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// allpapers is stored in a cookie
for (p of allPapers) {
    if (p.content.read) {
        // Chop off the .pdf
        console.log(p.content.pdf_url.slice(0, -4))
    }
}

// Improved version that doesn't have line numbers
console.log(allPapers.filter(p => p.content.read).map(p => p.content.pdf_url.slice(0, -4)).join('\n'))

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from typing import Text
import socket
import os
import comet_ml
import toml
from allennlp.training.callbacks.callback import Callback, handle_event
from allennlp.training.callbacks.events import Events


@Callback.register("log_to_comet")

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name: qb
dependencies:
  - python=3.7
  - pytorch=1.4.*
  - cudatoolkit=10.1
  - numpy
  - scipy
  - pandas=1.0.*
  - requests
  - flask

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$ allennlp make-vocab --include-package zero -s /tmp/stuff config/manual/qanta/ai2.jsonnet                                                                                            (zero) 
Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex.
2019-12-16 18:11:49,080 - INFO - allennlp.common.params - random_seed = 13370
2019-12-16 18:11:49,081 - INFO - allennlp.common.params - numpy_seed = 1337
2019-12-16 18:11:49,081 - INFO - allennlp.common.params - pytorch_seed = 133
2019-12-16 18:11:49,182 - INFO - allennlp.common.checks - Pytorch version: 1.0.1
2019-12-16 18:11:49,185 - INFO - allennlp.common.from_params - instantiating class <class 'allennlp.data.dataset_readers.dataset_reader.DatasetReader'> from params {'evidence': True, 'lazy': False, 'type': 'qanta_rc'} and extras {}
2019-12-16 18:11:49,185 - INFO - allennlp.common.params - dataset_reader.type = qanta_rc
2019-12-16 18:11:49,186 - INFO - allennlp.common.from_params - instantiating class <class 'zero.datasets.qanta.

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from typing import Dict, List, Union
import json

from overrides import overrides

from allennlp.data import DatasetReader, TokenIndexer, Instance
from allennlp.data.fields import TextField, LabelField, Field, MetadataField, ArrayField, ListField
from allennlp.data.token_indexers import SingleIdTokenIndexer, TokenCharactersIndexer
from allennlp.data.tokenizers import Tokenizer, WordTokenizer, Token
from allennlp.data.tokenizers.word_splitter import SpacyWordSplitter, WordSplitter

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(gdb) thread apply all backtrace

Thread 17 (Thread 0x7fc90f3ff700 (LWP 3228)):
#0  0x00007fc90fe56945 in pthread_cond_wait@@GLIBC_2.3.2 () from /lib64/libpthread.so.0
#1  0x00005563e30ed814 in rayon_core::sleep::Sleep::sleep::h403e051017a83b73 ()
#2  0x00005563e30ebd0e in rayon_core::registry::WorkerThread::wait_until_cold::h4c91d94806702f48 ()
#3  0x00005563e30ec54b in rayon_core::registry::main_loop::hbbba263316bb2911 ()
#4  0x00005563e30ed17c in std::panicking::try::do_call::h8a19372e663d596b ()
#5  0x00005563e310c48f in __rust_maybe_catch_panic () at /checkout/src/libpanic_unwind/lib.rs:101
#6  0x00005563e30e983e in _$LT$F$u20$as$u20$alloc..boxed..FnBox$LT$A$GT$$GT$::call_box::h24e52bc8c236002e ()

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from qanta.guesser.tfidf import TfidfGuesser

guesser = TfidfGuesser.load('output/guesser/qanta.guesser.tfidf.TfidfGuesser')

questions = [
    "Name this first president of the united states",
    "This man invented the theory of general relativity"
]

n_guesses = 1

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In [9]: model.predict(np.random.random((10, 3, 2)))
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
/home/pedro/anaconda3/lib/python3.6/site-pac

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[package]
name = "preferential_attachment"
version = "0.1.0"

[dependencies]
rand = "0.3"

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dummy_element_consumer
 data_size: 100, passes: 100
  grouper_it_0    3 0.003933991072699428
  grouper_it_1 3 0.0036308339331299067
  grouper_it_2 3 0.0039052229840308428
  grouper_impl 3 0.0020833380986005068
  grouper_it_0    10 0.0014350449200719595
  grouper_it_1 10 0.0015790120232850313
  grouper_it_2 10 0.0019499310292303562
  grouper_impl 10 0.001163481967523694