Overview

This is a checkpoint for the BERT Base model trained in NeMo on the uncased English Wikipedia and BookCorpus dataset on sequence length of 512. It was trained with Apex/Amp optimization level O1.

The model is trained for 2285714 iterations on a DGX1 with 8 V100 GPUs.

The model achieves EM/F1 of 82.74/89.79 on SQuADv1.1 and 71.24/74.32 on SQuADv2.0. On GLUE benchmark MRPC task the model achieves accuracy/F1 od 86.52/90.53.

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

BERT-STEP-2285714.pt - pretrained BERT encoder weights
BertTokenClassifier-STEP-2285714.pt - pretrained BERT masked language model head weights
SequenceClassifier-STEP-2285714.pt - pretrained BERT next sentence prediction head weights
bert-config.json - the config file used to initialize BERT network architecture in NeMo

More Details

BERT, or Bidirectional Encoder Representations from Transformers, is a neural approach to pre-train language representations which obtains near state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks, including the GLUE Benchmark and SQuAD Question Answering dataset. This model is based on the BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding paper and Hugging Face implementation, leveraging mixed precision arithmetic and Tensor Cores on V100 GPUs for faster training times while maintaining target accuracy.

The BERT architecture uses the same architecture as the encoder half 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 12 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. The total number of parameters is 110M.

The model is trained using masked language model loss and next sentence prediction loss.

Documentation

Source code and developer guide is available at https://github.com/NVIDIA/NeMo\nRefer to documentation at https://docs.nvidia.com/deeplearning/nemo/neural-modules-release-notes/index.html\nCode to pretrain and reproduce this model checkpoint are available at https://github.com/NVIDIA/NeMo.

This model checkpoint can be used for either finetuning BERT on your custom dataset, or finetuning downstream tasks, including GLUE benchmark tasks, question answering tasks e.g. SQuAD, joint intent and slot detection, punctuation and capitalization, named entity recognition, and speech recognition postprocessing model to correct mistakes. All of these tasks and scripts can be found at https://github.com/NVIDIA/NeMo.

In the following we show examples for how to train BERT and finetune two downstream tasks, GLUE MRPC and SQuAD.

Usage example 1: Pretraining BERT

Download and preprocess uncased Wikipedia and BookCorpus dataset:\n
- \n
  Clone https://github.com/NVIDIA/DeepLearningExamples
  \n
- \n
  Modify https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/LanguageModeling/BERT/data/create_datasets_from_start.sh to run
  \n
  python bertPrep.py with --max_seq_length 512 --max_predictions_per_seq 80 --vocab_file /path_to_vocab_dir/uncased_L-12_H-768_A-12/vocab.txt --do_lower_case 1 --n_processes 24 --fraction_test_set 0.1 --n_test_shards 1472 --n_training_shards 1472 --input_dir shard_1472_test_split_10
  \n
\n

Run the script and extract preprocessed hdf5 files into $train_data and $eval_data

\n
Run BERT base on the sequence length 512 and DGX1 with 8 V100 GPUs
\n
cd examples/nlp/language_modeling;
\n
python -m torch.distributed.launch --nproc_per_node=8 bert_pretraining.py --config_file bert-config.json --train_data $train_data --eval_data $eval_data --num_gpus 8 --batch_size 8 --amp_opt_level \"O1\" --lr_policy SquareRootAnnealing --beta1 0.9 --beta2 0.999 --lr_warmup_proportion 0.01 --optimizer adam_w --weight_decay 0.01 --lr 0.4375e-4 data_preprocessed --max_predictions_per_seq 80 --num_iters 2285714
\n

Checkpoints will be store at args.work_dir folder.

Usage example 2: Using BERT checkpoint for downstream task, using the example of GLUE benchmark task MRPC

Download BERT-STEP-2285714.pt and bert-config.json.

cd examples/nlp/glue_benchmark;\n\npython glue_benchmark_with_bert.py --data_dir $mrpc_dataset --task_name mrpc --pretrained_bert_model bert-base-uncased --bert_checkpoint /path_to/BERT-STEP-2285714.pt --bert_config /path_to/bert-config.json --lr 2e-5 \n

Usage example 3: Using BERT checkpoint for downstream task SQuAD Question Answering task

Download BERT-STEP-2285714.pt and bert-config.json.

