# Wolfram Neural Net Repository

Immediate Computable Access to Neural Net Models

Generate text in English and represent text as a sequence of vectors

Released in 2018, this Generative Pre-Training Transformer (GPT) model is pre-trained in an unsupervised fashion on a large corpus of English text. This model can be further fine-tuned with additional output layers to create highly accurate NLP models for a wide range of tasks. It uses bi-directional causal self-attention, often referred to as a transformer decoder.

Number of models: 2

- BookCorpus, a dataset consisting of 11,038 unpublished books from 16 different genres.

The model fine-tuned on various datasets obtains the following accuracy on various natural language inference tasks: 82.1%, 81.4%, 89.9%, 88.3%, 88.1% and 56% accuracy on MNLI-m, MNLI-mm, SNLI, SciTail, QNLI, and RTE datasets respectively.

For question answering and commonsense reasoning, the fine-tuned model obtains the following accuracies: 86.5%, 62.9%, 57.4%, and 59.0% accuracy on Story Cloze, RACE-m, RACE-h, and RACE datasets respectively

Get the pre-trained net:

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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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Given a piece of text, the GPT 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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The input string is first normalized and then tokenized, or split into words or subwords. This two-step process is accomplished using the NetEncoder "Function":

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The tokenization step is performed using the NetEncoder "BPESubwordTokens" and can be extracted using the following steps:

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The encoder produces an integer index for each subword token that corresponds to the position in the vocabulary:

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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 two embeddings are combined by summing elements with ThreadingLayer:

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The transformer architecture then processes the vectors using 12 structurally identical self-attention blocks stacked in a chain:

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

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Each head uses an AttentionLayer at its core:

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Attention is done with causal masking, which means that the embedding of a given subword token depends on the previous subword tokens and not on the subsequent ones. This is a prerequisite to be able to generate text with the language model. The following figures compare causal attention to other forms of connectivity between input tokens:

Retrieve the language model by specifying the "Task" parameter:

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Predict the next word in a given sequence:

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Obtain the top 15 probabilities:

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Plot the top 15 probabilities:

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Modify the language model so that it accepts the encoded token indices as input and creates the token indices as output:

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Create a new decoder that performs a lookup to find the corresponding string, followed by some text cleaning:

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Define a function to predict the next token using the modified language model:

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Get an input:

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Generate the next 20 tokens by using it on the input:

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The third optional argument is a “temperature” parameter that scales the input to the final softmax. A high temperature flattens the distribution from which tokens are sampled, increasing the probability of extracting less likely tokens:

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Decreasing the temperature sharpens the peaks of the sampling distribution, further decreasing the probability of extracting less likely tokens:

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Very high temperature settings are equivalent to random sampling:

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Very low temperature settings are equivalent to always picking the character with maximum probability. It is typical for sampling to “get stuck in a loop”:

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Define a sentence embedding that consists of the last subword embedding of GPT (this choice is justified by the fact that GPT is a forward causal model):

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Define some sentences in two broad categories for comparison:

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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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Get a text-processing dataset:

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View a random sample of the dataset:

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Define a sentence embedding that consists of the last subword embedding of GPT (this choice is justified by the fact that GPT is a forward causal model):

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

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Define a simple network for classification:

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Train the network on the precomputed GPT vectors:

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Check the classification error rate on the validation data:

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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 datasets (if available, GPU is recommended):

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Define a simple network for classification, using a max-pooling strategy:

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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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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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Wolfram Language 12.0 (April 2019) or above

- A. Radford, K. Narasimhan, T. Salimans, I. Sutskever, "Improving language understanding by generative pre-training," preprint (2018)
- (available from https://github.com/openai/finetune-transformer-lm)
- Rights: MIT License