jurbanhost
agricolamz
/ get_embedings_from_bert.qmd
Last active
November 14, 2023 15:30
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| --- | |
| title: "Get word embedings from Bert" | |
| format: html | |
| editor: source | |
| editor_options: | |
| chunk_output_type: console | |
| --- | |
| Убедимся, что у нас третий питон: |
jrosell
/ rpostgres-pdf.R
Created
December 9, 2021 10:20
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| library(RPostgres) | |
| # Get and read the PDF | |
| path <- file.path("os2.pdf") | |
| pdf <- readBin(con = path, what = raw(), n = file.info(path)$size) | |
| # Open it | |
| browseURL(path) | |
| # Connect to default DB and put seralized raw pdf in a data.frame |
behrica
/ PWI_top2vec.py
Last active
May 27, 2024 03:00
— forked from lcschv/PWI_top2vec.py
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| from nltk.probability import FreqDist | |
| import math | |
| import pickle | |
| from top2vec import Top2Vec | |
| import numpy as np | |
| from gensim.utils import simple_preprocess | |
| from gensim.parsing.preprocessing import strip_tags | |
| from tqdm import tqdm |
agricolamz
/ 2021-04-25_tidytuesday.R
Last active
July 4, 2021 13:39
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| # by G. Moroz | |
| # License: GPL-2 | |
| library(tidyverse) | |
| netflix_titles <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-04-20/netflix_titles.csv') | |
| glimpse(netflix_titles) | |
| library(tidytext) | |
| library(ggwordcloud) | |
| library(ggtext) |
adamlauretig
/ gensim_in_r.Rmd
Created
September 5, 2018 15:26
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| --- | |
| title: "Using Gensim in R" | |
| author: "Adam Lauretig" | |
| date: "3/17/2018" | |
| output: html_document | |
| --- | |
| ```{r setup, include=FALSE} | |
| knitr::opts_chunk$set(echo = TRUE) | |
| ``` |
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| #!/bin/bash | |
| OUTDIR=. | |
| while read -r db ; do | |
| while read -r table ; do | |
| if [ "$db" == "system" ]; then | |
| echo "skip system db" | |
| continue 2; |
tjvananne
/ process GloVe pre-trained word vector.R
Created
May 4, 2017 14:45
How to read and process a downloaded pre-trained GloVe word vector (turn it into a data.frame) in base R
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| #' A word vector is a giant matrix of words, and each word contains a numeric array that represents the semantic | |
| #' meaning of that word. This is useful so we can discover relationships and analogies between words programmatically. | |
| #' The classic example is "king" minus "man" plus "woman" is most similar to "queen" | |
| # function definition -------------------------------------------------------------------------- | |
| # input .txt file, exports list of list of values and character vector of names (words) | |
| proc_pretrained_vec <- function(p_vec) { |
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| # extracted from http//www.naturalearthdata.com/download/110m/cultural/ne_110m_admin_0_countries.zip | |
| # under public domain terms | |
| country_bounding_boxes = { | |
| 'AF': ('Afghanistan', (60.5284298033, 29.318572496, 75.1580277851, 38.4862816432)), | |
| 'AO': ('Angola', (11.6400960629, -17.9306364885, 24.0799052263, -4.43802336998)), | |
| 'AL': ('Albania', (19.3044861183, 39.624997667, 21.0200403175, 42.6882473822)), | |
| 'AE': ('United Arab Emirates', (51.5795186705, 22.4969475367, 56.3968473651, 26.055464179)), | |
| 'AR': ('Argentina', (-73.4154357571, -55.25, -53.628348965, -21.8323104794)), | |
| 'AM': ('Armenia', (43.5827458026, 38.7412014837, 46.5057198423, 41.2481285671)), |
hadley
/ clustergram-had.r
Created
June 15, 2010 21:30
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| ks.default <- function(rows) seq(2, max(3, rows %/% 4)) | |
| many_kmeans <- function(x, ks = ks.default(nrow(x)), ...) { | |
| ldply(seq_along(ks), function(i) { | |
| cl <- kmeans(x, centers = ks[i], ...) | |
| data.frame(obs = seq_len(nrow(x)), i = i, k = ks[i], cluster = cl$cluster) | |
| }) | |
| } | |
| all_hclust <- function(x, ks = ks.default(nrow(x)), point.dist = "euclidean", cluster.dist = "ward") { |