Upload README.md

README.md

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+---
+base_model: sentence-transformers/all-MiniLM-L6-v2
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:555
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: What does this text say about unclassified?
+  sentences:
+  - "these sources. \nErrors in third-party GAI components can also have downstream\
+    \ impacts on accuracy and robustness. \nFor example, test datasets commonly used\
+    \ to benchmark or validate models can contain label errors. \nInaccuracies in\
+    \ these labels can impact the “stability” or robustness of these benchmarks, which\
+    \ many \nGAI practitioners consider during the model selection process.  \nTrustworthy\
+    \ AI Characteristics: Accountable and Transparent, Explainable and Interpretable,\
+    \ Fair with \nHarmful Bias Managed, Privacy Enhanced, Safe, Secure and Resilient,\
+    \ Valid and Reliable \n3. \nSuggested Actions to Manage GAI Risks \nThe following\
+    \ suggested actions target risks unique to or exacerbated by GAI. \nIn addition\
+    \ to the suggested actions below, AI risk management activities and actions set\
+    \ forth in the AI \nRMF 1.0 and Playbook are already applicable for managing GAI\
+    \ risks. Organizations are encouraged to"
+  - "and hardware vulnerabilities; labor practices; data privacy and localization\
+    \ \ncompliance; geopolitical alignment). \nData Privacy; Information Security;\
+    \ \nValue Chain and Component \nIntegration; Harmful Bias and \nHomogenization\
+    \ \nMG-3.1-003 \nRe-assess model risks after fine-tuning or retrieval-augmented\
+    \ generation \nimplementation and for any third-party GAI models deployed for\
+    \ applications \nand/or use cases that were not evaluated in initial testing.\
+    \ \nValue Chain and Component \nIntegration \nMG-3.1-004 \nTake reasonable measures\
+    \ to review training data for CBRN information, and \nintellectual property, and\
+    \ where appropriate, remove it. Implement reasonable \nmeasures to prevent, flag,\
+    \ or take other action in response to outputs that \nreproduce particular training\
+    \ data (e.g., plagiarized, trademarked, patented, \nlicensed content or trade\
+    \ secret material). \nIntellectual Property; CBRN \nInformation or Capabilities\
+    \ \n \n43"
+  - "• \nStage of the AI lifecycle: Risks can arise during design, development, deployment,\
+    \ operation, \nand/or decommissioning. \n• \nScope: Risks may exist at individual\
+    \ model or system levels, at the application or implementation \nlevels (i.e.,\
+    \ for a specific use case), or at the ecosystem level – that is, beyond a single\
+    \ system or \norganizational context. Examples of the latter include the expansion\
+    \ of “algorithmic \nmonocultures,3” resulting from repeated use of the same model,\
+    \ or impacts on access to \nopportunity, labor markets, and the creative economies.4\
+    \ \n• \nSource of risk: Risks may emerge from factors related to the design, training,\
+    \ or operation of the \nGAI model itself, stemming in some cases from GAI model\
+    \ or system inputs, and in other cases, \nfrom GAI system outputs. Many GAI risks,\
+    \ however, originate from human behavior, including \n \n \n3 “Algorithmic monocultures”\
+    \ refers to the phenomenon in which repeated use of the same model or algorithm\
+    \ in"
+- source_sentence: What does this text say about unclassified?
