Model Overview

The TokenClassification Model in TAO supports Named Entity Recognition (NER) and other token-level classification tasks, as long as the data follows the format specified below. This model card will focus on the NER task.

Named entity recognition (NER), also referred to as entity chunking, identification or extraction, is the task of detecting and classifying key information (entities) in text. In other words, a NER model takes a piece of text as input and for each word in the text, the model identifies a category the word belongs to.\nFor example, in a sentence: Mary lives in Santa Clara and works at NVIDIA, the model should detect that Mary is a person, Santa Clara is a location and NVIDIA is a company.

Trained or fine-tuned NeMo models (with the file extenstion .nemo) can be converted to Riva models (with the file extension .riva) and then deployed. Here is a pre-trained Riva model for NER with BERT in English.

Model Architecture

The current version of the Named Entity Recognition model consists of the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model [1] followed by a token classification head. All model parameters are jointly fine-tuned on the downstream task. More specifically, an\ninput text is fed to the BERT model, and then the [CLS] representation of the text sequence is passed to the classification layer(s).

Training

The model was trained with NeMo BERT base uncased checkpoint.

Dataset

The model was trained on GMB (Groningen Meaning Bank) corpus for entity recognition.\nThe GMB dataset is a fairly large corpus with a lot of annotations. Note, that GMB is not completely human annotated and it’s not considered 100% correct. The data is labeled using the IOB format (short for inside, outside, beginning).\nThe following classes appear in the dataset:

LOC = Geographical Entity
ORG = Organization
PER = Person
GPE = Geopolitical Entity
TIME = Time indicator
ART = Artifact
EVE = Event
NAT = Natural Phenomenon

For this model, the classes ART, EVE, and NAT were combined into a MISC class due to small number of examples for these classes.

Pre-processed data that was used for training and evaluation could be found here.

Performance

Each word in the input sequence could be split into one or more tokens, as a result, there are two possible ways of the model evaluation:

marking the whole entity as a single label
perform evaluation on the sub token level

Here, the first approach was applied, and the predictions for the first token of the input were used to label the whole word.\nDue to high class unbalancing, the suggested metric for this model is F1 score (with macro averaging).

Evaluation on the GMB dataset dev set:

                            precision    recall  f1-score   support\n          O (label id: 0)     0.9913    0.9917    0.9915    131141\n      B-GPE (label id: 1)     0.9574    0.9420    0.9496      2362\n      B-LOC (label id: 2)     0.8402    0.9037    0.8708      5346\n     B-MISC (label id: 3)     0.4124    0.3077    0.3524       130\n      B-ORG (label id: 4)     0.7732    0.6805    0.7239      2980\n      B-PER (label id: 5)     0.8335    0.8510    0.8422      2577\n     B-TIME (label id: 6)     0.9176    0.9133    0.9154      2975\n      I-GPE (label id: 7)     0.8889    0.3478    0.5000        23\n      I-LOC (label id: 8)     0.7782    0.7835    0.7808      1030\n     I-MISC (label id: 9)     0.3036    0.2267    0.2595        75\n     I-ORG (label id: 10)     0.7712    0.7466    0.7587      2384\n     I-PER (label id: 11)     0.8710    0.8820    0.8765      2687\n    I-TIME (label id: 12)     0.8255    0.8273    0.8264       938\n    \n                 accuracy                         0.9689    154648\n                macro avg     0.7818    0.7234    0.7421    154648\n             weighted avg     0.9685    0.9689    0.9686    154648\n

How to use this model

The model is available for use in the NeMo toolkit [2], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.

Input

The model takes text as input.

Output

The model outputs category labels for each token in the input text.

Automatically load the model from NGC

import nemo\nimport nemo.collections.nlp as nemo_nlp\nmodel = nemo_nlp.models.TokenClassificationModel.from_pretrained(model_name=\"ner_en_bert\")\nmodel.add_predictions(['we bought four shirts from the nvidia gear store in santa clara.', 'NVIDIA is a company.'])\n

Limitations

The length of the input text is currently constrained by the maximum sequence length of the BERT base uncased model, which is 512 tokens after tokenization. The punctuation model supports commas, periods and question marks.

References

[1] Devlin, Jacob, et al. \"Bert: Pre-training of deep bidirectional transformers for language understanding.\" arXiv preprint arXiv:1810.04805 (2018).

