AdrianGonzalezSanchez/ModelTransparencySpec_EUAIAct

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The dataset generation failed

Error code:   DatasetGenerationError
Exception:    ArrowNotImplementedError
Message:      Cannot write struct type 'Approach 2' with no child field to Parquet. Consider adding a dummy child field.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 642, in write_table
                  self._build_writer(inferred_schema=pa_table.schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 457, in _build_writer
                  self.pa_writer = self._WRITER_CLASS(self.stream, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 1010, in __init__
                  self.writer = _parquet.ParquetWriter(
                File "pyarrow/_parquet.pyx", line 2157, in pyarrow._parquet.ParquetWriter.__cinit__
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowNotImplementedError: Cannot write struct type 'Approach 2' with no child field to Parquet. Consider adding a dummy child field.
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1847, in _prepare_split_single
                  num_examples, num_bytes = writer.finalize()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 661, in finalize
                  self._build_writer(self.schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 457, in _build_writer
                  self.pa_writer = self._WRITER_CLASS(self.stream, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 1010, in __init__
                  self.writer = _parquet.ParquetWriter(
                File "pyarrow/_parquet.pyx", line 2157, in pyarrow._parquet.ParquetWriter.__cinit__
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowNotImplementedError: Cannot write struct type 'Approach 2' with no child field to Parquet. Consider adding a dummy child field.
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1456, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1055, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 894, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 970, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1702, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1858, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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Part1_EUAICodeOfPractice_TransparencyChapter dict	Part2_AIRedTeaming dict
{ "GeneralInformation": { "LegalNameProvider": { "description": "Legal name for the model provider", "value": "", "AIO": true, "NCAs": true, "DPs": true, "explanation": "Legal name for the model provider." }, "ModelName": { "description": "Unique identifier for the model and publicly available versions", "value": "", "AIO": true, "NCAs": true, "DPs": true, "explanation": "The unique identifier for the model (e.g. Llama 3.1-405B), including identifiers for collections of models where applicable, and a list of publicly available versions." }, "ModelAuthenticity": { "description": "Evidence establishing provenance and authenticity (e.g. hash, URL endpoint)", "value": "", "AIO": true, "NCAs": true, "DPs": false, "explanation": "Evidence that establishes the provenance and authenticity of the model (e.g. a secure hash if binaries are distributed, or the URL endpoint in the case of a service), where available." }, "ReleaseDate": { "description": "Date when the model was first released", "value": "", "AIO": true, "NCAs": true, "DPs": true, "explanation": "Date when the model was first released through any distribution channel." }, "UnionMarketReleaseDate": { "description": "Date when the model was placed on the Union market", "value": "", "AIO": true, "NCAs": true, "DPs": true, "explanation": "Date when the model was placed on the Union market." }, "ModelDependencies": { "description": "List of model dependencies or 'N/A'", "value": [], "AIO": true, "NCAs": true, "DPs": true, "explanation": "If the model is the result of a modification or fine-tuning of one or more general-purpose AI models previously placed on the market, list those models. Otherwise write ‘N/A’." } }, "ModelProperties": { "Architecture": { "description": "General description of the model architecture", "value": "", "AIO": true, "NCAs": true, "DPs": true, "explanation": "A general description of the model architecture, e.g. a transformer architecture. [Recommended 20 words]." }, "DesignSpecifications": { "description": "Description of key design specifications, rationale, and assumptions", "value": "", "AIO": true, "NCAs": true, "DPs": false, "explanation": "A general description of the key design specifications of the model, including rationale and assumptions made, to provide basic insight into how the model was designed. [Recommended 100 words]." }, "InputModalities": { "description": "Supported input modalities and maximum input sizes", "options": [ "Text", "Images", "Audio", "Video", "Other" ], "selected": [], "maxSizes": { "Text": "", "Images": "", "Audio": "", "Video": "", "Other": "" }, "AIO": true, "NCAs": true, "DPs": true, "explanation": "Supported input modalities (Text, Images, Audio, Video, or