---
base_model: nomic-ai/nomic-embed-text-v1
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:2459
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: What types of applications may require confidentiality during their
launch?
sentences:
- "Taken together, the technical protections and practices laid out in the Blueprint\
\ for an AI Bill of Rights can help \nguard the American public against many of\
\ the potential and actual harms identified by researchers, technolo\ngists,\
\ advocates, journalists, policymakers, and communities in the United States and\
\ around the world. This \ntechnical companion is intended to be used as a reference\
\ by people across many circumstances – anyone"
- "deactivate AI systems that demonstrate performance or outcomes inconsistent with\
\ intended use. \nAction ID \nSuggested Action \nGAI Risks \nMG-2.4-001 \nEstablish\
\ and maintain communication plans to inform AI stakeholders as part of \nthe\
\ deactivation or disengagement process of a specific GAI system (including for\
\ \nopen-source models) or context of use, including reasons, workarounds, user\
\ \naccess removal, alternative processes, contact information, etc. \nHuman-AI\
\ Configuration"
- "launch may need to be confidential. Government applications, particularly law\
\ enforcement applications or \napplications that raise national security considerations,\
\ may require confidential or limited engagement based \non system sensitivities\
\ and preexisting oversight laws and structures. Concerns raised in this consultation\
\ \nshould be documented, and the automated system developers were proposing to\
\ create, use, or deploy should \nbe reconsidered based on this feedback."
- source_sentence: What is the main focus of the paper by Chandra et al. (2023) regarding
Chinese influence operations?
sentences:
- "https://arxiv.org/abs/2403.06634 \nChandra, B. et al. (2023) Dismantling the\
\ Disinformation Business of Chinese Influence Operations. \nRAND. https://www.rand.org/pubs/commentary/2023/10/dismantling-the-disinformation-business-of-\n\
chinese.html \nCiriello, R. et al. (2024) Ethical Tensions in Human-AI Companionship:\
\ A Dialectical Inquiry into Replika. \nResearchGate. https://www.researchgate.net/publication/374505266_Ethical_Tensions_in_Human-\n\
AI_Companionship_A_Dialectical_Inquiry_into_Replika"
- "monocultures,3” resulting from repeated use of the same model, or impacts on\
\ access to \nopportunity, labor markets, and the creative economies.4 \n• \n\
Source of risk: Risks may emerge from factors related to the design, training,\
\ or operation of the \nGAI model itself, stemming in some cases from GAI model\
\ or system inputs, and in other cases, \nfrom GAI system outputs. Many GAI risks,\
\ however, originate from human behavior, including"
- "limited to GAI model or system architecture, training mechanisms and libraries,\
\ data types used for \ntraining or fine-tuning, levels of model access or availability\
\ of model weights, and application or use \ncase 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"
- source_sentence: What steps are being taken to enhance transparency and accountability
in the GAI system?
sentences:
- "security, health, foreign relations, the environment, and the technological recovery\
\ and use of resources, among \nother topics. OSTP leads interagency science and\
\ technology policy coordination efforts, assists the Office of \nManagement and\
\ Budget (OMB) with an annual review and analysis of Federal research and development\
\ in \nbudgets, and serves as a source of scientific and technological analysis\
\ and judgment for the President with"
- "steps taken to update the GAI system to enhance transparency and \naccountability.\
\ \nHuman-AI Configuration; Harmful \nBias and Homogenization \nMG-4.1-006 \nTrack\
\ dataset modifications for provenance by monitoring data deletions, \nrectification\
\ requests, and other changes that may impact the verifiability of \ncontent origins.\
\ \nInformation Integrity"
- "content. Some well-known techniques for provenance data tracking include digital\
\ watermarking, \nmetadata recording, digital fingerprinting, and human authentication,\
\ among others. \nProvenance Data Tracking Approaches \nProvenance data tracking\
\ techniques for GAI systems can be used to track the history and origin of data\
\ \ninputs, metadata, and synthetic content. Provenance data tracking records\
\ the origin and history for"
- source_sentence: What are some examples of mechanisms for human consideration and
fallback mentioned in the context?
