BERT PyTorch checkpoint (Dist-6L-768D, SST-2, AMP)

BERT PyTorch checkpoint (Dist-6L-768D, SST-2, AMP)

Logo for BERT PyTorch checkpoint (Dist-6L-768D, SST-2, AMP)
Description
BERT Distilled 6L-768D PyTorch checkpoint finetuned on SST-2 dataset using AMP
Publisher
NVIDIA Deep Learning Examples
Latest Version
20.12.0_amp
Modified
April 4, 2023
Size
255.7 MB

Model Overview

BERT is a method of pre-training language representations which obtains state-of-the-art results on a wide array of NLP tasks.

Model Architecture

The BERT model uses the same architecture as the encoder of the Transformer. Input sequences are projected into an embedding space before being fed into the encoder structure. Additionally, positional and segment encodings are added to the embeddings to preserve positional information. The encoder structure is simply a stack of Transformer blocks, which consist of a multi-head attention layer followed by successive stages of feed-forward networks and layer normalization. The multi-head attention layer accomplishes self-attention on multiple input representations.

An illustration of the architecture taken from the Transformer paper is shown below.

BERT

Training

This model was trained using script available in GitHub repo.

Dataset

The following datasets were used to train this model:

Performance

Performance numbers for this model are available in GitHub readme performance section.

References

License

This model was trained using open-source software available in Deep Learning Examples repository. For terms of use, please refer to the license of the script and the datasets the model was derived from.