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 +1,752 @@
+---
+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: What is the purpose of the Artificial Intelligence Ethics for the
+    Intelligence Community as mentioned in the context?
+  sentences:
+  - "You should be able to opt out, where appropriate, and \nhave access to a person\
+    \ who can quickly consider and \nremedy problems you encounter. You should be\
+    \ able to opt \nout from automated systems in favor of a human alternative, where\
+    \ \nappropriate. Appropriateness should be determined based on rea­\nsonable expectations\
+    \ in a given context and with a focus on ensuring \nbroad accessibility and protecting\
+    \ the public from especially harm­\nful impacts. In some cases, a human or other\
+    \ alternative may be re­\nquired by law. You should have access to timely human\
+    \ consider­\nation and remedy by a fallback and escalation process if an automat­\n\
+    ed system fails, it produces an error, or you would like to appeal or \ncontest\
+    \ its impacts on you. Human consideration and fallback \nshould be accessible,\
+    \ equitable, effective, maintained, accompanied \nby appropriate operator training,\
+    \ and should not impose an unrea­\nsonable burden on the public. Automated systems\
+    \ with an intended"
+  - "points to numerous examples of effective and proactive stakeholder engagement,\
+    \ including the Community-\nBased Participatory Research Program developed by\
+    \ the National Institutes of Health and the participatory \ntechnology assessments\
+    \ developed by the National Oceanic and Atmospheric Administration.18\nThe National\
+    \ Institute of Standards and Technology (NIST) is developing a risk \nmanagement\
+    \ framework to better manage risks posed to individuals, organizations, and \n\
+    society by AI.19 The NIST AI Risk Management Framework, as mandated by Congress,\
+    \ is intended for \nvoluntary use to help incorporate trustworthiness considerations\
+    \ into the design, development, use, and \nevaluation of AI products, services,\
+    \ and systems. The NIST framework is being developed through a consensus-\ndriven,\
+    \ open, transparent, and collaborative process that includes workshops and other\
+    \ opportunities to provide \ninput. The NIST framework aims to foster the development\
+    \ of innovative approaches to address"
+  - "of Artificial Intelligence Ethics for the Intelligence Community to guide personnel\
+    \ on whether and how to \ndevelop and use AI in furtherance of the IC's mission,\
+    \ as well as an AI Ethics Framework to help implement \nthese principles.22\n\
+    The National Science Foundation (NSF) funds extensive research to help foster\
+    \ the \ndevelopment of automated systems that adhere to and advance their safety,\
+    \ security and \neffectiveness. Multiple NSF programs support research that directly\
+    \ addresses many of these principles: \nthe National AI Research Institutes23\
+    \ support research on all aspects of safe, trustworthy, fair, and explainable\
+    \ \nAI algorithms and systems; the Cyber Physical Systems24 program supports research\
+    \ on developing safe \nautonomous and cyber physical systems with AI components;\
+    \ the Secure and Trustworthy Cyberspace25 \nprogram supports research on cybersecurity\
+    \ and privacy enhancing technologies in automated systems; the"
+- source_sentence: How does the Department of Defense's approach to AI ethics differ
+    from that of the Department of Energy?
+  sentences:
+  - "NOTICE & \nEXPLANATION \nWHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS\nThe expectations\
+    \ for automated systems are meant to serve as a blueprint for the development\
+    \ of additional \ntechnical standards and practices that are tailored for particular\
+    \ sectors and contexts. \nTailored to the level of risk. An assessment should\
+    \ be done to determine the level of risk of the auto­\nmated system. In settings\
+    \ where the consequences are high as determined by a risk assessment, or extensive\
+    \ \noversight is expected (e.g., in criminal justice or some public sector settings),\
+    \ explanatory mechanisms should \nbe built into the system design so that the\
+    \ system’s full behavior can be explained in advance (i.e., only fully \ntransparent\
+    \ models should be used), rather than as an after-the-decision interpretation.\
+    \ In other settings, the \nextent of explanation provided should be tailored to\
+    \ the risk level."
