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

microsoft_trocr-base-printed.json

@@ -0,0 +1,54 @@
+{
+    "bomFormat": "CycloneDX",
+    "specVersion": "1.6",
+    "serialNumber": "urn:uuid:c7da9c3c-d2ac-492d-8ba9-c32ab0fee18d",
+    "version": 1,
+    "metadata": {
+        "timestamp": "2025-06-05T09:41:51.593289+00:00",
+        "component": {
+            "type": "machine-learning-model",
+            "bom-ref": "microsoft/trocr-base-printed-d1008ba0-f8e7-5f29-8d9d-5730d25177fd",
+            "name": "microsoft/trocr-base-printed",
+            "externalReferences": [
+                {
+                    "url": "https://huggingface.co/microsoft/trocr-base-printed",
+                    "type": "documentation"
+                }
+            ],
+            "modelCard": {
+                "modelParameters": {
+                    "task": "image-to-text",
+                    "architectureFamily": "vision-encoder-decoder",
+                    "modelArchitecture": "VisionEncoderDecoderModel"
+                },
+                "properties": [
+                    {
+                        "name": "library_name",
+                        "value": "transformers"
+                    }
+                ],
+                "consideration": {
+                    "useCases": "You can use the raw model for optical character recognition (OCR) on single text-line images. See the [model hub](https://huggingface.co/models?search=microsoft/trocr) to look for fine-tuned versions on a task that interests you."
+                }
+            },
+            "authors": [
+                {
+                    "name": "microsoft"
+                }
+            ],
+            "description": "The TrOCR model is an encoder-decoder model, consisting of an image Transformer as encoder, and a text Transformer as decoder. The image encoder was initialized from the weights of BEiT, while the text decoder was initialized from the weights of RoBERTa.Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder. Next, the Transformer text decoder autoregressively generates tokens.",
+            "tags": [
+                "transformers",
+                "pytorch",
+                "safetensors",
+                "vision-encoder-decoder",
+                "image-text-to-text",
+                "trocr",
+                "image-to-text",
+                "arxiv:2109.10282",
+                "endpoints_compatible",
+                "region:us"
+            ]
+        }
+    }
+}