TokenClassification (Named Entity Recognition) Model Card

Model Overview

The TokenClassification Model in TLT 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.

Intended Use

The primary use case of this model is to identify entities in a given input text.\nThe model supports the following categories:

LOC = Geographical Entity
ORG = Organization
PER = Person
GPE = Geopolitical Entity
TIME = Time indicator
MICS = Artifact, Event, Natural Phenomenon
O = none

Input of the model: Text\nOutput of the model: Category labels for each token in the input text.

Model Architecture

The current version of the Named Entity Recognition model consists of the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model 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).

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.

Training Data

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.

Evaluation

Evaluation results with BERT base uncased model.\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(1) marking the whole entity as a single label\n(2) 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

Evaluation on CoNLL-2003 dev set:

On CoNLL-2003 dev set https://www.clips.uantwerpen.be/conll2003/ner/\n                           precision    recall  f1-score   support\n          O (label id: 0)     0.9834    0.9313    0.9566     42759\n      B-GPE (label id: 1)     0.0000    0.0000    0.0000         0\n      B-LOC (label id: 2)     0.7847    0.7757    0.7802      1837\n     B-MISC (label id: 3)     0.2917    0.0304    0.0550       922\n      B-ORG (label id: 4)     0.5664    0.5884    0.5772      1341\n      B-PER (label id: 5)     0.8591    0.7845    0.8201      1842\n     B-TIME (label id: 6)     0.0000    0.0000    0.0000         0\n      I-GPE (label id: 7)     0.0000    0.0000    0.0000         0\n      I-LOC (label id: 8)     0.4624    0.6226    0.5307       257\n     I-MISC (label id: 9)     0.3605    0.0896    0.1435       346\n     I-ORG (label id: 10)     0.4510    0.6618    0.5364       751\n     I-PER (label id: 11)     0.8800    0.8585    0.8691      1307\n    I-TIME (label id: 12)     0.0000    0.0000    0.0000         0\n    \n                 accuracy                         0.8823     51362\n                macro avg     0.4338    0.4110    0.4053     51362\n             weighted avg     0.9313    0.8823    0.9034     51362\n

Note, that some labels of the GMB dataset are missing in the CoNLL dataset which led to the
\nmultiple zeros in the above table and the reduced values of the aggregated metrics (the three lines in the bottom of the table).

How to use this model

This pre-trained model needs to be used with NVIDIA Hardware and Software.\nThis model can be used with Transfer Learning Toolkit (TLT) with model load key: tlt_encode (Please make sure to use this as the key for all TLT commands that require a model load key).

For more details on how to prepare your data for this model and how to fine-tune the model with TLT, see TLT user guide for Token Classification model.

References

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

License

By downloading and using the models and resources packaged with TLT Conversational AI, you would be accepting the terms of the Jarvis license

Ethical AI

NVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications.\nConsider potential algorithmic bias when choosing or creating the models being deployed.\nWork with the model’s developer to ensure that it meets the requirements for the relevant industry and use case;\nthat the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended.