sentences:
- "consequences resulting from the utilization of content provenance approaches\
\ on users and \ncommunities. Furthermore, organizations can track and document\
\ the provenance of datasets to identify \ninstances in which AI-generated data\
\ is a potential root cause of performance issues with the GAI \nsystem. \nA.1.8.\
\ Incident Disclosure \nOverview \nAI incidents can be defined as an “event, circumstance,\
\ or series of events where the development, use,"
- "fully impact rights, opportunities, or access. Automated systems that have greater\
\ control over outcomes, \nprovide input to high-stakes decisions, relate to sensitive\
\ domains, or otherwise have a greater potential to \nmeaningfully impact rights,\
\ opportunities, or access should have greater availability (e.g., staffing) and\
\ over\nsight of human consideration and fallback mechanisms. \nAccessible. Mechanisms\
\ for human consideration and fallback, whether in-person, on paper, by phone,\
\ or"
- '•
Frida Polli, CEO, Pymetrics
•
Karen Levy, Assistant Professor, Department of Information Science, Cornell University
•
Natasha Duarte, Project Director, Upturn
•
Elana Zeide, Assistant Professor, University of Nebraska College of Law
•
Fabian Rogers, Constituent Advocate, Office of NY State Senator Jabari Brisport
and Community
Advocate and Floor Captain, Atlantic Plaza Towers Tenants Association'
- source_sentence: What mental health issues are associated with the increased use
of technologies in schools and workplaces?
sentences:
- "but this approach may still produce harmful recommendations in response to other\
\ less-explicit, novel \nprompts (also relevant to CBRN Information or Capabilities,\
\ Data Privacy, Information Security, and \nObscene, Degrading and/or Abusive\
\ Content). Crafting such prompts deliberately is known as \n“jailbreaking,” or,\
\ manipulating prompts to circumvent output controls. Limitations of GAI systems\
\ can be"
- "external use, narrow vs. broad application scope, fine-tuning, and varieties of\
\ \ndata sources (e.g., grounding, retrieval-augmented generation). \nData Privacy;\
\ Intellectual \nProperty"
- "technologies has increased in schools and workplaces, and, when coupled with\
\ consequential management and \nevaluation decisions, it is leading to mental\
\ health harms such as lowered self-confidence, anxiety, depression, and \na reduced\
\ ability to use analytical reasoning.61 Documented patterns show that personal\
\ data is being aggregated by \ndata brokers to profile communities in harmful\
\ ways.62 The impact of all this data harvesting is corrosive,"
model-index:
- name: SentenceTransformer based on nomic-ai/nomic-embed-text-v1
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy@1
value: 0.8584142394822006
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9838187702265372
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9951456310679612
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9991909385113269
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.8584142394822006
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.32793959007551243
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1990291262135922
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09991909385113268
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.8584142394822006
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9838187702265372
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9951456310679612
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9991909385113269
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9417951214306157
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9220443571171728
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9221065926163013
name: Cosine Map@100
- type: dot_accuracy@1
value: 0.8584142394822006
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.9838187702265372
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.9951456310679612
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.9991909385113269
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.8584142394822006
name: Dot Precision@1
- type: dot_precision@3
value: 0.32793959007551243
name: Dot Precision@3
- type: dot_precision@5
value: 0.1990291262135922
name: Dot Precision@5
- type: dot_precision@10
value: 0.09991909385113268
name: Dot Precision@10
- type: dot_recall@1
value: 0.8584142394822006
name: Dot Recall@1
- type: dot_recall@3
value: 0.9838187702265372
name: Dot Recall@3
- type: dot_recall@5
value: 0.9951456310679612
name: Dot Recall@5
- type: dot_recall@10
value: 0.9991909385113269
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.9417951214306157
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.9220443571171728
name: Dot Mrr@10
- type: dot_map@100
value: 0.9221065926163013
name: Dot Map@100
---
# SentenceTransformer based on nomic-ai/nomic-embed-text-v1
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/nomic-embed-text-v1](https://huggingface.co./nomic-ai/nomic-embed-text-v1). 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. In particular, **this model is trained on various documents which descibe frameworks for building ethical AI systems.** As such it performs well on matching questions to context in RAG applications.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [nomic-ai/nomic-embed-text-v1](https://huggingface.co./nomic-ai/nomic-embed-text-v1)
- **Maximum Sequence Length:** 8192 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': 8192, 'do_lower_case': False}) with Transformer model: NomicBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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("deman539/nomic-embed-text-v1")
# Run inference
sentences = [
'What mental health issues are associated with the increased use of technologies in schools and workplaces?',
'technologies has increased in schools and workplaces, and, when coupled with consequential management and \nevaluation decisions, it is leading to mental health harms such as lowered self-confidence, anxiety, depression, and \na reduced ability to use analytical reasoning.61 Documented patterns show that personal data is being aggregated by \ndata brokers to profile communities in harmful ways.62 The impact of all this data harvesting is corrosive,',
'but this approach may still produce harmful recommendations in response to other less-explicit, novel \nprompts (also relevant to CBRN Information or Capabilities, Data Privacy, Information Security, and \nObscene, Degrading and/or Abusive Content). Crafting such prompts deliberately is known as \n“jailbreaking,” or, manipulating prompts to circumvent output controls. Limitations of GAI systems can be',
]
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 | 0.8584 |
| cosine_accuracy@3 | 0.9838 |
| cosine_accuracy@5 | 0.9951 |
| cosine_accuracy@10 | 0.9992 |
| cosine_precision@1 | 0.8584 |
| cosine_precision@3 | 0.3279 |
| cosine_precision@5 | 0.199 |
| cosine_precision@10 | 0.0999 |
| cosine_recall@1 | 0.8584 |
| cosine_recall@3 | 0.9838 |
| cosine_recall@5 | 0.9951 |
| cosine_recall@10 | 0.9992 |
| cosine_ndcg@10 | 0.9418 |
| cosine_mrr@10 | 0.922 |
| **cosine_map@100** | **0.9221** |
| dot_accuracy@1 | 0.8584 |
| dot_accuracy@3 | 0.9838 |
| dot_accuracy@5 | 0.9951 |
| dot_accuracy@10 | 0.9992 |
| dot_precision@1 | 0.8584 |
| dot_precision@3 | 0.3279 |
| dot_precision@5 | 0.199 |
| dot_precision@10 | 0.0999 |
| dot_recall@1 | 0.8584 |
| dot_recall@3 | 0.9838 |
| dot_recall@5 | 0.9951 |
| dot_recall@10 | 0.9992 |
| dot_ndcg@10 | 0.9418 |
| dot_mrr@10 | 0.922 |
| dot_map@100 | 0.9221 |
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 2,459 training samples
* Columns: sentence_0 and sentence_1
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 |
|:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 2 tokens
- mean: 18.7 tokens
- max: 35 tokens
| - min: 22 tokens
- mean: 93.19 tokens
- max: 337 tokens
|
* Samples:
| sentence_0 | sentence_1 |
|:-----------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| What should organizations include in contracts to evaluate third-party GAI processes and standards? | services acquisition and value chain risk management; and legal compliance.
Data Privacy; Information
Integrity; Information Security;
Intellectual Property; Value Chain
and Component Integration
GV-6.1-006 Include clauses in contracts which allow an organization to evaluate third-party
GAI processes and standards.
Information Integrity
GV-6.1-007 Inventory all third-party entities with access to organizational content and
establish approved GAI technology and service provider lists. |
| What steps should be taken to manage third-party entities with access to organizational content? | services acquisition and value chain risk management; and legal compliance.
Data Privacy; Information
Integrity; Information Security;
Intellectual Property; Value Chain
and Component Integration
GV-6.1-006 Include clauses in contracts which allow an organization to evaluate third-party
GAI processes and standards.
Information Integrity
GV-6.1-007 Inventory all third-party entities with access to organizational content and
establish approved GAI technology and service provider lists. |
| What should entities responsible for automated systems establish before deploying the system? | Clear organizational oversight. Entities responsible for the development or use of automated systems
should lay out clear governance structures and procedures. This includes clearly-stated governance proce
dures before deploying the system, as well as responsibility of specific individuals or entities to oversee ongoing
assessment and mitigation. Organizational stakeholders including those with oversight of the business process |
* 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`: 32
- `per_device_eval_batch_size`: 32
- `num_train_epochs`: 20
- `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`: 32
- `per_device_eval_batch_size`: 32
- `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`: 20
- `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