Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -0,0 +1,982 @@
+---
+base_model: Snowflake/snowflake-arctic-embed-m
+datasets: []
+language: []
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+- dot_accuracy@1
+- dot_accuracy@3
+- dot_accuracy@5
+- dot_accuracy@10
+- dot_precision@1
+- dot_precision@3
+- dot_precision@5
+- dot_precision@10
+- dot_recall@1
+- dot_recall@3
+- dot_recall@5
+- dot_recall@10
+- dot_ndcg@10
+- dot_mrr@10
+- dot_map@100
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:678
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: What are some of the content types mentioned in the context?
+  sentences:
+  - 'and/or use cases that were not evaluated in initial testing. \\
+
+    \end{tabular} & \begin{tabular}{l}
+
+    Value Chain and Component \\
+
+    Integration \\
+
+    \end{tabular} \\
+
+    \hline
+
+    MG-3.1-004 & \begin{tabular}{l}
+
+    Take reasonable measures to review training data for CBRN information, and \\
+
+    intellectual property, and where appropriate, remove it. Implement reasonable
+    \\
+
+    measures to prevent, flag, or take other action in response to outputs that \\
+
+    reproduce particular training data (e.g., plagiarized, trademarked, patented,
+    \\
+
+    licensed content or trade secret material). \\
+
+    \end{tabular} & \begin{tabular}{l}
+
+    Intellectual Property; CBRN \\
+
+    Information or Capabilities \\
+
+    \end{tabular} \\
+
+    \hline
+
+    \end{tabular}
+
+    \end{center}'
+  - 'Bias and Homogenization \\
+
+    \end{tabular} \\
+
+    \hline
+
+    GV-6.2-004 & \begin{tabular}{l}
+
+    Establish policies and procedures for continuous monitoring of third-party GAI
+    \\
+
+    systems in deployment. \\
+
+    \end{tabular} & \begin{tabular}{l}
+
+    Value Chain and Component \\
+
+    Integration \\
+
+    \end{tabular} \\
+
+    \hline
+
+    GV-6.2-005 & \begin{tabular}{l}
+
+    Establish policies and procedures that address GAI data redundancy, including
+    \\
+
+    model weights and other system artifacts. \\
+
+    \end{tabular} & Harmful Bias and Homogenization \\
+
+    \hline
+
+    GV-6.2-006 & \begin{tabular}{l}
+
+    Establish policies and procedures to test and manage risks related to rollover
+    and \\
+
+    fallback technologies for GAI systems, acknowledging that rollover and fallback
+    \\
+
+    may include manual processing. \\
+
+    \end{tabular} & Information Integrity \\
+
+    \hline
+
+    GV-6.2-007 & \begin{tabular}{l}
+
+    Review vendor contracts and avoid arbitrary or capricious termination of critical
+    \\
+
+    GAI technologies or vendor services and non-standard terms that may amplify or
+    \\'
+  - 'time. \\
+
+    \end{tabular} & \begin{tabular}{l}
+
+    Information Integrity; Obscene, \\
+
+    Degrading, and/or Abusive \\
+
+    Content; Value Chain and \\
+
+    Component Integration; Harmful \\
+
+    Bias and Homogenization; \\
+
+    Dangerous, Violent, or Hateful \\
+
+    Content; CBRN Information or \\
+
+    Capabilities \\
+
+    \end{tabular} \\
+
+    \hline
+
+    GV-1.3-002 & \begin{tabular}{l}
+
+    Establish minimum thresholds for performance or assurance criteria and review
+    as \\
+
+    part of deployment approval ("go/"no-go") policies, procedures, and processes,
+    \\
+
+    with reviewed processes and approval thresholds reflecting measurement of GAI
+    \\
+
+    capabilities and risks. \\
+
+    \end{tabular} & \begin{tabular}{l}
+
+    CBRN Information or Capabilities; \\
+
+    Confabulation; Dangerous, \\
+
+    Violent, or Hateful Content \\
+
+    \end{tabular} \\
+
+    \hline
+
+    GV-1.3-003 & \begin{tabular}{l}
+
+    Establish a test plan and response policy, before developing highly capable models,
+    \\
+
+    to periodically evaluate whether the model may misuse CBRN information or \\'
+- source_sentence: What are the legal and regulatory requirements involving AI that
+    need to be understood, managed, and documented?
+  sentences:
+  - 'GOVERN 1.1: Legal and regulatory requirements involving Al are understood, managed,
+    and documented.
+
+
+    \begin{center}
+
+    \begin{tabular}{|l|l|l|}
+
+    \hline
+
+    Action ID & Suggested Action & GAI Risks \\
+
+    \hline
+
+    GV-1.1-001 & \begin{tabular}{l}
+
+    Align GAI development and use with applicable laws and regulations, including
+    \\
+
+    those related to data privacy, copyright and intellectual property law. \\
+
+    \end{tabular} & \begin{tabular}{l}
+
+    Data Privacy; Harmful Bias and \\
+
+    Homogenization; Intellectual \\
+
+    Property \\
+
+    \end{tabular} \\
+
+    \hline
+
+    \end{tabular}
+
+    \end{center}
+
+
+    Al Actor Tasks: Governance and Oversight\\
+
+    ${ }^{14} \mathrm{AI}$ Actors are defined by the OECD as "those who play an active
+    role in the AI system lifecycle, including organizations and individuals that
+    deploy or operate AI." See Appendix A of the AI RMF for additional descriptions
+    of Al Actors and AI Actor Tasks.'
+  - '\begin{center}
+
+    \begin{tabular}{|c|c|c|}
+
+    \hline
+
+    Action ID & Suggested Action & GAI Risks \\
+
+    \hline
+
+    GV-1.6-001 & \begin{tabular}{l}
+
+    Enumerate organizational GAI systems for incorporation into AI system inventory
+    \\
+
+    and adjust AI system inventory requirements to account for GAI risks. \\
+
+    \end{tabular} & Information Security \\
+
+    \hline
+
+    GV-1.6-002 & \begin{tabular}{l}
+
+    Define any inventory exemptions in organizational policies for GAI systems \\
+
+    embedded into application software. \\
+
+    \end{tabular} & \begin{tabular}{l}
+
+    Value Chain and Component \\
+
+    Integration \\
+
+    \end{tabular} \\
+
+    \hline
+
+    GV-1.6-003 & \begin{tabular}{l}
+
+    In addition to general model, governance, and risk information, consider the \\
+
+    following items in GAI system inventory entries: Data provenance information \\
+
+    (e.g., source, signatures, versioning, watermarks); Known issues reported from
+    \\
+
+    internal bug tracking or external information sharing resources (e.g., Al incident
+    \\'
+  - 'Wei, J. et al. (2024) Long Form Factuality in Large Language Models. arXiv. \href{https://arxiv.org/pdf/2403.18802}{https://arxiv.org/pdf/2403.18802}
+
+
+    Weidinger, L. et al. (2021) Ethical and social risks of harm from Language Models.
+    arXiv. \href{https://arxiv.org/pdf/2112.04359}{https://arxiv.org/pdf/2112.04359}
+
+
+    Weidinger, L. et al. (2023) Sociotechnical Safety Evaluation of Generative AI
+    Systems. arXiv. \href{https://arxiv.org/pdf/2310.11986}{https://arxiv.org/pdf/2310.11986}
+
+
+    Weidinger, L. et al. (2022) Taxonomy of Risks posed by Language Models. FAccT''
+    22. \href{https://dl.acm.org/doi/pdf/10.1145/3531146.3533088}{https://dl.acm.org/doi/pdf/10.1145/3531146.3533088}
+
+
+    West, D. (2023) Al poses disproportionate risks to women. Brookings. \href{https://www.brookings.edu/articles/ai-poses-disproportionate-risks-to-women/}{https://www.brookings.edu/articles/ai-poses-disproportionate-risks-to-women/}'
+- source_sentence: What are some known issues reported from internal bug tracking
+    or external information sharing resources?