+  - "SAFE AND EFFECTIVE \nSYSTEMS \nHOW THESE PRINCIPLES CAN MOVE INTO PRACTICE\n\
+    Real-life examples of how these principles can become reality, through laws, policies,\
+    \ and practical \ntechnical and sociotechnical approaches to protecting rights,\
+    \ opportunities, and access. ­\nSome U.S government agencies have developed specific\
+    \ frameworks for ethical use of AI \nsystems. The Department of Energy (DOE) has\
+    \ activated the AI Advancement Council that oversees coordina-\ntion and advises\
+    \ on implementation of the DOE AI Strategy and addresses issues and/or escalations\
+    \ on the \nethical use and development of AI systems.20 The Department of Defense\
+    \ has adopted Artificial Intelligence \nEthical Principles, and tenets for Responsible\
+    \ Artificial Intelligence specifically tailored to its national \nsecurity and\
+    \ defense activities.21 Similarly, the U.S. Intelligence Community (IC) has developed\
+    \ the Principles"
+  - "Formal Methods in the Field26 program supports research on rigorous formal verification\
+    \ and analysis of \nautomated systems and machine learning, and the Designing\
+    \ Accountable Software Systems27 program supports \nresearch on rigorous and reproducible\
+    \ methodologies for developing software systems with legal and regulatory \ncompliance\
+    \ in mind. \nSome state legislatures have placed strong transparency and validity\
+    \ requirements on \nthe use of pretrial risk assessments. The use of algorithmic\
+    \ pretrial risk assessments has been a \ncause of concern for civil rights groups.28\
+    \ Idaho Code Section 19-1910, enacted in 2019,29 requires that any \npretrial\
+    \ risk assessment, before use in the state, first be \"shown to be free of bias\
+    \ against any class of \nindividuals protected from discrimination by state or\
+    \ federal law\", that any locality using a pretrial risk \nassessment must first\
+    \ formally validate the claim of its being free of bias, that \"all documents,\
+    \ records, and"
+- source_sentence: What are the expectations for automated systems intended to serve
+    as a blueprint for?
+  sentences:
+  - "help to mitigate biases and potential harms. \nGuarding against proxies.  Directly\
+    \ using demographic information in the design, development, or \ndeployment of\
+    \ an automated system (for purposes other than evaluating a system for discrimination\
+    \ or using \na system to counter discrimination) runs a high risk of leading to\
+    \ algorithmic discrimination and should be \navoided. In many cases, attributes\
+    \ that are highly correlated with demographic features, known as proxies, can\
+    \ \ncontribute to algorithmic discrimination. In cases where use of the demographic\
+    \ features themselves would \nlead to illegal algorithmic discrimination, reliance\
+    \ on such proxies in decision-making (such as that facilitated \nby an algorithm)\
+    \ may also be prohibited by law. Proactive testing should be performed to identify\
+    \ proxies by \ntesting for correlation between demographic information and attributes\
+    \ in any data used as part of system"
+  - "describes three broad challenges for mitigating bias – datasets, testing and\
+    \ evaluation, and human factors – and \nintroduces preliminary guidance for addressing\
+    \ them. Throughout, the special publication takes a socio-\ntechnical perspective\
+    \ to identifying and managing AI bias. \n29\nAlgorithmic \nDiscrimination \nProtections"
+  - "SAFE AND EFFECTIVE \nSYSTEMS \nWHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS\n\
+    The expectations for automated systems are meant to serve as a blueprint for the\
+    \ development of additional \ntechnical standards and practices that are tailored\
+    \ for particular sectors and contexts. \nDerived data sources tracked and reviewed\
+    \ carefully. Data that is derived from other data through \nthe use of algorithms,\
+    \ such as data derived or inferred from prior model outputs, should be identified\
+    \ and \ntracked, e.g., via a specialized type in a data schema. Derived data should\
+    \ be viewed as potentially high-risk \ninputs that may lead to feedback loops,\
+    \ compounded harm, or inaccurate results. Such sources should be care­\nfully\
+    \ validated against the risk of collateral consequences. \nData reuse limits in\
+    \ sensitive domains. Data reuse, and especially data reuse in a new context, can\
+    \ result \nin the spreading and scaling of harms. Data from some domains, including\
+    \ criminal justice data and data indi­"
+- source_sentence: What should individuals have access to regarding their data decisions
+    and the impact of surveillance technologies?
