djg07

import json
import urllib.parse
import boto3

print('Loading function')

s3 = boto3.client('s3')

def lambda_handler(event, context):
    #1 - Get the bucket name

staugur

import re

ip_middle_octet = u"(?:\.(?:1?\d{1,2}|2[0-4]\d|25[0-5]))"
ip_last_octet = u"(?:\.(?:[1-9]\d?|1\d\d|2[0-4]\d|25[0-4]))"

regex = re.compile(
    u"^"
    # protocol identifier
    u"(?:(?:https?|ftp)://)"
    # user:pass authentication

aditya-malte

typokign

Zoom Sucks

• Zoom abuses the installer flow on MacOS to bypass permissions dialogs (source)
• Zoom sends identifying device info to Facebook, even when users don't have a Facebook account (source) (fixed)
• A bug in Zoom sent identifying information (including email addresses and profile pictures) of thousands of users to strangers (source)
• Zoom claims that meetings are end-to-end encrypted in their white paper and marketing materials, but meetings are only encrypted in transit, and are available in plaintext to Zoom servers and employees. (source)
• zoomAutenticationTool can be used to escalat

lovit

Huggingface

• NLP 관련 다양한 패키지를 제공하고 있으며, 특히 언어 모델 (language models) 을 학습하기 위하여 세 가지 패키지가 유용

package	note
transformers	Transformer 기반 (masked) language models 알고리즘, 기학습된 모델을 제공
tokenizers	transformers 에서 사용할 수 있는 토크나이저들을 학습/사용할 수 있는 기능 제공. transformers 와 분리된 패키지로 제공
nlp	데이터셋 및 평가 척도 (evaluation metrics) 을 제공

avidale

import torch
import copy
from torch import nn
from transformers import T5PreTrainedModel
from transformers.models.t5.modeling_t5 import T5Stack
from transformers.modeling_outputs import SequenceClassifierOutput
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss


def mean_pooling(inputs, mask):

pkubik

GFlowNets

Base paper: https://arxiv.org/abs/2106.04399

Extension: https://arxiv.org/pdf/2111.09266.pdf

Tutorial: https://milayb.notion.site/The-GFlowNet-Tutorial-95434ef0e2d94c24aab90e69b30be9b3

Lecture: https://www.youtube.com/watch?v=ZncOp1Dvw9o

LevanKvirkvelia

import torch.nn as nn
import torch.nn.functional as F
import math
from typing import Optional, Tuple

class BertSelfAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        
        if config.hidden_size % config.num_attention_heads != 0: