This is a sentence-transformers model finetuned from 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
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

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:

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("vincha77/finetuned_arctic")
# Run inference
sentences = [
    'What rights do applicants have if their application for credit is denied according to the CFPB?',
    'information in their credit report." The CFPB has also asserted that "[t]he law gives every applicant the right to \na specific explanation if their application for credit was denied, and that right is not diminished simply because \na company uses a complex algorithm that it doesn\'t understand."92 Such explanations illustrate a shared value \nthat certain decisions need to be explained. \nA California law requires that warehouse employees are provided with notice and explana-\ntion about quotas, potentially facilitated by automated systems, that apply to them. Warehous-\ning employers in California that use quota systems (often facilitated by algorithmic monitoring systems) are \nrequired to provide employees with a written description of each quota that applies to the employee, including \n“quantified number of tasks to be performed or materials to be produced or handled, within the defined',
    'even if the inferences are not accurate (e.g., confabulations), and especially if they reveal information \nthat the individual considers sensitive or that is used to disadvantage or harm them. \nBeyond harms from information exposure (such as extortion or dignitary harm), wrong or inappropriate \ninferences of PII can contribute to downstream or secondary harmful impacts. For example, predictive \ninferences made by GAI models based on PII or protected attributes can contribute to adverse decisions, \nleading to representational or allocative harms to individuals or groups (see Harmful Bias and \nHomogenization below).',
]
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

Metric	Value
cosine_accuracy@1	0.98
cosine_accuracy@3	1.0
cosine_accuracy@5	1.0
cosine_accuracy@10	1.0
cosine_precision@1	0.98
cosine_precision@3	0.3333
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	0.98
cosine_recall@3	1.0
cosine_recall@5	1.0
cosine_recall@10	1.0
cosine_ndcg@10	0.9913
cosine_mrr@10	0.9883
cosine_map@100	0.9883
dot_accuracy@1	0.98
dot_accuracy@3	1.0
dot_accuracy@5	1.0
dot_accuracy@10	1.0
dot_precision@1	0.98
dot_precision@3	0.3333
dot_precision@5	0.2
dot_precision@10	0.1
dot_recall@1	0.98
dot_recall@3	1.0
dot_recall@5	1.0
dot_recall@10	1.0
dot_ndcg@10	0.9913
dot_mrr@10	0.9883
dot_map@100	0.9883

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: 12 tokens
mean: 21.21 tokens
max: 39 tokens
min: 21 tokens
mean: 182.02 tokens
max: 512 tokens

Samples:

sentence_0	sentence_1
`What are the responsibilities of AI Actors in monitoring reported issues related to GAI system performance?`	45 [object Object]MG-4.1-007 [object Object]Verify that AI Actors responsible for monitoring reported issues can eﬀectively [object Object]evaluate GAI system performance including the application of content [object Object]provenance data tracking techniques, and promptly escalate issues for response. [object Object]Human-AI Conﬁguration; [object Object]Information Integrity [object Object]AI Actor Tasks: AI Deployment, Aﬀected Individuals and Communities, Domain Experts, End-Users, Human Factors, Operation and [object Object]Monitoring [object Object] [object Object]MANAGE 4.2: Measurable activities for continual improvements are integrated into AI system updates and include regular [object Object]engagement with interested parties, including relevant AI Actors. [object Object]Action ID [object Object]Suggested Action [object Object]GAI Risks [object Object]MG-4.2-001 Conduct regular monitoring of GAI systems and publish reports detailing the [object Object]performance, feedback received, and improvements made. [object Object]Harmful Bias and Homogenization [object Object]MG-4.2-002 [object Object]Practice and follow incident response plans for addressing the generation of
`How are measurable activities for continual improvements integrated into AI system updates according to the context provided?`	45 [object Object]MG-4.1-007 [object Object]Verify that AI Actors responsible for monitoring reported issues can eﬀectively [object Object]evaluate GAI system performance including the application of content [object Object]provenance data tracking techniques, and promptly escalate issues for response. [object Object]Human-AI Conﬁguration; [object Object]Information Integrity [object Object]AI Actor Tasks: AI Deployment, Aﬀected Individuals and Communities, Domain Experts, End-Users, Human Factors, Operation and [object Object]Monitoring [object Object] [object Object]MANAGE 4.2: Measurable activities for continual improvements are integrated into AI system updates and include regular [object Object]engagement with interested parties, including relevant AI Actors. [object Object]Action ID [object Object]Suggested Action [object Object]GAI Risks [object Object]MG-4.2-001 Conduct regular monitoring of GAI systems and publish reports detailing the [object Object]performance, feedback received, and improvements made. [object Object]Harmful Bias and Homogenization [object Object]MG-4.2-002 [object Object]Practice and follow incident response plans for addressing the generation of
`What is the main function of the app discussed in Samantha Cole's article from June 26, 2019?`	`them[object Object]10. Samantha Cole. This Horrifying App Undresses a Photo of Any Woman With a Single Click. Motherboard.[object Object]June 26, 2019. [object Object][object Object]11. Lauren Kaori Gurley. Amazon’s AI Cameras Are Punishing Drivers for Mistakes They Didn’t Make.[object Object]Motherboard. Sep. 20, 2021. [object Object][object Object]drivers-for-mistakes-they-didnt-make[object Object]63`

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 768, 5 512, 6 256, 7 128, 8 64 9 ], 10 "matryoshka_weights": [ 11 1, 12 1, 13 1, 14 1, 15 1 16 ], 17 "n_dims_per_step": -1 18}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
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: 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: 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

Training Logs

Epoch	Step	cosine_map@100
1.0	38	0.965
1.3158	50	0.9783
2.0	76	0.9767
2.6316	100	0.9833
3.0	114	0.9883

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.1.1
Transformers: 4.44.2
PyTorch: 2.4.1+cu121
Accelerate: 0.34.2
Datasets: 3.0.0
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}
}

	sentence_0	sentence_1
type	string	string
details	min: 12 tokens mean: 21.21 tokens max: 39 tokens	min: 21 tokens mean: 182.02 tokens max: 512 tokens