---
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 does the Blueprint for an AI Bill of Rights aim to protect
the rights of the American public?
sentences:
- "and use prohibitions. You and your communities should be free from unchecked\
\ surveillance; surveillance \ntechnologies should be subject to heightened oversight\
\ that includes at least pre-deployment assessment of their \npotential harms\
\ and scope limits to protect privacy and civil liberties. Continuous surveillance\
\ and monitoring"
- "steps to move these principles into practice and promote common approaches that\
\ allow technological \ninnovation to flourish while protecting people from harm.\
\ \n9"
- "ABOUT THIS FRAMEWORK\nThe Blueprint for an AI Bill of Rights is a set of\
\ five principles and associated practices to help guide the \ndesign, use, and\
\ deployment of automated systems to protect the rights of the American public\
\ in the age of \nartificial intel-ligence. Developed through extensive consultation\
\ with the American public, these principles are"
- source_sentence: How can organizations monitor the impact of proxy features on algorithmic
discrimination?
sentences:
- "sociodemographic variables that adjust or “correct” the algorithm’s output on\
\ the basis of a patient’s race or\nethnicity, which can lead to race-based health\
\ inequities.47\n25\nAlgorithmic \nDiscrimination \nProtections"
- "proxy; if needed, it may be possible to identify alternative attributes that\
\ can be used instead. At a minimum, \norganizations should ensure a proxy feature\
\ is not given undue weight and should monitor the system closely \nfor any resulting\
\ algorithmic discrimination. \n26\nAlgorithmic \nDiscrimination \nProtections"
- "velopment, and deployment of automated systems, and from the \ncompounded harm\
\ of its reuse. Independent evaluation and report\ning that confirms that the\
\ system is safe and effective, including re\nporting of steps taken to mitigate\
\ potential harms, should be per\nformed and the results made public whenever\
\ possible. \n15"
- source_sentence: What measures can be taken to ensure that AI systems are designed
to be accessible for people with disabilities?
sentences:
- "potential for meaningful impact on people’s rights, opportunities, or access\
\ and include those to impacted \ncommunities that may not be direct users of\
\ the automated system, risks resulting from purposeful misuse of \nthe system,\
\ and other concerns identified via the consultation process. Assessment and,\
\ where possible, mea"
- "and as a lifecycle minimum performance standard. Decision possibilities resulting\
\ from performance testing \nshould include the possibility of not deploying the\
\ system. \nRisk identification and mitigation. Before deployment, and in a proactive\
\ and ongoing manner, poten\ntial risks of the automated system should be identified\
\ and mitigated. Identified risks should focus on the"
- "individuals \nand \ncommunities \nfrom algorithmic \ndiscrimination and to use\
\ and design systems in an equitable way. This protection should include proactive\
\ \nequity assessments as part of the system design, use of representative data\
\ and protection against proxies \nfor demographic features, ensuring accessibility\
\ for people with disabilities in design and development,"
- source_sentence: 'How should organizations address concerns raised during public
consultations regarding AI data processing and interpretation? '
sentences:
- "and testing and evaluation of AI technologies and systems. It is expected to\
\ be released in the winter of 2022-23. \n21"
- "provide guidance whenever automated systems can meaningfully impact the public’s\
\ rights, opportunities, \nor access to critical needs. \n3"
- "learning models or for other purposes, including how data sources were processed\
\ and interpreted, a \nsummary of what data might be missing, incomplete, or erroneous,\
\ and data relevancy justifications; the \nresults of public consultation such\
\ as concerns raised and any decisions made due to these concerns; risk"
- source_sentence: What role do ethical considerations play in the development and
implementation of automated systems?
sentences:
- "tial to meaningfully impact rights, opportunities, or access. Additionally, this\
\ framework does not analyze or \ntake a position on legislative and regulatory\
\ proposals in municipal, state, and federal government, or those in \nother countries.\
\ \nWe have seen modest progress in recent years, with some state and local governments\
\ responding to these prob"
- '•
Searches for “Black girls,” “Asian girls,” or “Latina girls” return predominantly39
sexualized content, rather
than role models, toys, or activities.40 Some search engines have been working
to reduce the prevalence of
these results, but the problem remains.41
•
Advertisement delivery systems that predict who is most likely to click on a job
advertisement end up deliv-'
- "particularly relevant to automated systems, without articulating a specific set\
\ of FIPPs or scoping \napplicability or the interests served to a single particular\
\ domain, like privacy, civil rights and civil liberties, \nethics, or risk management.\
\ The Technical Companion builds on this prior work to provide practical next"
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: 0.83
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: 0.99
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.83
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: 0.09899999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.83
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: 0.99
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9195971547817925
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8960000000000001
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8966666666666666
name: Cosine Map@100
- type: dot_accuracy@1
value: 0.83
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: 0.99
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.83
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: 0.09899999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.83
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: 0.99
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.9195971547817925
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.8960000000000001
name: Dot Mrr@10
- type: dot_map@100
value: 0.8966666666666666
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("niting089/finetuned_arctic")
# Run inference
sentences = [
'What role do ethical considerations play in the development and implementation of automated systems?',
'particularly relevant to automated systems, without articulating a specific set of FIPPs or scoping \napplicability or the interests served to a single particular domain, like privacy, civil rights and civil liberties, \nethics, or risk management. The Technical Companion builds on this prior work to provide practical next',
'•\nSearches for “Black girls,” “Asian girls,” or “Latina girls” return predominantly39 sexualized content, rather\nthan role models, toys, or activities.40 Some search engines have been working to reduce the prevalence of\nthese results, but the problem remains.41\n•\nAdvertisement delivery systems that predict who is most likely to click on a job advertisement end up deliv-',
]
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 | 0.83 |
| cosine_accuracy@3 | 0.96 |
| cosine_accuracy@5 | 0.98 |
| cosine_accuracy@10 | 0.99 |
| cosine_precision@1 | 0.83 |
| cosine_precision@3 | 0.32 |
| cosine_precision@5 | 0.196 |
| cosine_precision@10 | 0.099 |
| cosine_recall@1 | 0.83 |
| cosine_recall@3 | 0.96 |
| cosine_recall@5 | 0.98 |
| cosine_recall@10 | 0.99 |
| cosine_ndcg@10 | 0.9196 |
| cosine_mrr@10 | 0.896 |
| **cosine_map@100** | **0.8967** |
| dot_accuracy@1 | 0.83 |
| dot_accuracy@3 | 0.96 |
| dot_accuracy@5 | 0.98 |
| dot_accuracy@10 | 0.99 |
| dot_precision@1 | 0.83 |
| dot_precision@3 | 0.32 |
| dot_precision@5 | 0.196 |
| dot_precision@10 | 0.099 |
| dot_recall@1 | 0.83 |
| dot_recall@3 | 0.96 |
| dot_recall@5 | 0.98 |
| dot_recall@10 | 0.99 |
| dot_ndcg@10 | 0.9196 |
| dot_mrr@10 | 0.896 |
| dot_map@100 | 0.8967 |
## 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: 19.86 tokens
- max: 36 tokens
| - min: 16 tokens
- mean: 60.47 tokens
- max: 94 tokens
|
* Samples:
| sentence_0 | sentence_1 |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| What are the key principles outlined in the AI Bill of Rights aimed at ensuring automated systems benefit the American people? | BLUEPRINT FOR AN
AI BILL OF
RIGHTS
MAKING AUTOMATED
SYSTEMS WORK FOR
THE AMERICAN PEOPLE
OCTOBER 2022 |
| How does the AI Bill of Rights address potential ethical concerns related to automated decision-making systems? | BLUEPRINT FOR AN
AI BILL OF
RIGHTS
MAKING AUTOMATED
SYSTEMS WORK FOR
THE AMERICAN PEOPLE
OCTOBER 2022 |
| What is the purpose of the Blueprint for an AI Bill of Rights as outlined by the White House Office of Science and Technology Policy? | 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 |
* 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`: 20
- `per_device_eval_batch_size`: 20
- `num_train_epochs`: 5
- `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`: 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