---
base_model: Alibaba-NLP/gte-large-en-v1.5
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:500
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: "1. What measures should be taken to avoid \"mission creep\" when\
\ identifying goals for data collection? \n2. Why is it important to assess new\
\ privacy risks before using collected data in a different context?"
sentences:
- "narrow identified goals, to avoid \"mission creep.\" Anticipated data collection\
\ should be determined to be \nstrictly necessary to the identified goals and\
\ should be minimized as much as possible. Data collected based on \nthese identified\
\ goals and for a specific context should not be used in a different context without\
\ assessing for \nnew privacy risks and implementing appropriate mitigation measures,\
\ which may include express consent."
- "Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government\
\ (December 2020). \nThis white paper recognizes that national security (which\
\ includes certain law enforcement and \nhomeland security activities) and defense\
\ activities are of increased sensitivity and interest to our nation’s \nadversaries\
\ and are often subject to special requirements, such as those governing classified\
\ information and \nother protected data. Such activities require alternative,\
\ compatible safeguards through existing policies that"
- "establish and maintain the capabilities that will allow individuals to use their\
\ own automated systems to help \nthem make consent, access, and control decisions\
\ in a complex data ecosystem. Capabilities include machine \nreadable data, standardized\
\ data formats, metadata or tags for expressing data processing permissions and\
\ \npreferences and data provenance and lineage, context of use and access-specific\
\ tags, and training models for \nassessing privacy risk."
- source_sentence: "1. What types of discrimination are mentioned in the context that\
\ can impact individuals based on their race and ethnicity? \n2. How does the\
\ context address discrimination related to gender identity and sexual orientation?"
sentences:
- "HUMAN ALTERNATIVES, CONSIDERATION\nALLBACK\nF\nAND\n, \n46"
- "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"
- "impacts disfavoring people based on their race, color, ethnicity, \nsex \n(including\
\ \npregnancy, \nchildbirth, \nand \nrelated \nmedical \nconditions, \ngender\
\ \nidentity, \nintersex \nstatus, \nand \nsexual \norientation), religion, age,\
\ national origin, disability, veteran status,"
- source_sentence: "1. What roles do the panelists hold in their respective organizations?\
\ \n2. How are AI systems and other technologies being discussed in relation\
\ to their impact by the individual panelists?"
sentences:
- "requirements of the Federal agencies that enforce them. These principles are\
\ not intended to, and do not, \nprohibit or limit any lawful activity of a government\
\ agency, including law enforcement, national security, or \nintelligence activities.\
\ \nThe appropriate application of the principles set forth in this white paper\
\ depends significantly on the \ncontext in which automated systems are being\
\ utilized. In some circumstances, application of these principles"
- '•
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
The individual panelists described the ways in which AI systems and other technologies
are increasingly being'
- "SECTION TITLE\nFOREWORD\nAmong the great challenges posed to democracy today\
\ is the use of technology, data, and automated systems in \nways that threaten\
\ the rights of the American public. Too often, these tools are used to limit\
\ our opportunities and \nprevent our access to critical resources or services.\
\ These problems are well documented. In America and around \nthe world, systems\
\ supposed to help with patient care have proven unsafe, ineffective, or biased.\
\ Algorithms used"
- source_sentence: "1. What are the key tenets of the Department of Defense's Artificial\
\ Intelligence Ethical Principles? \n2. How do the Principles of Artificial Intelligence\
\ Ethics for the Intelligence Community guide personnel in their use of AI?"
sentences:
- "different treatment or impacts disfavoring people based on their race, color,\
\ ethnicity, sex (including \npregnancy, childbirth, and related medical conditions,\
\ gender identity, intersex status, and sexual \norientation), religion, age,\
\ national origin, disability, veteran status, genetic information, or any other\
\ \nclassification protected by law. Depending on the specific circumstances,\
\ such algorithmic discrimination"
- "ethical 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\
\ \nof Artificial Intelligence Ethics for the Intelligence Community to guide\
\ personnel on whether and how to"
- "DATA PRIVACY \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. \nProtect the public from unchecked surveillance \nHeightened\
\ oversight of surveillance. Surveillance or monitoring systems should be subject\
\ to"
- source_sentence: "1. What measures should be taken to ensure the accuracy and timeliness\
\ of data? \n2. Why is it important to limit access to sensitive data and derived\
\ data?"
sentences:
- "maintain accurate, timely, and complete data. \nLimit access to sensitive data\
\ and derived data. Sensitive data and derived data should not be sold, \nshared,\
\ or made public as part of data brokerage or other agreements. Sensitive data\
\ includes data that can be \nused to infer sensitive information; even systems\
\ that are not directly marketed as sensitive domain technologies \nare expected\
\ to keep sensitive data private. Access to such data should be limited based\
\ on necessity and based"
- "comply with the Privacy Act’s requirements. Among other things, a court may order\
\ a federal agency to amend or \ncorrect an individual’s information in its records\
\ or award monetary damages if an inaccurate, irrelevant, untimely, \nor incomplete\
\ record results in an adverse determination about an individual’s “qualifications,\
\ character, rights, … \nopportunities…, or benefits.” \nNIST’s Privacy Framework\
\ provides a comprehensive, detailed and actionable approach for"
- "made public whenever possible. Care will need to be taken to balance individual\
\ privacy with evaluation data \naccess needs. \nReporting. When members of the\
\ public wish to know what data about them is being used in a system, the \nentity\
\ responsible for the development of the system should respond quickly with a\
\ report on the data it has \ncollected or stored about them. Such a report should\
\ be machine-readable, understandable by most users, and"
model-index:
- name: SentenceTransformer based on Alibaba-NLP/gte-large-en-v1.5
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy@1
value: 0.9733333333333334
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 1.0
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 1.0
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1.0
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.9733333333333334
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.33333333333333326
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19999999999999996
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09999999999999998
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.9733333333333334
name: Cosine Recall@1
- type: cosine_recall@3
value: 1.0
name: Cosine Recall@3
- type: cosine_recall@5
value: 1.0
name: Cosine Recall@5
- type: cosine_recall@10
value: 1.0
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9901581267619055
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9866666666666667
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9866666666666667
name: Cosine Map@100
- type: dot_accuracy@1
value: 0.9733333333333334
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 1.0
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 1.0
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 1.0
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.9733333333333334
name: Dot Precision@1
- type: dot_precision@3
value: 0.33333333333333326
name: Dot Precision@3
- type: dot_precision@5
value: 0.19999999999999996
name: Dot Precision@5
- type: dot_precision@10
value: 0.09999999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.9733333333333334
name: Dot Recall@1
- type: dot_recall@3
value: 1.0
name: Dot Recall@3
- type: dot_recall@5
value: 1.0
name: Dot Recall@5
- type: dot_recall@10
value: 1.0
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.9901581267619055
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.9866666666666667
name: Dot Mrr@10
- type: dot_map@100
value: 0.9866666666666667
name: Dot Map@100
---
# SentenceTransformer based on Alibaba-NLP/gte-large-en-v1.5
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-large-en-v1.5](https://huggingface.co./Alibaba-NLP/gte-large-en-v1.5). It maps sentences & paragraphs to a 1024-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:** [Alibaba-NLP/gte-large-en-v1.5](https://huggingface.co./Alibaba-NLP/gte-large-en-v1.5)
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 1024 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: NewModel
(1): Pooling({'word_embedding_dimension': 1024, '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})
)
```
## 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("lw2134/policy_gte_large")
# Run inference
sentences = [
'1. What measures should be taken to ensure the accuracy and timeliness of data? \n2. Why is it important to limit access to sensitive data and derived data?',
'maintain accurate, timely, and complete data. \nLimit access to sensitive data and derived data. Sensitive data and derived data should not be sold, \nshared, or made public as part of data brokerage or other agreements. Sensitive data includes data that can be \nused to infer sensitive information; even systems that are not directly marketed as sensitive domain technologies \nare expected to keep sensitive data private. Access to such data should be limited based on necessity and based',
'comply with the Privacy Act’s requirements. Among other things, a court may order a federal agency to amend or \ncorrect an individual’s information in its records or award monetary damages if an inaccurate, irrelevant, untimely, \nor incomplete record results in an adverse determination about an individual’s “qualifications, character, rights, … \nopportunities…, or benefits.” \nNIST’s Privacy Framework provides a comprehensive, detailed and actionable approach for',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# 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 | 0.9733 |
| cosine_accuracy@3 | 1.0 |
| cosine_accuracy@5 | 1.0 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.9733 |
| cosine_precision@3 | 0.3333 |
| cosine_precision@5 | 0.2 |
| cosine_precision@10 | 0.1 |
| cosine_recall@1 | 0.9733 |
| cosine_recall@3 | 1.0 |
| cosine_recall@5 | 1.0 |
| cosine_recall@10 | 1.0 |
| cosine_ndcg@10 | 0.9902 |
| cosine_mrr@10 | 0.9867 |
| **cosine_map@100** | **0.9867** |
| dot_accuracy@1 | 0.9733 |
| dot_accuracy@3 | 1.0 |
| dot_accuracy@5 | 1.0 |
| dot_accuracy@10 | 1.0 |
| dot_precision@1 | 0.9733 |
| dot_precision@3 | 0.3333 |
| dot_precision@5 | 0.2 |
| dot_precision@10 | 0.1 |
| dot_recall@1 | 0.9733 |
| dot_recall@3 | 1.0 |
| dot_recall@5 | 1.0 |
| dot_recall@10 | 1.0 |
| dot_ndcg@10 | 0.9902 |
| dot_mrr@10 | 0.9867 |
| dot_map@100 | 0.9867 |
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 500 training samples
* Columns: sentence_0 and sentence_1
* Approximate statistics based on the first 500 samples:
| | sentence_0 | sentence_1 |
|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 27 tokens
- mean: 40.71 tokens
- max: 62 tokens
| - min: 11 tokens
- mean: 78.92 tokens
- max: 104 tokens
|
* Samples:
| sentence_0 | sentence_1 |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1. What is the purpose of the AI Bill of Rights mentioned in the context?
2. When was the Blueprint for an AI Bill of Rights published? | BLUEPRINT FOR AN
AI BILL OF
RIGHTS
MAKING AUTOMATED
SYSTEMS WORK FOR
THE AMERICAN PEOPLE
OCTOBER 2022 |
| 1. What is the purpose of the Blueprint for an AI Bill of Rights published by the White House Office of Science and Technology Policy?
2. When was the Blueprint for an AI Bill of Rights released in relation to the announcement of the process to develop it? | About this Document
The Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People was
published by the White House Office of Science and Technology Policy in October 2022. This framework was
released one year after OSTP announced the launch of a process to develop “a bill of rights for an AI-powered |
| 1. What initiative did the OSTP announce the launch of one year prior to the release mentioned in the context?
2. Where can the framework for the AI bill of rights be accessed online? | released one year after OSTP announced the launch of a process to develop “a bill of rights for an AI-powered
world.” Its release follows a year of public engagement to inform this initiative. The framework is available
online at: https://www.whitehouse.gov/ostp/ai-bill-of-rights
About the Office of Science and Technology Policy
The Office of Science and Technology Policy (OSTP) was established by the National Science and Technology |
* Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
1024,
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
- `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`: 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`: 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