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dmlc · Jun 4, 2019 · 501189c · 501189c
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commit 501189c
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Showing 3 changed files with 12 additions and 2 deletions.
diff --git a/scripts/bert/compare_tf_gluon_model.py → ...onversion_tools/compare_tf_gluon_model.py b/scripts/bert/compare_tf_gluon_model.py → ...onversion_tools/compare_tf_gluon_model.py
@@ -22,11 +22,13 @@
 
 import sys
 import os
+# import os.path
 import argparse
 import numpy as np
 import mxnet as mx
 import gluonnlp as nlp
 
+sys.path.insert(0, os.path.abspath(os.path.join(__file__, os.pardir, os.pardir)))
 from utils import tf_vocab_to_gluon_vocab
 
 parser = argparse.ArgumentParser(description='Comparison script for BERT model in Tensorflow'

diff --git a/scripts/bert/convert_tf_model.py → ...bert/conversion_tools/convert_tf_model.py b/scripts/bert/convert_tf_model.py → ...bert/conversion_tools/convert_tf_model.py
@@ -23,10 +23,18 @@
 import json
 import logging
 import os
+import sys
+
 import mxnet as mx
+
 from gluonnlp.model import BERTEncoder, BERTModel
 from gluonnlp.model.bert import bert_hparams
-from utils import load_tf_vocab, tf_vocab_to_gluon_vocab, get_hash, read_tf_checkpoint
+
+sys.path.insert(0, os.path.abspath(os.path.join(__file__, os.pardir, os.pardir)))
+
+from utils import (get_hash, load_tf_vocab, read_tf_checkpoint,
+                   tf_vocab_to_gluon_vocab)
+
 
 parser = argparse.ArgumentParser(
     description='Conversion script for Tensorflow BERT model',

diff --git a/scripts/bert/index.rst b/scripts/bert/index.rst
@@ -340,7 +340,7 @@ Command line interface
 
 .. code-block:: shell
 
-    python bert/bert.py --sentences "GluonNLP is a toolkit that enables easy text preprocessing, datasets loading and neural models building to help you speed up your Natural Language Processing (NLP) research."
+    python bert/embedding.py --sentences "GluonNLP is a toolkit that enables easy text preprocessing, datasets loading and neural models building to help you speed up your Natural Language Processing (NLP) research."
     Text: GluonNLP is a toolkit that enables easy text preprocessing, datasets loading and neural models building to help you speed up your Natural Language Processing (NLP) research.
     Tokens embedding: [array([-0.11881411, -0.59530115,  0.627092  , ...,  0.00648153,
        -0.03886228,  0.03406909], dtype=float32), array([-0.7995638 , -0.6540758 , -0.00521846, ..., -0.42272145,