Understand your Mac and iPhone more deeply by tracing the evolution of Mac OS X from prelease to Swift. John Siracusa delivers the details.
You've got two main options:
/** | |
* Configuration object for API settings. | |
* Contains key constants used throughout the extraction process. | |
*/ | |
const API_CONFIG = { | |
key: "...", // Your Firecrawl API key | |
baseUrl: 'https://api.firecrawl.dev/v1', // Base URL for Firecrawl API endpoints | |
timeout: 30, // Timeout for HTTP requests in seconds | |
maxAttempts: 10, // Maximum number of attempts to poll for job completion | |
initialDelay: 1000 // Initial delay (in ms) before polling for job status |
################################## | |
# Data functions | |
################################## | |
import re | |
from datasets import load_dataset | |
def clean(text): | |
text = text.replace("\n", " ").strip() |
package main | |
import ( | |
"fmt" | |
"github.com/codegangsta/negroni" | |
"github.com/gorilla/mux" | |
"log" | |
"net/http" | |
) |
aws s3 sync s3://oldbucket s3://newbucket --source-region us-west-1 --region us-west-2 |
aws s3 sync s3://oldbucket s3://newbucket --source-region us-west-1 --region us-west-2 |
Bolukbasi et al. "Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings"
http://papers.nips.cc/paper/6228-man-is-to-computer-programmer-as-woman-is-to-homemaker-debiasing-word-embeddings.pdf
Bolukbasi et al. "Quantifying and Reducing Stereotypes in Word Embeddings" https://arxiv.org/pdf/1606.06121.pdf
Brunet et al. "Understanding the Origins of Bias in Word Embeddings" https://arxiv.org/pdf/1810.03611v1.pdf
Costa-jussá and Font "Equalizing Gender Biases in Neural Machine Translation with Word Embeddings Techniques"