---
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 organizations tailor their measurement of GAI risks based
on specific characteristics?
sentences:
- "3 \nthe abuse, misuse, and unsafe repurposing by humans (adversarial or not),\
\ and others result \nfrom interactions between a human and an AI system. \n\
• \nTime scale: GAI risks may materialize abruptly or across extended periods.\
\ Examples include \nimmediate (and/or prolonged) emotional harm and potential\
\ risks to physical safety due to the \ndistribution of harmful deepfake images,\
\ or the long-term effect of disinformation on societal \ntrust in public institutions."
- "12 \nCSAM. Even when trained on “clean” data, increasingly capable GAI models\
\ can synthesize or produce \nsynthetic NCII and CSAM. Websites, mobile apps,\
\ and custom-built models that generate synthetic NCII \nhave moved from niche\
\ internet forums to mainstream, automated, and scaled online businesses. \n\
Trustworthy AI Characteristics: Fair with Harmful Bias Managed, Safe, Privacy\
\ Enhanced \n2.12. \nValue Chain and Component Integration"
- "case context. \nOrganizations may choose to tailor how they measure GAI risks\
\ based on these characteristics. They may \nadditionally wish to allocate risk\
\ management resources relative to the severity and likelihood of \nnegative impacts,\
\ including where and how these risks manifest, and their direct and material\
\ impacts \nharms in the context of GAI use. Mitigations for model or system level\
\ risks may differ from mitigations \nfor use-case or ecosystem level risks."
- source_sentence: What methods are suggested for measuring the reliability of content
authentication techniques in the context of content provenance?
sentences:
- "updates. \nInformation Integrity; Data Privacy \nMG-3.2-003 \nDocument sources\
\ and types of training data and their origins, potential biases \npresent in\
\ the data related to the GAI application and its content provenance, \narchitecture,\
\ training process of the pre-trained model including information on \nhyperparameters,\
\ training duration, and any fine-tuning or retrieval-augmented \ngeneration processes\
\ applied. \nInformation Integrity; Harmful Bias \nand Homogenization; Intellectual\
\ \nProperty"
- "Security \nMS-2.7-005 \nMeasure reliability of content authentication methods,\
\ such as watermarking, \ncryptographic signatures, digital fingerprints, as well\
\ as access controls, \nconformity assessment, and model integrity verification,\
\ which can help support \nthe effective implementation of content provenance techniques.\
\ Evaluate the \nrate of false positives and false negatives in content provenance,\
\ as well as true \npositives and true negatives for verification. \nInformation\
\ Integrity \nMS-2.7-006"
- "GV-1.6-003 \nIn addition to general model, governance, and risk information,\
\ consider the \nfollowing items in GAI system inventory entries: Data provenance\
\ information \n(e.g., source, signatures, versioning, watermarks); Known issues\
\ reported from \ninternal bug tracking or external information sharing resources\
\ (e.g., AI incident \ndatabase, AVID, CVE, NVD, or OECD AI incident monitor);\
\ Human oversight roles \nand responsibilities; Special rights and considerations\
\ for intellectual property,"
- source_sentence: What are the suggested actions an organization can take to manage
GAI risks?
sentences:
- "Information Integrity; Dangerous, \nViolent, or Hateful Content; CBRN \nInformation\
\ or Capabilities \nGV-1.3-007 Devise a plan to halt development or deployment\
\ of a GAI system that poses \nunacceptable negative risk. \nCBRN Information\
\ and Capability; \nInformation Security; Information \nIntegrity \nAI Actor Tasks:\
\ Governance and Oversight \n \nGOVERN 1.4: The risk management process and its\
\ outcomes are established through transparent policies, procedures, and other"
- "match the statistical properties of real-world data without disclosing personally\
\ \nidentifiable information or contributing to homogenization. \nData Privacy;\
\ Intellectual Property; \nInformation Integrity; \nConfabulation; Harmful Bias\
\ and \nHomogenization \nAI Actor Tasks: AI Deployment, AI Impact Assessment,\
\ Governance and Oversight, Operation and Monitoring \n \nMANAGE 2.3: Procedures\
\ are followed to respond to and recover from a previously unknown risk when it\
\ is identified. \nAction ID"
- "• \nSuggested Action: Steps an organization or AI actor can take to manage GAI\
\ risks. \n• \nGAI Risks: Tags linking suggested actions with relevant GAI risks.\
\ \n• \nAI Actor Tasks: Pertinent AI Actor Tasks for each subcategory. Not every\
\ AI Actor Task listed will \napply to every suggested action in the subcategory\
\ (i.e., some apply to AI development and \nothers apply to AI deployment). \n\
The tables below begin with the AI RMF subcategory, shaded in blue, followed by\
\ suggested actions."
- source_sentence: How can harmful bias and homogenization be addressed in the context
of human-AI configuration?
sentences:
- "on GAI, apply general fairness metrics (e.g., demographic parity, equalized odds,\
\ \nequal opportunity, statistical hypothesis tests), to the pipeline or business\
\ \noutcome where appropriate; Custom, context-specific metrics developed in \n\
collaboration with domain experts and affected communities; Measurements of \n\
the prevalence of denigration in generated content in deployment (e.g., sub-\n\
sampling a fraction of traffic and manually annotating denigrating content). \n\
Harmful Bias and Homogenization;"
- "MP-5.1-001 Apply TEVV practices for content provenance (e.g., probing a system's\
\ synthetic \ndata generation capabilities for potential misuse or vulnerabilities.\
\ \nInformation Integrity; Information \nSecurity \nMP-5.1-002 \nIdentify potential\
\ content provenance harms of GAI, such as misinformation or \ndisinformation,\
\ deepfakes, including NCII, or tampered content. Enumerate and \nrank risks based\
\ on their likelihood and potential impact, and determine how well"
- "MS-1.3-002 \nEngage in internal and external evaluations, GAI red-teaming, impact\
\ \nassessments, or other structured human feedback exercises in consultation\
\ \nwith representative AI Actors with expertise and familiarity in the context\
\ of \nuse, and/or who are representative of the populations associated with the\
\ \ncontext of use. \nHuman-AI Configuration; Harmful \nBias and Homogenization;\
\ CBRN \nInformation or Capabilities \nMS-1.3-003"
- source_sentence: How can structured human feedback exercises, such as GAI red-teaming,
contribute to GAI risk measurement and management?
sentences:
- "rank risks based on their likelihood and potential impact, and determine how\
\ well \nprovenance solutions address specific risks and/or harms. \nInformation\
\ Integrity; Dangerous, \nViolent, or Hateful Content; \nObscene, Degrading, and/or\
\ \nAbusive Content \nMP-5.1-003 \nConsider disclosing use of GAI to end users\
\ in relevant contexts, while considering \nthe objective of disclosure, the context\
\ of use, the likelihood and magnitude of the"
- "15 \nGV-1.3-004 Obtain input from stakeholder communities to identify unacceptable\
\ use, in \naccordance with activities in the AI RMF Map function. \nCBRN Information\
\ or Capabilities; \nObscene, Degrading, and/or \nAbusive Content; Harmful Bias\
\ \nand Homogenization; Dangerous, \nViolent, or Hateful Content \nGV-1.3-005\
\ \nMaintain an updated hierarchy of identified and expected GAI risks connected\
\ to \ncontexts of GAI model advancement and use, potentially including specialized\
\ risk"
- "AI-generated content, for example by employing techniques like chaos \nengineering\
\ and seeking stakeholder feedback. \nInformation Integrity \nMS-1.1-008 \nDefine\
\ use cases, contexts of use, capabilities, and negative impacts where \nstructured\
\ human feedback exercises, e.g., GAI red-teaming, would be most \nbeneficial for\
\ GAI risk measurement and management based on the context of \nuse. \nHarmful\
\ Bias and \nHomogenization; CBRN \nInformation or Capabilities \nMS-1.1-009"
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: 0.85
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.96
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.98
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1.0
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.85
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.31999999999999995
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19599999999999995
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09999999999999998
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.85
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.96
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.98
name: Cosine Recall@5
- type: cosine_recall@10
value: 1.0
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9342942871848772
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9124166666666668
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9124166666666668
name: Cosine Map@100
- type: dot_accuracy@1
value: 0.85
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.96
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.98
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 1.0
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.85
name: Dot Precision@1
- type: dot_precision@3
value: 0.31999999999999995
name: Dot Precision@3
- type: dot_precision@5
value: 0.19599999999999995
