.. _tut_bert:
=================================
BERT in DLATK
=================================
BERT is a contextual word embedding model. It converts words to numeric vectors that capture semantic information. Recommended reading about BERT:
* `Illustrated BERT `_
DLATK enables extraction of BERT features from messages. These features are stored, as you might expect, in feature tables!
Prerequisites
-------------
You must have a ``msgs`` table in MySQL containing a ``message`` column.
You must also ensure that punkt is installed for NLTK. This can be accomplished with the following command:
.. code-block:: bash
python -m nltk.downloader punkt
1. Preparing messages
---------------------
Messages need to be split using :doc:`../fwinterface/fwflag_add_sent_tokenized`:
.. code-block:: bash
dlatkInterface.py -d dla_tutorial -t msgs_xxx -c user_id --add_sent_tokenized
This will create the table ``msgs_xxx_stoks`` with a ``messages`` column containing arrays of messages. Remember to replace ``xxx`` with your initials!
2. Adding BERT features
-----------------------
The simplest way to add BERT embeddings, accepting all defaults, is with the :doc:`../fwinterface/fwflag_add_bert` flag:
.. code-block:: bash
dlatkInterface.py -d dla_tutorial -t msgs_xxx -c user_id --add_bert
Note that the BERT features this adds will be aggregated at several levels:
* Layers
* Words
* Messages
BERT layers are aggregated to produce a single vector representation of a word. Words are aggregated to produce message-level vector representations. Messages are aggregated to produce vector representations at the level of the grouping factor (:doc:`../fwinterface/fwflag_c`). In the command above, this means messages are aggregated to produce user-level vector representations.
This command uses all BERT defaults. However, it is possible to customize BERT features in a number of ways:
* :doc:`../fwinterface/fwflag_bert_model`
* :doc:`../fwinterface/fwflag_bert_layers`
* :doc:`../fwinterface/fwflag_bert_layer_aggregation`
* :doc:`../fwinterface/fwflag_bert_word_aggregation`
In the following subsections, we discuss these flags in more detail.
--bert_model
^^^^^^^^^^^^
The most important option for BERT is the choice of model using the :doc:`../fwinterface/fwflag_bert_model` flag. Any `Hugging Face pretrained models `_ may be used here. By default, BERT features are extracted using the ``bert-base-uncased`` model; you can specify other models like so:
.. code-block:: bash
dlatkInterface.py -d dla_tutorial -t msgs_xxx -c user_id --add_bert --bert_model large-uncased
Note that the command above doesn't specify ``bert-``. If you use a model named ``base-*`` or ``large-*``, DLATK will assume you're referring to a BERT model. You can specify other models using the full name, for example:
.. code-block:: bash
dlatkInterface.py -d dla_tutorial -t msgs_xxx -c user_id --add_bert --bert_model cl-tohoku/bert-base-japanese
--bert_layers
^^^^^^^^^^^^^
BERT produces multiple layers of embeddings (because it is a *deep* network, so each network layer produces a layer of embeddings!). Roughly speaking, later layers embed more abstract representations of features, while earlier layers represent more concrete ones. It is typical to combine some or all of these layers in order to capture this breadth of representation:
.. image:: ../_static/bert-layers.png
To specify which layers you want to aggregate over, use the :doc:`../fwinterface/fwflag_bert_layers` flag. This flag takes as arguments the indexes of each layer you want to keep. For example, we might run the following to keep the last two layers:
.. code-block:: bash
dlatkInterface.py -d dla_tutorial -t msgs_xxx -c user_id --add_bert --bert_model large-uncased --bert_layers 10 11
Notice that layers are 0-indexed, i.e., the 0th layer is the earliest one, while the 11th is the last one.
Aggregation
^^^^^^^^^^^
As discussed above, there are two levels of aggregation when adding BERT features: layer and message. These can be specified with the following flags:
* :doc:`../fwinterface/fwflag_bert_layer_aggregation`
* :doc:`../fwinterface/fwflag_bert_msg_aggregation`
It is important when running these aggregations to remember that you're choosing a numpy method, and that it will be applied to the 0th axis (i.e., it will be applied across layers). Here's an example:
.. code-block:: bash
dlatkInterface.py -d dla_tutorial -t msgs_xxx -c user_id --add_bert --bert_model large-uncased --bert_layer_aggregation mean --bert_msg_aggregation max
It is also possible to specify multiple aggregations (though this currently does not work for layer aggregations). Aggregations will be applied in the order that you specify them.
3. Understanding BERT Feature Table Names
-----------------------------------------
BERT feature tables have names that might look confusing, but actually reveal all the details about how the features were computed. If you don't yet understand feature table naming conventions in DLATK, please read :doc:`tut_feat_tables` before continuing.
