exportBERTtoMatlab: Load pre-trained BERT models
Version 1.0.0 (26.7 KB) by Moritz Scherrmann
This toolbox imports pre-trained BERT transformer models from Python and stores the models to be directly used in Matlab.
Updated 24 Feb 2023
Export BERT models from Python to Matlab for NLP
This toolbox exports pre-trained BERT transformer models from Python into Matlab and stores the models such that they can be directly used with the Mathworks' BERT implementation.
This allows for example to use a pre-trained German BERT model (like GBERT) from HuggingFace and use it directly for NLP applications, without having to rely on accessing Python from Matlab:
- We have tested to export models from PyTorch and TensorFlow.
- Pre-trained models for a downstream task are supported. This comprises text-classification (e.g. sentiment, multiclass or multilabel models) as well token-classification (e.g. for named entity recognition NER).
- Models with a different structure then BERT (like Roberta etc.) are not supported.
- Only models that use the sentence-piece tokenizer are currently supported.
The workflow comprises:
- Install the Mathworks implementation of BERT and add it to the Matlab path (not included).
- Install Python (we have only tested Python 3.9.x)
- Extract the toolbox in a specific folder (e.g. “exportBertToMatlab”) and add it to the Matlab path.
- Generate an environment using the “bert2matlab.yml” provided in our “Python”-folder. This installs PyTorch, TensorFlow, and HuggingFace’s “transformers” libraries, to be able to import the pre-trained Python models. GPU support is not necessary.
- A specific IDE is not necessary to export models, you can use the Python command line interface.
- For example, these commands will export a plain, pre-trained German BERT model from HuggingFace, where the import syntax consists of the HuggingFace model name, the type of model (“none”, “text-classification”, or “token-classification”), and the model format (“tf” or “py”):
# Open command window
# Plain pre-trained BERT model
from helperFunctions import modelToMatlab #(tensorflow model)
- For the Python syntax to import a model from HugingFace, see the included "MinimalExample.txt" file.
- Use the "readBertFromPython.m" function to load the model into Matlab and use powerful NLP.
By default, the models are imported into the subfolder “Models”, but you can optionally provide any path where to store the imported model.
We provide demo files to test the imported models both in Python as well as in Matlab (after import).
Note that in principle it would also possible to import pre-trained models from the “sentence-transformers” library (sbert) developed by Nils Reimers to get high quality sentence/paragraph embeddings, but:
- The sbert-embeddings are mostly based on the MPNet-BERT model with relative positional encoding, which would require a modification of the Mathworks BERT-implementation.
- Multilingual sentence embeddings rely on Byte-Pair-Encoding (BPE) for which we don’t have a compatible implementation.
We therefore refrained from adding sentence-transformers to this toolbox.
Disclaimer: We have tested to import several models, comprising plain BERT models and models with downstream tasks for NER, sentiment, multilabel classification etc. But we cannot guarantee that the code works for all BERT implementations.
The code is provided solely for illustrative purposes. We can neither guarantee that it works nor that it's free of errors, and do not take on any liability.
The code/toolbox is licensed under the GNU General Public License v3.0.
Moritz Scherrmann (2023). exportBERTtoMatlab: Load pre-trained BERT models (https://www.mathworks.com/matlabcentral/fileexchange/125305-exportberttomatlab-load-pre-trained-bert-models), MATLAB Central File Exchange. Retrieved .
MATLAB Release Compatibility
Created with R2022b
Compatible with R2021a and later releases
Platform CompatibilityWindows macOS Linux
Inspired by: Transformer Models
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