+  sentences:
+  - "Security; Dangerous, Violent, or \nHateful Content \n \n34 \nMS-2.7-009 Regularly\
+    \ assess and verify that security measures remain effective and have not \nbeen\
+    \ compromised. \nInformation Security \nAI Actor Tasks: AI Deployment, AI Impact\
+    \ Assessment, Domain Experts, Operation and Monitoring, TEVV \n \nMEASURE 2.8:\
+    \ Risks associated with transparency and accountability – as identified in the\
+    \ MAP function – are examined and \ndocumented. \nAction ID \nSuggested Action\
+    \ \nGAI Risks \nMS-2.8-001 \nCompile statistics on actual policy violations, take-down\
+    \ requests, and intellectual \nproperty infringement for organizational GAI systems:\
+    \ Analyze transparency \nreports across demographic groups, languages groups.\
+    \ \nIntellectual Property; Harmful Bias \nand Homogenization \nMS-2.8-002 Document\
+    \ the instructions given to data annotators or AI red-teamers. \nHuman-AI Configuration\
+    \ \nMS-2.8-003 \nUse digital content transparency solutions to enable the documentation\
+    \ of each"
+  - "information during GAI training and maintenance. \nHuman-AI Configuration; Obscene,\
+    \ \nDegrading, and/or Abusive \nContent; Value Chain and \nComponent Integration;\
+    \ \nDangerous, Violent, or Hateful \nContent \nMS-2.6-002 \nAssess existence or\
+    \ levels of harmful bias, intellectual property infringement, \ndata privacy violations,\
+    \ obscenity, extremism, violence, or CBRN information in \nsystem training data.\
+    \ \nData Privacy; Intellectual Property; \nObscene, Degrading, and/or \nAbusive\
+    \ Content; Harmful Bias and \nHomogenization; Dangerous, \nViolent, or Hateful\
+    \ Content; CBRN \nInformation or Capabilities \nMS-2.6-003 Re-evaluate safety\
+    \ features of fine-tuned models when the negative risk exceeds \norganizational\
+    \ risk tolerance. \nDangerous, Violent, or Hateful \nContent \nMS-2.6-004 Review\
+    \ GAI system outputs for validity and safety: Review generated code to \nassess\
+    \ risks that may arise from unreliable downstream decision-making. \nValue Chain\
+    \ and Component \nIntegration; Dangerous, Violent, or \nHateful Content"
+  - "Information Integrity; Harmful Bias \nand Homogenization \nAI Actor Tasks: AI\
+    \ Deployment, AI Impact Assessment, Domain Experts, End-Users, Operation and Monitoring,\
+    \ TEVV \n \nMEASURE 2.10: Privacy risk of the AI system – as identified in the\
+    \ MAP function – is examined and documented. \nAction ID \nSuggested Action \n\
+    GAI Risks \nMS-2.10-001 \nConduct AI red-teaming to assess issues such as: Outputting\
+    \ of training data \nsamples, and subsequent reverse engineering, model extraction,\
+    \ and \nmembership inference risks; Revealing biometric, confidential, copyrighted,\
+    \ \nlicensed, patented, personal, proprietary, sensitive, or trade-marked information;\
+    \ \nTracking or revealing location information of users or members of training\
+    \ \ndatasets. \nHuman-AI Configuration; \nInformation Integrity; Intellectual \n\
+    Property \nMS-2.10-002 \nEngage directly with end-users and other stakeholders\
+    \ to understand their \nexpectations and concerns regarding content provenance.\
+    \ Use this feedback to"
+- source_sentence: What does this text say about risk management?
+  sentences:
+  - "robust watermarking techniques and corresponding detectors to identify the source\
+    \ of content or \nmetadata recording techniques and metadata management tools\
+    \ and repositories to trace content \norigins and modifications. Further narrowing\
+    \ of GAI task definitions to include provenance data can \nenable organizations\
+    \ to maximize the utility of provenance data and risk management efforts. \nA.1.7.\
+    \ Enhancing Content Provenance through Structured Public Feedback \nWhile indirect\
+    \ feedback methods such as automated error collection systems are useful, they\
+    \ often lack \nthe context and depth that direct input from end users can provide.\
+    \ Organizations can leverage feedback \napproaches described in the Pre-Deployment\
+    \ Testing section to capture input from external sources such \nas through AI\
+    \ red-teaming.  \nIntegrating pre- and post-deployment external feedback into\
+    \ the monitoring process for GAI models and"
+  - "tools for monitoring third-party GAI risks; Consider policy adjustments across\
+    \ GAI \nmodeling libraries, tools and APIs, fine-tuned models, and embedded tools;\
+    \ \nAssess GAI vendors, open-source or proprietary GAI tools, or GAI service \n\
+    providers against incident or vulnerability databases. \nData Privacy; Human-AI\
+    \ \nConfiguration; Information \nSecurity; Intellectual Property; \nValue Chain\
+    \ and Component \nIntegration; Harmful Bias and \nHomogenization \nGV-6.1-010\
+    \ \nUpdate GAI acceptable use policies to address proprietary and open-source\
+    \ GAI \ntechnologies and data, and contractors, consultants, and other third-party\
+    \ \npersonnel. \nIntellectual Property; Value Chain \nand Component Integration\
+    \ \nAI Actor Tasks: Operation and Monitoring, Procurement, Third-party entities\
+    \ \n \nGOVERN 6.2: Contingency processes are in place to handle failures or incidents\
+    \ in third-party data or AI systems deemed to be \nhigh-risk. \nAction ID \nSuggested\
+    \ Action \nGAI Risks \nGV-6.2-001"
+  - "MEASURE 2.3: AI system performance or assurance criteria are measured qualitatively\
+    \ or quantitatively and demonstrated for \nconditions similar to deployment setting(s).\
+    \ Measures are documented. \nAction ID \nSuggested Action \nGAI Risks \nMS-2.3-001\
+    \ Consider baseline model performance on suites of benchmarks when selecting a\
+    \ \nmodel for fine tuning or enhancement with retrieval-augmented generation. \n\
+    Information Security; \nConfabulation \nMS-2.3-002 Evaluate claims of model capabilities\
+    \ using empirically validated methods. \nConfabulation; Information \nSecurity\
+    \ \nMS-2.3-003 Share results of pre-deployment testing with relevant GAI Actors,\
+    \ such as those \nwith system release approval authority. \nHuman-AI Configuration\
+    \ \n \n31 \nMS-2.3-004 \nUtilize a purpose-built testing environment such as NIST\
+    \ Dioptra to empirically \nevaluate GAI trustworthy characteristics. \nCBRN Information\
+    \ or Capabilities; \nData Privacy; Confabulation; \nInformation Integrity; Information\
+    \ \nSecurity; Dangerous, Violent, or"
+- source_sentence: What does this text say about unclassified?
+  sentences:
+  - "techniques such as re-sampling, re-ranking, or adversarial training to mitigate\
+    \ \nbiases in the generated content. \nInformation Security; Harmful Bias \nand\
+    \ Homogenization \nMG-2.2-005 \nEngage in due diligence to analyze GAI output\
+    \ for harmful content, potential \nmisinformation, and CBRN-related or NCII content.\
+    \ \nCBRN Information or Capabilities; \nObscene, Degrading, and/or \nAbusive Content;\
+    \ Harmful Bias and \nHomogenization; Dangerous, \nViolent, or Hateful Content\
+    \ \n \n41 \nMG-2.2-006 \nUse feedback from internal and external AI Actors, users,\
+    \ individuals, and \ncommunities, to assess impact of AI-generated content. \n\
+    Human-AI Configuration \nMG-2.2-007 \nUse real-time auditing tools where they can\
+    \ be demonstrated to aid in the \ntracking and validation of the lineage and authenticity\
+    \ of AI-generated data. \nInformation Integrity \nMG-2.2-008 \nUse structured\
+    \ feedback mechanisms to solicit and capture user input about AI-\ngenerated content\
+    \ to detect subtle shifts in quality or alignment with"
+  - "Human-AI Configuration; Value \nChain and Component Integration \nMP-5.2-002 \n\
+    Plan regular engagements with AI Actors responsible for inputs to GAI systems,\
+    \ \nincluding third-party data and algorithms, to review and evaluate unanticipated\
+    \ \nimpacts. \nHuman-AI Configuration; Value \nChain and Component Integration\
+    \ \nAI Actor Tasks: AI Deployment, AI Design, AI Impact Assessment, Affected Individuals\
+    \ and Communities, Domain Experts, End-\nUsers, Human Factors, Operation and Monitoring\
+    \  \n \nMEASURE 1.1: Approaches and metrics for measurement of AI risks enumerated\
+    \ during the MAP function are selected for \nimplementation starting with the\
+    \ most significant AI risks. The risks or trustworthiness characteristics that\
+    \ will not – or cannot – be \nmeasured are properly documented. \nAction ID \n\
+    Suggested Action \nGAI Risks \nMS-1.1-001 Employ methods to trace the origin and\
+    \ modifications of digital content. \nInformation Integrity \nMS-1.1-002"
+  - "input them directly to a GAI system, with a variety of downstream negative consequences\
+    \ to \ninterconnected systems. Indirect prompt injection attacks occur when adversaries\
+    \ remotely (i.e., without \na direct interface) exploit LLM-integrated applications\
+    \ by injecting prompts into data likely to be \nretrieved. Security researchers\
+    \ have already demonstrated how indirect prompt injections can exploit \nvulnerabilities\
+    \ by stealing proprietary data or running malicious code remotely on a machine.\
+    \ Merely \nquerying a closed production model can elicit previously undisclosed\
+    \ information about that model. \nAnother cybersecurity risk to GAI is data poisoning,\
+    \ in which an adversary compromises a training \ndataset used by a model to manipulate\
+    \ its outputs or operation. Malicious tampering with data or parts \nof the model\
+    \ could exacerbate risks associated with GAI system outputs. \nTrustworthy AI\
+    \ Characteristics: Privacy Enhanced, Safe, Secure and Resilient, Valid and Reliable\
+    \ \n2.10."
+- source_sentence: What does this text say about data privacy?
