Index Out of Range Error in tokenization using TF Hub for Pretrained Albert Models

I am getting Index out of Range error in tokenization.py when running a finetune Albert large model with TF Hub. I printed out the vocab file and printing out the token before the error. You can see the error and print-outs below.

Vocab File: b'/tmp/tfhub_modules/c88f9d4ac7469966b2fab3b577a8031ae23e125a/assets/30k-clean.model'
Token:  

Traceback (most recent call last):
  File "/home/[user]/anaconda3/envs/tf-gpu/lib/python3.7/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/home/[user]/anaconda3/envs/tf-gpu/lib/python3.7/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/home/[user]/Documents/ml-tests/falling-albert/albert/run_classifier_with_tfhub.py", line 318, in <module>
    tf.compat.v1.app.run()
  File "/home/[user]/anaconda3/envs/tf-gpu/lib/python3.7/site-packages/tensorflow_core/python/platform/app.py", line 40, in run
    _run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef)
  File "/home/[user]/anaconda3/envs/tf-gpu/lib/python3.7/site-packages/absl/app.py", line 299, in run
    _run_main(main, args)
  File "/home/[user]/anaconda3/envs/tf-gpu/lib/python3.7/site-packages/absl/app.py", line 250, in _run_main
    sys.exit(main(argv))
  File "/home/[user]/Documents/ml-tests/falling-albert/albert/run_classifier_with_tfhub.py", line 185, in main
    tokenizer = create_tokenizer_from_hub_module(FLAGS.albert_hub_module_handle)
  File "/home/[user]/Documents/ml-tests/falling-albert/albert/run_classifier_with_tfhub.py", line 161, in create_tokenizer_from_hub_module
    spm_model_file=FLAGS.spm_model_file)
  File "/home/[user]/Documents/ml-tests/falling-albert/albert/tokenization.py", line 249, in __init__
    self.vocab = load_vocab(vocab_file)
  File "/home/[user]/Documents/ml-tests/falling-albert/albert/tokenization.py", line 203, in load_vocab
    token = token.strip().split()[0]
IndexError: list index out of range

Albert Finetune Shell Script

#!/bin/bash
pip install -r albert/requirements.txt
python -m albert.run_classifier_with_tfhub \
--albert_hub_module_handle=https://tfhub.dev/google/albert_xlarge/1 \
--task_name=cola \
--do_train=true \
--do_eval=true  \
--data_dir=./data-to-albert \
--max_seq_length=128  \
--train_batch_size=32  \
--learning_rate=2e-05 \
--num_train_epochs=3.0  \
--output_dir=./checkpoints/test

[KodairaTomonori]

‘30k-clean.model’ is ‘spm_model_file’, not ‘vocab_file’. The code changes are:

diff --git a/albert/run_classifier_with_tfhub.py b/albert/run_classifier_with_tfhub.py
index 92fef74..26f4339 100644                                                         
--- a/albert/run_classifier_with_tfhub.py                                             
+++ b/albert/run_classifier_with_tfhub.py                                             
@@ -156,6 +156,7 @@ def create_tokenizer_from_hub_module(albert_hub_module_handle):   
     with tf.Session() as sess:                                                       
       vocab_file, do_lower_case = sess.run([tokenization_info["vocab_file"],         
                                             tokenization_info["do_lower_case"]])     
+    FLAGS.spm_model_file = vocab_file                                                
   return tokenization.FullTokenizer(                                                 
       vocab_file=vocab_file, do_lower_case=do_lower_case,                            
       spm_model_file=FLAGS.spm_model_file)                                           

[mnsrmov]

Upgrade your tensorflow to at least 1.15.