- Instalaciones:
yarn add --dev jest babel-jest @babel/preset-env @babel/preset-react
yarn add --dev @testing-library/react @types/jest jest-environment-jsdom
- Opcional: Si usamos Fetch API en el proyecto:
Bootstrap 3 - Carousel/Slider Collection | |
Article: http://sevenx.de/blog | |
Demo: http://sevenx.de/demo/bootstrap-carousel | |
Working Examples (inline Styles, CDN Scripts) | |
- minimal Bootstrap Markup changes | |
- minimal CSS Styles | |
- minimal jQuery |
import { ObjectId } from "bson" | |
let movies | |
let mflix | |
const DEFAULT_SORT = [["tomatoes.viewer.numReviews", -1]] | |
export default class MoviesDAO { | |
static async injectDB(conn) { | |
if (movies) { | |
return |
How are documents usually evaluated in the simplest form of keyword-based search? According to the length of the documents Based on the number of images and videos contained in the documents By the complexity of language used in the documents ** Based on the presence and frequency of the user-provided keywords
When is fine-tuning an appropriate method for customizing a Large Language Model (LLM)? When you want to optimize the model without any instructions When the LLM requires access to the latest data for generating outputs ** When the LLM does not perform well on a task and the data for prompt engineering is too large