Remove fine_tuned_embedding_model from Git tracking

fine_tuned_embedding_model/1_Pooling/config.json

@@ -1,10 +0,0 @@
-{
-  "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
-}

fine_tuned_embedding_model/README.md

@@ -1,520 +0,0 @@
----
-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.*
--->
-
-<!--
-## Model Card Authors
-
-*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
--->
-
-<!--
-## Model Card Contact
-
-*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--->

fine_tuned_embedding_model/config.json

@@ -1,26 +0,0 @@
-{
-  "_name_or_path": "sentence-transformers/all-MiniLM-L6-v2",
-  "architectures": [
-    "BertModel"
-  ],
-  "attention_probs_dropout_prob": 0.1,
-  "classifier_dropout": null,
-  "gradient_checkpointing": false,
-  "hidden_act": "gelu",
-  "hidden_dropout_prob": 0.1,
-  "hidden_size": 384,
-  "initializer_range": 0.02,
-  "intermediate_size": 1536,
-  "layer_norm_eps": 1e-12,
-  "max_position_embeddings": 512,
-  "model_type": "bert",
-  "num_attention_heads": 12,
-  "num_hidden_layers": 6,
-  "pad_token_id": 0,
-  "position_embedding_type": "absolute",
-  "torch_dtype": "float32",
-  "transformers_version": "4.44.2",
-  "type_vocab_size": 2,
-  "use_cache": true,
-  "vocab_size": 30522
-}

fine_tuned_embedding_model/config_sentence_transformers.json

@@ -1,10 +0,0 @@
-{
-  "__version__": {
-    "sentence_transformers": "3.1.1",
-    "transformers": "4.44.2",
-    "pytorch": "2.4.1+cpu"
-  },
-  "prompts": {},
-  "default_prompt_name": null,
-  "similarity_fn_name": null
-}

fine_tuned_embedding_model/modules.json

@@ -1,20 +0,0 @@
-[
-  {
-    "idx": 0,
-    "name": "0",
-    "path": "",
-    "type": "sentence_transformers.models.Transformer"
-  },
-  {
-    "idx": 1,
-    "name": "1",
-    "path": "1_Pooling",
-    "type": "sentence_transformers.models.Pooling"
-  },
-  {
-    "idx": 2,
-    "name": "2",
-    "path": "2_Normalize",
-    "type": "sentence_transformers.models.Normalize"
-  }
-]

fine_tuned_embedding_model/sentence_bert_config.json

@@ -1,4 +0,0 @@
-{
-  "max_seq_length": 256,
-  "do_lower_case": false
-}

fine_tuned_embedding_model/special_tokens_map.json

@@ -1,37 +0,0 @@
-{
-  "cls_token": {
-    "content": "[CLS]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "mask_token": {
-    "content": "[MASK]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "pad_token": {
-    "content": "[PAD]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "sep_token": {
-    "content": "[SEP]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "unk_token": {
-    "content": "[UNK]",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  }
-}

fine_tuned_embedding_model/tokenizer_config.json

@@ -1,64 +0,0 @@
-{
-  "added_tokens_decoder": {
-    "0": {
-      "content": "[PAD]",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "100": {
-      "content": "[UNK]",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "101": {
-      "content": "[CLS]",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "102": {
-      "content": "[SEP]",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "103": {
-      "content": "[MASK]",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    }
-  },
-  "clean_up_tokenization_spaces": true,
-  "cls_token": "[CLS]",
-  "do_basic_tokenize": true,
-  "do_lower_case": true,
-  "mask_token": "[MASK]",
-  "max_length": 128,
-  "model_max_length": 256,
-  "never_split": null,
-  "pad_to_multiple_of": null,
-  "pad_token": "[PAD]",
-  "pad_token_type_id": 0,
-  "padding_side": "right",
-  "sep_token": "[SEP]",
-  "stride": 0,
-  "strip_accents": null,
-  "tokenize_chinese_chars": true,
-  "tokenizer_class": "BertTokenizer",
-  "truncation_side": "right",
-  "truncation_strategy": "longest_first",
-  "unk_token": "[UNK]"
-}