Model Overview

This is a pre-trained autoencoding language model trained on English Wikipedia and BookCorpus using a sequence length of 512.\nThe 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].\nOn 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\nimport nemo.collections.nlp as nemo_nlp\nmodel = nemo_nlp.models.language_modeling.BERTLMModel.from_pretrained(model_name=\"bertbaseuncased\")\n

Training Model

python [NEMO_GIT_FOLDER]/examples/nlp/language_modeling/bert_pretraining.py --config-name=bert_pretraining_from_preprocessed_config.yaml\n

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/

[4] https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/LanguageModeling/BERT/data/create_datasets_from_start.sh

[5] NVIDIA NeMo Toolkit

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.

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In this particular instance, the model has 12 Transformer blocks. It is using WordPiece tokenizer [2].\n\n## Training\n\nThe model was trained from scratch on preprocessed English Wikipedia and BookCorpus using a sequence length of 512.\n\n### Dataset\n\nThe 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](https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/LanguageModeling/BERT/data/create_datasets_from_start.sh) [4].\n\n\n## Performance\nThe accuracy of language models are often measured on downstream tasks such as SQuAD [3].\nOn SQuADv1.1 it reaches EM=82.78, F1=89.97, on SQuADv2.0 EM=75.04, F1=78.08 \n\n\n## How to use this model\n\nThe 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.\n\n### Automatically load the model from NGC\n```python\nimport nemo\nimport nemo.collections.nlp as nemo_nlp\nmodel = nemo_nlp.models.language_modeling.BERTLMModel.from_pretrained(model_name=\"bertbaseuncased\")\n```\n\n### Training Model\n```bash\npython [NEMO_GIT_FOLDER]/examples/nlp/language_modeling/bert_pretraining.py --config-name=bert_pretraining_from_preprocessed_config.yaml\n```\n### Input\n\nThe model takes preprocessed data as input. \n\n### Output\n\nThe model outputs masked language model loss and optional next sentence prediction.\n\n\n## Limitations\n\nThe length of the input text is currently constrained by the maximum sequence length of the model, which is 512 tokens after tokenization. \n\n## References\n\n[1] https://arxiv.org/pdf/1810.04805.pdf\n\n[2] https://arxiv.org/abs/1609.08144\n\n[3] https://rajpurkar.github.io/SQuAD-explorer/\n\n[4] https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/LanguageModeling/BERT/data/create_datasets_from_start.sh\n\n[5] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)\n\n\n## Licence\n\nLicense to use this model is covered by the NGC [TERMS OF USE](https://ngc.nvidia.com/legal/terms) unless another License/Terms Of Use/EULA is clearly specified. 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Key	Value
SQuADv1.1 EM	82.78
SQuADv1.1 F1	89.97
SQuADv2.0 EM	75.04
SQuADv2.0 F1	78.08

Key	Value
Architecture	BERT uncased_L-12_H-768_A-12
Inputs	tokenized English data in hdf5 format
Outputs	Masked Language Model Loss, Next Sentence Prediction Loss