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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\nIntended Use\n------------\n\nThe primary use case of this model is to identify entities in a given input text.\nThe model supports the following categories:\n\n- LOC = Geographical Entity\n- ORG = Organization\n- PER = Person\n- GPE = Geopolitical Entity\n- TIME = Time indicator\n- MICS = Artifact, Event, Natural Phenomenon\n- O = none\n\nInput of the model: Text\nOutput of the model: Category labels for each token in the input text.\n\n\nModel Architecture\n------------------\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 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\nLimitations\n-----------\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.\n\n\nTraining Data\n-------------\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\nEvaluation\n----------\nEvaluation results with [BERT](https://arxiv.org/pdf/1810.04805.pdf) base uncased model.\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(1) marking the whole entity as a single label\n(2) 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\nEvaluation on CoNLL-2003 dev set:\n\n On CoNLL-2003 dev set https://www.clips.uantwerpen.be/conll2003/ner/\n precision recall f1-score support\n O (label id: 0) 0.9834 0.9313 0.9566 42759\n B-GPE (label id: 1) 0.0000 0.0000 0.0000 0\n B-LOC (label id: 2) 0.7847 0.7757 0.7802 1837\n B-MISC (label id: 3) 0.2917 0.0304 0.0550 922\n B-ORG (label id: 4) 0.5664 0.5884 0.5772 1341\n B-PER (label id: 5) 0.8591 0.7845 0.8201 1842\n B-TIME (label id: 6) 0.0000 0.0000 0.0000 0\n I-GPE (label id: 7) 0.0000 0.0000 0.0000 0\n I-LOC (label id: 8) 0.4624 0.6226 0.5307 257\n I-MISC (label id: 9) 0.3605 0.0896 0.1435 346\n I-ORG (label id: 10) 0.4510 0.6618 0.5364 751\n I-PER (label id: 11) 0.8800 0.8585 0.8691 1307\n I-TIME (label id: 12) 0.0000 0.0000 0.0000 0\n \n accuracy 0.8823 51362\n macro avg 0.4338 0.4110 0.4053 51362\n weighted avg 0.9313 0.8823 0.9034 51362\n \n\nNote, that some labels of the GMB dataset are missing in the CoNLL dataset which led to the \\\nmultiple zeros in the above table and the reduced values of the aggregated metrics (the three lines in the bottom of the table).\n\nHow to use this model\n---------------------\n\nThis pre-trained model needs to be used with NVIDIA Hardware and Software. \nThis model can be used with [Transfer Learning Toolkit](https://developer.nvidia.com/transfer-learning-toolkit) (TLT) with model load key: `tlt_encode` (Please make sure to use this as the key for all TLT commands that require a model load key).\n\nFor more details on how to prepare your data for this model and how to fine-tune the model with TLT, see [TLT user guide for Token Classification model](https://docs.nvidia.com/metropolis/TLT/tlt-user-guide/text/nlp/token_classification.html).\n\n\nReferences\n----------\n- [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\nSuggested Reading\n-----------------\n- More information about the Transfer Learning Toolkit can be found at the NVIDIA Developer Zone: https://developer.nvidia.com/transfer-learning-toolkit\n- Read the [TLT getting Started guide](https://docs.nvidia.com/TLT/tlt-user-guide) and [release notes](https://docs.nvidia.com/TLT/tlt-user-guide/text/release_notes.html).\n- More information about the experiment spec files can be found in the [TLT User Guide](https://docs.nvidia.com/TLT/tlt-user-guide/text/overview.html)\n- More information about deploying the model with Jarvis Services see [this](http://docs.jarvis-ai.nvidia.com/release-1-0/model-servicemaker.html#)\n- If you have any questions or feedback, please refer to the discussions on [TLT Developer Forums](https://devtalk.nvidia.com/default/board/417/transfer-learning-toolkit/)\n\nLicense\n-------\n\nBy downloading and using the models and resources packaged with TLT Conversational AI, you would be accepting the terms of the Jarvis [license](https://developer.nvidia.com/ngc/nvidia-jarvis-license)\n\nEthical AI\n----------\n\nNVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications.\nConsider potential algorithmic bias when choosing or creating the models being deployed.\nWork with the model’s developer to ensure that it meets the requirements for the relevant industry and use case;\nthat the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended.","Named Entity Recognition 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We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558],[422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438],{"_439":532,"_91":532,"_90":257,"_412":533},{"_439":528,"_91":528,"_90":257,"_412":529},{"_439":523,"_91":523,"_90":257,"_412":524},{"_439":519,"_91":519,"_90":257,"_412":520},{"_439":514,"_91":514,"_90":257,"_412":515},{"_439":509,"_91":509,"_90":257,"_412":510},{"_439":501,"_91":501,"_90":257,"_412":502},{"_439":493,"_91":493,"_90":257,"_412":494},{"_439":488,"_91":488,"_90":257,"_412":489},{"_439":483,"_91":483,"_90":257,"_412":484},{"_439":478,"_91":478,"_90":257,"_412":479},{"_439":473,"_91":473,"_90":257,"_412":474},{"_439":468,"_91":468,"_90":257,"_412":469},{"_439":461,"_91":461,"_90":257,"_412":462},{"_439":456,"_91":456,"_90":257,"_412":457},{"_439":449,"_91":449,"_90":257,"_412":450},{"_439":440,"_91":440,"_90":257,"_412":441},"hostName","amazon-adsystem.com",[442,443],{"_284":448,"_445":440,"_446":26,"_90":447},{"_284":444,"_445":440,"_446":26,"_90":447},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[451,452],{"_284":455,"_445":449,"_446":26,"_90":454},{"_284":453,"_445":449,"_446":26,"_90":454},"IDE","This domain is owned by Doubleclick (Google). 