Other). For each selected modality please include maximum input size or 'N/A' if not defined." }, "OutputModalities": { "description": "Supported output modalities and maximum output sizes", "options": [ "Text", "Images", "Audio", "Video", "Other" ], "selected": [], "maxSizes": { "Text": "", "Images": "", "Audio": "", "Video": "", "Other": "" }, "AIO": true, "NCAs": false, "DPs": true, "explanation": "Supported output modalities (Text, Images, Audio, Video, or Other). For each selected modality include maximum output size or 'N/A' if not defined." }, "TotalModelSize": { "description": "Total number of parameters and parameter range", "value": "", "ranges": [ "1—500M", "500M—5B", "5B—15B", "15B—50B", "50B—100B", "100B—500B", "500B—1T", ">1T" ], "selectedRange": "", "AIO": true, "NCAs": false, "DPs": false, "explanation": "The total number of parameters of the model, recorded with at least two significant figures, and the range within which the total number of parameters falls." } }, "DistributionAndLicenses": { "DistributionChannels": { "description": "List of methods of distribution with access levels", "options": [ "Enterprise/subscription software suites", "Public/subscription API access", "IDE/device-specific apps or firmware", "Open-source repositories", "Other" ], "selected": [], "AIO": true, "NCAs": true, "DPs": false, "explanation": "A list of the methods of distribution (enterprise, subscription, API, IDEs, firmware, open-source, etc.) through which the model has been made available in the Union market, with access level details." }, "DistributionChannelsForDPs": { "description": "Methods of distribution available to downstream providers", "options": [ "Enterprise/subscription software suites", "Public/subscription API access", "IDE/device-specific apps or firmware", "Open-source repositories", "Other" ], "selected": [], "AIO": false, "NCAs": false, "DPs": true, "explanation": "List of the methods of distribution through which the model can be made available to downstream providers." }, "License": { "description": "Link or copy of model license(s)", "value": "", "AIO": true, "NCAs": true, "DPs": false, "explanation": "A link to model license(s) (or provide upon request by the AIO) or indicate that no model license exists." }, "LicenseForDPs": { "description": "Types/categories of licenses for downstream use", "options": [ "Free and open source", "Less permissive (restricted use)", "Proprietary", "No license (access via terms of service)" ], "selected": [], "AIO": false, "NCAs": false, "DPs": true, "explanation": "Types of licences for downstream use: open source, less permissive (restricted), proprietary, or absence of license (via terms of service)." }, "AdditionalAssets": { "description": "List of additional assets with access and licenses", "options": [ "Training data", "Processing code", "Training code", "Inference code", "Evaluation code", "Other" ], "selected": [], "AIO": true, "NCAs": false, "DPs": true, "explanation": "A list of additional assets (e.g. training data, training/inference code, evaluation code) that are made available, with details of how to access them and related licenses." } }, "Use": { "AcceptableUsePolicy": { "description": "Link to acceptable use policy or statement that none exists", "value": "", "AIO": true, "NCAs": true, "DPs": true, "explanation": "Provide a link to the acceptable use policy (or attach to the document) or indicate that none exists." }, "IntendedUses": { "description": "Description of intended and restricted uses", "value": "", "AIO": true, "NCAs": true, "DPs": true, "explanation": "A description of intended or restricted uses as specified in instructions for use, terms and conditions, promotional materials, or technical documentation. [Recommended 200 words]." }, "IntegrationTypes": { "description": "AI systems in which model can/cannot be integrated", "examples": [ "Autonomous systems", "Conversational assistants", "Decision support systems", "Creative AI systems", "Predictive systems", "Cybersecurity", "Surveillance", "Human-AI collaboration" ], "selected": [], "AIO": true, "NCAs": true, "DPs": true, "explanation": "Type and nature of AI systems in which the model can/cannot be integrated, e.g. autonomous systems, assistants, predictive systems. [Recommended 300 words]." }, "TechnicalMeansForIntegration": { "description": "Technical means required for model integration", "value": "", "AIO": false, "NCAs": false, "DPs": true, "explanation": "A general description of the technical means (instructions, infrastructure, tools) required for integration into AI systems. [Recommended 100 words]." }, "RequiredHardware": { "description": "Hardware requirements (if any)", "value": "", "AIO": false, "NCAs": false, "DPs": true, "explanation": "Description of hardware required to use the model, or 'N/A' if not applicable (e.g. API access). [Recommended 100 words]." }, "RequiredSoftware": { "description": "Software requirements (if any)", "value": "", "AIO": false, "NCAs": false, "DPs": true, "explanation": "Description of software required to use the model, or 'N/A' if not applicable. [Recommended 100 words]." } }, "TrainingData": { "DataType": { "description": "Types/modalities of training, testing, validation data", "options": [ "Text", "Images", "Audio", "Video", "Other" ], "selected": [], "AIO": true, "NCAs": true, "DPs": true, "explanation": "Modalities of data used in training, testing, and validation (Text, Images, Audio, Video, or Other)." }, "DataProvenance": { "description": "Sources of data", "options": [ "Web crawling", "Private third-party datasets", "User data", "Publicly available datasets", "Other collected data", "Synthetic data (non-public)" ], "selected": [], "AIO": true, "NCAs": true, "DPs": true, "explanation": "Sources of data (Web crawl, private datasets, user data, public datasets, synthetic, or other)." }, "NumberOfDataPoints": { "description": "Size of datasets with units and required precision", "training": "", "testing": "", "validation": "", "unit": "", "precision": [ "≥1 sig fig", "≥2 sig fig" ], "AIO": true, "NCAs": true, "DPs": false, "explanation": "The size of the datasets (training, testing, validation) with definition of the unit of data points (e.g. tokens, documents, images, hours of video), recorded with required precision." } }, "ComputationalResources": { "TrainingTime": { "description": "Training duration", "ranges": [ "<1 month", "1–3 months", "3–6 months", ">6 months" ], "selectedRange": "", "precise": { "wallClockDays": "", "hardwareDays": "" }, "AIO": true, "NCAs": true, "DPs": false, "explanation": "Duration of training measured either as a range (<1 month, 1–3 months, 3–6 months, >6 months) or precisely in wall clock days and hardware days." }, "ComputationUsed": { "description": "Amount of computation used for training", "value": "", "precision": [ "Order of magnitude", "≥2 sig fig" ], "AIO": true, "NCAs": true, "DPs": false, "explanation": "Measured or estimated amount of computation used for training, reported in FLOPs (order of magnitude or ≥2 significant figures)." } }, "EnergyConsumption": { "TrainingEnergy": { "description": "Energy used for training (MWh)", "value": "", "precision": "≥2 sig fig", "AIO": true, "NCAs": true, "DPs": false, "explanation": "Measured or estimated energy used for training (MWh), recorded with ≥2 significant figures. Enter ‘N/A’ if not estimable." }, "InferenceComputation": { "description": "Benchmarked computation for inference (FLOPs)", "value": "", "precision": "≥2 sig fig", "AIO": true, "NCAs": true, "DPs": false, "explanation": "Benchmarked computation for inference, reported in FLOPs with ≥2 significant figures." } } }	{ "Approach 1": { "description": "e.g. multilingual red teaming with baseline test questions for FSI", "mode": "e.g., manual, automated, hybrid", "language": "one or several - EN, IT, SP, HI, etc.", "attacker_model": "provider and version", "target_industry": "FSI, healthcare, all", "AIsafety_ON": "YES or NO, depending on scenario 1) only usage of base model, 2) base model + AI safety layer (e.g. Azure)", "results": { "number_attacks": "total number of tested attacks", "percentage_success_blocked": "total percentage of blocked attacks", "description_failed_blocked": "plain language description of the type of effective red teaming attacks that won vs model", "attack_examples": { "prompt1": { "input": "example prompt", "type_of_attack": "type attack - injection" }, "prompt2": { "input": "example prompt", "type_of_attack": "type attack - role" }, "promptN": { "input": "example prompt", "type_of_attack": "type of attack" } }, "qualitative_notes": "insights, future risks, pending evaluations, promising future paths" } }, "Approach 2": {}, "Approach N": {} }

Part1_EUAICodeOfPractice_TransparencyChapter

dict

Part2_AIRedTeaming

dict

{ "GeneralInformation": { "LegalNameProvider": { "description": "Legal name for the model provider", "value": "", "AIO": true, "NCAs": true, "DPs": true, "explanation": "Legal name for the model provider." }, "ModelName": { "description": "Unique identifier for the model and publicly available versions", "value": "", "AIO": true, "NCAs": true, "DPs": true, "explanation": "The unique identifier for the model (e.g. Llama 3.1-405B), including identifiers for collections of models where applicable, and a list of publicly available versions." }, "ModelAuthenticity": { "description": "Evidence establishing provenance and authenticity (e.g. hash, URL endpoint)", "value": "", "AIO": true, "NCAs": true, "DPs": false, "explanation": "Evidence that establishes the provenance and authenticity of the model (e.g. a secure hash if binaries are distributed, or the URL endpoint in the case of a service), where available." }, "ReleaseDate": { "description": "Date when the model was first released", "value": "", "AIO": true, "NCAs": true, "DPs": true, "explanation": "Date when the model was first released through any distribution channel." }, "UnionMarketReleaseDate": { "description": "Date when the model was placed on the Union market", "value": "", "AIO": true, "NCAs": true, "DPs": true, "explanation": "Date when