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 +1,679 @@
+---
+base_model: Snowflake/snowflake-arctic-embed-m
+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:600
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: How can high compute resource utilization in training GAI models
+    affect ecosystems?
+  sentences:
+  - "should not be used in education, work, housing, or in other contexts where the\
+    \ use of such surveillance \ntechnologies is likely to limit rights, opportunities,\
+    \ or access. Whenever possible, you should have access to \nreporting that confirms\
+    \ your data decisions have been respected and provides an assessment of the \n\
+    potential impact of surveillance technologies on your rights, opportunities, or\
+    \ access. \nNOTICE AND EXPLANATION"
+  - "Legal Disclaimer \nThe Blueprint for an AI Bill of Rights: Making Automated Systems\
+    \ Work for the American People is a white paper \npublished by the White House\
+    \ Office of Science and Technology Policy. It is intended to support the \ndevelopment\
+    \ of policies and practices that protect civil rights and promote democratic values\
+    \ in the building, \ndeployment, and governance of automated systems. \nThe Blueprint\
+    \ for an AI Bill of Rights is non-binding and does not constitute U.S. government\
+    \ policy. It \ndoes not supersede, modify, or direct an interpretation of any\
+    \ existing statute, regulation, policy, or \ninternational instrument. It does\
+    \ not constitute binding guidance for the public or Federal agencies and"
+  - "or stereotyping content . \n4. Data Privacy: Impacts due to l eakage and unauthorized\
+    \ use, disclosure , or de -anonymization of \nbiometric, health, location , or\
+    \ other  personally identifiable information  or sensitive data .7 \n5. Environmental\
+    \ Impacts: Impacts due to high compute resource utilization in training or \n\
+    operating GAI models, and related outcomes that may adversely impact ecosystems.\
+    \  \n6. Harmful Bias or Homogenization: Amplification and exacerbation of  historical,\
+    \  societal, and \nsystemic  biases ; performance disparities8 between  sub- groups\
+    \ or languages , possibly due to \nnon- representative training data , that result\
+    \ in discrimination, amplification of biases, or"
+- source_sentence: What are the potential risks associated with human-AI configuration
+    in GAI systems?
+  sentences:
+  - "establish approved GAI technology and service provider lists.  Value Chain and\
+    \ Component \nIntegration  \nGV-6.1-0 08 Maintain records of changes to content\
+    \ made by third parties to promote content \nprovenance, including sources, timestamps,\
+    \ metadata . Information Integrity ; Value Chain \nand Component Integration;\
+    \ Intellectual Property  \nGV-6.1-0 09 Update and integrate due diligence processes\
+    \ for GAI acquisition and \nprocurement vendor assessments to include intellectual\
+    \ property, data privacy, security, and other risks. For example, update p rocesses\
+    \ \nto: Address solutions that \nmay rely on embedded GAI technologies; Address\
+    \ ongoing monitoring , \nassessments, and alerting, dynamic risk assessments,\
+    \ and real -time reporting"
+  - "could lead to homogenized outputs, including by amplifying any homogenization\
+    \ from the model used to \ngenerate the synthetic training data . \nTrustworthy\
+    \ AI Characteristics:  Fair with Harmful Bias Managed, Valid and Reliable  \n\
+    2.7. Human -AI Configuration  \nGAI system  use can involve varying risks of misconfigurations\
+    \  and poor interactions  between a system \nand a human who is interacti ng with\
+    \ it. Humans bring their  unique perspectives , experiences , or domain -\nspecific\
+    \ expertise to interactions with AI systems  but may not have detailed knowledge\
+    \ of AI systems and \nhow they work.  As a result, h uman experts may be unnecessarily\
+    \ “averse ” to GAI systems , and thus \ndeprive themselves  or others  of GAI’s\
+    \ beneficial uses ."
+  - "requests image features that are inconsistent with the  stereotypes.  Harmful\
+    \ b ias in GAI models , which \nmay stem from their training data , can also \
+    \ cause representational  harm s or perpetuate  or exacerbate  \nbias based on\
+    \ race, gender, disability, or other protected classes .  \nHarmful b ias in GAI\
+    \ systems can also lead to harms via disparities between how a model performs\
+    \ for \ndifferent subgroups or languages  (e.g., an LLM may perform  less well\
+    \ for non- English languages  or \ncertain dialects ). Such disparities can contribute\
+    \ to discriminatory decision -making or amplification of \nexisting societal biases.\
+    \  In addition,  GAI systems may be inappropriately trusted to perform similarly"
+- source_sentence: What types of content are considered harmful biases in the context
+    of information security?