+  sentences:
+  - 'Kirchenbauer, J. et al. (2023) A Watermark for Large Language Models. OpenReview.
+    \href{https://openreview.net/forum?id=aX8ig9X2a7}{https://openreview.net/forum?id=aX8ig9X2a7}
+
+
+    Kleinberg, J. et al. (May 2021) Algorithmic monoculture and social welfare. PNAS.\\
+
+    \href{https://www.pnas.org/doi/10.1073/pnas}{https://www.pnas.org/doi/10.1073/pnas}.
+    2018340118\\
+
+    Lakatos, S. (2023) A Revealing Picture. Graphika. \href{https://graphika.com/reports/a-revealing-picture}{https://graphika.com/reports/a-revealing-picture}\\
+
+    Lee, H. et al. (2024) Deepfakes, Phrenology, Surveillance, and More! A Taxonomy
+    of AI Privacy Risks. arXiv. \href{https://arxiv.org/pdf/2310.07879}{https://arxiv.org/pdf/2310.07879}
+
+
+    Lenaerts-Bergmans, B. (2024) Data Poisoning: The Exploitation of Generative AI.
+    Crowdstrike. \href{https://www.crowdstrike.com/cybersecurity-101/cyberattacks/data-poisoning/}{https://www.crowdstrike.com/cybersecurity-101/cyberattacks/data-poisoning/}'
+  - '(e.g., source, signatures, versioning, watermarks); Known issues reported from
+    \\
+
+    internal bug tracking or external information sharing resources (e.g., Al incident
+    \\
+
+    database, AVID, CVE, NVD, or OECD AI incident monitor); Human oversight roles
+    \\
+
+    and responsibilities; Special rights and considerations for intellectual property,
+    \\
+
+    licensed works, or personal, privileged, proprietary or sensitive data; Underlying
+    \\
+
+    foundation models, versions of underlying models, and access modes. \\
+
+    \end{tabular} & \begin{tabular}{l}
+
+    Data Privacy; Human-AI \\
+
+    Configuration; Information \\
+
+    Integrity; Intellectual Property; \\
+
+    Value Chain and Component \\
+
+    Integration \\
+
+    \end{tabular} \\
+
+    \hline
+
+    \multicolumn{3}{|l|}{AI Actor Tasks: Governance and Oversight} \\
+
+    \hline
+
+    \end{tabular}
+
+    \end{center}'
+  - 'Trustworthy AI Characteristic: Safe, Explainable and Interpretable
+
+    \subsection*{2.2. Confabulation}
+
+    "Confabulation" refers to a phenomenon in which GAI systems generate and confidently
+    present erroneous or false content in response to prompts. Confabulations also
+    include generated outputs that diverge from the prompts or other input or that
+    contradict previously generated statements in the same context. These phenomena
+    are colloquially also referred to as "hallucinations" or "fabrications."'
+- source_sentence: Why do image generator models struggle to produce non-stereotyped
+    content even when prompted?
+  sentences:
+  - Bias exists in many forms and can become ingrained in automated systems. Al systems,
+    including GAI systems, can increase the speed and scale at which harmful biases
+    manifest and are acted upon, potentially perpetuating and amplifying harms to
+    individuals, groups, communities, organizations, and society. For example, when
+    prompted to generate images of CEOs, doctors, lawyers, and judges, current text-to-image
+    models underrepresent women and/or racial minorities, and people with disabilities.
+    Image generator models have also produced biased or stereotyped output for various
+    demographic groups and have difficulty producing non-stereotyped content even
+    when the prompt specifically requests image features that are inconsistent with
+    the stereotypes. Harmful bias in GAI models, which may stem from their training
+    data, can also cause representational harms or perpetuate or exacerbate bias based
+    on race, gender, disability, or other protected classes.
