BLEU Scores

Data details

There are 100 rows and 1 columns. The data source¹ is used to create our data that is stored in our pins table. You can access this pin from a connection to posit.byui.edu using hathawayj/bleu_scores.

This data is available to all.

Variable description

• BleuScore: Each data point represents the BLEU score for a block of text that was translated from Spanish to English. BLEU scores evaluate the quality of text translated by machine learning. Scores go from 0 to 1, and a score closer to 1 indicates a higher quality translation.

Variable summary

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
BleuScore	0	1	0.29	0.03	0.21	0.27	0.29	0.31	0.37	▁▃▇▂▁

NULL

library(tidyverse)
library(pins)
library(connectapi)

bleu_scores <- read_csv('https://github.com/byuistats/data/raw/master/BLEU-Scores/BLEU-Scores.csv') %>% 
  rename(BleuScore = x)


# Publish the data to the server with Bro. Hathaway as the owner.
board <- board_connect()
pin_write(board, bleu_scores, type = "parquet", access_type = "all")

pin_name <- "bleu_scores"
meta <- pin_meta(board, paste0("hathawayj/", pin_name))
client <- connect()
my_app <- content_item(client, meta$local$content_id)
set_vanity_url(my_app, paste0("data/", pin_name))

Access data

This data is available to all.

Direct Download: bleu_scores.parquet

R and Python Download:

URL Connections:

For public data, any user can connect and read the data using pins::board_connect_url() in R.

library(pins)
url_data <- "https://posit.byui.edu/data/bleu_scores/"
board_url <- board_connect_url(c("dat" = url_data))
dat <- pin_read(board_url, "dat")

Use this custom function in Python to have the data in a Pandas DataFrame.

import pandas as pd
import requests
from io import BytesIO

def read_url_pin(name):
  url = "https://posit.byui.edu/data/" + name + "/" + name + ".parquet"
  response = requests.get(url)
  if response.status_code == 200:
    parquet_content = BytesIO(response.content)
    pandas_dataframe = pd.read_parquet(parquet_content)
    return pandas_dataframe
  else:
    print(f"Failed to retrieve data. Status code: {response.status_code}")
    return None

# Example usage:
pandas_df = read_url_pin("bleu_scores")

Authenticated Connection:

Our connect server is https://posit.byui.edu which you assign to your CONNECT_SERVER environment variable. You must create an API key and store it in your environment under CONNECT_API_KEY.

Read more about environment variables and the pins package to understand how these environment variables are stored and accessed in R and Python with pins.

library(pins)
board <- board_connect(auth = "auto")
dat <- pin_read(board, "hathawayj/bleu_scores")

import os
from pins import board_rsconnect
from dotenv import load_dotenv
load_dotenv()
API_KEY = os.getenv('CONNECT_API_KEY')
SERVER = os.getenv('CONNECT_SERVER')

board = board_rsconnect(server_url=SERVER, api_key=API_KEY)
dat = board.pin_read("hathawayj/bleu_scores")

Footnotes

1. Data were extracted from Figure 2 in the article: Philipp Koehn. Statistical significance tests for machine translation evaluation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP).