{
    "bomFormat": "CycloneDX",
    "specVersion": "1.6",
    "serialNumber": "urn:uuid:2d71328a-f87c-48dc-9f4e-ba83929b1cb0",
    "version": 1,
    "metadata": {
        "timestamp": "2025-06-05T09:39:43.530276+00:00",
        "component": {
            "type": "machine-learning-model",
            "bom-ref": "dslim/bert-base-NER-40551b74-59a4-53a9-ae36-e1dca4f66e41",
            "name": "dslim/bert-base-NER",
            "externalReferences": [
                {
                    "url": "https://huggingface.co/dslim/bert-base-NER",
                    "type": "documentation"
                }
            ],
            "modelCard": {
                "modelParameters": {
                    "task": "token-classification",
                    "architectureFamily": "bert",
                    "modelArchitecture": "BertForTokenClassification",
                    "datasets": [
                        {
                            "ref": "conll2003-be67a053-25af-52ad-93c8-134501f8fa4b"
                        }
                    ]
                },
                "properties": [
                    {
                        "name": "library_name",
                        "value": "transformers"
                    }
                ],
                "quantitativeAnalysis": {
                    "performanceMetrics": [
                        {
                            "slice": "dataset: conll2003, split: test, config: conll2003",
                            "type": "accuracy",
                            "value": 0.9118041001560013
                        },
                        {
                            "slice": "dataset: conll2003, split: test, config: conll2003",
                            "type": "precision",
                            "value": 0.9211550382257732
                        },
                        {
                            "slice": "dataset: conll2003, split: test, config: conll2003",
                            "type": "recall",
                            "value": 0.9306415698281261
                        },
                        {
                            "slice": "dataset: conll2003, split: test, config: conll2003",
                            "type": "f1",
                            "value": 0.9258740048459675
                        },
                        {
                            "slice": "dataset: conll2003, split: test, config: conll2003",
                            "type": "loss",
                            "value": 0.48325642943382263
                        }
                    ]
                }
            },
            "authors": [
                {
                    "name": "dslim"
                }
            ],
            "licenses": [
                {
                    "license": {
                        "id": "MIT",
                        "url": "https://spdx.org/licenses/MIT.html"
                    }
                }
            ],
            "description": "**bert-base-NER** is a fine-tuned BERT model that is ready to use for **Named Entity Recognition** and achieves **state-of-the-art performance** for the NER task. It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PER) and Miscellaneous (MISC).Specifically, this model is a *bert-base-cased* model that was fine-tuned on the English version of the standard [CoNLL-2003 Named Entity Recognition](https://www.aclweb.org/anthology/W03-0419.pdf) dataset.If you'd like to use a larger BERT-large model fine-tuned on the same dataset, a [**bert-large-NER**](https://huggingface.co/dslim/bert-large-NER/) version is also available.",
            "tags": [
                "transformers",
                "pytorch",
                "tf",
                "jax",
                "onnx",
                "safetensors",
                "bert",
                "token-classification",
                "en",
                "dataset:conll2003",
                "arxiv:1810.04805",
                "license:mit",
                "model-index",
                "autotrain_compatible",
                "endpoints_compatible",
                "region:us"
            ]
        }
    },
    "components": [
        {
            "type": "data",
            "bom-ref": "conll2003-be67a053-25af-52ad-93c8-134501f8fa4b",
            "name": "conll2003",
            "data": [
                {
                    "type": "dataset",
                    "bom-ref": "conll2003-be67a053-25af-52ad-93c8-134501f8fa4b",
                    "name": "conll2003",
                    "contents": {
                        "url": "https://huggingface.co/datasets/conll2003",
                        "properties": [
                            {
                                "name": "task_categories",
                                "value": "token-classification"
                            },
                            {
                                "name": "task_ids",
                                "value": "named-entity-recognition, part-of-speech"
                            },
                            {
                                "name": "language",
                                "value": "en"
                            },
                            {
                                "name": "size_categories",
                                "value": "10K<n<100K"
                            },
                            {
                                "name": "annotations_creators",
                                "value": "crowdsourced"
                            },
                            {
                                "name": "language_creators",
                                "value": "found"
                            },
                            {
                                "name": "pretty_name",
                                "value": "CoNLL-2003"
                            },
                            {
                                "name": "source_datasets",
                                "value": "extended|other-reuters-corpus"
                            },
                            {
                                "name": "paperswithcode_id",
                                "value": "conll-2003"
                            },
                            {
                                "name": "license",
                                "value": "other"
                            }
                        ]
                    },
                    "governance": {
                        "owners": [
                            {
                                "organization": {
                                    "name": "eriktks",
                                    "url": "https://huggingface.co/eriktks"
                                }
                            }
                        ]
                    },
                    "description": "The shared task of CoNLL-2003 concerns language-independent named entity recognition. We will concentrate on\nfour types of named entities: persons, locations, organizations and names of miscellaneous entities that do\nnot belong to the previous three groups.\n\nThe CoNLL-2003 shared task data files contain four columns separated by a single space. Each word has been put on\na separate line and there is an empty line after each sentence. The first item on each line is a word, the second\na part-of-speech (POS) tag, the third a syntactic chunk tag and the fourth the named entity tag. The chunk tags\nand the named entity tags have the format I-TYPE which means that the word is inside a phrase of type TYPE. Only\nif two phrases of the same type immediately follow each other, the first word of the second phrase will have tag\nB-TYPE to show that it starts a new phrase. A word with tag O is not part of a phrase. Note the dataset uses IOB2\ntagging scheme, whereas the original dataset uses IOB1.\n\nFor more details see https://www.clips.uantwerpen.be/conll2003/ner/ and https://www.aclweb.org/anthology/W03-0419"
                }
            ]
        }
    ]
}