[2] 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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This model card will focus on the NER task.\n\nNamed entity recognition (NER), also referred to as entity chunking, identification or extraction, is the task of detecting and classifying key information (entities) in text. In other words, a NER model takes a piece of text as input and for each word in the text, the model identifies a category the word belongs to.\nFor example, in a sentence: `Mary lives in Santa Clara and works at NVIDIA`, the model should detect that `Mary` is a person, `Santa Clara` is a location and `NVIDIA` is a company.\n\nTrained or fine-tuned NeMo models (with the file extenstion `.nemo`) can be converted to Riva models (with the file extension `.riva`) and then deployed. [Here](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/namedentityrecognition_english_bert) is a pre-trained Riva model for NER with BERT in English.\n\n## Model Architecture\n\nThe current version of the Named Entity Recognition model consists of the pre-trained [Bidirectional Encoder Representations from Transformers (BERT)](https://arxiv.org/pdf/1810.04805.pdf) model [1] followed by a token classification head. All model parameters are jointly fine-tuned on the downstream task. More specifically, an \ninput text is fed to the BERT model, and then the [CLS] representation of the text sequence is passed to the classification layer(s).\n\n## Training\n\nThe model was trained with [NeMo BERT base uncased checkpoint](https://ngc.nvidia.com/catalog/models/nvidia:bertbaseuncasedfornemo).\n\n### Dataset\n\nThe model was trained on [GMB (Groningen Meaning Bank)](http://www.let.rug.nl/bjerva/gmb/about.php) corpus for entity recognition.\nThe GMB dataset is a fairly large corpus with a lot of annotations. Note, that GMB is not completely human annotated and it’s not considered 100% correct. The data is labeled using the [IOB format](https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)) (short for inside, outside, beginning).\nThe following classes appear in the dataset:\n\n- LOC = Geographical Entity\n- ORG = Organization\n- PER = Person\n- GPE = Geopolitical Entity\n- TIME = Time indicator\n- ART = Artifact\n- EVE = Event\n- NAT = Natural Phenomenon\n\nFor this model, the classes ART, EVE, and NAT were combined into a MISC class due to small number of examples for these classes.\n\nPre-processed data that was used for training and evaluation could be found [here](https://dldata-public.s3.us-east-2.amazonaws.com/gmb_v_2.2.0_clean.zip).\n\n## Performance\n\nEach word in the input sequence could be split into one or more tokens, as a result, there are two possible ways of the model evaluation: \n- marking the whole entity as a single label\n- perform evaluation on the sub token level\n\nHere, the first approach was applied, and the predictions for the first token of the input were used to label the whole word. \nDue to high class unbalancing, the suggested metric for this model is F1 score (with macro averaging).\n\n\nEvaluation on the GMB dataset dev set:\n \n precision recall f1-score support\n O (label id: 0) 0.9913 0.9917 0.9915 131141\n B-GPE (label id: 1) 0.9574 0.9420 0.9496 2362\n B-LOC (label id: 2) 0.8402 0.9037 0.8708 5346\n B-MISC (label id: 3) 0.4124 0.3077 0.3524 130\n B-ORG (label id: 4) 0.7732 0.6805 0.7239 2980\n B-PER (label id: 5) 0.8335 0.8510 0.8422 2577\n B-TIME (label id: 6) 0.9176 0.9133 0.9154 2975\n I-GPE (label id: 7) 0.8889 0.3478 0.5000 23\n I-LOC (label id: 8) 0.7782 0.7835 0.7808 1030\n I-MISC (label id: 9) 0.3036 0.2267 0.2595 75\n I-ORG (label id: 10) 0.7712 0.7466 0.7587 2384\n I-PER (label id: 11) 0.8710 0.8820 0.8765 2687\n I-TIME (label id: 12) 0.8255 0.8273 0.8264 938\n \n accuracy 0.9689 154648\n macro avg 0.7818 0.7234 0.7421 154648\n weighted avg 0.9685 0.9689 0.9686 154648\n\n\n## How to use this model\n\nThe model is available for use in the NeMo toolkit [2], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.\n\n### Input\n\nThe model takes text as input.\n\n### Output\n\nThe model outputs category labels for each token in the input text.\n\nAutomatically load the model from NGC\n-------------------------------------\n\n\timport nemo\n\timport nemo.collections.nlp as nemo_nlp\n\tmodel = nemo_nlp.models.TokenClassificationModel.from_pretrained(model_name=\"ner_en_bert\")\n model.add_predictions(['we bought four shirts from the nvidia gear store in santa clara.', 'NVIDIA is a company.'])\n\n## Limitations\n\nThe length of the input text is currently constrained by the maximum sequence length of the BERT base uncased model, which is 512 tokens after tokenization. The punctuation model supports commas, periods and question marks.\n\n## References\n\n[1] [Devlin, Jacob, et al. \"Bert: Pre-training of deep bidirectional transformers for language understanding.\" arXiv preprint arXiv:1810.04805 (2018).](https://arxiv.org/pdf/1810.04805.pdf)\n\n[2] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)\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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