+  sentences:
+  - '•
+
+    Searches for “Black girls,” “Asian girls,” or “Latina girls” return predominantly39
+    sexualized content, rather
+
+    than role models, toys, or activities.40 Some search engines have been working
+    to reduce the prevalence of
+
+    these results, but the problem remains.41
+
+    •
+
+    Advertisement delivery systems that predict who is most likely to click on a job
+    advertisement end up deliv-
+
+    ering ads in ways that reinforce racial and gender stereotypes, such as overwhelmingly
+    directing supermar-
+
+    ket cashier ads to women and jobs with taxi companies to primarily Black people.42­
+
+    •
+
+    Body scanners, used by TSA at airport checkpoints, require the operator to select
+    a “male” or “female”
+
+    scanning setting based on the passenger’s sex, but the setting is chosen based
+    on the operator’s perception of
+
+    the passenger’s gender identity. These scanners are more likely to flag transgender
+    travelers as requiring
+
+    extra screening done by a person. Transgender travelers have described degrading
+    experiences associated'
+  - "information used to build or validate the risk assessment shall be open to public\
+    \ inspection,\" and that assertions \nof trade secrets cannot be used \"to quash\
+    \ discovery in a criminal matter by a party to a criminal case.\" \n22"
+  - "tect privacy and civil liberties. Continuous surveillance and monitoring \nshould\
+    \ not be used in education, work, housing, or in other contexts where the \nuse\
+    \ of such surveillance technologies is likely to limit rights, opportunities,\
+    \ or \naccess. Whenever possible, you should have access to reporting that confirms\
+    \ \nyour data decisions have been respected and provides an assessment of the\
+    \ \npotential impact of surveillance technologies on your rights, opportunities,\
+    \ or \naccess. \nDATA PRIVACY\n30"
+- source_sentence: What are the implications of the digital divide highlighted in
+    Andrew Kenney's article regarding unemployment benefits?
+  sentences:
+  - "cating adverse outcomes in domains such as finance, employment, and housing,\
+    \ is especially sensitive, and in \nsome cases its reuse is limited by law. Accordingly,\
+    \ such data should be subject to extra oversight to ensure \nsafety and efficacy.\
+    \ Data reuse of sensitive domain data in other contexts (e.g., criminal data reuse\
+    \ for civil legal \nmatters or private sector use) should only occur where use\
+    \ of such data is legally authorized and, after examina­\ntion, has benefits for\
+    \ those impacted by the system that outweigh identified risks and, as appropriate,\
+    \ reason­\nable measures have been implemented to mitigate the identified risks.\
+    \ Such data should be clearly labeled to \nidentify contexts for limited reuse\
+    \ based on sensitivity. Where possible, aggregated datasets may be useful for\
+    \ \nreplacing individual-level sensitive data. \nDemonstrate the safety and effectiveness\
+    \ of the system \nIndependent evaluation. Automated systems should be designed\
+    \ to allow for independent evaluation (e.g.,"
+  - "5. Environmental Impacts: Impacts due to high compute resource utilization in\
+    \ training or \noperating 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 \nincorrect presumptions\
+    \ about performance; undesired homogeneity that skews system or model \noutputs,\
+    \ which may be erroneous, lead to ill-founded decision-making, or amplify harmful\
+    \ \nbiases.  \n7. Human-AI Configuration: Arrangements of or interactions between\
+    \ a human and an AI system \nwhich can result in the human inappropriately anthropomorphizing\
+    \ GAI systems or experiencing \nalgorithmic aversion, automation bias, over-reliance,\
+    \ or emotional entanglement with GAI \nsystems."
