maulberto3

"""
This is a workaround for `examples/run_mlm.py` for pretraining models 
with big text files line-by-line. 

For the time being, `datasets` is facing some issues dealing with really 
big text files, so we use a custom dataset until this is fixed. 

August 3th 2021

Author: Juan Manuel Pérez

maulberto3

const arr = [["dog", 1], ["cat", 2], ["bear", 3]];
const func1 = ([key, v1]) => ({ [key]: v1 }); // creates an object out of the array
const func2 = (obj, prop) => Object.assign(obj, prop); // assigns each object to an accumulator object
const obj3 = arr.map(func1).reduce(func2); // nesting
console.log(JSON.stringify(obj3, null, 2)); // Prints {  "dog": 1,  "cat": 2,  "bear": 3  }

maulberto3

const arr = [["dog", 1], ["cat", 2], ["bear", 3]];
const func1 = ([key, v1]) => ({ [key]: v1 }); // creates an object out of the array

const obj2 = arr.map(func1) 
console.log(JSON.stringify(obj2, null, 2)); // Prints [ { "dog": 1 }, { "cat": 2 }, { "bear": 3 } ]

maulberto3

const reducer = (accum, curr) => accum + curr;
const result = [0, 1, 2, 3, 4].reduce(reducer);
console.log("result is", result); // result is 10

maulberto3

const result = [0, 1, 2, 3, 4].reduce( (accum, curr) => accum + curr )
console.log('result is', result) // result is 10