SQuAD v1.1

cd examples/nlp/question_answering;\n\npython -m torch.distributed.launch --nproc_per_node=8 question_answering_squad.py --mode train_eval --amp_opt_level O1 --num_gpus 8 --train_file=/path_to/squad/v1.1/train-v1.1.json --eval_file /path_to/squad/v1.1/dev-v1.1.json --bert_checkpoint /path_to/BERT-STEP-2285714.pt --bert_config /path_to/bert-config.json --pretrained_model_name bert-base-uncased --batch_size 3 --num_epochs 2 --lr_policy SquareRootAnnealing --optimizer adam_w --lr 3e-5 --do_lower_case --no_data_cache\n

SQuAD v2.0

cd examples/nlp/question_answering;\n\npython -m torch.distributed.launch --nproc_per_node=8 question_answering_squad.py --mode train_eval --amp_opt_level O1 --num_gpus 8 --train_file /path_to/squad/v2.0/train-v2.0.json --eval_file /path_to/squad/v2.0/dev-v2.0.json --bert_checkpoint /path_to/BERT-STEP-2285714.pt --bert_config /path_to/bert-config.json --pretrained_model_name=bert-base-uncased --batch_size 3 --num_epochs 2 --lr_policy SquareRootAnnealing --optimizer adam_w --lr 3e-5 --do_lower_case --version_2_with_negative --no_data_cache\n