+  sentences:
+  - "MS-2.5-0 05 Verify GAI system training data and TEVV data provenance, and that\
+    \ fine -tuning  \nor retrieval- augmented generation data is grounded.  Information\
+    \ Integrity  \nMS-2.5-0 06 Regularly review security and safety guardrails, especially\
+    \ if the GAI system is \nbeing operated in novel circumstances. This includes\
+    \ reviewing reasons why the \nGAI system was initially assessed as being safe\
+    \ to deploy.  Information Security ; Dangerous , \nViolent, or Hateful  Content\
+    \  \nAI Actor Tasks:  Domain Experts, TEVV"
+  - "to diminished transparency or accountability for downstream users.  While  this\
+    \ is a risk for traditional AI \nsystems  and some other digital technologies\
+    \ , the risk is exacerbated for GAI due to the scale of the \ntraining data, which\
+    \ may be too large for humans to vet; the  difficulty of training foundation models,\
+    \ \nwhich leads to extensive reuse of limited numbers of models; an d the extent\
+    \ to which GAI may be \nintegrat ed into other  devices and services.  As GAI\
+    \ systems often involve many distinct  third -party \ncomponents and data sources\
+    \ , it may be difficult to attribute issues in a system’s behavior to any one of\
+    \ \nthese sources.  \nErrors in t hird-party GAI components can also have downstream\
+    \ impacts  on accuracy and robustness ."
+  - "biases in the generated content.  Information Security ; Harmful Bias \nand Homogenization\
+    \  \nMG-2.2-005 Engage in due diligence to analyze  GAI output for harmful content,\
+    \ potential \nmisinformation , and CBRN -related or NCII content . CBRN Information\
+    \ or Capabilities ; \nObscene, Degrading, and/or \nAbusive Content ; Harmful Bias\
+    \ and \nHomogenization ; Dangerous , \nViolent, or Hateful Content"
+- source_sentence: What is the focus of the paper by Padmakumar et al (2024) regarding
+    language models and content diversity?
+  sentences:
+  - "Content  \nMS-2.12- 002 Document anticipated environmental impacts of model development,\
+    \ \nmaintenance, and deployment in product design decisions.  Environmental  \n\
+    MS-2.12- 003 Measure or estimate environmental impacts (e.g., energy and water\
+    \ \nconsumption) for training, fine tuning, and deploying models: Verify tradeoffs\
+    \ \nbetween resources used at inference time versus additional resources required\
+    \ at training time.  Environmental  \nMS-2.12- 004 Verify effectiveness of carbon\
+    \ capture or offset programs  for GAI training and \napplications , and address\
+    \ green -washing concerns . Environmental  \nAI Actor Tasks:  AI Deployment, AI\
+    \ Impact Assessment, Domain Experts, Operation and Monitoring, TEVV"
+  - "opportunities, undermine their privac y, or pervasively track their activity—often\
+    \ without their knowledge or \nconsent. \nThese outcomes are deeply harmful—but\
+    \ they are not inevitable. Automated systems have brought about extraor-\ndinary\
+    \ benefits, from technology that helps farmers grow food more efficiently and\
+    \ computers that predict storm \npaths, to algorithms that can identify diseases\
+    \ in patients. These tools now drive important decisions across \nsectors, while\
+    \ data is helping to revolutionize global industries. Fueled by the power of American\
+    \ innovation, \nthese tools hold the potential to redefine every part of our society\
+    \ and make life better for everyone."
+  - "Publishing, Paris . https://doi.org/10.1787/d1a8d965- en \nOpenAI  (2023) GPT-4\
+    \ System Card . https://cdn.openai.com/papers/gpt -4-system -card.pdf  \nOpenAI\
+    \  (2024) GPT-4 Technical Report. https://arxiv.org/pdf/2303.08774  \nPadmakumar,\
+    \ V. et al. (2024) Does writing with language models reduce content diversity?\
+    \  ICLR . \nhttps://arxiv.org/pdf/2309.05196  \nPark,  P.  et. al. (2024)  AI\
+    \ deception: A survey of  examples, risks, and potential solutions. Patterns,\
+    \ 5(5).  \narXiv . https://arxiv.org/pdf/2308.14752  \nPartnership on AI  (2023)\
+    \ Building a Glossary for Synthetic Media Transparency Methods, Part 1: Indirect\
+    \ \nDisclosure . https://partnershiponai.org/glossary -for-synthetic -media- transparency\
+    \ -methods -part-1-\nindirect -disclosure/"
+- source_sentence: What are the key components involved in ensuring data quality and
+    ethical considerations in AI systems?
+  sentences:
+  - "(such as where significant negative impacts are imminent, severe harms are actually\
+    \ occurring, or large -scale risks could occur); and broad GAI negative risks,\
+    \ \nincluding: Immature safety or risk cultures related to AI and GAI design,\
+    \ development and deployment, public information integrity risks, including impacts\
+    \ on democratic processes, unknown long -term performance characteristics of GAI.\
+    \  Information Integrity ; Dangerous , \nViolent, or Hateful Content ; CBRN \n\
+    Information or Capabilities  \nGV-1.3-007 Devise a plan to halt development or\
+    \ deployment of a GAI system that poses unacceptable negative risk.  CBRN Information\
+    \ and Capability ; \nInformation Security ; Information \nIntegrity  \nAI Actor\
+    \ Tasks: Governance and Oversight"
+  - "30 MEASURE 2.2:  Evaluations involving human subjects meet applicable requirements\
+    \ (including human subject protection) and are \nrepresentative of the relevant\
+    \ population.  \nAction ID  Suggested Action  GAI Risks  \nMS-2.2-001 Assess and\
+    \ manage statistical biases related to GAI content provenance through \ntechniques\
+    \ such as re -sampling, re -weighting, or adversarial training.  Information Integrity\
+    \ ; Information \nSecurity ; Harmful Bias and \nHomogenization  \nMS-2.2-002 Document\
+    \ how content provenance data  is tracked  and how that data interact s \nwith\
+    \  privacy and security . Consider : Anonymiz ing data to protect the privacy\
+    \ of \nhuman subjects; Leverag ing privacy output filters; Remov ing any personally"
+  - "Data quality; Model architecture (e.g., convolutional neural network, transformers,\
+    \ etc.); Optimizatio n objectives; Training algorithms; RLHF \napproaches; Fine\
+    \ -tuning or retrieval- augmented generation approaches; \nEvaluation data; Ethical\
+    \ considerations; Legal and regulatory requirements.  Information Integrity ;\
+    \ Harmful Bias \nand Homogenization  \nAI Actor Tasks:  AI Deployment, AI Impact\
+    \ Assessment, Domain Experts, End -Users, Operation and Monitoring, TEVV  \n \n\
+    MEASURE 2.10:  Privacy risk of the AI system – as identified in the MAP function\
+    \ – is examined and documented.  \nAction ID  Suggested Action  GAI Risks  \n\
+    MS-2.10- 001 Conduct AI red -teaming to assess issues  such as: Outputting of\
+    \ training data"
+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: 0.8
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.99
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.99
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.8
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.33000000000000007
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.19799999999999998
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09999999999999998
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.8
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.99
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.99
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 1.0
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.9195108324425135
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.8916666666666667
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.8916666666666666
+      name: Cosine Map@100
+    - type: dot_accuracy@1
+      value: 0.8
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.99
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.99
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 1.0
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.8
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.33000000000000007
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.19799999999999998
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.09999999999999998
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.8
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.99
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 0.99
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 1.0
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.9195108324425135
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.8916666666666667
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.8916666666666666
+      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("XicoC/midterm-finetuned-arctic")
+# Run inference
+sentences = [
+    'What are the key components involved in ensuring data quality and ethical considerations in AI systems?',
+    'Data quality; Model architecture (e.g., convolutional neural network, transformers, etc.); Optimizatio n objectives; Training algorithms; RLHF \napproaches; Fine -tuning or retrieval- augmented generation approaches; \nEvaluation data; Ethical considerations; Legal and regulatory requirements.  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  Suggested Action  GAI Risks  \nMS-2.10- 001 Conduct AI red -teaming to assess issues  such as: Outputting of training data',
+    '30 MEASURE 2.2:  Evaluations involving human subjects meet applicable requirements (including human subject protection) and are \nrepresentative of the relevant population.  \nAction ID  Suggested Action  GAI Risks  \nMS-2.2-001 Assess and manage statistical biases related to GAI content provenance through \ntechniques such as re -sampling, re -weighting, or adversarial training.  Information Integrity ; Information \nSecurity ; Harmful Bias and \nHomogenization  \nMS-2.2-002 Document how content provenance data  is tracked  and how that data interact s \nwith  privacy and security . Consider : Anonymiz ing data to protect the privacy of \nhuman subjects; Leverag ing privacy output filters; Remov ing any personally',
+]
+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   | 0.8        |
+| cosine_accuracy@3   | 0.99       |
+| cosine_accuracy@5   | 0.99       |
+| cosine_accuracy@10  | 1.0        |
+| cosine_precision@1  | 0.8        |
+| cosine_precision@3  | 0.33       |
+| cosine_precision@5  | 0.198      |
+| cosine_precision@10 | 0.1        |
+| cosine_recall@1     | 0.8        |
+| cosine_recall@3     | 0.99       |
+| cosine_recall@5     | 0.99       |
+| cosine_recall@10    | 1.0        |
+| cosine_ndcg@10      | 0.9195     |
+| cosine_mrr@10       | 0.8917     |
+| **cosine_map@100**  | **0.8917** |
+| dot_accuracy@1      | 0.8        |
+| dot_accuracy@3      | 0.99       |
+| dot_accuracy@5      | 0.99       |
+| dot_accuracy@10     | 1.0        |
+| dot_precision@1     | 0.8        |
+| dot_precision@3     | 0.33       |
+| dot_precision@5     | 0.198      |
+| dot_precision@10    | 0.1        |
+| dot_recall@1        | 0.8        |
+| dot_recall@3        | 0.99       |
+| dot_recall@5        | 0.99       |
+| dot_recall@10       | 1.0        |
+| dot_ndcg@10         | 0.9195     |
+| dot_mrr@10          | 0.8917     |
+| dot_map@100         | 0.8917     |
+
+<!--
+## 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: 600 training samples
+* Columns: <code>sentence_0</code> and <code>sentence_1</code>
+* Approximate statistics based on the first 600 samples:
+  |         | sentence_0                                                                         | sentence_1                                                                          |
+  |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                              |
+  | details | <ul><li>min: 13 tokens</li><li>mean: 21.67 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 132.86 tokens</li><li>max: 512 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                                                         | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
+  |:-------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What is the title of the NIST publication related to Artificial Intelligence Risk Management?</code>         | <code>NIST Trustworthy and Responsible AI  <br>NIST AI 600 -1 <br>Artificial Intelligence Risk Management <br>Framework: Generative Artificial <br>Intelligence Profile <br> <br>  <br>This publication is available free of charge from:  <br>https://doi.org/10.6028/NIST.AI.600 -1</code>                                                                                                                                                                                                                                                                 |
+  | <code>Where can the NIST AI 600 -1 publication be accessed for free?</code>                                        | <code>NIST Trustworthy and Responsible AI  <br>NIST AI 600 -1 <br>Artificial Intelligence Risk Management <br>Framework: Generative Artificial <br>Intelligence Profile <br> <br>  <br>This publication is available free of charge from:  <br>https://doi.org/10.6028/NIST.AI.600 -1</code>                                                                                                                                                                                                                                                                 |
+  | <code>What is the title of the publication released by NIST in July 2024 regarding artificial intelligence?</code> | <code>NIST Trustworthy and Responsible AI  <br>NIST AI 600 -1 <br>Artificial Intelligence Risk Management <br>Framework: Generative Artificial <br>Intelligence Profile <br> <br>  <br>This publication is available free of charge from:  <br>https://doi.org/10.6028/NIST.AI.600 -1 <br> <br>July 2024  <br> <br> <br> <br> <br>U.S. Department of Commerce  <br>Gina M. Raimondo, Secretary <br>National Institute of Standards and Technology  <br>Laurie E. Locascio, NIST Director and Under Secretary of Commerce for Standards and Technology</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    | 30   | 0.8722         |
+| 1.6667 | 50   | 0.8817         |
+| 2.0    | 60   | 0.8867         |
+| 3.0    | 90   | 0.8867         |
+| 3.3333 | 100  | 0.8917         |
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.1.0
+- Transformers: 4.44.2
+- PyTorch: 2.4.0+cu121
+- Accelerate: 0.34.2
+- Datasets: 2.19.2
+- 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": "midterm-finetuned_arctic",
+  "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": "3.1.0",
+    "transformers": "4.44.2",
+    "pytorch": "2.4.0+cu121"
+  },
+  "prompts": {
+    "query": "Represent this sentence for searching relevant passages: "
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

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+oid sha256:e0023811e92eb44d32fdb9fe8bd88a6fd762711e7b567617175a836f424f844a
+size 437951328

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
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+    "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": {
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+    "lstrip": false,
+    "normalized": false,
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+    "single_word": false
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
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+    "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

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
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+      "lstrip": false,
+      "normalized": false,
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+  "cls_token": "[CLS]",
+  "do_lower_case": true,
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+  "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]"
+}