Using Information from the rest of a Sequence to Predict the Label for any one Item I have a dictionary of variable-length sequences:

[(file_name[-10:], len(tag_is_header_list)) for file_name,
 tag_is_header_list in HEADER_PATTERN_DICT.items()]

[('37bd1.html', 25),
 ('0bcce.html', 40),
 ('90364.html', 28),
 ('8f9c7.html', 24),
 ('d12d4.html', 73),
 ('46837.html', 37),
 ('adb92.html', 53),
 ('0a1e7.html', 69),
 ('da077.html', 43),
 ('9366a.html', 21),
 ('6ae4d.html', 37),
 ('f62ee.html', 19),
 ('73aee.html', 33),
 ('e090a.html', 35),
 ('8b093.html', 44)]

These contain a label for each item as to whether or not they are a subject heading:

HEADER_PATTERN_DICT[sorted([(file_name, len(tag_is_header_list)) for file_name,
                            tag_is_header_list in HEADER_PATTERN_DICT.items()],
                           key=lambda x: x[1])[0][0]]

[(None, True),
 ('&lt;div', False),
 ('&lt;div', False),
 (None, True),
 (None, False),
 ('&lt;li', False),
 ('&lt;li', False),
 ('&lt;li', False),
 (None, False),
 (None, False),
 ('&lt;li', False),
 ('&lt;li', False),
 ('&lt;li', False),
 (None, True),
 (None, True),
 ('&lt;li', False),
 ('&lt;li', False),
 ('&lt;li', False),
 ('&lt;div', False)]

Every item in the sequence is an instance for which the label should be predicted. So, what is the best way to use some variable-length sequence vectorization to train a model to predict the label?
The fastest way to train a model to predict each item's label is using Conditional Random Fields (CRT) like in this example. h/t @erwin