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It uses the data gathered from most of these services to profile the interests of web users and sell advertising space to organisations based on such interest profiles as well as aligning adverts to the content on the pages where its customer's adverts appear.","linkedin.com",[490],{"_284":491,"_445":488,"_446":26,"_90":492},"bcookie","This domain is owned by LinkedIn, the business networking platform. It typically acts as a third party host where website owners have placed one of its content sharing buttons in their pages, although its content and services can be embedded in other ways. Although such buttons add functionality to the website they are on, cookies are set regardless of whether or not the visitor has an active Linkedin profile, or agreed to their terms and conditions. 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This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_bc_session_id","ttcsid_XXXXXXXXXXXXXXXXXXXXCategory","This cookie name is associated with TikTok which is used to track your activity on the platform, including how you interact with their embedded services on other websites.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","_fbp","Used by Facebook to deliver a series of advertisement products such as real time bidding from third party advertisers","_zitok","_twpid","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. 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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653],[600,601,602,603,604],{"_439":514,"_91":514,"_90":257,"_412":624},{"_439":488,"_91":488,"_90":257,"_412":619},{"_439":614,"_91":614,"_90":257,"_412":615},{"_439":609,"_91":609,"_90":257,"_412":610},{"_439":605,"_91":605,"_90":257,"_412":606},"nvidia.partners",[607],{"_284":608,"_445":605,"_446":26,"_90":482},"edge","app.hushly.com",[611],{"_284":612,"_445":609,"_446":26,"_90":613},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[616],{"_284":617,"_445":614,"_446":26,"_90":618},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",3,[713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741],[693,694,695,696],{"_439":708,"_91":708,"_90":257,"_412":709},{"_439":703,"_91":703,"_90":257,"_412":704},{"_439":501,"_91":501,"_90":257,"_412":700},{"_439":614,"_91":614,"_90":257,"_412":697},[698],{"_284":699,"_445":614,"_446":177,"_90":618},"_gh_sess",[701],{"_284":702,"_445":501,"_446":177,"_90":508},"YSC","bizible.com",[705],{"_284":706,"_445":703,"_446":26,"_90":707},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[710],{"_284":711,"_445":708,"_446":26,"_90":712},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_284":783,"_445":560,"_446":26,"_90":784},{"_284":768,"_445":782,"_446":177,"_90":769},{"_284":751,"_445":563,"_446":26,"_90":675},{"_284":751,"_445":663,"_446":26,"_90":675},{"_284":780,"_445":560,"_446":26,"_90":781},{"_284":768,"_445":566,"_446":177,"_90":769},{"_284":778,"_445":560,"_446":26,"_90":779},{"_284":768,"_445":563,"_446":177,"_90":769},{"_284":776,"_445":662,"_446":26,"_90":777},{"_284":768,"_445":684,"_446":177,"_90":769},{"_284":748,"_445":563,"_446":26,"_90":750},{"_284":774,"_445":560,"_446":177,"_90":775},{"_284":772,"_445":560,"_446":26,"_90":773},{"_284":770,"_445":560,"_446":177,"_90":771},{"_284":768,"_445":658,"_446":177,"_90":769},{"_284":766,"_445":560,"_446":26,"_90":767},{"_284":764,"_445":560,"_446":26,"_90":765},{"_284":762,"_445":560,"_446":177,"_90":763},{"_284":760,"_445":667,"_446":26,"_90":761},{"_284":748,"_445":658,"_446":26,"_90":750},{"_284":758,"_445":560,"_446":26,"_90":759},{"_284":756,"_445":560,"_446":177,"_90":757},{"_284":754,"_445":560,"_446":26,"_90":755},{"_284":752,"_445":560,"_446":26,"_90":753},{"_284":751,"_445":560,"_446":26,"_90":675},{"_284":748,"_445":749,"_446":26,"_90":750},{"_284":746,"_445":560,"_446":26,"_90":747},{"_284":744,"_445":560,"_446":177,"_90":745},{"_284":742,"_445":560,"_446":26,"_90":743},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. It contains the current session ID, whether the session is excluded due to sampling, and the expiration date of the session. The cookie is extended for an extra 15 minutes every time the user interacts with the website, up to the maximum user session duration (4 hours).","nvweb_A","_ga","This cookie name is associated with Google Universal Analytics - which is a significant update to Google's more commonly used analytics service. This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. 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