+  sentences:
+  - "Property. We also note that some risks are cross-cutting between these categories.\
+    \  \n \n4 \n1. CBRN Information or Capabilities: Eased access to or synthesis\
+    \ of materially nefarious \ninformation or design capabilities related to chemical,\
+    \ biological, radiological, or nuclear (CBRN) \nweapons or other dangerous materials\
+    \ or agents. \n2. Confabulation: The production of confidently stated but erroneous\
+    \ or false content (known \ncolloquially as “hallucinations” or “fabrications”)\
+    \ by which users may be misled or deceived.6 \n3. Dangerous, Violent, or Hateful\
+    \ Content: Eased production of and access to violent, inciting, \nradicalizing,\
+    \ or threatening content as well as recommendations to carry out self-harm or\
+    \ \nconduct illegal activities. Includes difficulty controlling public exposure\
+    \ to hateful and disparaging \nor stereotyping content. \n4. Data Privacy: Impacts\
+    \ due to leakage and unauthorized use, disclosure, or de-anonymization of"
+  - "information during GAI training and maintenance. \nHuman-AI Configuration; Obscene,\
+    \ \nDegrading, and/or Abusive \nContent; Value Chain and \nComponent Integration;\
+    \ \nDangerous, Violent, or Hateful \nContent \nMS-2.6-002 \nAssess existence or\
+    \ levels of harmful bias, intellectual property infringement, \ndata privacy violations,\
+    \ obscenity, extremism, violence, or CBRN information in \nsystem training data.\
+    \ \nData Privacy; Intellectual Property; \nObscene, Degrading, and/or \nAbusive\
+    \ Content; Harmful Bias and \nHomogenization; Dangerous, \nViolent, or Hateful\
+    \ Content; CBRN \nInformation or Capabilities \nMS-2.6-003 Re-evaluate safety\
+    \ features of fine-tuned models when the negative risk exceeds \norganizational\
+    \ risk tolerance. \nDangerous, Violent, or Hateful \nContent \nMS-2.6-004 Review\
+    \ GAI system outputs for validity and safety: Review generated code to \nassess\
+    \ risks that may arise from unreliable downstream decision-making. \nValue Chain\
+    \ and Component \nIntegration; Dangerous, Violent, or \nHateful Content"
+  - "Scheurer, J. et al. (2023) Technical report: Large language models can strategically\
+    \ deceive their users \nwhen put under pressure. arXiv. https://arxiv.org/abs/2311.07590\
+    \ \nShelby, R. et al. (2023) Sociotechnical Harms of Algorithmic Systems: Scoping\
+    \ a Taxonomy for Harm \nReduction. arXiv. https://arxiv.org/pdf/2210.05791 \n\
+    Shevlane, T. et al. (2023) Model evaluation for extreme risks. arXiv. https://arxiv.org/pdf/2305.15324\
+    \ \nShumailov, I. et al. (2023) The curse of recursion: training on generated\
+    \ data makes models forget. arXiv. \nhttps://arxiv.org/pdf/2305.17493v2 \nSmith,\
+    \ A. et al. (2023) Hallucination or Confabulation? Neuroanatomy as metaphor in\
+    \ Large Language \nModels. PLOS Digital Health. \nhttps://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000388\
+    \ \nSoice, E. et al. (2023) Can large language models democratize access to dual-use\
+    \ biotechnology? arXiv. \nhttps://arxiv.org/abs/2306.03809"
+---
+
+# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision 8b3219a92973c328a8e22fadcfa821b5dc75636a -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 384 tokens
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'What does this text say about data privacy?',
+    'information during GAI training and maintenance. \nHuman-AI Configuration; Obscene, \nDegrading, and/or Abusive \nContent; Value Chain and \nComponent Integration; \nDangerous, Violent, or Hateful \nContent \nMS-2.6-002 \nAssess existence or levels of harmful bias, intellectual property infringement, \ndata privacy violations, obscenity, extremism, violence, or CBRN information in \nsystem training data. \nData Privacy; Intellectual Property; \nObscene, Degrading, and/or \nAbusive Content; Harmful Bias and \nHomogenization; Dangerous, \nViolent, or Hateful Content; CBRN \nInformation or Capabilities \nMS-2.6-003 Re-evaluate safety features of fine-tuned models when the negative risk exceeds \norganizational risk tolerance. \nDangerous, Violent, or Hateful \nContent \nMS-2.6-004 Review GAI system outputs for validity and safety: Review generated code to \nassess risks that may arise from unreliable downstream decision-making. \nValue Chain and Component \nIntegration; Dangerous, Violent, or \nHateful Content',
+    'Scheurer, J. et al. (2023) Technical report: Large language models can strategically deceive their users \nwhen put under pressure. arXiv. https://arxiv.org/abs/2311.07590 \nShelby, R. et al. (2023) Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm \nReduction. arXiv. https://arxiv.org/pdf/2210.05791 \nShevlane, T. et al. (2023) Model evaluation for extreme risks. arXiv. https://arxiv.org/pdf/2305.15324 \nShumailov, I. et al. (2023) The curse of recursion: training on generated data makes models forget. arXiv. \nhttps://arxiv.org/pdf/2305.17493v2 \nSmith, A. et al. (2023) Hallucination or Confabulation? Neuroanatomy as metaphor in Large Language \nModels. PLOS Digital Health. \nhttps://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000388 \nSoice, E. et al. (2023) Can large language models democratize access to dual-use biotechnology? arXiv. \nhttps://arxiv.org/abs/2306.03809',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 555 training samples