+  - 'The White House (2016) Circular No. A-130, Managing Information as a Strategic
+    Resource. \href{https://www.whitehouse.gov/wp-}{https://www.whitehouse.gov/wp-}\\
+
+    content/uploads/legacy drupal files/omb/circulars/A130/a130revised.pdf\\
+
+    The White House (2023) Executive Order on the Safe, Secure, and Trustworthy Development
+    and Use of Artificial Intelligence. \href{https://www.whitehouse.gov/briefing-room/presidentialactions/2023/10/30/executive-order-on-the-safe-secure-and-trustworthy-development-and-use-ofartificial-intelligence/}{https://www.whitehouse.gov/briefing-room/presidentialactions/2023/10/30/executive-order-on-the-safe-secure-and-trustworthy-development-and-use-ofartificial-intelligence/}'
+  - "%Overriding the \\footnotetext command to hide the marker if its value is `0`\n\
+    \\let\\svfootnotetext\\footnotetext\n\\renewcommand\\footnotetext[2][?]{%\n  \\\
+    if\\relax#1\\relax%\n    \\ifnum\\value{footnote}=0\\blfootnotetext{#2}\\else\\\
+    svfootnotetext{#2}\\fi%\n  \\else%\n    \\if?#1\\ifnum\\value{footnote}=0\\blfootnotetext{#2}\\\
+    else\\svfootnotetext{#2}\\fi%\n    \\else\\svfootnotetext[#1]{#2}\\fi%\n  \\fi\n\
+    }\n\n\\begin{document}\n\\maketitle\n\\section*{Artificial Intelligence Risk Management\
+    \ Framework: Generative Artificial Intelligence Profile}\n\\section*{NIST Trustworthy\
+    \ and Responsible AI NIST AI 600-1}\n\\section*{Artificial Intelligence Risk Management\
+    \ Framework: Generative Artificial Intelligence Profile}\nThis publication is\
+    \ available free of charge from:\\\\\n\\href{https://doi.org/10.6028/NIST.Al.600-1}{https://doi.org/10.6028/NIST.Al.600-1}\n\
+    \nJuly 2024\n\n\\includegraphics[max width=\\textwidth, center]{2024_09_22_1b8d52aa873ff5f60066g-02}\\\
+    \\\nU.S. Department of Commerce Gina M. Raimondo, Secretary"
+- source_sentence: What processes should be updated for GAI acquisition and procurement
+    vendor assessments?
+  sentences:
+  - 'Inventory all third-party entities with access to organizational content and
+    \\
+
+    establish approved GAI technology and service provider lists. \\
+
+    \end{tabular} & \begin{tabular}{l}
+
+    Value Chain and Component \\
+
+    Integration \\
+
+    \end{tabular} \\
+
+    \hline
+
+    GV-6.1-008 & \begin{tabular}{l}
+
+    Maintain records of changes to content made by third parties to promote content
+    \\
+
+    provenance, including sources, timestamps, metadata. \\
+
+    \end{tabular} & \begin{tabular}{l}
+
+    Information Integrity; Value Chain \\
+
+    and Component Integration; \\
+
+    Intellectual Property \\
+
+    \end{tabular} \\
+
+    \hline
+
+    GV-6.1-009 & \begin{tabular}{l}
+
+    Update and integrate due diligence processes for GAI acquisition and \\
+
+    procurement vendor assessments to include intellectual property, data privacy,
+    \\
+
+    security, and other risks. For example, update processes to: Address solutions
+    that \\
+
+    may rely on embedded GAI technologies; Address ongoing monitoring, \\
+
+    assessments, and alerting, dynamic risk assessments, and real-time reporting \\'
+  - "\\item Information Integrity: Lowered barrier to entry to generate and support\
+    \ the exchange and consumption of content which may not distinguish fact from\
+    \ opinion or fiction or acknowledge uncertainties, or could be leveraged for large-scale\
+    \ dis- and mis-information campaigns.\n  \\item Information Security: Lowered\
+    \ barriers for offensive cyber capabilities, including via automated discovery\
+    \ and exploitation of vulnerabilities to ease hacking, malware, phishing, offensive\
+    \ cyber\n\\end{enumerate}\n\\footnotetext{${ }^{6}$ Some commenters have noted\
+    \ that the terms \"hallucination\" and \"fabrication\" anthropomorphize GAI, which\
+    \ itself is a risk related to GAI systems as it can inappropriately attribute\
+    \ human characteristics to non-human entities.\\\\"
+  - 'Evaluation data; Ethical considerations; Legal and regulatory requirements. \\
+
+    \end{tabular} & \begin{tabular}{l}
+
+    Information Integrity; Harmful Bias \\
+
+    and Homogenization \\
+
+    \end{tabular} \\
+
+    \hline
+
+    AI Actor Tasks: Al Deployment, Al Impact Assessment, Domain Experts, End-Users,
+    Operation and Monitoring, TEVV &  &  \\
+
+    \hline
+
+    \end{tabular}
+
+    \end{center}'
+model-index:
+- name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-m
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.8850574712643678
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.9540229885057471
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 1.0
