joaopgrassi

$remoteport = bash.exe -c "ifconfig eth0 | grep 'inet '"
$found = $remoteport -match '\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}';

if( $found ){
  $remoteport = $matches[0];
  echo $remoteport
} else{
  echo "The Script Exited, the ip address of WSL 2 cannot be found";
  exit;
}

tamuhey

How to calculate the alignment between BERT and spaCy tokens effectively and robustly

site: https://tamuhey.github.io/tokenizations/

Natural Language Processing (NLP) has made great progress in recent years because of neural networks, which allows us to solve various tasks with end-to-end architecture. However, many NLP systems still require language-specific pre- and post-processing, especially in tokenizations. In this article, I describe an algorithm that simplifies calculating correspondence between tokens (e.g. BERT vs. spaCy), one such process. And I introduce Python and Rust libraries that implement this algorithm. Here are the library and the demo site links:

• repo: https://github.com/tamuhey/tokenizations

thomwolf

def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')):
    """ Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
        Args:
            logits: logits distribution shape (vocabulary size)
            top_k >0: keep only top k tokens with highest probability (top-k filtering).
            top_p >0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
                Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
    """
    assert logits.dim() == 1  # batch size 1 for now - could be updated for more but the code would be less clear
    top_k = min(top_k, logits.size(-1))  # Safety check

L0SG

import torch
from torch import nn
from torch.autograd import Variable
import torch.nn.functional as F
import torch.optim as optim


# toy feed-forward net
class Net(nn.Module):
    def __init__(self):

andreh7

andreasvc

"""Extract metadata from Project Gutenberg RDF catalog into a Python dict.

Based on https://bitbucket.org/c-w/gutenberg/

>>> md = readmetadata()
>>> md[123]
{'LCC': {'PS'},
 'author': u'Burroughs, Edgar Rice',
 'authoryearofbirth': 1875,
 'authoryearofdeath': 1950,