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FSI : Financial Megatron 345m parameters model with bert vocabulary (28k size) uncased, pre-trained on subsets of CC-100 text corpus.
Latest Version
April 4, 2023
1.25 GB


This is a nemo file for FSI Financial Megatron BERT 345m with cased BERT vocab.

Please be sure to download the latest version in order to ensure compatibility with the latest NeMo release.

Model Architecture

NeMo Megatron is a new capability in the NeMo framework that allows developers to effectively train and scale language models to billions of parameters. Unlike BERT, the position of the layer normalization and the residual connection in the model architecture (similar to GPT-2 architucture) are swapped, which allowed the models to continue to improve as they were scaled up. This model reaches higher scores compared to BERT on a range of Natural Language Processing (NLP) tasks.

This 345m papameter model has 24 layers (Transformer blocks), 1024 hidden-units, and 16 attention heads.

For more information about NeMo Megatron visit


This model was trained on text sourced from financial related documents from Wikipedia, RealNews, OpenWebText, and CC-Stories.

How to use this Model

NVIDIA NeMo can be used for easy fine-tuning to a number of different tasks. The usage of Financial megatron model can refer to other domains alike: Tutorial notebooks on fine-tuning the model for Named Entity Recognition, Relation Extraction can be found on the tutorials page of NeMo.

Source code and developer guide is available at Refer to documentation at


No known limitations available at this time.


License to use this model is covered by the NGC TERMS OF USE unless another License/Terms Of Use/EULA is clearly specified. By downloading the public and release version of the model, you accept the terms and conditions of the NGC TERMS OF USE.