def main():
parser = argparse.ArgumentParser()
parser.add_argument('--save_dir', help='Location of checkpoint files')
parser.add_argument('--out_dir', help='Location to keep vocabs, weights and options')
args = parser.parse_args()
if not os.path.isdir(args.out_dir):
os.system("mkdir %s"%args.out_dir)
dw(args.save_dir, os.path.join(args.out_dir, 'weights.hdf5'))
os.system("cp %s/vocabs.txt %s/"%(args.save_dir, args.out_dir))
options = load_options(os.path.join(args.save_dir, 'options.json'))
# fix "n_characters" in options.json
options['char_cnn']["n_characters"] += 1
save_options(options, os.path.join(args.out_dir, 'options.json'))
import sys, os
sys.path.append(os.getcwd())
import argparse
from bilm.training import dump_weights as dw
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--save_dir', help='Location of checkpoint files')
parser.add_argument('--outfile', help='Output hdf5 file with weights')
args = parser.parse_args()
dw(args.save_dir, args.outfile)
#!/usr/bin/env python
# coding: utf-8
# In[ ]:
get_ipython().system('pip install bilm razdel')
# In[ ]:
from bilm.training import dump_weights as dw
dw('../input/model-elmo/', 'weights.hdf5')
# In[ ]:
import pandas as pd
df = (pd.read_csv('../input/avito-dataset/train.csv',
usecols=['description', 'category_id'],
nrows=25_000).rename(columns={
'description': 'text',
'category_id': 'label'
}))
# In[ ]:
import json
with open('../input/model-elmo/options.json', 'r') as file:
options = json.load(file)
import argparse
from bilm.training import dump_weights as dw
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--save_dir', help='Location of checkpoint files')
parser.add_argument('--outfile', help='Output hdf5 file with weights')
args = parser.parse_args()
dw(args.save_dir, args.outfile)
def main(args):
dw(args.save_dir, args.out)
