younesbelkada

import torch
import os
import argparse
import matplotlib.pyplot as plt
from tqdm import tqdm
from transformers import AutoModelForCausalLM, AutoTokenizer
import seaborn as sns


def get_parser():

younesbelkada

# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software

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

athiyadeviyani

# BASIC TKINTER CHEATSHEET
# Build basic GUIs with Python

from tkinter import *
from tkinter import scrolledtext
from tkinter import messagebox
from tkinter.ttk import Progressbar
from tkinter import filedialog
from tkinter import Menu

xylcbd

#coding: utf-8
#demo of beam search for seq2seq model
import numpy as np
import random

vocab = {
    0: 'a',
    1: 'b',
    2: 'c',
    3: 'd',

ingo-m

How to install CUDA on Debian 8 (Jessie)

This document describes how to install nvidia drivers & CUDA in one go on a fresh debian install.

Work in progress

Preparations

• Start with a fresh Debian install.

udibr

# variation to https://github.com/ryankiros/skip-thoughts/blob/master/decoding/search.py

def keras_rnn_predict(samples, empty=empty, rnn_model=model, maxlen=maxlen):
    """for every sample, calculate probability for every possible label
    you need to supply your RNN model and maxlen - the length of sequences it can handle
    """
    data = sequence.pad_sequences(samples, maxlen=maxlen, value=empty)
    return rnn_model.predict(data, verbose=0)

def beamsearch(predict=keras_rnn_predict,

joyrexus

Hi:

perl -e 'print "hello world!\n"'

A simple filter:

perl -ne 'print if /REGEX/'

Filter out blank lines (in place):

raisch

// tokenize(str)
// extracts semantically useful tokens from a string containing English-language sentences
// @param {String}    the string to tokenize
// @returns {Array}   contains extracted tokens

function tokenize(str) {

   var punct='\\['+ '\\!'+ '\\"'+ '\\#'+ '\\$'+              // since javascript does not
             '\\%'+ '\\&'+ '\\\''+ '\\('+ '\\)'+             // support POSIX character
             '\\*'+ '\\+'+ '\\,'+ '\\\\'+ '\\-'+             // classes, we'll need our