mrdrozdov

2018-11-20 12:25:06,998 [INFO] Accuracy=0.926402290907022
2018-11-20 12:25:07,011 [INFO] Confusion Matrix:
        t/p        CARDINAL        DATE       EVENT         FAC         GPE    LANGUAGE         LAW         LOC       MONEY        NORP     ORDINAL         ORG     PERCENT      PERSON     PRODUCT    QUANTITY        TIME WORK_OF_ART
       CARDINAL       477.0         4.0         0.0         0.0         1.0         0.0         0.0         0.0        18.0         0.0         1.0         1.0         0.0         1.0         0.0         3.0         1.0         0.0
           DATE         6.0       795.0         3.0         0.0         2.0         0.0         0.0         0.0         1.0         0.0         2.0         0.0         0.0         1.0         0.0         1.0         5.0         0.0
          EVENT         0.0         2.0        31.0         1.0         0.0         0.0         0.0         0.0         0.0         0.0         0.0         3.0         0.0         2.0         1.0         0.0         0.0  

mrdrozdov

--- PROFILING ---
--- INSIDE ---
_unsafe_view                           19 127.431us  108.259us
t                                      19 158.285us  160.697us
split                                  19 272.784us  276.931us
exp                                    19 276.274us  283.493us
th_sub                                 19 282.381us  296.449us
div                                    19 280.799us  299.139us
max                                    19 332.444us  343.678us
bmm                                    19 307.893us  362.781us

mrdrozdov

import torch
import argparse
from tqdm import tqdm

parser = argparse.ArgumentParser()
parser.add_argument('--flip', action='store_true')
options = parser.parse_args()

mrdrozdov

punctuation_words = set(['.', ',', ':', '-LRB-', '-RRB-', '\'\'', '``', '--', ';', '-', '?', '!', '...', '-LCB-', '-RCB-'])
currency_tags_words = set(['#', '$', 'C$', 'A$'])
ellipsis = set(['*', '*?*', '0', '*T*', '*ICH*', '*U*', '*RNR*', '*EXP*', '*PPA*', '*NOT*'])
other = set(['HK$', '&', '**'])

mrdrozdov

import argparse
import json

import numpy as np

from tqdm import tqdm


class Node(object):
    def __init__(self, id, parent, label, depth=None):

mrdrozdov

"""
A tree is a nested tuple representation.
  tree = ('A', ('B', 'C'))

It can also be represented as a set of spans.
  tree_spans = get_spans(tree)  # {(0, 3), (1, 3)}

Sometimes you need to convert a string into a tree.
  tree_str = '( A ( B C ) )'
  tokens, transitions = convert_binary_bracketing(tree_str)

mrdrozdov

create user

https://www.digitalocean.com/community/tutorials/how-to-create-a-new-user-and-grant-permissions-in-mysql

CREATE USER 'newuser'@'localhost' IDENTIFIED BY 'password';
GRANT ALL PRIVILEGES ON * . * TO 'newuser'@'localhost';
FLUSH PRIVILEGES;

mrdrozdov

tensor                                  1 13.868us   12.288us
_unsafe_view                            9 68.084us   61.634us
t                                       9 88.332us   88.192us
split                                   9 141.167us  142.527us
exp                                     9 143.811us  146.658us
th_sub                                  9 151.269us  172.068us
div                                     9 151.436us  172.991us
bmm                                     9 171.693us  195.647us
max                                     9 198.883us  202.752us
chunk                                   9 240.174us  238.307us

mrdrozdov

import argparse
import json

import torch
import torch.nn as nn

from tqdm import tqdm


class Model(nn.Module):

mrdrozdov

import argparse


def run(options):
    print(json.dumps(options.__dict__, sort_keys=True, indent=4))


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--log', default=None, type=str)