Let's say you have a BERT feature table called ``feat$bert_ba_un_meL10co$messages_en$user_id$16to16``. Here's how to interpret the segment ``bert_ba_un_meL10co``:
#. **bert**
#. **ba**\ se\_\ **un**\ cased
#. **me**\ an aggregated messages
#. **L**\ ayer 10
#. **con**\ catenated layers
Some of these may be repeated: each layer selected with :doc:`../fwinterface/fwflag_bert_layers`, for example, will appear in the name.
4. Using BERT features
----------------------
Let's say you've generated default BERT features with the following command:
.. code-block:: bash
dlatkInterface.py -d dla_tutorial -t msgs_xxx -c user_id --add_bert
This will create the table ``feat$bert_ba_un_meL10con$msgs_xxx$user_id$16to16`` in the ``dla_tutorial`` database. How do you make use of these features?
The answer is, essentially, like any other feature table in DLATK! (See :doc:`tut_pred` if you don't know how to use feature tables.) For example, let's say you want to predict age from the ``blog_outcomes`` table in the ``dla_tutorial`` database (just like in :doc:`tut_pred`). This would look like:
.. code-block:: bash
dlatkInterface.py -d dla_tutorial -t msgs_xxx -c user_id -f 'feat$bert_ba_un_meL10con$msgs_xxx$user_id$16to16' --outcome_table blog_outcomes --group_freq_thresh 500 --outcomes age --output_name xxx_age_output --nfold_test_regression --model ridgecv --folds 10
This will run a ridge regression model through 10-fold cross validation, predicting age, and using BERT embeddings as features in the model. You should see lots of output, ending with something like this:
.. code-block:: bash
[TEST COMPLETE]
{'age': {(): {1: {'N': 978,
'R': 0.6618386965904822,
'R2': 0.43803046030458836,
'R2_folds': 0.4233643081827411,
'mae': 4.619034298134363,
'mae_folds': 4.616423448270654,
'mse': 38.854440157161314,
'mse_folds': 38.820150245521624,
'num_features': 768,
'r': 0.6621098985283438,
'r_folds': 0.6694097453791333,
'r_p': 2.0416764169778933e-124,
'r_p_folds': 1.783371417616072e-11,
'rho': 0.7107187086007063,
'rho_p': 2.9543434601896134e-151,
'se_R2_folds': 0.02206729102836306,
'se_mae_folds': 0.11535169076175808,
'se_mse_folds': 1.859884969097588,
'se_r_folds': 0.01581679229802067,
'se_r_p_folds': 9.484973024855944e-12,
'se_train_mean_mae_folds': 0.20067386714575627,
'test_size': 105,
'train_mean_mae': 4.335374453708489,
'train_mean_mae_folds': 6.464315148935017,
'train_size': 873,
'{modelFS_desc}': 'None',
'{model_desc}': 'RidgeCV(alphas=array([1.e+03, 1.e-01, '
'1.e+00, 1.e+01, 1.e+02, 1.e+04, 1.e+05]), '
'cv=None, fit_intercept=True, gcv_mode=None, '
'normalize=False, scoring=None, '
'store_cv_values=False)'}}}}
Comparing these results against those from :doc:`tut_pred`, we can see that BERT features get a Pearson r of 0.6621, outperforming LDA topics + unigrams, which get an r of 0.6496.
There's a natural question we've glossed over here: what exactly do the BERT features look like? We can check the contents of the feature table in MySQL:
.. code-block:: bash
mysql> SELECT * FROM feat$bert_ba_un_meL10con$msgs_xxx$user_id$16to16 LIMIT 10;
+----+----------+------+-----------------------+-----------------------+
| id | group_id | feat | value | group_norm |
+----+----------+------+-----------------------+-----------------------+
| 1 | 666 | 0me | -0.20481809973716736 | -0.20481809973716736 |
| 2 | 666 | 1me | -0.48483654856681824 | -0.48483654856681824 |
| 3 | 666 | 2me | 1.1650058031082153 | 1.1650058031082153 |
| 4 | 666 | 3me | -0.5072966814041138 | -0.5072966814041138 |
| 5 | 666 | 4me | 0.40456074476242065 | 0.40456074476242065 |
| 6 | 666 | 5me | -0.6585525274276733 | -0.6585525274276733 |
| 7 | 666 | 6me | -0.019926181063055992 | -0.019926181063055992 |
| 8 | 666 | 7me | 0.2585161030292511 | 0.2585161030292511 |
| 9 | 666 | 8me | -0.2901904881000519 | -0.2901904881000519 |
| 10 | 666 | 9me | -0.2664993405342102 | -0.2664993405342102 |
+----+----------+------+-----------------------+-----------------------+
The names of the ``feat`` column may seem a bit opaque at first, but they are simple to interpret: the number indicates the index of the dimension in the BERT embedding vector, while the ``me`` indicates that the message embeddings were aggregated using the mean. If you have specified multiple message aggregations, these will appear as separate features. Since BERT produces vectors of length 768, this means each ``group_id`` will have ``768 * [number of message aggregations]`` features. Each dimension of the aggregated BERT embedding vector then serves as a distinct feature in the predictive model.