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It was trained with Apex/Amp optimization level O1.\n\nThe model is trained for 2285714 iterations on a DGX1 with 8 V100 GPUs.\n\nThe model achieves EM/F1 of 82.74/89.79 on SQuADv1.1 and 71.24/74.32 on SQuADv2.0. On GLUE benchmark MRPC task the model achieves accuracy/F1 od 86.52/90.53.\n\nPlease be sure to download the latest version in order to ensure compatibility with the latest NeMo release.\n* BERT-STEP-2285714.pt - pretrained BERT encoder weights \n* BertTokenClassifier-STEP-2285714.pt - pretrained BERT masked language model head weights\n* SequenceClassifier-STEP-2285714.pt - pretrained BERT next sentence prediction head weights\n* bert-config.json - the config file used to initialize BERT network architecture in NeMo\n\n# More Details\nBERT, or Bidirectional Encoder Representations from Transformers, is a neural approach to pre-train language representations which obtains near state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks, including the GLUE Benchmark and SQuAD Question Answering dataset. This model is based on the BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding paper and Hugging Face implementation, leveraging mixed precision arithmetic and Tensor Cores on V100 GPUs for faster training times while maintaining target accuracy.\n\nThe BERT architecture uses the same architecture as the encoder half 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 12 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. The total number of parameters is 110M.\n\nThe model is trained using masked language model loss and next sentence prediction loss.\n\n\n# Documentation\nSource code and developer guide is available at https://github.com/NVIDIA/NeMo\nRefer to documentation at https://docs.nvidia.com/deeplearning/nemo/neural-modules-release-notes/index.html\nCode to pretrain and reproduce this model checkpoint are available at https://github.com/NVIDIA/NeMo. \n\nThis model checkpoint can be used for either finetuning BERT on your custom dataset, or finetuning downstream tasks, including GLUE benchmark tasks, question answering tasks e.g. SQuAD, joint intent and slot detection, punctuation and capitalization, named entity recognition, and speech recognition postprocessing model to correct mistakes. All of these tasks and scripts can be found at https://github.com/NVIDIA/NeMo.\n\nIn the following we show examples for how to train BERT and finetune two downstream tasks, GLUE MRPC and SQuAD. \n\n## Usage example 1: Pretraining BERT\n1. Download and preprocess uncased Wikipedia and BookCorpus dataset:\n\t* Clone https://github.com/NVIDIA/DeepLearningExamples\n * Modify https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/LanguageModeling/BERT/data/create_datasets_from_start.sh to run \n \n python bertPrep.py with --max_seq_length 512 --max_predictions_per_seq 80 --vocab_file /path_to_vocab_dir/uncased_L-12_H-768_A-12/vocab.txt --do_lower_case 1 --n_processes 24 --fraction_test_set 0.1 --n_test_shards 1472 --n_training_shards 1472 --input_dir shard_1472_test_split_10\n \n * Run the script and extract preprocessed hdf5 files into $train_data and $eval_data\n\n2. Run BERT base on the sequence length 512 and DGX1 with 8 V100 GPUs\n\n\n\tcd examples/nlp/language_modeling;\n\n\tpython -m torch.distributed.launch --nproc_per_node=8 bert_pretraining.py --config_file bert-config.json --train_data $train_data --eval_data $eval_data --num_gpus 8 --batch_size 8 --amp_opt_level \"O1\" --lr_policy SquareRootAnnealing --beta1 0.9 --beta2 0.999 --lr_warmup_proportion 0.01 --optimizer adam_w --weight_decay 0.01 --lr 0.4375e-4 data_preprocessed --max_predictions_per_seq 80 --num_iters 2285714 \n\nCheckpoints will be store at args.work_dir folder.\n\n\n## Usage example 2: Using BERT checkpoint for downstream task, using the example of GLUE benchmark task MRPC\nDownload BERT-STEP-2285714.pt and bert-config.json.\n\n\tcd examples/nlp/glue_benchmark;\n \n python glue_benchmark_with_bert.py --data_dir $mrpc_dataset --task_name mrpc --pretrained_bert_model bert-base-uncased --bert_checkpoint /path_to/BERT-STEP-2285714.pt --bert_config /path_to/bert-config.json --lr 2e-5 \n\n## Usage example 3: Using BERT checkpoint for downstream task SQuAD Question Answering task\nDownload BERT-STEP-2285714.pt and bert-config.json.\n### SQuAD v1.1\n\n \tcd examples/nlp/question_answering;\n \n python -m torch.distributed.launch --nproc_per_node=8 question_answering_squad.py --mode train_eval --amp_opt_level O1 --num_gpus 8 --train_file=/path_to/squad/v1.1/train-v1.1.json --eval_file /path_to/squad/v1.1/dev-v1.1.json --bert_checkpoint /path_to/BERT-STEP-2285714.pt --bert_config /path_to/bert-config.json --pretrained_model_name bert-base-uncased --batch_size 3 --num_epochs 2 --lr_policy SquareRootAnnealing --optimizer adam_w --lr 3e-5 --do_lower_case --no_data_cache\n\n### SQuAD v2.0\n\n \tcd examples/nlp/question_answering;\n\n \tpython -m torch.distributed.launch --nproc_per_node=8 question_answering_squad.py --mode train_eval --amp_opt_level O1 --num_gpus 8 --train_file /path_to/squad/v2.0/train-v2.0.json --eval_file /path_to/squad/v2.0/dev-v2.0.json 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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_biz_pendingA","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","AMP_TEST","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","_biz_flagsA","This cookie is used to remember users' settings as well as for authentication and analytics.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. 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It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. 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Usually associated with managing sessions on load balanced servers, to ensure user requests are routed consistently to the correct server. The common root is BIGipServer most commonly followed by a domain name, usually the one that it is hosted on, but not always.","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a one year lifespan and contains no personal information.","default","

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Name	Size	Updated	Actions
bert-config.json	313 B	February 28, 2025 UTC	curl wget Download
BERT-STEP-2285714.pt	417.69 MB	February 28, 2025 UTC	curl wget Download
BertTokenClassifier-STEP-2285714.pt	91.8 MB	February 28, 2025 UTC	curl wget Download
SequenceClassifier-STEP-2285714.pt	2.26 MB	February 28, 2025 UTC	curl wget Download