the model was placed on the Union market." }, "ModelDependencies": { "description": "List of model dependencies or 'N/A'", "value": [], "AIO": true, "NCAs": true, "DPs": true, "explanation": "If the model is the result of a modification or fine-tuning of one or more general-purpose AI models previously placed on the market, list those models. Otherwise write ‘N/A’." } }, "ModelProperties": { "Architecture": { "description": "General description of the model architecture", "value": "", "AIO": true, "NCAs": true, "DPs": true, "explanation": "A general description of the model architecture, e.g. a transformer architecture. [Recommended 20 words]." }, "DesignSpecifications": { "description": "Description of key design specifications, rationale, and assumptions", "value": "", "AIO": true, "NCAs": true, "DPs": false, "explanation": "A general description of the key design specifications of the model, including rationale and assumptions made, to provide basic insight into how the model was designed. [Recommended 100 words]." }, "InputModalities": { "description": "Supported input modalities and maximum input sizes", "options": [ "Text", "Images", "Audio", "Video", "Other" ], "selected": [], "maxSizes": { "Text": "", "Images": "", "Audio": "", "Video": "", "Other": "" }, "AIO": true, "NCAs": true, "DPs": true, "explanation": "Supported input modalities (Text, Images, Audio, Video, or Other). For each selected modality please include maximum input size or 'N/A' if not defined." }, "OutputModalities": { "description": "Supported output modalities and maximum output sizes", "options": [ "Text", "Images", "Audio", "Video", "Other" ], "selected": [], "maxSizes": { "Text": "", "Images": "", "Audio": "", "Video": "", "Other": "" }, "AIO": true, "NCAs": false, "DPs": true, "explanation": "Supported output modalities (Text, Images, Audio, Video, or Other). For each selected modality include maximum output size or 'N/A' if not defined." }, "TotalModelSize": { "description": "Total number of parameters and parameter range", "value": "", "ranges": [ "1—500M", "500M—5B", "5B—15B", "15B—50B", "50B—100B", "100B—500B", "500B—1T", ">1T" ], "selectedRange": "", "AIO": true, "NCAs": false, "DPs": false, "explanation": "The total number of parameters of the model, recorded with at least two significant figures, and the range within which the total number of parameters falls." } }, "DistributionAndLicenses": { "DistributionChannels": { "description": "List of methods of distribution with access levels", "options": [ "Enterprise/subscription software suites", "Public/subscription API access", "IDE/device-specific apps or firmware", "Open-source repositories", "Other" ], "selected": [], "AIO": true, "NCAs": true, "DPs": false, "explanation": "A list of the methods of distribution (enterprise, subscription, API, IDEs, firmware, open-source, etc.) through which the model has been made available in the Union market, with access level details." }, "DistributionChannelsForDPs": { "description": "Methods of distribution available to downstream providers", "options": [ "Enterprise/subscription software suites", "Public/subscription API access", "IDE/device-specific apps or firmware", "Open-source repositories", "Other" ], "selected": [], "AIO": false, "NCAs": false, "DPs": true, "explanation": "List of the methods of distribution through which the model can be made available to downstream providers." }, "License": { "description": "Link or copy of model license(s)", "value": "", "AIO": true, "NCAs": true, "DPs": false, "explanation": "A link to model license(s) (or provide upon request by the AIO) or indicate that no model license exists." }, "LicenseForDPs": { "description": "Types/categories of licenses for downstream use", "options": [ "Free and open source", "Less permissive (restricted use)", "Proprietary", "No license (access via terms of service)" ], "selected": [], "AIO": false, "NCAs": false, "DPs": true, "explanation": "Types of licences for downstream use: open source, less permissive (restricted), proprietary, or absence of license (via terms of service)." }, "AdditionalAssets": { "description": "List of additional assets with access and licenses", "options": [ "Training data", "Processing code", "Training code", "Inference code", "Evaluation code", "Other" ], "selected": [], "AIO": true, "NCAs": false, "DPs": true, "explanation": "A list of additional assets (e.g. training data, training/inference code, evaluation code) that are made available, with details of how to access them and related licenses." } }, "Use": { "AcceptableUsePolicy": { "description": "Link to acceptable use policy or statement that none exists", "value": "", "AIO": true, "NCAs": true, "DPs": true, "explanation": "Provide a link to the acceptable use policy (or attach to the document) or indicate that none exists." }, "IntendedUses": { "description": "Description of intended and restricted uses", "value": "", "AIO": true, "NCAs": true, "DPs": true, "explanation": "A description of intended or restricted uses as specified in instructions for