add AIBOM

Dear model owner(s),
We are a group of researchers investigating the usefulness of sharing AIBOMs (Artificial Intelligence Bill of Materials) to document AI models – AIBOMs are machine-readable structured lists of components (e.g., datasets and models) used to enhance transparency in AI-model supply chains.

To pursue the above-mentioned objective, we identified popular models on HuggingFace and, based on your model card (and some configuration information available in HuggingFace), we generated your AIBOM according to the CyclonDX (v1.6) standard (see https://cyclonedx.org/docs/1.6/json/). AIBOMs are generated as JSON files by using the following open-source supporting tool: https://github.com/MSR4SBOM/ALOHA (technical details are available in the research paper: https://github.com/MSR4SBOM/ALOHA/blob/main/ALOHA.pdf).

The JSON file in this pull request is your AIBOM (see https://github.com/MSR4SBOM/ALOHA/blob/main/documentation.json for details on its structure).

Clearly, the submitted AIBOM matches the current model information, yet it can be easily regenerated when the model evolves, using the aforementioned AIBOM generator tool.

We open this pull request containing an AIBOM of your AI model, and hope it will be considered. We would also like to hear your opinion on the usefulness (or not) of AIBOM by answering a 3-minute anonymous survey: https://forms.gle/WGffSQD5dLoWttEe7.

Thanks in advance, and regards,
Riccardo D’Avino, Fatima Ahmed, Sabato Nocera, Simone Romano, Giuseppe Scanniello (University of Salerno, Italy),
Massimiliano Di Penta (University of Sannio, Italy),
The MSR4SBOM team

dslim_bert-base-NER.json

@@ -0,0 +1,169 @@
+{
+    "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"
+                }
+            ]
+        }
+    ]
+}