Model Overview

This is a checkpoint for BioMegatron 345m uncased. BioMegatron is Megatron language model pretrained on uncased PubMed https://catalog.data.gov/dataset/pubmed, a biomedical domain dataset, which gives state-of-the-art results on a range of biomedical natural language downstream tasks. The model has around 345 million paramters.\nThe model is trained with https://github.com/NVIDIA/Megatron-LM.

Model Architecture

Megatron is a large, powerful transformer developed by the Applied Deep Learning Research team at NVIDIA, which was trained with multinode and using mixed precision. 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.\nBioMegatron has the same network architecture as the Megatron-LM/BERT, but is pretrained on a different dataset - PubMed, a large biomedical text corpus, which achieves better performance in biomedical downstream tasks than the original Megatron.

This 345m papameter model has 24 layers (Transformer blocks), 1024 hidden-units, and 16 attention heads.\nIt uses the original BERT uncased vocabulary learned from Wikipedia and Books corpus.\nFor more detail about the Megatron-LM/BERT architecture, please refer to the Megatron-LM or BERT papers.

For more information about BioBERT or Megatron visit https://ngc.nvidia.com/catalog/models/nvidia:biobertbasecasedfornemo or https://github.com/NVIDIA/Megatron-LM.

Training

Training BioMegatron was done using the Megatron-LM codebase based on PyTorch.

The entire pre-training takes about 400 hours on 8 DGX-2 machines with Tesla V100 GPUs.\nLoss function and hyper-parameter settings are the same as pre-training the BERT language models with the Megatron-LM codebase.

Dataset

Pre-training was done on 4.5 billion-word PubMed abstract set and the 1.6 billion-word CC0-licensed Commercial Use Collection of the PMC full-text corpus.

Performance

The model achieves state-of-the-art results on BioASQ-7b-factoid biomedical question answering:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Benchmark	SAcc	LAcc	MRR
BioASQ-7b-factoid	46.2	62.6	52.5

How to use this Model

NVIDIA NeMo can be used for easy fine-tuning to a number of different tasks.\nTutorial notebooks on fine-tuning the model for Named Entity Recognition, Relation Extraction, and Question Answering can be found on the tutorials page of NeMo.

Ulternatively, users can also choose to develop their own fine-tuning script from the Megatron-LM codebase, or use any other PyTorch-based framework such as Huggingface Transformers by installing the Megatron-LM package from PyPi.

Limitations

No known limitations available at this time.

References

Shin, Hoo-Chang and Zhang, Yang and Bakhturina, Evelina and Puri, Raul and Patwary, Mostofa and Shoeybi, Mohammad and Mani, Raghav, 2020, November. BioMegatron: Larger Biomedical Domain Language Model. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 4700--4706).

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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[BioMegatron](https://arxiv.org/abs/2010.06060) is [Megatron](https://arxiv.org/abs/1909.08053) language model pretrained on uncased PubMed https://catalog.data.gov/dataset/pubmed, a biomedical domain dataset, which gives state-of-the-art results on a range of biomedical natural language downstream tasks. The model has around 345 million paramters.\nThe model is trained with https://github.com/NVIDIA/Megatron-LM.\n\n## Model Architecture\n\nMegatron is a large, powerful transformer developed by the Applied Deep Learning Research team at NVIDIA, which was trained with multinode and using mixed precision. 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. \nBioMegatron has the same network architecture as the Megatron-LM/BERT, but is pretrained on a different dataset - PubMed, a large biomedical text corpus, which achieves better performance in biomedical downstream tasks than the original Megatron.\n\nThis 345m papameter model has 24 layers (Transformer blocks), 1024 hidden-units, and 16 attention heads. \nIt uses the original [BERT](https://arxiv.org/pdf/1810.04805.pdf) uncased vocabulary learned from Wikipedia and Books corpus.\nFor more detail about the Megatron-LM/BERT architecture, please refer to the [Megatron-LM](https://arxiv.org/abs/1909.08053) or [BERT](https://arxiv.org/pdf/1810.04805.pdf) papers.\n\nFor more information about BioBERT or Megatron visit https://ngc.nvidia.com/catalog/models/nvidia:biobertbasecasedfornemo or https://github.com/NVIDIA/Megatron-LM.\n\n## Training\n\nTraining BioMegatron was done using the [Megatron-LM codebase](https://github.com/NVIDIA/Megatron-LM) based on [PyTorch](https://pytorch.org/).\n\nThe entire pre-training takes about 400 hours on 8 DGX-2 machines with Tesla V100 GPUs.\nLoss function and hyper-parameter settings are the same as pre-training the BERT language models with the Megatron-LM codebase.\n\n### Dataset\n\nPre-training was done on 4.5 billion-word [PubMed](https://pubmed.ncbi.nlm.nih.gov/) abstract set and the 1.6 billion-word CC0-licensed Commercial Use Collection of the [PMC full-text corpus](www.ncbi.nlm.nih.gov/pmc).\n\n## Performance\n\nThe model achieves state-of-the-art results on BioASQ-7b-factoid biomedical question answering:\n\n| Benchmark | SAcc | LAcc | MRR |\n|------------------------|------|------|------|\n| BioASQ-7b-factoid | 46.2 | 62.6 | 52.5 |\n\n\n## How to use this Model\n\nNVIDIA [NeMo](https://github.com/nvidia/nemo) can be used for easy fine-tuning to a number of different tasks.\nTutorial notebooks on fine-tuning the model for [Named Entity Recognition](https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/Token_Classification-BioMegatron.ipynb), [Relation Extraction](https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/Relation_Extraction-BioMegatron.ipynb), and [Question Answering](https://github.com/NVIDIA/NeMo/blob/main/tutorials/nlp/Question_Answering_Squad.ipynb) can be found on the [tutorials page](https://github.com/NVIDIA/NeMo/tree/main/tutorials/nlp) of NeMo.\n\nUlternatively, users can also choose to develop their own fine-tuning script from the [Megatron-LM codebase](https://github.com/NVIDIA/Megatron-LM), or use any other PyTorch-based framework such as [Huggingface Transformers](https://github.com/huggingface/transformers) by installing the [Megatron-LM package](https://pypi.org/project/megatron-lm/) from PyPi.\n\n## Limitations\nNo known limitations available at this time.\n\n## References\n\n1. Shin, Hoo-Chang and Zhang, Yang and Bakhturina, Evelina and Puri, Raul and Patwary, Mostofa and Shoeybi, Mohammad and Mani, Raghav, 2020, November. BioMegatron: Larger Biomedical Domain Language Model. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 4700--4706).\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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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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