+  - 'https://bipartisanpolicy.org/blog/the-low-down-on-ballot-curing/
+
+    101. Andrew Kenney. ''I''m shocked that they need to have a smartphone'': System
+    for unemployment
+
+    benefits exposes digital divide. USA Today. May 2, 2021.
+
+    https://www.usatoday.com/story/tech/news/2021/05/02/unemployment-benefits-system-leaving­
+
+    people-behind/4915248001/
+
+    102. Allie Gross. UIA lawsuit shows how the state criminalizes the unemployed.
+    Detroit Metro-Times.
+
+    Sep. 18, 2015.
+
+    https://www.metrotimes.com/news/uia-lawsuit-shows-how-the-state-criminalizes-the­
+
+    unemployed-2369412
+
+    103. Maia Szalavitz. The Pain Was Unbearable. So Why Did Doctors Turn Her Away?
+    Wired. Aug. 11,
+
+    2021. https://www.wired.com/story/opioid-drug-addiction-algorithm-chronic-pain/
+
+    104. Spencer Soper. Fired by Bot at Amazon: "It''s You Against the Machine". Bloomberg,
+    Jun. 28, 2021.
+
+    https://www.bloomberg.com/news/features/2021-06-28/fired-by-bot-amazon-turns-to-machine­
+
+    managers-and-workers-are-losing-out'
+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: 0.73
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.9
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.935
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.96
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.73
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.3
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.187
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.096
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.73
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.9
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.935
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.96
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.8511693160760204
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.8155396825396827
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.8172228277187864
+      name: Cosine Map@100
+    - type: dot_accuracy@1
+      value: 0.73
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.9
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.935
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 0.96
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.73
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.3
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.187
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.096
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.73
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.9
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 0.935
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.96
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.8511693160760204
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.8155396825396827
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.8172228277187864
+      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("ldldld/snowflake-arctic-embed-m-finetuned")
+# Run inference
+sentences = [
+    "What are the implications of the digital divide highlighted in Andrew Kenney's article regarding unemployment benefits?",
+    'https://bipartisanpolicy.org/blog/the-low-down-on-ballot-curing/\n101. Andrew Kenney. \'I\'m shocked that they need to have a smartphone\': System for unemployment\nbenefits exposes digital divide. USA Today. May 2, 2021.\nhttps://www.usatoday.com/story/tech/news/2021/05/02/unemployment-benefits-system-leaving\xad\npeople-behind/4915248001/\n102. Allie Gross. UIA lawsuit shows how the state criminalizes the unemployed. Detroit Metro-Times.\nSep. 18, 2015.\nhttps://www.metrotimes.com/news/uia-lawsuit-shows-how-the-state-criminalizes-the\xad\nunemployed-2369412\n103. Maia Szalavitz. The Pain Was Unbearable. So Why Did Doctors Turn Her Away? Wired. Aug. 11,\n2021. https://www.wired.com/story/opioid-drug-addiction-algorithm-chronic-pain/\n104. Spencer Soper. Fired by Bot at Amazon: "It\'s You Against the Machine". Bloomberg, Jun. 28, 2021.\nhttps://www.bloomberg.com/news/features/2021-06-28/fired-by-bot-amazon-turns-to-machine\xad\nmanagers-and-workers-are-losing-out',
