Description
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Model Overview
This is a pre-trained autoencoding language model trained on English Wikipedia and BookCorpus using a sequence length of 512. The model is based on the architecture presented in "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" paper [1].
Model Architecture
The model is based on the architecture presented in "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" paper [1]. In this particular instance, the model has 12 Transformer blocks. It is using WordPiece tokenizer [2].
Training
The model was trained from scratch on preprocessed English Wikipedia and BookCorpus using a sequence length of 512.
Dataset
The model was trained from scratch on preprocessed English Wikipedia and BookCorpus using a sequence length of 512. The processing was done with NVIDIA Deep Learning Examples [4].
Performance
The accuracy of language models are often measured on downstream tasks such as SQuAD [3]. On SQuADv1.1 it reaches EM=82.78, F1=89.97, on SQuADv2.0 EM=75.04, F1=78.08
How to use this model
The model is available for use in the NeMo toolkit [5], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
Automatically load the model from NGC
import nemo
import nemo.collections.nlp as nemo_nlp
model = nemo_nlp.models.language_modeling.BERTLMModel.from_pretrained(model_name="bertbaseuncased")
Training Model
python [NEMO_GIT_FOLDER]/examples/nlp/language_modeling/bert_pretraining.py --config-name=bert_pretraining_from_preprocessed_config.yaml
Input
The model takes preprocessed data as input.
Output
The model outputs masked language model loss and optional next sentence prediction.
Limitations
The length of the input text is currently constrained by the maximum sequence length of the model, which is 512 tokens after tokenization.
References
[1] https://arxiv.org/pdf/1810.04805.pdf
[2] https://arxiv.org/abs/1609.08144
[3] https://rajpurkar.github.io/SQuAD-explorer/
Licence
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.