+* Columns: <code>sentence_0</code> and <code>sentence_1</code>
+* Approximate statistics based on the first 555 samples:
+  |         | sentence_0                                                                        | sentence_1                                                                            |
+  |:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                                |
+  | details | <ul><li>min: 10 tokens</li><li>mean: 11.2 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>min: 156 tokens</li><li>mean: 199.37 tokens</li><li>max: 256 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                  | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
+  |:------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What does this text say about trustworthiness?</code> | <code>other systems. <br>Information Integrity; Value Chain <br>and Component Integration <br>MP-2.2-002 <br>Observe and analyze how the GAI system interacts with external networks, and <br>identify any potential for negative externalities, particularly where content <br>provenance might be compromised. <br>Information Integrity <br>AI Actor Tasks: End Users <br> <br>MAP 2.3: Scientific integrity and TEVV considerations are identified and documented, including those related to experimental <br>design, data collection and selection (e.g., availability, representativeness, suitability), system trustworthiness, and construct <br>validation <br>Action ID <br>Suggested Action <br>GAI Risks <br>MP-2.3-001 <br>Assess the accuracy, quality, reliability, and authenticity of GAI output by <br>comparing it to a set of known ground truth data and by using a variety of <br>evaluation methods (e.g., human oversight and automated evaluation, proven <br>cryptographic techniques, review of content inputs). <br>Information Integrity <br> <br>25</code>                     |
+  | <code>What does this text say about unclassified?</code>    | <code>training and TEVV data; Filtering of hate speech or content in GAI system <br>training data; Prevalence of GAI-generated data in GAI system training data. <br>Harmful Bias and Homogenization <br> <br> <br>15 Winogender Schemas is a sample set of paired sentences which differ only by gender of the pronouns used, <br>which can be used to evaluate gender bias in natural language processing coreference resolution systems.  <br> <br>37 <br>MS-2.11-005 <br>Assess the proportion of synthetic to non-synthetic training data and verify <br>training data is not overly homogenous or GAI-produced to mitigate concerns of <br>model collapse. <br>Harmful Bias and Homogenization <br>AI Actor Tasks: AI Deployment, AI Impact Assessment, Affected Individuals and Communities, Domain Experts, End-Users, <br>Operation and Monitoring, TEVV <br> <br>MEASURE 2.12: Environmental impact and sustainability of AI model training and management activities – as identified in the MAP <br>function – are assessed and documented. <br>Action ID <br>Suggested Action <br>GAI Risks</code> |
+  | <code>What does this text say about unclassified?</code>    | <code>Padmakumar, V. et al. (2024) Does writing with language models reduce content diversity? ICLR. <br>https://arxiv.org/pdf/2309.05196 <br>Park, P. et. al. (2024) AI deception: A survey of examples, risks, and potential solutions. Patterns, 5(5). <br>arXiv. https://arxiv.org/pdf/2308.14752 <br>Partnership on AI (2023) Building a Glossary for Synthetic Media Transparency Methods, Part 1: Indirect <br>Disclosure. https://partnershiponai.org/glossary-for-synthetic-media-transparency-methods-part-1-<br>indirect-disclosure/ <br>Qu, Y. et al. (2023) Unsafe Diffusion: On the Generation of Unsafe Images and Hateful Memes From Text-<br>To-Image Models. arXiv. https://arxiv.org/pdf/2305.13873 <br>Rafat, K. et al. (2023) Mitigating carbon footprint for knowledge distillation based deep learning model <br>compression. PLOS One. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0285668 <br>Said, I. et al. (2022) Nonconsensual Distribution of Intimate Images: Exploring the Role of Legal Attitudes</code>                                              |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1
+- `num_train_epochs`: 3
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `eval_use_gather_object`: False
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Framework Versions
+- Python: 3.11.5
+- Sentence Transformers: 3.1.1
+- Transformers: 4.44.2
+- PyTorch: 2.4.1+cpu
+- Accelerate: 0.34.2
+- Datasets: 3.0.0
+- Tokenizers: 0.19.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
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+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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