farach

library(tidyverse)
library(gganimate)

# Parameters
h <- 0.02 # Time cost per worker for the solver
r <- 2 # Rental rate for one unit of compute
worker_grid <- seq(0.2, 0.8, by = 0.1) # Range of human worker knowledge
solver_grid <- seq(0.3, 1.0, by = 0.1) # Range of human solver knowledge
zAI_values <- seq(0.25, 0.95, by = 0.05) # Range of AI knowledge levels

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# This R script generates synthetic data representing job functions in various languages, translates them to English using a local language model, and detects the original language. The output includes the original job function, the translated job function, and the detected language, and is saved to a CSV file for further use.

# Load necessary libraries
library(httr)
library(jsonlite)
library(textcat)
library(tidyverse)
library(glue)
library(here)

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# Load necessary libraries
library(janitor)
library(httr)
library(jsonlite)
library(tidyverse)
library(furrr)
library(stringr)
library(glue)

# Setup parallel processing

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# Instructions:
# - Download LM Studio
# - Download Phi-3 Model (within LM Studio)
# - Load the model into LM Studio
# - Start the Local Server (instructions here: https://lmstudio.ai/docs/local-server)

# Load necessary libraries
library(httr)
library(jsonlite)
library(tidyverse)

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import pandas as pd

def pep_talk ():
  pep_csv = pd.read_csv(
    "https://raw.githubusercontent.com/farach/pep/main/pep_talk.csv",
    encoding = 'unicode_escape'
    )
  
  pepText = list(
    map(

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pep_talk <- function() {
  read.csv("https://raw.githubusercontent.com/farach/pep/main/pep_talk.csv") |> 
    purrr::map_chr(~ sample(.x, 1)) |> 
    glue::glue_collapse(sep = " ")
}

farach

library(fredr)
library(tidyverse)
library(geofacet)
library(ggrepel)
library(ggtext)

set.seed(42) # For reproducibility

# Prepare state series IDs
state_ids <- c(

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---
title: "Using SpacyR on scraped glassdoor reviews"
author: "Alex Farach"
date: "10/17/2021"
output: html_document
---

```{r setup, include=FALSE}
library(knitr)
knitr::opts_chunk$set(

farach

library(spotifyr)
library(tidyverse)
library(lubridate)
library(glue)
library(geniusr)
library(rvest)
library(tidytext)

# Create a ggplot2 theme
theme_alex <- function() {

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library(tidyverse)
library(tidytext)
library(vader)
library(glue)

theme_alex <- function() {
  font <- "Arial"
  theme_minimal()
  theme(
    strip.text = element_text(