+    '5. Environmental Impacts: Impacts due to high compute resource utilization in training or \noperating 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 \nincorrect presumptions about performance; undesired homogeneity that skews system or model \noutputs, which may be erroneous, lead to ill-founded decision-making, or amplify harmful \nbiases.  \n7. Human-AI Configuration: Arrangements of or interactions between a human and an AI system \nwhich can result in the human inappropriately anthropomorphizing GAI systems or experiencing \nalgorithmic aversion, automation bias, over-reliance, or emotional entanglement with GAI \nsystems.',
+]
+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   | 0.73       |
+| cosine_accuracy@3   | 0.9        |
+| cosine_accuracy@5   | 0.935      |
+| cosine_accuracy@10  | 0.96       |
+| cosine_precision@1  | 0.73       |
+| cosine_precision@3  | 0.3        |
+| cosine_precision@5  | 0.187      |
+| cosine_precision@10 | 0.096      |
+| cosine_recall@1     | 0.73       |
+| cosine_recall@3     | 0.9        |
+| cosine_recall@5     | 0.935      |
+| cosine_recall@10    | 0.96       |
+| cosine_ndcg@10      | 0.8512     |
+| cosine_mrr@10       | 0.8155     |
+| **cosine_map@100**  | **0.8172** |
+| dot_accuracy@1      | 0.73       |
+| dot_accuracy@3      | 0.9        |
+| dot_accuracy@5      | 0.935      |
+| dot_accuracy@10     | 0.96       |
+| dot_precision@1     | 0.73       |
+| dot_precision@3     | 0.3        |
+| dot_precision@5     | 0.187      |
+| dot_precision@10    | 0.096      |
+| dot_recall@1        | 0.73       |
+| dot_recall@3        | 0.9        |
+| dot_recall@5        | 0.935      |
+| dot_recall@10       | 0.96       |
+| dot_ndcg@10         | 0.8512     |
+| dot_mrr@10          | 0.8155     |
+| dot_map@100         | 0.8172     |
+
+<!--
+## 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: 12 tokens</li><li>mean: 20.66 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 165.88 tokens</li><li>max: 512 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                                        | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
+  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What is the main purpose of the "Blueprint for an AI Bill of Rights" as indicated in the context?</code>                                                    | <code>BLUEPRINT FOR AN <br>AI BILL OF <br>RIGHTS <br>MAKING AUTOMATED <br>SYSTEMS WORK FOR <br>THE AMERICAN PEOPLE <br>OCTOBER 2022</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
+  | <code>When was the "Blueprint for an AI Bill of Rights" created?</code>                                                                                           | <code>BLUEPRINT FOR AN <br>AI BILL OF <br>RIGHTS <br>MAKING AUTOMATED <br>SYSTEMS WORK FOR <br>THE AMERICAN PEOPLE <br>OCTOBER 2022</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
+  | <code>What was the purpose of the Blueprint for an AI Bill of Rights published by the White House Office of Science and Technology Policy in October 2022?</code> | <code>About this Document <br>The Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People was <br>published by the White House Office of Science and Technology Policy in October 2022. This framework was <br>released one year after OSTP announced the launch of a process to develop “a bill of rights for an AI-powered <br>world.” Its release follows a year of public engagement to inform this initiative. The framework is available <br>online at: https://www.whitehouse.gov/ostp/ai-bill-of-rights <br>About the Office of Science and Technology Policy <br>The Office of Science and Technology Policy (OSTP) was established by the National Science and Technology <br>Policy, Organization, and Priorities Act of 1976 to provide the President and others within the Executive Office <br>of the President with advice on the scientific, engineering, and technological aspects of the economy, national</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   | 0.7953         |
+| 1.6667 | 50   | 0.8326         |
+| 2.0    | 60   | 0.8277         |
+| 3.0    | 90   | 0.8250         |
+| 3.3333 | 100  | 0.8284         |
+| 4.0    | 120  | 0.8200         |
+| 5.0    | 150  | 0.8172         |
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.1.1
+- Transformers: 4.44.2
+- PyTorch: 2.4.1+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": "3.1.1",
+    "transformers": "4.44.2",
+    "pytorch": "2.4.1+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 sha256:7027427cf7714fa771f875b485493d13f5540576c12633f755bfebc207cdae37
+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]"
+}