lumpidu

"""Linear cross entropy loss for masked language modeling.

Memory-efficient cross entropy that avoids materializing full logit tensors.
Instead of computing logits for all tokens (including padding), only computes
logits for positions that contribute to the loss (non-ignored tokens).

For MLM with 15% masking, this reduces memory by ~85% compared to standard
cross entropy, since we only compute logits for masked positions.
"""

lumpidu

import argparse
import hashlib
import os
import urllib
import warnings
from collections import OrderedDict

import torch
from torch import nn
from tqdm import tqdm

lumpidu

import torch
import transformers
import argparse
from pytictoc import TicToc


def load_model_and_tokenizer(model_name):
    the_tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
    tokenizer_model = transformers.AutoModel.from_pretrained(model_name)
    return tokenizer_model, the_tokenizer

lumpidu

46c46
< #define GETSERVBYPORT_R_ARGS 6
---
> #define GETSERVBYPORT_R_ARGS
131c131
< #define HAVE_GETSERVBYPORT_R
---
> /* #undef HAVE_GETSERVBYPORT_R */
46c46
< #define GETSERVBYPORT_R_ARGS 6

lumpidu

sudo add-apt-repository ppa:ubuntu-toolchain-r/test
sudo apt-get update
sudo apt-get install gcc-5 g++-5

sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-5 60 --slave /usr/bin/g++ g++ /usr/bin/g++-5

lumpidu

#!/usr/bin/ruby

require 'json'
require 'json-schema'

schema = '{
    "$schema": "http://json-schema.org/draft-04/schema#",
    "title": "demo",
    "type": "object",
    "properties": {

lumpidu

#!/usr/bin/ruby

require 'json'
require 'json-schema'

schema = '{
    "$schema": "http://json-schema.org/draft-04/schema#",
    "title": "demo",
    "type": "object",
    "properties": {

lumpidu

{
  "posts" : {
    "post" : {
      "properties" : {
        "converted_text" : {
          "type" : "string"
        },
        "converted_text_size" : {
          "type" : "long"
        },

lumpidu

class Post  < ActiveRecord::Base

  include Tire::Model::Search
  include Tire::Model::Callbacks

  mapping do
    indexes :id,                    :index    => :not_analyzed
    indexes :title,                 :analyzer => 'snowball', :boost => 3
    indexes :url,                   :analyzer => 'url'
    indexes :created_at,            :type => 'date', :include_in_all => false

lumpidu

# Searches ElasticSearch fulltext engine via Tire
  #
  # @param term     Search term to be used
  #                 It has to start with a prefix specifying the field(s) to be searched:
  #                 e.g.
  #                   _all:Apple
  #                   converted_text:Apple
  #                   field_summary:Apple, etc.
  #
  #                 Each Lucene search term can be used