-hide-content/ 29 | 30 | @mixin sr-only { 31 | border: 0; 32 | clip: rect(0, 0, 0, 0); 33 | height: 1px; 34 | margin: -1px; 35 | overflow: hidden; 36 | padding: 0; 37 | position: absolute; 38 | width: 1px; 39 | } 40 | 41 | // Use in conjunction with .sr-only to only display content when it's focused. 42 | // 43 | // Useful for "Skip to main content" links; see http://www.w3.org/TR/2013/NOTE-WCAG20-TECHS-20130905/G1 44 | // 45 | // Credit: HTML5 Boilerplate 46 | 47 | @mixin sr-only-focusable { 48 | &:active, 49 | &:focus { 50 | clip: auto; 51 | height: auto; 52 | margin: 0; 53 | overflow: visible; 54 | position: static; 55 | width: auto; 56 | } 57 | } 58 | -------------------------------------------------------------------------------- /libs/font-awesome-5.3.1/scss/_rotated-flipped.scss: -------------------------------------------------------------------------------- 1 | // Rotated & Flipped Icons 2 | // ------------------------- 3 | 4 | .#{$fa-css-prefix}-rotate-90 { @include fa-icon-rotate(90deg, 1); } 5 | .#{$fa-css-prefix}-rotate-180 { @include fa-icon-rotate(180deg, 2); } 6 | .#{$fa-css-prefix}-rotate-270 { @include fa-icon-rotate(270deg, 3); } 7 | 8 | .#{$fa-css-prefix}-flip-horizontal { @include fa-icon-flip(-1, 1, 0); } 9 | .#{$fa-css-prefix}-flip-vertical { @include fa-icon-flip(1, -1, 2); } 10 | .#{$fa-css-prefix}-flip-horizontal.#{$fa-css-prefix}-flip-vertical { @include fa-icon-flip(-1, -1, 2); } 11 | 12 | // Hook for IE8-9 13 | // ------------------------- 14 | 15 | :root { 16 | .#{$fa-css-prefix}-rotate-90, 17 | .#{$fa-css-prefix}-rotate-180, 18 | .#{$fa-css-prefix}-rotate-270, 19 | .#{$fa-css-prefix}-flip-horizontal, 20 | .#{$fa-css-prefix}-flip-vertical { 21 | filter: none; 22 | } 23 | } 24 | -------------------------------------------------------------------------------- /libs/font-awesome-5.3.1/scss/_screen-reader.scss: -------------------------------------------------------------------------------- 1 | // Screen Readers 2 | // ------------------------- 3 | 4 | .sr-only { @include sr-only; } 5 | .sr-only-focusable { @include sr-only-focusable; } 6 | -------------------------------------------------------------------------------- /libs/font-awesome-5.3.1/scss/_stacked.scss: -------------------------------------------------------------------------------- 1 | // Stacked Icons 2 | // ------------------------- 3 | 4 | .#{$fa-css-prefix}-stack { 5 | display: inline-block; 6 | height: 2em; 7 | line-height: 2em; 8 | position: relative; 9 | vertical-align: middle; 10 | width: 2em; 11 | } 12 | 13 | .#{$fa-css-prefix}-stack-1x, 14 | .#{$fa-css-prefix}-stack-2x { 15 | left: 0; 16 | position: absolute; 17 | text-align: center; 18 | width: 100%; 19 | } 20 | 21 | .#{$fa-css-prefix}-stack-1x { 22 | line-height: inherit; 23 | } 24 | 25 | .#{$fa-css-prefix}-stack-2x { 26 | font-size: 2em; 27 | } 28 | 29 | .#{$fa-css-prefix}-inverse { 30 | color: $fa-inverse; 31 | } 32 | -------------------------------------------------------------------------------- /libs/font-awesome-5.3.1/scss/_variables.scss: -------------------------------------------------------------------------------- 1 | // Variables 2 | // -------------------------- 3 | 4 | $fa-font-path: "../webfonts" !default; 5 | $fa-font-size-base: 16px !default; 6 | $fa-css-prefix: fa !default; 7 | $fa-version: "5.3.1" !default; 8 | $fa-border-color: #eee !default; 9 | $fa-inverse: #fff !default; 10 | $fa-li-width: 2em !default; 11 | 12 | // Convenience function used to set content property 13 | @function fa-content($fa-var) { 14 | @return unquote("\"#{ $fa-var }\""); 15 | } 16 | 17 | $fa-var-500px: \f26e; 18 | $fa-var-accessible-icon: \f368; 19 | $fa-var-accusoft: \f369; 20 | $fa-var-ad: \f641; 21 | $fa-var-address-book: \f2b9; 22 | $fa-var-address-card: \f2bb; 23 | $fa-var-adjust: \f042; 24 | $fa-var-adn: \f170; 25 | $fa-var-adversal: \f36a; 26 | $fa-var-affiliatetheme: \f36b; 27 | $fa-var-air-freshener: \f5d0; 28 | $fa-var-algolia: \f36c; 29 | $fa-var-align-center: \f037; 30 | $fa-var-align-justify: \f039; 31 | $fa-var-align-left: \f036; 32 | $fa-var-align-right: \f038; 33 | $fa-var-alipay: \f642; 34 | $fa-var-allergies: \f461; 35 | $fa-var-amazon: \f270; 36 | $fa-var-amazon-pay: \f42c; 37 | $fa-var-ambulance: \f0f9; 38 | $fa-var-american-sign-language-interpreting: \f2a3; 39 | $fa-var-amilia: \f36d; 40 | $fa-var-anchor: \f13d; 41 | $fa-var-android: \f17b; 42 | $fa-var-angellist: \f209; 43 | $fa-var-angle-double-down: \f103; 44 | $fa-var-angle-double-left: \f100; 45 | $fa-var-angle-double-right: \f101; 46 | $fa-var-angle-double-up: \f102; 47 | $fa-var-angle-down: \f107; 48 | $fa-var-angle-left: \f104; 49 | $fa-var-angle-right: \f105; 50 | $fa-var-angle-up: \f106; 51 | $fa-var-angry: \f556; 52 | $fa-var-angrycreative: \f36e; 53 | $fa-var-angular: \f420; 54 | $fa-var-ankh: \f644; 55 | $fa-var-app-store: \f36f; 56 | $fa-var-app-store-ios: \f370; 57 | $fa-var-apper: \f371; 58 | $fa-var-apple: \f179; 59 | $fa-var-apple-alt: \f5d1; 