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

chandar-lab_NeoBERT.json

@@ -0,0 +1,115 @@
+{
+    "bomFormat": "CycloneDX",
+    "specVersion": "1.6",
+    "serialNumber": "urn:uuid:1c879a20-16da-4bf5-901d-c0989128f99f",
+    "version": 1,
+    "metadata": {
+        "timestamp": "2025-06-05T09:41:58.948971+00:00",
+        "component": {
+            "type": "machine-learning-model",
+            "bom-ref": "chandar-lab/NeoBERT-d613029c-4864-500f-a464-bcbfff6cda00",
+            "name": "chandar-lab/NeoBERT",
+            "externalReferences": [
+                {
+                    "url": "https://huggingface.co/chandar-lab/NeoBERT",
+                    "type": "documentation"
+                }
+            ],
+            "modelCard": {
+                "modelParameters": {
+                    "task": "feature-extraction",
+                    "architectureFamily": "neobert",
+                    "modelArchitecture": "NeoBERTLMHead",
+                    "datasets": [
+                        {
+                            "ref": "tiiuae/falcon-refinedweb-0f0f969c-55e1-583f-88a6-b6a6fc0a250c"
+                        }
+                    ]
+                },
+                "properties": [
+                    {
+                        "name": "library_name",
+                        "value": "transformers"
+                    }
+                ]
+            },
+            "authors": [
+                {
+                    "name": "chandar-lab"
+                }
+            ],
+            "licenses": [
+                {
+                    "license": {
+                        "id": "MIT",
+                        "url": "https://spdx.org/licenses/MIT.html"
+                    }
+                }
+            ],
+            "tags": [
+                "transformers",
+                "safetensors",
+                "neobert",
+                "fill-mask",
+                "feature-extraction",
+                "custom_code",
+                "en",
+                "dataset:tiiuae/falcon-refinedweb",
+                "arxiv:2502.19587",
+                "license:mit",
+                "autotrain_compatible",
+                "region:us"
+            ]
+        }
+    },
+    "components": [
+        {
+            "type": "data",
+            "bom-ref": "tiiuae/falcon-refinedweb-0f0f969c-55e1-583f-88a6-b6a6fc0a250c",
+            "name": "tiiuae/falcon-refinedweb",
+            "data": [
+                {
+                    "type": "dataset",
+                    "bom-ref": "tiiuae/falcon-refinedweb-0f0f969c-55e1-583f-88a6-b6a6fc0a250c",
+                    "name": "tiiuae/falcon-refinedweb",
+                    "contents": {
+                        "url": "https://huggingface.co/datasets/tiiuae/falcon-refinedweb",
+                        "properties": [
+                            {
+                                "name": "task_categories",
+                                "value": "text-generation"
+                            },
+                            {
+                                "name": "language",
+                                "value": "en"
+                            },
+                            {
+                                "name": "size_categories",
+                                "value": "100B<n<1T"
+                            },
+                            {
+                                "name": "pretty_name",
+                                "value": "Falcon RefinedWeb"
+                            },
+                            {
+                                "name": "license",
+                                "value": "odc-by"
+                            }
+                        ]
+                    },
+                    "governance": {
+                        "owners": [
+                            {
+                                "organization": {
+                                    "name": "tiiuae",
+                                    "url": "https://huggingface.co/tiiuae"
+                                }
+                            }
+                        ]
+                    },
+                    "description": "\n\t\n\t\t\n\t\t\ud83d\udcc0 Falcon RefinedWeb\n\t\n\nFalcon RefinedWeb is a massive English web dataset built by TII and released under an ODC-By 1.0 license.\nSee the \ud83d\udcd3 paper on arXiv for more details. \nRefinedWeb is built through stringent filtering and large-scale deduplication of CommonCrawl; we found models trained on RefinedWeb to achieve performance in-line or better than models trained on curated datasets, while only relying on web data. \nRefinedWeb is also \"multimodal-friendly\": it contains links and alt\u2026 See the full description on the dataset page: https://huggingface.co/datasets/tiiuae/falcon-refinedweb."
+                }
+            ]
+        }
+    ]
+}