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Wolfram Neural Net Repository

Immediate Computable Access to Neural Net Models

Represent text as a sequence of vectors

Released in 2019, these four pre-trained feature extractors leverage a large multidomain scientific corpus with a total of 3.17 billion tokens. Two vocabularies are available, each coming in both cased and uncased versions: the original BERT vocabulary and a new "Scivocab", with overlapping by 42% with the original. The resulting model has improved performance on a suite of downstream scientific NLP tasks including sequence tagging, sentence classification and dependency parsing, with datasets from a variety of scientific domains.

Number of models: 4

- SemanticScholar, a dataset consisting of 1.14 million scientific publications with 18% of the papers from computer science domain and 82% from the broad biomedical domain.

F1 scores for fine-tuned BERT and Uncased-Scivocab SciBERT models for various natural language inference tasks:

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, SciBERT net produces a sequence of feature vectors of size 768, which corresponds to the sequence of input words or subwords:

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Obtain the dimensions of the embeddings:

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Visualize the embeddings:

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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 corresponds 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 12 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.” Each head uses an AttentionLayer at its core:

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SciBERT uses 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:

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

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Define a list of sentences in three broad categories (diseases, medicines and NLP models):

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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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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 12.1 (March 2020) or above

- I. Beltagy, K. Lo, A. Cohan, "SciBERT: A Pretrained Language Model for Scientific Text," arXiv:1903.10676 (2019)
- (available from https://github.com/allenai/scibert)
- Rights: Apache 2.0 License