60 | $fa-var-apple-pay: \f415; 61 | $fa-var-archive: \f187; 62 | $fa-var-archway: \f557; 63 | $fa-var-arrow-alt-circle-down: \f358; 64 | $fa-var-arrow-alt-circle-left: \f359; 65 | $fa-var-arrow-alt-circle-right: \f35a; 66 | $fa-var-arrow-alt-circle-up: \f35b; 67 | $fa-var-arrow-circle-down: \f0ab; 68 | $fa-var-arrow-circle-left: \f0a8; 69 | $fa-var-arrow-circle-right: \f0a9; 70 | $fa-var-arrow-circle-up: \f0aa; 71 | $fa-var-arrow-down: \f063; 72 | $fa-var-arrow-left: \f060; 73 | $fa-var-arrow-right: \f061; 74 | $fa-var-arrow-up: \f062; 75 | $fa-var-arrows-alt: \f0b2; 76 | $fa-var-arrows-alt-h: \f337; 77 | $fa-var-arrows-alt-v: \f338; 78 | $fa-var-assistive-listening-systems: \f2a2; 79 | $fa-var-asterisk: \f069; 80 | $fa-var-asymmetrik: \f372; 81 | $fa-var-at: \f1fa; 82 | $fa-var-atlas: \f558; 83 | $fa-var-atom: \f5d2; 84 | $fa-var-audible: \f373; 85 | $fa-var-audio-description: \f29e; 86 | $fa-var-autoprefixer: \f41c; 87 | $fa-var-avianex: \f374; 88 | $fa-var-aviato: \f421; 89 | $fa-var-award: \f559; 90 | $fa-var-aws: \f375; 91 | $fa-var-backspace: \f55a; 92 | $fa-var-backward: \f04a; 93 | $fa-var-balance-scale: \f24e; 94 | $fa-var-ban: \f05e; 95 | $fa-var-band-aid: \f462; 96 | $fa-var-bandcamp: \f2d5; 97 | $fa-var-barcode: \f02a; 98 | $fa-var-bars: \f0c9; 99 | $fa-var-baseball-ball: \f433; 100 | $fa-var-basketball-ball: \f434; 101 | $fa-var-bath: \f2cd; 102 | $fa-var-battery-empty: \f244; 103 | $fa-var-battery-full: \f240; 104 | $fa-var-battery-half: \f242; 105 | $fa-var-battery-quarter: \f243; 106 | $fa-var-battery-three-quarters: \f241; 107 | $fa-var-bed: \f236; 108 | $fa-var-beer: \f0fc; 109 | $fa-var-behance: \f1b4; 110 | $fa-var-behance-square: \f1b5; 111 | $fa-var-bell: \f0f3; 112 | $fa-var-bell-slash: \f1f6; 113 | $fa-var-bezier-curve: \f55b; 114 | $fa-var-bible: \f647; 115 | $fa-var-bicycle: \f206; 116 | $fa-var-bimobject: \f378; 117 | $fa-var-binoculars: \f1e5; 118 | $fa-var-birthday-cake: \f1fd; 119 | $fa-var-bitbucket: \f171; 120 | $fa-var-bitcoin: \f379; 121 | $fa-var-bity: \f37a; 122 | $fa-var-black-tie: \f27e; 123 | $fa-var-blackberry: \f37b; 124 | $fa-var-blender: \f517; 125 | $fa-var-blind: \f29d; 126 | $fa-var-blogger: \f37c; 127 | $fa-var-blogger-b: \f37d; 128 | $fa-var-bluetooth: \f293; 129 | $fa-var-bluetooth-b: \f294; 130 | $fa-var-bold: \f032; 131 | $fa-var-bolt: \f0e7; 132 | $fa-var-bomb: \f1e2; 133 | $fa-var-bone: \f5d7; 134 | $fa-var-bong: \f55c; 135 | $fa-var-book: \f02d; 136 | $fa-var-book-open: \f518; 137 | $fa-var-book-reader: \f5da; 138 | $fa-var-bookmark: \f02e; 139 | $fa-var-bowling-ball: \f436; 140 | $fa-var-box: \f466; 141 | $fa-var-box-open: \f49e; 142 | $fa-var-boxes: \f468; 143 | $fa-var-braille: \f2a1; 144 | $fa-var-brain: \f5dc; 145 | $fa-var-briefcase: \f0b1; 146 | $fa-var-briefcase-medical: \f469; 147 | $fa-var-broadcast-tower: \f519; 148 | $fa-var-broom: \f51a; 149 | $fa-var-brush: \f55d; 150 | $fa-var-btc: \f15a; 151 | $fa-var-bug: \f188; 152 | $fa-var-building: \f1ad; 153 | $fa-var-bullhorn: \f0a1; 154 | $fa-var-bullseye: \f140; 155 | $fa-var-burn: \f46a; 156 | $fa-var-buromobelexperte: \f37f; 157 | $fa-var-bus: \f207; 158 | $fa-var-bus-alt: \f55e; 159 | $fa-var-business-time: \f64a; 160 | $fa-var-buysellads: \f20d; 161 | $fa-var-calculator: \f1ec; 162 | $fa-var-calendar: \f133; 163 | $fa-var-calendar-alt: \f073; 164 | $fa-var-calendar-check: \f274; 165 | $fa-var-calendar-minus: \f272; 166 | $fa-var-calendar-plus: \f271; 167 | $fa-var-calendar-times: \f273; 168 | $fa-var-camera: \f030; 169 | $fa-var-camera-retro: \f083; 170 | $fa-var-cannabis: \f55f; 171 | $fa-var-capsules: \f46b; 172 | $fa-var-car: \f1b9; 173 | $fa-var-car-alt: \f5de; 174 | $fa-var-car-battery: \f5df; 175 | $fa-var-car-crash: \f5e1; 176 | $fa-var-car-side: \f5e4; 177 | $fa-var-caret-down: \f0d7; 178 | $fa-var-caret-left: \f0d9; 179 | $fa-var-caret-right: \f0da; 180 | $fa-var-caret-square-down: \f150; 181 | $fa-var-caret-square-left: \f191; 182 | $fa-var-caret-square-right: \f152; 183 | $fa-var-caret-square-up: \f151; 184 | $fa-var-caret-up: \f0d8; 185 | $fa-var-cart-arrow-down: \f218; 186 | $fa-var-cart-plus: \f217; 187 | $fa-var-cc-amazon-pay: \f42d; 188 | $fa-var-cc-amex: \f1f3; 189 | $fa-var-cc-apple-pay: \f416; 190 | $fa-var-cc-diners-club: \f24c; 191 | $fa-var-cc-discover: \f1f2; 192 | $fa-var-cc-jcb: \f24b; 193 | $fa-var-cc-mastercard: \f1f1; 194 | $fa-var-cc-paypal: \f1f4; 195 | $fa-var-cc-stripe: \f1f5; 196 | $fa-var-cc-visa: \f1f0; 197 | $fa-var-centercode: \f380; 198 | $fa-var-certificate: \f0a3; 199 | $fa-var-chalkboard: \f51b; 200 | $fa-var-chalkboard-teacher: \f51c; 201 | $fa-var-charging-station: \f5e7; 202 | $fa-var-chart-area: \f1fe; 203 | $fa-var-chart-bar: \f080; 204 | $fa-var-chart-line: \f201; 205 | $fa-var-chart-pie: \f200; 206 | $fa-var-check: \f00c; 207 | $fa-var-check-circle: \f058; 208 | $fa-var-check-double: \f560; 209 | $fa-var-check-square: \f14a; 210 | $fa-var-chess: \f439; 211 | $fa-var-chess-bishop: \f43a; 212 | $fa-var-chess-board: \f43c; 213 | $fa-var-chess-king: \f43f; 214 | $fa-var-chess-knight: \f441; 215 | $fa-var-chess-pawn: \f443; 216 | $fa-var-chess-queen: \f445; 217 | $fa-var-chess-rook: \f447; 218 | $fa-var-chevron-circle-down: \f13a; 219 | $fa-var-chevron-circle-left: \f137; 220 | $fa-var-chevron-circle-right: \f138; 221 | $fa-var-chevron-circle-up: \f139; 222 | $fa-var-chevron-down: \f078; 223 | $fa-var-chevron-left: \f053; 224 | $fa-var-chevron-right: \f054; 225 | $fa-var-chevron-up: \f077; 226 | $fa-var-child: \f1ae; 227 | $fa-var-chrome: \f268; 228 | $fa-var-church: \f51d; 229 | $fa-var-circle: \f111; 230 | $fa-var-circle-notch: \f1ce; 231 | $fa-var-city: \f64f; 232 | $fa-var-clipboard: \f328; 233 | $fa-var-clipboard-check: \f46c; 234 | $fa-var-clipboard-list: \f46d; 235 | $fa-var-clock: \f017; 236 | $fa-var-clone: \f24d; 237 | $fa-var-closed-captioning: \f20a; 238 | $fa-var-cloud: \f0c2; 239 | $fa-var-cloud-download-alt: \f381; 240 | $fa-var-cloud-upload-alt: \f382; 241 | $fa-var-cloudscale: \f383; 242 | $fa-var-cloudsmith: \f384; 243 | $fa-var-cloudversify: \f385; 244 | $fa-var-cocktail: \f561; 245 | $fa-var-code: \f121; 246 | $fa-var-code-branch: \f126; 247 | $fa-var-codepen: \f1cb; 248 | $fa-var-codiepie: \f284; 249 | $fa-var-coffee: \f0f4; 250 | $fa-var-cog: \f013; 251 | $fa-var-cogs: \f085; 252 | $fa-var-coins: \f51e; 253 | $fa-var-columns: \f0db; 254 | $fa-var-comment: \f075; 255 | $fa-var-comment-alt: \f27a; 256 | $fa-var-comment-dollar: \f651; 257 | $fa-var-comment-dots: \f4ad; 258 | $fa-var-comment-slash: \f4b3; 259 | $fa-var-comments: \f086; 260 | $fa-var-comments-dollar: \f653; 261 | $fa-var-compact-disc: \f51f; 262 | $fa-var-compass: \f14e; 263 | $fa-var-compress: \f066; 264 | $fa-var-concierge-bell: \f562; 265 | $fa-var-connectdevelop: \f20e; 266 | $fa-var-contao: \f26d; 267 | $fa-var-cookie: \f563; 268 | $fa-var-cookie-bite: \f564; 269 | 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| $fa-var-magnet: \f076; 667 | $fa-var-mail-bulk: \f674; 668 | $fa-var-mailchimp: \f59e; 669 | $fa-var-male: \f183; 670 | $fa-var-mandalorian: \f50f; 671 | $fa-var-map: \f279; 672 | $fa-var-map-marked: \f59f; 673 | $fa-var-map-marked-alt: \f5a0; 674 | $fa-var-map-marker: \f041; 675 | $fa-var-map-marker-alt: \f3c5; 676 | $fa-var-map-pin: \f276; 677 | $fa-var-map-signs: \f277; 678 | $fa-var-markdown: \f60f; 679 | $fa-var-marker: \f5a1; 680 | $fa-var-mars: \f222; 681 | $fa-var-mars-double: \f227; 682 | $fa-var-mars-stroke: \f229; 683 | $fa-var-mars-stroke-h: \f22b; 684 | $fa-var-mars-stroke-v: \f22a; 685 | $fa-var-mastodon: \f4f6; 686 | $fa-var-maxcdn: \f136; 687 | $fa-var-medal: \f5a2; 688 | $fa-var-medapps: \f3c6; 689 | $fa-var-medium: \f23a; 690 | $fa-var-medium-m: \f3c7; 691 | $fa-var-medkit: \f0fa; 692 | $fa-var-medrt: \f3c8; 693 | $fa-var-meetup: \f2e0; 694 | $fa-var-megaport: \f5a3; 695 | $fa-var-meh: \f11a; 696 | $fa-var-meh-blank: \f5a4; 697 | $fa-var-meh-rolling-eyes: \f5a5; 698 | $fa-var-memory: \f538; 699 | $fa-var-menorah: \f676; 700 | $fa-var-mercury: \f223; 701 | $fa-var-microchip: \f2db; 702 | $fa-var-microphone: \f130; 703 | $fa-var-microphone-alt: \f3c9; 704 | $fa-var-microphone-alt-slash: \f539; 705 | $fa-var-microphone-slash: \f131; 706 | $fa-var-microscope: \f610; 707 | $fa-var-microsoft: \f3ca; 708 | $fa-var-minus: \f068; 709 | $fa-var-minus-circle: \f056; 710 | $fa-var-minus-square: \f146; 711 | $fa-var-mix: \f3cb; 712 | $fa-var-mixcloud: \f289; 713 | $fa-var-mizuni: \f3cc; 714 | $fa-var-mobile: \f10b; 715 | $fa-var-mobile-alt: \f3cd; 716 | $fa-var-modx: \f285; 717 | $fa-var-monero: \f3d0; 718 | $fa-var-money-bill: \f0d6; 719 | $fa-var-money-bill-alt: \f3d1; 720 | $fa-var-money-bill-wave: \f53a; 721 | $fa-var-money-bill-wave-alt: \f53b; 722 | $fa-var-money-check: \f53c; 723 | $fa-var-money-check-alt: \f53d; 724 | $fa-var-monument: \f5a6; 725 | $fa-var-moon: \f186; 726 | $fa-var-mortar-pestle: \f5a7; 727 | $fa-var-mosque: \f678; 728 | $fa-var-motorcycle: \f21c; 729 | $fa-var-mouse-pointer: \f245; 730 | $fa-var-music: \f001; 731 | $fa-var-napster: \f3d2; 732 | $fa-var-neos: \f612; 733 | $fa-var-neuter: \f22c; 734 | $fa-var-newspaper: \f1ea; 735 | $fa-var-nimblr: \f5a8; 736 | $fa-var-nintendo-switch: \f418; 737 | $fa-var-node: \f419; 738 | $fa-var-node-js: \f3d3; 739 | $fa-var-not-equal: \f53e; 740 | $fa-var-notes-medical: \f481; 741 | $fa-var-npm: \f3d4; 742 | $fa-var-ns8: \f3d5; 743 | $fa-var-nutritionix: \f3d6; 744 | $fa-var-object-group: \f247; 745 | $fa-var-object-ungroup: \f248; 746 | $fa-var-odnoklassniki: \f263; 747 | $fa-var-odnoklassniki-square: \f264; 748 | $fa-var-oil-can: \f613; 749 | $fa-var-old-republic: \f510; 750 | $fa-var-om: \f679; 751 | $fa-var-opencart: \f23d; 752 | $fa-var-openid: \f19b; 753 | $fa-var-opera: \f26a; 754 | $fa-var-optin-monster: \f23c; 755 | $fa-var-osi: \f41a; 756 | $fa-var-outdent: \f03b; 757 | $fa-var-page4: \f3d7; 758 | $fa-var-pagelines: \f18c; 759 | $fa-var-paint-brush: \f1fc; 760 | $fa-var-paint-roller: \f5aa; 761 | $fa-var-palette: \f53f; 762 | $fa-var-palfed: \f3d8; 763 | $fa-var-pallet: \f482; 764 | $fa-var-paper-plane: \f1d8; 765 | $fa-var-paperclip: \f0c6; 766 | $fa-var-parachute-box: \f4cd; 767 | $fa-var-paragraph: \f1dd; 768 | $fa-var-parking: \f540; 769 | $fa-var-passport: \f5ab; 770 | $fa-var-pastafarianism: \f67b; 771 | $fa-var-paste: \f0ea; 772 | $fa-var-patreon: \f3d9; 773 | $fa-var-pause: \f04c; 774 | $fa-var-pause-circle: \f28b; 775 | $fa-var-paw: \f1b0; 776 | $fa-var-paypal: \f1ed; 777 | $fa-var-peace: \f67c; 778 | $fa-var-pen: \f304; 779 | $fa-var-pen-alt: \f305; 780 | $fa-var-pen-fancy: \f5ac; 781 | $fa-var-pen-nib: \f5ad; 782 | $fa-var-pen-square: \f14b; 783 | $fa-var-pencil-alt: \f303; 784 | $fa-var-pencil-ruler: \f5ae; 785 | $fa-var-people-carry: \f4ce; 786 | $fa-var-percent: \f295; 787 | $fa-var-percentage: \f541; 788 | $fa-var-periscope: \f3da; 789 | $fa-var-phabricator: \f3db; 790 | $fa-var-phoenix-framework: \f3dc; 791 | $fa-var-phoenix-squadron: \f511; 792 | $fa-var-phone: \f095; 793 | $fa-var-phone-slash: \f3dd; 794 | $fa-var-phone-square: \f098; 795 | $fa-var-phone-volume: \f2a0; 796 | $fa-var-php: \f457; 797 | $fa-var-pied-piper: \f2ae; 798 | $fa-var-pied-piper-alt: \f1a8; 799 | $fa-var-pied-piper-hat: \f4e5; 800 | $fa-var-pied-piper-pp: \f1a7; 801 | $fa-var-piggy-bank: \f4d3; 802 | $fa-var-pills: \f484; 803 | $fa-var-pinterest: \f0d2; 804 | $fa-var-pinterest-p: \f231; 805 | $fa-var-pinterest-square: \f0d3; 806 | $fa-var-place-of-worship: \f67f; 807 | $fa-var-plane: \f072; 808 | $fa-var-plane-arrival: \f5af; 809 | $fa-var-plane-departure: \f5b0; 810 | $fa-var-play: \f04b; 811 | $fa-var-play-circle: \f144; 812 | $fa-var-playstation: \f3df; 813 | $fa-var-plug: \f1e6; 814 | $fa-var-plus: \f067; 815 | $fa-var-plus-circle: \f055; 816 | $fa-var-plus-square: \f0fe; 817 | $fa-var-podcast: \f2ce; 818 | $fa-var-poll: \f681; 819 | $fa-var-poll-h: \f682; 820 | $fa-var-poo: \f2fe; 821 | $fa-var-poop: \f619; 822 | $fa-var-portrait: \f3e0; 823 | $fa-var-pound-sign: \f154; 824 | $fa-var-power-off: \f011; 825 | $fa-var-pray: \f683; 826 | $fa-var-praying-hands: \f684; 827 | $fa-var-prescription: \f5b1; 828 | $fa-var-prescription-bottle: \f485; 829 | $fa-var-prescription-bottle-alt: \f486; 830 | $fa-var-print: \f02f; 831 | $fa-var-procedures: \f487; 832 | $fa-var-product-hunt: \f288; 833 | $fa-var-project-diagram: \f542; 834 | $fa-var-pushed: \f3e1; 835 | $fa-var-puzzle-piece: \f12e; 836 | $fa-var-python: \f3e2; 837 | $fa-var-qq: \f1d6; 838 | $fa-var-qrcode: \f029; 839 | $fa-var-question: \f128; 840 | $fa-var-question-circle: \f059; 841 | $fa-var-quidditch: \f458; 842 | $fa-var-quinscape: \f459; 843 | $fa-var-quora: \f2c4; 844 | $fa-var-quote-left: \f10d; 845 | $fa-var-quote-right: \f10e; 846 | $fa-var-quran: \f687; 847 | $fa-var-r-project: \f4f7; 848 | $fa-var-random: \f074; 849 | $fa-var-ravelry: \f2d9; 850 | $fa-var-react: \f41b; 851 | $fa-var-readme: \f4d5; 852 | $fa-var-rebel: \f1d0; 853 | $fa-var-receipt: \f543; 854 | $fa-var-recycle: \f1b8; 855 | $fa-var-red-river: \f3e3; 856 | $fa-var-reddit: \f1a1; 857 | $fa-var-reddit-alien: \f281; 858 | $fa-var-reddit-square: \f1a2; 859 | $fa-var-redo: \f01e; 860 | $fa-var-redo-alt: \f2f9; 861 | $fa-var-registered: \f25d; 862 | $fa-var-rendact: \f3e4; 863 | $fa-var-renren: \f18b; 864 | $fa-var-reply: \f3e5; 865 | $fa-var-reply-all: \f122; 866 | $fa-var-replyd: \f3e6; 867 | $fa-var-researchgate: \f4f8; 868 | $fa-var-resolving: \f3e7; 869 | $fa-var-retweet: \f079; 870 | $fa-var-rev: \f5b2; 871 | $fa-var-ribbon: \f4d6; 872 | $fa-var-road: \f018; 873 | $fa-var-robot: \f544; 874 | $fa-var-rocket: \f135; 875 | $fa-var-rocketchat: \f3e8; 876 | $fa-var-rockrms: \f3e9; 877 | $fa-var-route: \f4d7; 878 | $fa-var-rss: \f09e; 879 | $fa-var-rss-square: \f143; 880 | $fa-var-ruble-sign: \f158; 881 | $fa-var-ruler: \f545; 882 | $fa-var-ruler-combined: \f546; 883 | $fa-var-ruler-horizontal: \f547; 884 | $fa-var-ruler-vertical: \f548; 885 | $fa-var-rupee-sign: \f156; 886 | $fa-var-sad-cry: \f5b3; 887 | $fa-var-sad-tear: \f5b4; 888 | $fa-var-safari: \f267; 889 | $fa-var-sass: \f41e; 890 | $fa-var-save: \f0c7; 891 | $fa-var-schlix: \f3ea; 892 | $fa-var-school: \f549; 893 | $fa-var-screwdriver: \f54a; 894 | $fa-var-scribd: \f28a; 895 | $fa-var-search: \f002; 896 | $fa-var-search-dollar: \f688; 897 | $fa-var-search-location: \f689; 898 | $fa-var-search-minus: \f010; 899 | $fa-var-search-plus: \f00e; 900 | $fa-var-searchengin: \f3eb; 901 | $fa-var-seedling: \f4d8; 902 | $fa-var-sellcast: \f2da; 903 | $fa-var-sellsy: \f213; 904 | $fa-var-server: \f233; 905 | $fa-var-servicestack: \f3ec; 906 | $fa-var-shapes: \f61f; 907 | $fa-var-share: \f064; 908 | $fa-var-share-alt: \f1e0; 909 | $fa-var-share-alt-square: \f1e1; 910 | $fa-var-share-square: \f14d; 911 | $fa-var-shekel-sign: \f20b; 912 | $fa-var-shield-alt: \f3ed; 913 | $fa-var-ship: \f21a; 914 | $fa-var-shipping-fast: \f48b; 915 | $fa-var-shirtsinbulk: \f214; 916 | $fa-var-shoe-prints: \f54b; 917 | $fa-var-shopping-bag: \f290; 918 | $fa-var-shopping-basket: \f291; 919 | $fa-var-shopping-cart: \f07a; 920 | $fa-var-shopware: \f5b5; 921 | $fa-var-shower: \f2cc; 922 | $fa-var-shuttle-van: \f5b6; 923 | $fa-var-sign: \f4d9; 924 | $fa-var-sign-in-alt: \f2f6; 925 | $fa-var-sign-language: \f2a7; 926 | $fa-var-sign-out-alt: \f2f5; 927 | $fa-var-signal: \f012; 928 | $fa-var-signature: \f5b7; 929 | $fa-var-simplybuilt: \f215; 930 | $fa-var-sistrix: \f3ee; 931 | $fa-var-sitemap: \f0e8; 932 | $fa-var-sith: \f512; 933 | $fa-var-skull: \f54c; 934 | $fa-var-skyatlas: \f216; 935 | $fa-var-skype: \f17e; 936 | $fa-var-slack: \f198; 937 | $fa-var-slack-hash: \f3ef; 938 | $fa-var-sliders-h: \f1de; 939 | $fa-var-slideshare: \f1e7; 940 | $fa-var-smile: \f118; 941 | $fa-var-smile-beam: \f5b8; 942 | $fa-var-smile-wink: \f4da; 943 | $fa-var-smoking: \f48d; 944 | $fa-var-smoking-ban: \f54d; 945 | $fa-var-snapchat: \f2ab; 946 | $fa-var-snapchat-ghost: \f2ac; 947 | $fa-var-snapchat-square: \f2ad; 948 | $fa-var-snowflake: \f2dc; 949 | $fa-var-socks: \f696; 950 | $fa-var-solar-panel: \f5ba; 951 | $fa-var-sort: \f0dc; 952 | $fa-var-sort-alpha-down: \f15d; 953 | $fa-var-sort-alpha-up: \f15e; 954 | $fa-var-sort-amount-down: \f160; 955 | $fa-var-sort-amount-up: \f161; 956 | $fa-var-sort-down: \f0dd; 957 | $fa-var-sort-numeric-down: \f162; 958 | $fa-var-sort-numeric-up: \f163; 959 | $fa-var-sort-up: \f0de; 960 | $fa-var-soundcloud: \f1be; 961 | $fa-var-spa: \f5bb; 962 | $fa-var-space-shuttle: \f197; 963 | $fa-var-speakap: \f3f3; 964 | $fa-var-spinner: \f110; 965 | $fa-var-splotch: \f5bc; 966 | $fa-var-spotify: \f1bc; 967 | $fa-var-spray-can: \f5bd; 968 | $fa-var-square: \f0c8; 969 | $fa-var-square-full: \f45c; 970 | $fa-var-square-root-alt: \f698; 971 | $fa-var-squarespace: \f5be; 972 | $fa-var-stack-exchange: \f18d; 973 | $fa-var-stack-overflow: \f16c; 974 | $fa-var-stamp: \f5bf; 975 | $fa-var-star: \f005; 976 | $fa-var-star-and-crescent: \f699; 977 | $fa-var-star-half: \f089; 978 | $fa-var-star-half-alt: \f5c0; 979 | $fa-var-star-of-david: \f69a; 980 | $fa-var-star-of-life: \f621; 981 | $fa-var-staylinked: \f3f5; 982 | $fa-var-steam: \f1b6; 983 | $fa-var-steam-square: \f1b7; 984 | $fa-var-steam-symbol: \f3f6; 985 | $fa-var-step-backward: \f048; 986 | $fa-var-step-forward: \f051; 987 | $fa-var-stethoscope: \f0f1; 988 | $fa-var-sticker-mule: \f3f7; 989 | $fa-var-sticky-note: \f249; 990 | $fa-var-stop: \f04d; 991 | $fa-var-stop-circle: \f28d; 992 | $fa-var-stopwatch: \f2f2; 993 | $fa-var-store: \f54e; 994 | $fa-var-store-alt: \f54f; 995 | $fa-var-strava: \f428; 996 | $fa-var-stream: \f550; 997 | $fa-var-street-view: \f21d; 998 | $fa-var-strikethrough: \f0cc; 999 | $fa-var-stripe: \f429; 1000 | $fa-var-stripe-s: \f42a; 1001 | $fa-var-stroopwafel: \f551; 1002 | $fa-var-studiovinari: \f3f8; 1003 | $fa-var-stumbleupon: \f1a4; 1004 | $fa-var-stumbleupon-circle: \f1a3; 1005 | $fa-var-subscript: \f12c; 1006 | $fa-var-subway: \f239; 1007 | $fa-var-suitcase: \f0f2; 1008 | $fa-var-suitcase-rolling: \f5c1; 1009 | $fa-var-sun: \f185; 1010 | $fa-var-superpowers: \f2dd; 1011 | $fa-var-superscript: \f12b; 1012 | $fa-var-supple: \f3f9; 1013 | $fa-var-surprise: \f5c2; 1014 | $fa-var-swatchbook: \f5c3; 1015 | $fa-var-swimmer: \f5c4; 1016 | $fa-var-swimming-pool: \f5c5; 1017 | $fa-var-synagogue: \f69b; 1018 | $fa-var-sync: \f021; 1019 | $fa-var-sync-alt: \f2f1; 1020 | $fa-var-syringe: \f48e; 1021 | $fa-var-table: \f0ce; 1022 | $fa-var-table-tennis: \f45d; 1023 | $fa-var-tablet: \f10a; 1024 | $fa-var-tablet-alt: \f3fa; 1025 | $fa-var-tablets: \f490; 1026 | $fa-var-tachometer-alt: \f3fd; 1027 | $fa-var-tag: \f02b; 1028 | $fa-var-tags: \f02c; 1029 | $fa-var-tape: \f4db; 1030 | $fa-var-tasks: \f0ae; 1031 | $fa-var-taxi: \f1ba; 1032 | $fa-var-teamspeak: \f4f9; 1033 | $fa-var-teeth: \f62e; 1034 | $fa-var-teeth-open: \f62f; 1035 | $fa-var-telegram: \f2c6; 1036 | $fa-var-telegram-plane: \f3fe; 1037 | $fa-var-tencent-weibo: \f1d5; 1038 | $fa-var-terminal: \f120; 1039 | $fa-var-text-height: \f034; 1040 | $fa-var-text-width: \f035; 1041 | $fa-var-th: \f00a; 1042 | $fa-var-th-large: \f009; 1043 | $fa-var-th-list: \f00b; 1044 | $fa-var-the-red-yeti: \f69d; 1045 | $fa-var-theater-masks: \f630; 1046 | $fa-var-themeco: \f5c6; 1047 | $fa-var-themeisle: \f2b2; 1048 | $fa-var-thermometer: \f491; 1049 | $fa-var-thermometer-empty: \f2cb; 1050 | $fa-var-thermometer-full: \f2c7; 1051 | $fa-var-thermometer-half: \f2c9; 1052 | $fa-var-thermometer-quarter: \f2ca; 1053 | $fa-var-thermometer-three-quarters: \f2c8; 1054 | $fa-var-thumbs-down: \f165; 1055 | $fa-var-thumbs-up: \f164; 1056 | $fa-var-thumbtack: \f08d; 1057 | $fa-var-ticket-alt: \f3ff; 1058 | $fa-var-times: \f00d; 1059 | $fa-var-times-circle: \f057; 1060 | $fa-var-tint: \f043; 1061 | $fa-var-tint-slash: \f5c7; 1062 | $fa-var-tired: \f5c8; 1063 | $fa-var-toggle-off: \f204; 1064 | $fa-var-toggle-on: \f205; 1065 | $fa-var-toolbox: \f552; 1066 | $fa-var-tooth: \f5c9; 1067 | 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\f411; 1189 | $fa-var-wpbeginner: \f297; 1190 | $fa-var-wpexplorer: \f2de; 1191 | $fa-var-wpforms: \f298; 1192 | $fa-var-wrench: \f0ad; 1193 | $fa-var-x-ray: \f497; 1194 | $fa-var-xbox: \f412; 1195 | $fa-var-xing: \f168; 1196 | $fa-var-xing-square: \f169; 1197 | $fa-var-y-combinator: \f23b; 1198 | $fa-var-yahoo: \f19e; 1199 | $fa-var-yandex: \f413; 1200 | $fa-var-yandex-international: \f414; 1201 | $fa-var-yelp: \f1e9; 1202 | $fa-var-yen-sign: \f157; 1203 | $fa-var-yin-yang: \f6ad; 1204 | $fa-var-yoast: \f2b1; 1205 | $fa-var-youtube: \f167; 1206 | $fa-var-youtube-square: \f431; 1207 | $fa-var-zhihu: \f63f; 1208 | -------------------------------------------------------------------------------- /libs/font-awesome-5.3.1/scss/brands.scss: -------------------------------------------------------------------------------- 1 | /*! 2 | * Font Awesome Free 5.3.1 by @fontawesome - https://fontawesome.com 3 | * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) 4 | */ 5 | @import 'variables'; 6 | 7 | @font-face { 8 | font-family: 'Font Awesome 5 Brands'; 9 | font-style: normal; 10 | font-weight: normal; 11 | src: url('#{$fa-font-path}/fa-brands-400.eot'); 12 | src: url('#{$fa-font-path}/fa-brands-400.eot?#iefix') format('embedded-opentype'), 13 | url('#{$fa-font-path}/fa-brands-400.woff2') format('woff2'), 14 | url('#{$fa-font-path}/fa-brands-400.woff') format('woff'), 15 | url('#{$fa-font-path}/fa-brands-400.ttf') format('truetype'), 16 | url('#{$fa-font-path}/fa-brands-400.svg#fontawesome') format('svg'); 17 | } 18 | 19 | .fab { 20 | font-family: 'Font Awesome 5 Brands'; 21 | } 22 | -------------------------------------------------------------------------------- /libs/font-awesome-5.3.1/scss/fontawesome.scss: -------------------------------------------------------------------------------- 1 | /*! 2 | * Font Awesome Free 5.3.1 by @fontawesome - https://fontawesome.com 3 | * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) 4 | */ 5 | @import 'variables'; 6 | @import 'mixins'; 7 | @import 'core'; 8 | @import 'larger'; 9 | @import 'fixed-width'; 10 | @import 'list'; 11 | @import 'bordered-pulled'; 12 | @import 'animated'; 13 | @import 'rotated-flipped'; 14 | @import 'stacked'; 15 | @import 'icons'; 16 | @import 'screen-reader'; 17 | -------------------------------------------------------------------------------- /libs/font-awesome-5.3.1/scss/regular.scss: -------------------------------------------------------------------------------- 1 | /*! 2 | * Font Awesome Free 5.3.1 by @fontawesome - https://fontawesome.com 3 | * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) 4 | */ 5 | @import 'variables'; 6 | 7 | @font-face { 8 | font-family: 'Font Awesome 5 Free'; 9 | font-style: normal; 10 | font-weight: 400; 11 | src: url('#{$fa-font-path}/fa-regular-400.eot'); 12 | src: url('#{$fa-font-path}/fa-regular-400.eot?#iefix') format('embedded-opentype'), 13 | url('#{$fa-font-path}/fa-regular-400.woff2') format('woff2'), 14 | url('#{$fa-font-path}/fa-regular-400.woff') format('woff'), 15 | url('#{$fa-font-path}/fa-regular-400.ttf') format('truetype'), 16 | url('#{$fa-font-path}/fa-regular-400.svg#fontawesome') format('svg'); 17 | } 18 | 19 | .far { 20 | font-family: 'Font Awesome 5 Free'; 21 | font-weight: 400; 22 | } 23 | -------------------------------------------------------------------------------- /libs/font-awesome-5.3.1/scss/solid.scss: -------------------------------------------------------------------------------- 1 | /*! 2 | * Font Awesome Free 5.3.1 by @fontawesome - https://fontawesome.com 3 | * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) 4 | */ 5 | @import 'variables'; 6 | 7 | @font-face { 8 | font-family: 'Font Awesome 5 Free'; 9 | font-style: normal; 10 | font-weight: 900; 11 | src: url('#{$fa-font-path}/fa-solid-900.eot'); 12 | src: url('#{$fa-font-path}/fa-solid-900.eot?#iefix') format('embedded-opentype'), 13 | url('#{$fa-font-path}/fa-solid-900.woff2') format('woff2'), 14 | url('#{$fa-font-path}/fa-solid-900.woff') format('woff'), 15 | url('#{$fa-font-path}/fa-solid-900.ttf') format('truetype'), 16 | url('#{$fa-font-path}/fa-solid-900.svg#fontawesome') format('svg'); 17 | } 18 | 19 | .fa, 20 | .fas { 21 | font-family: 'Font Awesome 5 Free'; 22 | font-weight: 900; 23 | } 24 | -------------------------------------------------------------------------------- /libs/font-awesome-5.3.1/scss/v4-shims.scss: -------------------------------------------------------------------------------- 1 | /*! 