name: Dot Precision@5
- type: dot_precision@10
value: 0.09999999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.85
name: Dot Recall@1
- type: dot_recall@3
value: 0.96
name: Dot Recall@3
- type: dot_recall@5
value: 0.98
name: Dot Recall@5
- type: dot_recall@10
value: 1.0
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.9342942871848772
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.9124166666666668
name: Dot Mrr@10
- type: dot_map@100
value: 0.9124166666666668
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)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
### 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("Cheselle/finetuned-arctic-sentence")
# Run inference
sentences = [
'How can structured human feedback exercises, such as GAI red-teaming, contribute to GAI risk measurement and management?',
'AI-generated content, for example by employing techniques like chaos \nengineering and seeking stakeholder feedback. \nInformation Integrity \nMS-1.1-008 \nDefine use cases, contexts of use, capabilities, and negative impacts where \nstructured human feedback exercises, e.g., GAI red-teaming, would be most \nbeneficial for GAI risk measurement and management based on the context of \nuse. \nHarmful Bias and \nHomogenization; CBRN \nInformation or Capabilities \nMS-1.1-009',
'15 \nGV-1.3-004 Obtain input from stakeholder communities to identify unacceptable use, in \naccordance with activities in the AI RMF Map function. \nCBRN Information or Capabilities; \nObscene, Degrading, and/or \nAbusive Content; Harmful Bias \nand Homogenization; Dangerous, \nViolent, or Hateful Content \nGV-1.3-005 \nMaintain an updated hierarchy of identified and expected GAI risks connected to \ncontexts of GAI model advancement and use, potentially including specialized risk',
]
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]
```
## Evaluation
### Metrics
#### Information Retrieval
* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.85 |
| cosine_accuracy@3 | 0.96 |
| cosine_accuracy@5 | 0.98 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.85 |
| cosine_precision@3 | 0.32 |
| cosine_precision@5 | 0.196 |
| cosine_precision@10 | 0.1 |
| cosine_recall@1 | 0.85 |
| cosine_recall@3 | 0.96 |
| cosine_recall@5 | 0.98 |
| cosine_recall@10 | 1.0 |
| cosine_ndcg@10 | 0.9343 |
| cosine_mrr@10 | 0.9124 |
| **cosine_map@100** | **0.9124** |
| dot_accuracy@1 | 0.85 |
| dot_accuracy@3 | 0.96 |
| dot_accuracy@5 | 0.98 |
| dot_accuracy@10 | 1.0 |
| dot_precision@1 | 0.85 |
| dot_precision@3 | 0.32 |
| dot_precision@5 | 0.196 |
| dot_precision@10 | 0.1 |
| dot_recall@1 | 0.85 |
| dot_recall@3 | 0.96 |
| dot_recall@5 | 0.98 |
| dot_recall@10 | 1.0 |
| dot_ndcg@10 | 0.9343 |
| dot_mrr@10 | 0.9124 |
| dot_map@100 | 0.9124 |
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 600 training samples
* Columns: sentence_0 and sentence_1
* Approximate statistics based on the first 600 samples:
| | sentence_0 | sentence_1 |
|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 11 tokens
- mean: 21.05 tokens
- max: 34 tokens
| - min: 14 tokens
- mean: 91.74 tokens
- max: 335 tokens
|
* Samples:
| sentence_0 | sentence_1 |
|:--------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| What is the title of the publication related to Artificial Intelligence Risk Management by NIST? | NIST Trustworthy and Responsible AI
NIST AI 600-1
Artificial Intelligence Risk Management
Framework: Generative Artificial
Intelligence Profile
This publication is available free of charge from:
https://doi.org/10.6028/NIST.AI.600-1 |
| Where can the NIST AI 600-1 publication be accessed for free? | NIST Trustworthy and Responsible AI
NIST AI 600-1
Artificial Intelligence Risk Management
Framework: Generative Artificial
Intelligence Profile
This publication is available free of charge from:
https://doi.org/10.6028/NIST.AI.600-1 |
| What is the title of the publication released by NIST in July 2024 regarding AI risk management? | NIST Trustworthy and Responsible AI
NIST AI 600-1
Artificial Intelligence Risk Management
Framework: Generative Artificial
Intelligence Profile
This publication is available free of charge from:
https://doi.org/10.6028/NIST.AI.600-1
July 2024
U.S. Department of Commerce
Gina M. Raimondo, Secretary
National Institute of Standards and Technology
Laurie E. Locascio, NIST Director and Under Secretary of Commerce for Standards and Technology |
* Loss: [MatryoshkaLoss](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`: 16
- `per_device_eval_batch_size`: 16
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin