Trouble running main.py missing module attribute-distutils-util

Hey, thanks for the code. Unfortunately I am having difficulty running the pretrained conll_2003_en model. mu parameters.ini file looks like this:

#----- Possible modes of operation -----------------------------------------------------------------------------------------------------------------#
# training mode (from scratch): set train_model to True, and use_pretrained_model to False (if training from scratch).                        #
#				 				Must have train and valid sets in the dataset_text_folder, and test and deployment sets are optional.               #
# training mode (from pretrained model): set train_model to True, and use_pretrained_model to True (if training from a pretrained model).     #
#				 						 Must have train and valid sets in the dataset_text_folder, and test and deployment sets are optional.      #
# prediction mode (using pretrained model): set train_model to False, and use_pretrained_model to True.                                       #
#											Must have either a test set or a deployment set.                                                        #
# NOTE: Whenever use_pretrained_model is set to True, pretrained_model_folder must be set to the folder containing the pretrained model to use, and #
# 		model.ckpt, dataset.pickle and parameters.ini must exist in the same folder as the checkpoint file.                                         #
#---------------------------------------------------------------------------------------------------------------------------------------------------#

[mode]
# At least one of use_pretrained_model and train_model must be set to True.
train_model = False
use_pretrained_model = True
pretrained_model_folder = ../trained_models/conll_2003_en

[dataset]
dataset_text_folder = ../data/conll2003/en

# main_evaluation_mode should be either 'conll', 'bio', 'token', or 'binary'. ('conll' is entity-based)
# It determines which metric to use for early stopping, displaying during training, and plotting F1-score vs. epoch.
main_evaluation_mode = conll

output_folder = ../output

#---------------------------------------------------------------------------------------------------------------------#
# The parameters below are for advanced users. Their default values should yield good performance in most cases.      #
#---------------------------------------------------------------------------------------------------------------------#

[ann]
use_character_lstm = True
character_embedding_dimension = 25
character_lstm_hidden_state_dimension = 25

# In order to use random initialization instead, set token_pretrained_embedding_filepath to empty string, as below:
# token_pretrained_embedding_filepath =
token_pretrained_embedding_filepath = ../data/word_vectors/glove.6B.100d.txt
token_embedding_dimension = 100
token_lstm_hidden_state_dimension = 100

use_crf = True

[training]
patience = 10
maximum_number_of_epochs = 100

# optimizer should be either 'sgd', 'adam', or 'adadelta'
optimizer = sgd
learning_rate = 0.005
# gradients will be clipped above |gradient_clipping_value| and below -|gradient_clipping_value|, if gradient_clipping_value is non-zero
# (set to 0 to disable gradient clipping)
gradient_clipping_value = 5.0

# dropout_rate should be between 0 and 1
dropout_rate = 0.5

# Upper bound on the number of CPU threads NeuroNER will use
number_of_cpu_threads = 8

# Upper bound on the number of GPU NeuroNER will use
# If number_of_gpus > 0, you need to have installed tensorflow-gpu
number_of_gpus = 0

[advanced]
experiment_name = test

# tagging_format should be either 'bioes' or 'bio'
tagging_format = bioes

# tokenizer should be either 'spacy' or 'stanford'. The tokenizer is only used when the original data is provided only in BRAT format.
# - 'spacy' refers to spaCy (https://spacy.io). To install spacy: pip install -U spacy
# - 'stanford' refers to Stanford CoreNLP (https://stanfordnlp.github.io/CoreNLP/). Stanford CoreNLP is written in Java: to use it one has to start a
#              Stanford CoreNLP server, which can tokenize sentences given on the fly. Stanford CoreNLP is portable, which means that it can be run
#              without any installation.
#              To download Stanford CoreNLP: https://stanfordnlp.github.io/CoreNLP/download.html
#              To run Stanford CoreNLP, execute in the terminal: `java -mx4g -cp "*" edu.stanford.nlp.pipeline.StanfordCoreNLPServer -port 9000 -timeout 50000`
#              By default Stanford CoreNLP is in English. To use it in other languages, see: https://stanfordnlp.github.io/CoreNLP/human-languages.html
#              Stanford CoreNLP 3.6.0 and higher requires Java 8. We have tested NeuroNER with Stanford CoreNLP 3.6.0.
tokenizer = spacy
# spacylanguage should be either 'de' (German), 'en' (English) or 'fr' (French). (https://spacy.io/docs/api/language-models)
# To install the spaCy language: `python -m spacy.de.download`; or `python -m spacy.en.download`; or `python -m spacy.fr.download`
spacylanguage = en

# If remap_unknown_tokens is set to True, map to UNK any token that hasn't been seen in neither the training set nor the pre-trained token embeddings.
remap_unknown_tokens_to_unk = True

# If load_only_pretrained_token_embeddings is set to True, then token embeddings will only be loaded if it exists in token_pretrained_embedding_filepath
# or in pretrained_model_checkpoint_filepath, even for the training set.
load_only_pretrained_token_embeddings = False

# If load_all_pretrained_token_embeddings is set to True, then all pretrained token embeddings will be loaded even for the tokens that do not appear in the dataset.
load_all_pretrained_token_embeddings = False

# If check_for_lowercase is set to True, the lowercased version of each token will also be checked when loading the pretrained embeddings.
# For example, if the token 'Boston' does not exist in the pretrained embeddings, then it is mapped to the embedding of its lowercased version 'boston',
# if it exists among the pretrained embeddings.
check_for_lowercase = True

# If check_for_digits_replaced_with_zeros is set to True, each token with digits replaced with zeros will also be checked when loading pretrained embeddings.
# For example, if the token '123-456-7890' does not exist in the pretrained embeddings, then it is mapped to the embedding of '000-000-0000',
# if it exists among the pretrained embeddings.
# If both check_for_lowercase and check_for_digits_replaced_with_zeros are set to True, then the lowercased version is checked before the digit-zeroed version.
check_for_digits_replaced_with_zeros = True

# If freeze_token_embeddings is set to True, token embedding will remain frozen (not be trained).
freeze_token_embeddings = False

# If debug is set to True, only 200 lines will be loaded for each split of the dataset.
debug = False
verbose = False

# plot_format specifies the format of the plots generated by NeuroNER. It should be either 'png' or 'pdf'.
plot_format = pdf

# specify which layers to reload from the pretrained model
reload_character_embeddings = True
reload_character_lstm = True
reload_token_embeddings = True
reload_token_lstm = True
reload_feedforward = True
reload_crf = True

parameters_filepath = ./parameters.ini

However when I run the python3.5 main.py or python3.5 main.py --train_model=False --use_pretrained_model=True --dataset_text_folder=../data/example_unannotated_texts --pretrained_model_folder=../trained_models/conll_2003_en I get this:

TensorFlow version: 1.3.0
Traceback (most recent call last):
  File "main.py", line 250, in <module>
    main()
  File "main.py", line 245, in main
    nn = NeuroNER(**arguments)
  File "/home/beast/Documents/NeuroNER-master/src/neuroner.py", line 257, in __init__
    parameters, conf_parameters = self._load_parameters(arguments['parameters_filepath'], arguments=arguments)
  File "/home/beast/Documents/NeuroNER-master/src/neuroner.py", line 118, in _load_parameters
    parameters[k] = distutils.util.strtobool(v)
AttributeError: module 'distutils' has no attribute 'util'
Exception ignored in: <bound method NeuroNER.__del__ of <neuroner.NeuroNER object at 0x7fb575c84588>>
Traceback (most recent call last):
  File "/home/beast/Documents/NeuroNER-master/src/neuroner.py", line 489, in __del__
    self.sess.close()
AttributeError: 'NeuroNER' object has no attribute 'sess'```

Any ideas? 
Thanks

[abhishek-jain11]

It seems something has changed in distutils. Try changing import distutils to import distutils.util in neuroner.py,

[Blair-Young]

Thanks for the advice, the problem I am getting now is the when testing the pretrained model using python3.5 main.py --train_model=False --use_pretrained_model=True --dataset_text_folder=../data/example_unannotated_texts/ --pretrained_model_folder=../trained_models/conll_2003_en I get this back.

Training completed in 0.00 seconds
Predict labels for the deploy set
Formatting 000_deploy set from CONLL to BRAT... Done.
Finishing the experiment

I cannot seem to find the output of the test. I notice that it seems like its training a new model for 0 epochs. Is this 0 epoch model being used for testing?