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.8850574712643678
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.31800766283524895
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.19999999999999996
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09999999999999998
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.02458492975734355
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.026500638569604086
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.027777777777777776
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.027777777777777776
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.20817571346541755
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.927969348659004
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.025776926351638994
+      name: Cosine Map@100
+    - type: dot_accuracy@1
+      value: 0.8850574712643678
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.9540229885057471
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 1.0
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 1.0
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.8850574712643678
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.31800766283524895
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.19999999999999996
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.09999999999999998
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.02458492975734355
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.026500638569604086
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 0.027777777777777776
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.027777777777777776
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.20817571346541755
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.927969348659004
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.025776926351638994
+      name: Dot Map@100
+---
+
+# SentenceTransformer based on Snowflake/snowflake-arctic-embed-m
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m). It maps sentences & paragraphs to a 768-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:** [Snowflake/snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m) <!-- at revision e2b128b9fa60c82b4585512b33e1544224ffff42 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("Mr-Cool/midterm-finetuned-embedding")
+# Run inference
+sentences = [
+    'What processes should be updated for GAI acquisition and procurement vendor assessments?',
+    'Inventory all third-party entities with access to organizational content and \\\\\nestablish approved GAI technology and service provider lists. \\\\\n\\end{tabular} & \\begin{tabular}{l}\nValue Chain and Component \\\\\nIntegration \\\\\n\\end{tabular} \\\\\n\\hline\nGV-6.1-008 & \\begin{tabular}{l}\nMaintain records of changes to content made by third parties to promote content \\\\\nprovenance, including sources, timestamps, metadata. \\\\\n\\end{tabular} & \\begin{tabular}{l}\nInformation Integrity; Value Chain \\\\\nand Component Integration; \\\\\nIntellectual Property \\\\\n\\end{tabular} \\\\\n\\hline\nGV-6.1-009 & \\begin{tabular}{l}\nUpdate and integrate due diligence processes for GAI acquisition and \\\\\nprocurement vendor assessments to include intellectual property, data privacy, \\\\\nsecurity, and other risks. For example, update processes to: Address solutions that \\\\\nmay rely on embedded GAI technologies; Address ongoing monitoring, \\\\\nassessments, and alerting, dynamic risk assessments, and real-time reporting \\\\',
+    'Evaluation data; Ethical considerations; Legal and regulatory requirements. \\\\\n\\end{tabular} & \\begin{tabular}{l}\nInformation Integrity; Harmful Bias \\\\\nand Homogenization \\\\\n\\end{tabular} \\\\\n\\hline\nAI Actor Tasks: Al Deployment, Al Impact Assessment, Domain Experts, End-Users, Operation and Monitoring, TEVV &  &  \\\\\n\\hline\n\\end{tabular}\n\\end{center}',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# 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.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.8851     |
+| cosine_accuracy@3   | 0.954      |
+| cosine_accuracy@5   | 1.0        |
+| cosine_accuracy@10  | 1.0        |
+| cosine_precision@1  | 0.8851     |
+| cosine_precision@3  | 0.318      |
+| cosine_precision@5  | 0.2        |
+| cosine_precision@10 | 0.1        |
+| cosine_recall@1     | 0.0246     |
+| cosine_recall@3     | 0.0265     |
+| cosine_recall@5     | 0.0278     |
+| cosine_recall@10    | 0.0278     |
+| cosine_ndcg@10      | 0.2082     |
+| cosine_mrr@10       | 0.928      |
+| **cosine_map@100**  | **0.0258** |
