|
--- |
|
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} |
|
} |
|
``` |
|
|
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