use, terms and conditions, promotional materials, or technical documentation. [Recommended 200 words]." }, "IntegrationTypes": { "description": "AI systems in which model can/cannot be integrated", "examples": [ "Autonomous systems", "Conversational assistants", "Decision support systems", "Creative AI systems", "Predictive systems", "Cybersecurity", "Surveillance", "Human-AI collaboration" ], "selected": [], "AIO": true, "NCAs": true, "DPs": true, "explanation": "Type and nature of AI systems in which the model can/cannot be integrated, e.g. autonomous systems, assistants, predictive systems. [Recommended 300 words]." }, "TechnicalMeansForIntegration": { "description": "Technical means required for model integration", "value": "", "AIO": false, "NCAs": false, "DPs": true, "explanation": "A general description of the technical means (instructions, infrastructure, tools) required for integration into AI systems. [Recommended 100 words]." }, "RequiredHardware": { "description": "Hardware requirements (if any)", "value": "", "AIO": false, "NCAs": false, "DPs": true, "explanation": "Description of hardware required to use the model, or 'N/A' if not applicable (e.g. API access). [Recommended 100 words]." }, "RequiredSoftware": { "description": "Software requirements (if any)", "value": "", "AIO": false, "NCAs": false, "DPs": true, "explanation": "Description of software required to use the model, or 'N/A' if not applicable. [Recommended 100 words]." } }, "TrainingData": { "DataType": { "description": "Types/modalities of training, testing, validation data", "options": [ "Text", "Images", "Audio", "Video", "Other" ], "selected": [], "AIO": true, "NCAs": true, "DPs": true, "explanation": "Modalities of data used in training, testing, and validation (Text, Images, Audio, Video, or Other)." }, "DataProvenance": { "description": "Sources of data", "options": [ "Web crawling", "Private third-party datasets", "User data", "Publicly available datasets", "Other collected data", "Synthetic data (non-public)" ], "selected": [], "AIO": true, "NCAs": true, "DPs": true, "explanation": "Sources of data (Web crawl, private datasets, user data, public datasets, synthetic, or other)." }, "NumberOfDataPoints": { "description": "Size of datasets with units and required precision", "training": "", "testing": "", "validation": "", "unit": "", "precision": [ "≥1 sig fig", "≥2 sig fig" ], "AIO": true, "NCAs": true, "DPs": false, "explanation": "The size of the datasets (training, testing, validation) with definition of the unit of data points (e.g. tokens, documents, images, hours of video), recorded with required precision." } }, "ComputationalResources": { "TrainingTime": { "description": "Training duration", "ranges": [ "<1 month", "1–3 months", "3–6 months", ">6 months" ], "selectedRange": "", "precise": { "wallClockDays": "", "hardwareDays": "" }, "AIO": true, "NCAs": true, "DPs": false, "explanation": "Duration of training measured either as a range (<1 month, 1–3 months, 3–6 months, >6 months) or precisely in wall clock days and hardware days." }, "ComputationUsed": { "description": "Amount of computation used for training", "value": "", "precision": [ "Order of magnitude", "≥2 sig fig" ], "AIO": true, "NCAs": true, "DPs": false, "explanation": "Measured or estimated amount of computation used for training, reported in FLOPs (order of magnitude or ≥2 significant figures)." } }, "EnergyConsumption": { "TrainingEnergy": { "description": "Energy used for training (MWh)", "value": "", "precision": "≥2 sig fig", "AIO": true, "NCAs": true, "DPs": false, "explanation": "Measured or estimated energy used for training (MWh), recorded with ≥2 significant figures. Enter ‘N/A’ if not estimable." }, "InferenceComputation": { "description": "Benchmarked computation for inference (FLOPs)", "value": "", "precision": "≥2 sig fig", "AIO": true, "NCAs": true, "DPs": false, "explanation": "Benchmarked computation for inference, reported in FLOPs with ≥2 significant figures." } } }

{ "Approach 1": { "description": "e.g. multilingual red teaming with baseline test questions for FSI", "mode": "e.g., manual, automated, hybrid", "language": "one or several - EN, IT, SP, HI, etc.", "attacker_model": "provider and version", "target_industry": "FSI, healthcare, all", "AIsafety_ON": "YES or NO, depending on scenario 1) only usage of base model, 2) base model + AI safety layer (e.g. Azure)", "results": { "number_attacks": "total number of tested attacks", "percentage_success_blocked": "total percentage of blocked attacks", "description_failed_blocked": "plain language description of the type of effective red teaming attacks that won vs model", "attack_examples": { "prompt1": { "input": "example prompt", "type_of_attack": "type attack - injection" }, "prompt2": { "input": "example prompt", "type_of_attack": "type attack - role" }, "promptN": { "input": "example prompt", "type_of_attack": "type of attack" } }, "qualitative_notes": "insights, future risks, pending evaluations, promising future paths" } }, "Approach 2": {}, "Approach N": {} }