+| dot_accuracy@1      | 0.8851     |
+| dot_accuracy@3      | 0.954      |
+| dot_accuracy@5      | 1.0        |
+| dot_accuracy@10     | 1.0        |
+| dot_precision@1     | 0.8851     |
+| dot_precision@3     | 0.318      |
+| dot_precision@5     | 0.2        |
+| dot_precision@10    | 0.1        |
+| dot_recall@1        | 0.0246     |
+| dot_recall@3        | 0.0265     |
+| dot_recall@5        | 0.0278     |
+| dot_recall@10       | 0.0278     |
+| dot_ndcg@10         | 0.2082     |
+| dot_mrr@10          | 0.928      |
+| dot_map@100         | 0.0258     |
+
+<!--
+## 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: 678 training samples
+* Columns: <code>sentence_0</code> and <code>sentence_1</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                        | sentence_1                                                                         |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                             |
+  | details | <ul><li>min: 7 tokens</li><li>mean: 18.37 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 188.5 tokens</li><li>max: 396 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                                                  | sentence_1                                                                                                                                        |
+  |:------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What are the characteristics of trustworthy AI?</code>                                                | <code>GOVERN 1.2: The characteristics of trustworthy AI are integrated into organizational policies, processes, procedures, and practices.</code> |
+  | <code>How are the characteristics of trustworthy AI integrated into organizational policies?</code>         | <code>GOVERN 1.2: The characteristics of trustworthy AI are integrated into organizational policies, processes, procedures, and practices.</code> |
+  | <code>Why is it important to integrate trustworthy AI characteristics into organizational processes?</code> | <code>GOVERN 1.2: The characteristics of trustworthy AI are integrated into organizational policies, processes, procedures, and practices.</code> |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "MultipleNegativesRankingLoss",
+      "matryoshka_dims": [
+          768,
+          512,
+          256,
+          128,
+          64
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 20
+- `per_device_eval_batch_size`: 20
+- `num_train_epochs`: 5
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 20
+- `per_device_eval_batch_size`: 20
+- `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`: 5
+- `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>
+
+### Training Logs
+| Epoch  | Step | cosine_map@100 |
+|:------:|:----:|:--------------:|
+| 1.0    | 34   | 0.0250         |
+| 1.4706 | 50   | 0.0258         |
+| 2.0    | 68   | 0.0257         |
+| 2.9412 | 100  | 0.0258         |
+| 3.0    | 102  | 0.0258         |
+| 4.0    | 136  | 0.0258         |
+| 4.4118 | 150  | 0.0258         |
+| 5.0    | 170  | 0.0258         |
+
+
+### Framework Versions
+- Python: 3.12.3
+- Sentence Transformers: 3.0.1
+- Transformers: 4.44.2
+- PyTorch: 2.6.0.dev20240922+cu121
+- 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",
+}
+```
+
+#### MatryoshkaLoss
+```bibtex
+@misc{kusupati2024matryoshka,
+    title={Matryoshka Representation Learning}, 
+    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
+    year={2024},
+    eprint={2205.13147},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+#### 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.*
+-->

config.json

@@ -0,0 +1,26 @@
+{
+  "_name_or_path": "Snowflake/snowflake-arctic-embed-m",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "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
+}

config_sentence_transformers.json

@@ -0,0 +1,12 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.44.2",
+    "pytorch": "2.6.0.dev20240922+cu121"
+  },
+  "prompts": {
+    "query": "Represent this sentence for searching relevant passages: "
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8f522fcd942f9887a83b12368d5a76d875cf43a70120e74ecb87288a987d8d52
+size 435588776

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "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"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "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
+  }
+}

tokenizer_config.json

@@ -0,0 +1,62 @@
+{
+  "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_lower_case": true,
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "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]"
+}