Resource retrieval

Get the pre-trained net:

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NetModel parameters

This model consists of a family of individual nets, each identified by a specific parameter combination. Inspect the available parameters:

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Pick a non-default net by specifying the parameters:

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Pick a non-default uninitialized net:

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Basic usage

Given a piece of text, the DistilBERT net produces a sequence of feature vectors of size 768, which correspond to the sequence of input words or subwords:

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Obtain dimensions of the embeddings:

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Visualize the embeddings:

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Transformer architecture

Each input text segment is first tokenized into words or subwords using a word-piece tokenizer and additional text normalization. Integer codes called token indices are generated from these tokens, together with additional segment indices:

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For each input subword token, the encoder yields a pair of indices that correspond to the token index in the vocabulary, and the index of the sentence within the list of input sentences:

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The list of tokens always starts with special token index 102, which corresponds to the classification index. Also the special token index 103 is used as a separator between the different text segments. Each subword token is also assigned a positional index:

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A lookup is done to map these indices to numeric vectors of size 768:

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For each subword token, these three embeddings are combined by summing elements with ThreadingLayer:

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The transformer architecture then processes the vectors using six structurally identical self-attention blocks stacked in a chain:

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The key part of these blocks is the attention module comprising of 12 parallel self-attention transformations, also called “attention heads”:

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BERT-like models use self-attention, where the embedding of a given subword depends on the full input text. The following figure compares self-attention (lower left) to other types of connectivity patterns that are popular in deep learning:

Sentence analogies

Define a sentence embedding that takes the last feature vector from DistilBERT subword embeddings (as an arbitrary choice):

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Define a list of sentences in two broad categories (food and music):

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Precompute the embeddings for a list of sentences:

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Visualize the similarity between the sentences using the net as a feature extractor:

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Train a classifier model with the subword embeddings

Get a text-processing dataset:

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View a random sample of the dataset:

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Precompute the DistilBERT vectors for the training and the validation datasets (if available, GPU is highly recommended):

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Define a network to classify the sequences of subword embeddings, using a max-pooling strategy:

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Train the network on the precomputed vectors from DistilBERT:

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Check the classification error rate on the validation data:

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Let’s compare the results with the performance of a classifier trained on context-independent word embeddings. Precompute the GloVe vectors for the training and the validation dataset:

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Train the classifier on the precomputed GloVe vectors:

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Compare the results obtained with GPT and with GloVe:

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Net information

Inspect the number of parameters of all arrays in the net:

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Obtain the total number of parameters:

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Obtain the layer type counts:

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Display the summary graphic:

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Export to MXNet

Export the net into a format that can be opened in MXNet:

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Export also creates a net.params file containing parameters:

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Get the size of the parameter file:

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