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NLP Deep Studying
Some ideas about transformer and an instance on methods to construct a sequence mannequin with transformer
Customers’ behaviour sequence information can be utilized to foretell customers’ profile and preferences. For example, we are able to study a person’s gender from the person’s historic buy information, or we are able to predict whether or not a person will resolve a mortgage based mostly on this person’s reimbursement historical past.
On this publish, we are going to attempt to predict customers’ monetary threat degree by modelling the app instalment sequence on their telephone. Right here, we take the app instalment sequence modelling process as an NLP drawback. Apps put in by a bunch of customers kind a corpus. Every app is analogous to a phrase, whereas every app instalment sequence by a person is analogous to a sentence within the NLP drawback. To resolve this app instalment sequence modelling process, we select to make use of a neural community with transformers.
On this publish, the followings can be coated:
- Transformers and Why Transformers Work
- Neural Community with Transformers Improvement and Deployment on Spark
Transformers have been extensively utilized in NLP issues, since Google revealed the paper Attention Is All You Need. The eye module differentiates the transformers from the RNN constructions. Ketan Doshi has a wonderful series of Medium posts that nicely explains transformers and a focus mechanism, each mathematically and visually. In case you are , don’t neglect to take a look at his collection of posts. Right here, I’ll summarise some key take-aways that I discover notably helpful to grasp transformers and a focus mechanism.
- In every consideration module, the embedding enter goes via three linear transformations to kind Question, Key, and Worth. These three linear transformations are achieved by matrix multiplication, the place the transformation matrix accommodates trainable parameters. Within the coaching course of, these trainable parameters can be tuned to align the enter embedding for optimised prediction output.
- The eye rating within the consideration module is dot product between Question and Key to get Question-Key rating, after which dot product between the Question-Key and Worth. By utilizing consideration rating, every phrase pays consideration to all the opposite phrases within the enter sequence. For every phrase, the eye scores to all the opposite phrases within the enter sequence are calculated, and bigger consideration rating means greater relevance.
- There are a lot of variations of transformer constructions. A transformer containing stacks of encoders and stacks of decoders can deal with sequence-to-sequence NLP drawback, whereas a transformer containing stacks of encoders adopted by classification layers can be utilized for classification duties.
- Earlier than an enter sequence is handed to a transformer, the enter sequence will undergo an embedding layer and a place encoding layer. The embedding layer incorporates the which means of the enter, whereas the place encoding layer retrieves the place info of the enter phrases. Completely different from RNN, the place the enter sequence place info is retained, consideration mechanism parallelly calculates the eye scores with out contemplating the place info. Thus, the place encoding layer helps so as to add again the place info.
On this session, we are going to undergo the complete means of app sequence modelling. The transformer used right here takes a reference from Transformer Model for Language Understanding tutorial revealed by TensorFlow and Text Classification with Transformer tutorial revealed by Keras.
The enter used on this instance is a Pandas DataFrame df_app_sequence, and the DataFrame has three columns: userid, label, and app_sequence.
The next code goes via the complete means of the app sequence modelling.
1. Tokenization and Sequence Padding
The app_sequence is a listing of put in app_id within the type of strings. Since a neural community doesn’t take string enter, the very first thing we have to do is to tokenize the app_sequence and switch every app_id from a string into an integer. Right here NUM_APPS is the variety of apps within the corpus.
After tokenization, we might want to pad every person’s put in app sequence to the identical size. Right here MAX_LENGTH is a hyperparameter. App sequences longer than MAX_LENGTH can be truncated, whereas app sequences shorter than MAX_LENGTH can be padded with zero on the finish.
2. Outline the Encoder Layer
By the point this publish publishes, tensorflow.keras.layers.MultiHeadAttention
is barely supported in TensorFlow variations greater than 2.4.0. Right here, we are going to implement the MultiHeadAttention given by the Transformer Model for Language Understanding tutorial.
The scaled dot product operate:
The MultiHeadAttention operate:
Level-wise feed-forward layer:
Encoder layer:
Encoder:
3. Outline the Neural Community
Within the neural community, we implement stacks of transformer encoders, adopted by a collection of classification layers.
The neural community construction is:
The code is:
4. Coaching and Analysis
An Adam optimizer is used. Coaching might be examined after epochs of coaching.
5. Saving and Reloading the Mannequin
Because the mannequin has customized object (the encoder layer), we selected to avoid wasting the mannequin as TensorFlow SavedModel format. Each TensorFlow SavedModel format and Keras mannequin work for saving mannequin with customized object, nevertheless we discovered TensorFlow SavedModel format is simpler and easy. In case you are to study the distinction in saving mannequin with customized object as TensorFlow SavedModel and Keras mannequin, I’d suggest you to take a look at Murat Karakaya’s post.
Right here, the mannequin is saved as TensorFlow SavedModel by utilizing tf.operate
.
The mannequin might be reloaded and used for prediction.
6. Deployment on Spark
In the course of the mannequin coaching, we had been utilizing Pandas DataFrame on a single GPU server. Nonetheless, in the case of mannequin deployment for batch inference, we hope to deploy the educated TensorFlow mannequin on Spark and make inference on large-scale datasets on distributed Spark surroundings.
To deploy the TensorFlow mannequin with customized object (the encoder layer) on Spark, the important thing problem is methods to serialise the educated mannequin. I discovered a useful post on fixing this subject.
The answer is to add the educated mannequin repository from native path to HDFS first.
Then, use SparkFiles to add the HDFS mannequin repository to each working node within the cluster. By setting recursive=True, the complete repository will get uploaded.
Thirdly, wrap the prediction operate.
In the long run, name get_model_prediction operate within the Pandas UDF operate, and inference on Spark is finished!
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