TokenClassification (Named Entity Recognition) Model Card

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.

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\nO (label id: 0) 0.9913 0.9917 0.9915 131141\nB-GPE (label id: 1) 0.9574 0.9420 0.9496 2362\nB-LOC (label id: 2) 0.8402 0.9037 0.8708 5346\nB-MISC (label id: 3) 0.4124 0.3077 0.3524 130\nB-ORG (label id: 4) 0.7732 0.6805 0.7239 2980\nB-PER (label id: 5) 0.8335 0.8510 0.8422 2577\nB-TIME (label id: 6) 0.9176 0.9133 0.9154 2975\nI-GPE (label id: 7) 0.8889 0.3478 0.5000 23\nI-LOC (label id: 8) 0.7782 0.7835 0.7808 1030\nI-MISC (label id: 9) 0.3036 0.2267 0.2595 75\nI-ORG (label id: 10) 0.7712 0.7466 0.7587 2384\nI-PER (label id: 11) 0.8710 0.8820 0.8765 2687\nI-TIME (label id: 12) 0.8255 0.8273 0.8264 938

accuracy 0.9689 154648\nmacro avg 0.7818 0.7234 0.7421 154648\nweighted avg 0.9685 0.9689 0.9686 154648

Evaluation on CoNLL-2003 dev set:

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

accuracy 0.8823 51362\nmacro avg 0.4338 0.4110 0.4053 51362\nweighted avg 0.9313 0.8823 0.9034 51362

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 Train Adapt Optimize (TAO) Toolkit with model load key: tlt_encode (Please make sure to use this as the key for all TAO 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 TAO, see TAO 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 TAO Conversational AI, you would be accepting the terms of the Riva 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 [Train Adapt Optimize (TAO) Toolkit](https://developer.nvidia.com/tao-toolkit) with model load key: `tlt_encode` (Please make sure to use this as the key for all TAO 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 TAO, see [TAO user guide for Token Classification model](https://docs.nvidia.com/metropolis/TAO/tao-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 TAO Toolkit can be found at the NVIDIA Developer Zone: https://developer.nvidia.com/tao-toolkit\n- Read the [TAO getting Started guide](https://docs.nvidia.com/tao/tao-toolkit/text/tao_quick_start_guide.html) and [release notes](https://docs.nvidia.com/tao/tao-toolkit/text/release_notes.html).\n- More information about the experiment spec files can be found in the [TAO User Guide](https://docs.nvidia.com/tao/tao-toolkit/text/overview.html)\n- More information about deploying the model with Riva Services see [this](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/model-overview.html#)\n- If you have any questions or feedback, please refer to the discussions on [TAO Toolkit Developer Forums](https://forums.developer.nvidia.com/c/accelerated-computing/intelligent-video-analytics/tao-toolkit/17)\n\nLicense\n-------\n\nBy downloading and using the models and resources packaged with TAO Conversational AI, you would be accepting the terms of the Riva [license](https://developer.nvidia.com/riva/ga/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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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. 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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.",[592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616],[563,564,565,566,567],{"_402":477,"_92":477,"_91":217,"_375":587},{"_402":451,"_92":451,"_91":217,"_375":582},{"_402":577,"_92":577,"_91":217,"_375":578},{"_402":572,"_92":572,"_91":217,"_375":573},{"_402":568,"_92":568,"_91":217,"_375":569},"nvidia.partners",[570],{"_245":571,"_408":568,"_409":26,"_91":445},"edge","app.hushly.com",[574],{"_245":575,"_408":572,"_409":26,"_91":576},"*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",[579],{"_245":580,"_408":577,"_409":26,"_91":581},"_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. 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