2 | * Font Awesome Free 5.3.1 by @fontawesome - https://fontawesome.com 3 | * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) 4 | */ 5 | @import 'variables'; 6 | @import 'shims'; 7 | 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color: rgb(249, 38, 114); 3 | text-decoration: none; 4 | } 5 | .footnote { 6 | position: absolute; 7 | bottom: 3em; 8 | padding-right: 4em; 9 | font-size: 90%; 10 | } 11 | .remark-code-line-highlighted { background-color: #ffff88; } 12 | 13 | .inverse { 14 | background-color: #272822; 15 | color: #d6d6d6; 16 | text-shadow: 0 0 20px #333; 17 | } 18 | .inverse h1, .inverse h2, .inverse h3 { 19 | color: #f3f3f3; 20 | } 21 | /* Two-column layout */ 22 | .left-column { 23 | color: #777; 24 | width: 20%; 25 | height: 92%; 26 | float: left; 27 | } 28 | .left-column h2:last-of-type, .left-column h3:last-child { 29 | color: #000; 30 | } 31 | .right-column { 32 | width: 75%; 33 | float: right; 34 | padding-top: 1em; 35 | } 36 | .pull-left { 37 | float: left; 38 | width: 47%; 39 | } 40 | .pull-right { 41 | float: right; 42 | width: 47%; 43 | } 44 | .pull-right ~ * { 45 | clear: both; 46 | } 47 | img, video, iframe { 48 | max-width: 100%; 49 | } 50 | blockquote { 51 | border-left: solid 5px lightgray; 52 | padding-left: 1em; 53 | } 54 | .remark-slide table { 55 | margin: auto; 56 | border-top: 1px solid #666; 57 | border-bottom: 1px solid #666; 58 | } 59 | .remark-slide table thead th { border-bottom: 1px solid #ddd; } 60 | th, td { padding: 5px; } 61 | .remark-slide thead, .remark-slide tfoot, .remark-slide tr:nth-child(even) { background: #eee } 62 | 63 | @page { margin: 0; } 64 | @media print { 65 | .remark-slide-scaler { 66 | width: 100% !important; 67 | height: 100% !important; 68 | transform: scale(1) !important; 69 | top: 0 !important; 70 | left: 0 !important; 71 | } 72 | } 73 | -------------------------------------------------------------------------------- /macros.js: -------------------------------------------------------------------------------- 1 | remark.macros.scale = function(w) { 2 | var url = this; 3 | return ''; 4 | }; 5 | -------------------------------------------------------------------------------- /text_modeling.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Predictive modeling with text using tidy data principles" 3 | author: "Julia Silge & Emil Hvitfeldt" 4 | date: "2019-7-24" 5 | output: html_document 6 | --- 7 | 8 | ```{r setup, include=FALSE} 9 | knitr::opts_chunk$set(echo = TRUE) 10 | ``` 11 | 12 | For [this predictive modeling analysis](https://youtu.be/Sz8RB_fPYOk), you will need to install up-to-date versions of 13 | 14 | - tidyverse 15 | - tidymodels 16 | - textrecipes 17 | - stopwords 18 | - vip 19 | 20 | ## Read in the data 21 | 22 | ```{r} 23 | library(tidyverse) 24 | 25 | set.seed(123) 26 | complaints <- read_csv("data/complaints.csv.gz") %>% 27 | slice_sample(prop = 0.1) ## try with more data if you like, but training will take longer 28 | 29 | names(complaints) 30 | ``` 31 | 32 | What do some example complaints look like? 33 | 34 | ```{r} 35 | complaints %>% 36 | sample_n(10) %>% 37 | pull(consumer_complaint_narrative) 38 | ``` 39 | 40 | How is the `product` variable distributed? 41 | 42 | ```{r} 43 | complaints %>% 44 | mutate(product = str_wrap(product, 50), 45 | product = fct_rev(fct_infreq(factor(product)))) %>% 46 | ggplot(aes(y = product)) + 47 | geom_bar() + 48 | labs(x = NULL, y = NULL) 49 | ``` 50 | 51 | Try this yourself with some of the other variables like `tag`! 52 | 53 | 54 | ## Create a binary classification outcome 55 | 56 | ```{r} 57 | credit <- "Credit reporting, credit repair services, or other personal consumer reports" 58 | 59 | complaints2class <- complaints %>% 60 | mutate(product = factor(if_else( 61 | condition = product == credit, 62 | true = "Credit", 63 | false = "Other"))) %>% 64 | rename(text = consumer_complaint_narrative) 65 | ``` 66 | 67 | 68 | ## Data splitting 69 | 70 | Let's split our data into **testing** and **training** sets. 71 | 72 | ```{r} 73 | library(tidymodels) 74 | set.seed(1234) 75 | 76 | complaints_split <- initial_split(complaints2class, strata = product) 77 | 78 | complaints_train <- training(complaints_split) 79 | complaints_test <- testing(complaints_split) 80 | ``` 81 | 82 | 83 | ## Data preprocessing 84 | 85 | This is the same as feature engineering. 86 | 87 | ```{r} 88 | library(textrecipes) 89 | 90 | complaints_rec <- 91 | recipe(product ~ date_received + tags + text, 92 | data = complaints_train 93 | ) %>% 94 | step_date(date_received, features = c("month", "dow"), role = "dates") %>% 95 | step_rm(date_received) %>% 96 | step_dummy(has_role("dates")) %>% 97 | step_unknown(tags) %>% 98 | step_dummy(tags) %>% 99 | step_tokenize(text) %>% 100 | step_stopwords(text) %>% 101 | step_ngram(text, num_tokens = 2, min_num_tokens = 1) %>% 102 | step_tokenfilter(text, max_tokens = tune(), min_times = 5) %>% 103 | step_tfidf(text) 104 | 105 | complaints_rec 106 | ``` 107 | 108 | Notice `max_tokens = tune()`, i.e. we are tuning the number of tokens being used in the model. 109 | 110 | ## Model specification 111 | 112 | 113 | ```{r} 114 | lasso_spec <- logistic_reg(penalty = tune(), mixture = 1) %>% 115 | set_mode("classification") %>% 116 | set_engine("glmnet") 117 | 118 | lasso_spec 119 | ``` 120 | 121 | If we had used `mixture = 0`, we would have a ridge model. With `penalty = tune()`, we will tune the regularization parameter. 122 | 123 | ## Possible hyperparameter to try out 124 | 125 | ```{r} 126 | param_grid <- grid_regular( 127 | penalty(range = c(-4, 0)), 128 | max_tokens(range = c(500, 2000)), 129 | levels = 5 130 | ) 131 | 132 | param_grid 133 | ``` 134 | 135 | ## Cross-validation folds 136 | 137 | We can use resampling to create simulated validation sets, for comparing and evaluating all our possible models. 138 | 139 | ```{r} 140 | set.seed(123) 141 | complaints_folds <- vfold_cv(complaints_train, strata = product) 142 | 143 | complaints_folds 144 | ``` 145 | 146 | ## Create a workflow 147 | 148 | A workflow combines machine learning components like a data preprocessor and a model specification in an easy-to-handle container object. 149 | 150 | ```{r} 151 | complaints_wf <- workflow() %>% 152 | add_recipe(complaints_rec) %>% 153 | add_model(lasso_spec) 154 | 155 | complaints_wf 156 | ``` 157 | 158 | 159 | ## Time to tune! 160 | 161 | We have everything we need (resamples, model, features) so now we can train all these different possible models and see which ones turn out best. Be patient, as fitting all these models takes a while. 162 | 163 | (Optionally, you can uncomment the line to set up parallel processing. Different operating systems may need to use different approaches for parallel processing. If you use parallel processing, `verbose` won't print anything.) 164 | 165 | ```{r} 166 | set.seed(42) 167 | 168 | ##doParallel::registerDoParallel() 169 | 170 | lasso_rs <- tune_grid( 171 | complaints_wf, 172 | resamples = complaints_folds, 173 | grid = param_grid, 174 | control = control_grid(save_pred = TRUE, verbose = TRUE) 175 | ) 176 | 177 | lasso_rs 178 | ``` 179 | 180 | ## Evaluate our results 181 | 182 | What **metrics** did we get for each of the models we trained? 183 | 184 | ```{r} 185 | collect_metrics(lasso_rs) 186 | ``` 187 | 188 | We can find just the best ones. 189 | 190 | ```{r} 191 | lasso_rs %>% 192 | show_best("roc_auc") 193 | ``` 194 | 195 | We can visualize the results as well. 196 | 197 | ```{r} 198 | autoplot(lasso_rs) 199 | ``` 200 | 201 | Which one was the **best**? 202 | 203 | ```{r} 204 | best_roc_auc <- select_best(lasso_rs, "roc_auc") 205 | 206 | best_roc_auc 207 | ``` 208 | 209 | We can also look at _predictions_ from these models. For example, from the best performing model: 210 | 211 | ```{r} 212 | collect_predictions(lasso_rs, parameters = best_roc_auc) 213 | ``` 214 | 215 | Let's make an ROC plot. 216 | 217 | ```{r} 218 | collect_predictions(lasso_rs, parameters = best_roc_auc) %>% 219 | group_by(id) %>% 220 | roc_curve(truth = product, .pred_Credit) %>% 221 | autoplot() 222 | ``` 223 | 224 | Each resample fold is shown in a different color. 225 | 226 | We can update our workflow with the best performing hyperparameters. 227 | 228 | ```{r} 229 | wf_spec_final <- finalize_workflow(complaints_wf, best_roc_auc) 230 | 231 | wf_spec_final 232 | ``` 233 | 234 | This workflow is ready to go! It can now be applied to new data. 235 | 236 | ## Variable importance 237 | 238 | Which variables drive our model to predict one way or the other? 239 | 240 | ```{r} 241 | library(vip) 242 | 243 | vi_data <- wf_spec_final %>% 244 | fit(complaints_train) %>% 245 | pull_workflow_fit() %>% 246 | vi(lambda = best_roc_auc$penalty) %>% 247 | mutate(Variable = str_remove_all(Variable, "tfidf_text_")) %>% 248 | filter(Importance != 0) 249 | 250 | vi_data 251 | ``` 252 | 253 | ```{r} 254 | vi_data %>% 255 | mutate( 256 | Importance = abs(Importance) 257 | ) %>% 258 | filter(Importance != 0) %>% 259 | group_by(Sign) %>% 260 | top_n(20, Importance) %>% 261 | ungroup() %>% 262 | mutate(Sign = factor(Sign, c("POS", "NEG"), c("Other", "Credit"))) %>% 263 | ggplot(aes( 264 | x = Importance, 265 | y = fct_reorder(Variable, Importance), 266 | fill = Sign 267 | )) + 268 | geom_col(show.legend = FALSE) + 269 | scale_x_continuous(expand = c(0, 0)) + 270 | facet_wrap(~Sign, scales = "free") + 271 | labs( 272 | y = NULL 273 | ) 274 | ``` 275 | 276 | ## Final fit 277 | 278 | We will now use `last_fit()` to **fit** our model one last time on our training data and **evaluate** it on our testing data. 279 | 280 | ```{r} 281 | final_fit <- last_fit( 282 | wf_spec_final, 283 | complaints_split 284 | ) 285 | 286 | final_fit 287 | ``` 288 | 289 | These are metrics computed on the _testing_ set. 290 | 291 | ```{r} 292 | final_fit %>% 293 | collect_metrics() 294 | ``` 295 | 296 | ```{r} 297 | final_fit %>% 298 | collect_predictions() %>% 299 | roc_curve(truth = product, .pred_Credit) %>% 300 | autoplot() 301 | ``` 302 | 303 | These results indicate that we did not overfit during tuning. 304 | -------------------------------------------------------------------------------- /theme.css: -------------------------------------------------------------------------------- 1 | @import url("https://fonts.googleapis.com/css?family=Montserrat:400,700"); 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