This is a sentence-transformers model finetuned from nomic-ai/nomic-embed-text-v1.5. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: nomic-ai/nomic-embed-text-v1.5
Maximum Sequence Length: 8192 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Supported Modality: Text

Model Sources

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

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'NomicBertModel'})
  (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'mean', 'include_prompt': True})
)

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("sentence_transformers_model_id")
# Run inference
sentences = [
    'How is the Initial Daily Benefit (the Applicable Daily Benefit for the first policy year) determined and stated in the policy schedule?',
    'provided any such part\nexceeds a connuous period of 4 hours (aer having\nstay\ncompleted the 24 hours as above) in a non-ICU ward/room of a hospital, an\namount equal to the Applicable Daily Beneﬁt (ADB) available under the policy\nduring that policy year shall be payable subject to beneﬁt limits and condions\nmenonedinPara11A)andexclusionsmenonedinPara15below.\nDuring the ﬁrst\nof cover commencement in respect of each insured, the\nyear\nApplicableDailyBeneﬁtshallbetheInialDailyBeneﬁtamountchosenbyyouand\nmenonedinthepolicySchedule.\nTheamountof DBforeachpolicyyear,aertheﬁrstpolicyyear,shallconsistof2parts:\nA\n\nAn arithmec addion of an amount equal to 5% (ﬁve percent) of the Inial Daily',
    'Periodwithoutanymaximumlimit.\nFor members\nsubsequently under the policy, the beneﬁt in the ﬁrst year\nincluded\nshall be equal to Inial Daily Beneﬁt amount and thereaer the Applicable Daily\nBeneﬁtshallincreaseasabove.\nIfanyofthememberinsuredisrequiredtostayinanIntensiveCareUnitofahospital,\nt\nsubject\nbeneﬁt limits and\nwo mes the\nDaily\nwill be payable\nto\nApplicable\nBeneﬁt\ncondionsmenonedinPara11A)andexclusionsmenonedinPara15below.\nDuring one period of 24 connuous hours (i.e. one day) of Hospitalisaon (aer\nhaving completed the 24 hours as above), if the said Hospitalisaon included stay\ninanIntensiveCareUnitaswellasinanyotherin-paent(non-IntensiveCareUnit)',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.6203, 0.6283],
#         [0.6203, 1.0000, 0.8679],
#         [0.6283, 0.8679, 1.0000]])

Evaluation

Metrics

Information Retrieval

Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.5455
cosine_accuracy@3	0.7727
cosine_accuracy@5	0.9091
cosine_accuracy@10	1.0
cosine_precision@1	0.5455
cosine_precision@3	0.2576
cosine_precision@5	0.1818
cosine_precision@10	0.1
cosine_recall@1	0.5455
cosine_recall@3	0.7727
cosine_recall@5	0.9091
cosine_recall@10	1.0
cosine_ndcg@10	0.7731
cosine_mrr@10	0.7011
cosine_map@100	0.7011

Training Details

Training Dataset

Unnamed Dataset

Size: 20 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 20 samples:
sentence_0 sentence_1
type string string
details
min: 19 tokens
mean: 29.65 tokens
max: 56 tokens
min: 44 tokens
mean: 173.0 tokens
max: 226 tokens

Samples:

sentence_0	sentence_1
`Which specific benefits (e.g., Hospital Cash Benefit, Major Surgical Benefit, Day Care Procedure Benefit, etc.) are available to the insured if they are hospitalized for a continuous period of 24 hours or more?`	65 years (last birthday)[object Object]75 (last birthday)[object Object]17 years (last birthday)[object Object]Howlongareeachinsuredunderthispolicy?[object Object]Each of the insured are covered for[object Object]risks up to age (80). Children are insured up[object Object]Health[object Object]toage25years.[object Object]•[object Object]Hospitalcashbeneﬁt(HCB)[object Object]•[object Object]MajorSurgicalBeneﬁt(MSB)[object Object]•[object Object]DayCareProcedureBeneﬁt[object Object]•[object Object]OtherSurgicalBeneﬁt[object Object]•[object Object]AmbulanceBeneﬁt[object Object]•[object Object]PremiumwaiverBeneﬁt(PWB)[object Object]A) HospitalCashBeneﬁt:[object Object]due to[object Object]If you or any of the insured lives covered under the policy is hospitalised[object Object]Accidental Body Injury or Sickness and the stay in hospital exceeds a connuous[object Object]periodof24hours,thenforanyconnuousperiodof24hoursorpartthereof,[object Object]1. Beneﬁts oﬀered under the plan are
`What are the four daily Hospital Cash Benefit options available when choosing the initial Daily Benefit for the LIC Jeevan Arogya policy?`	emergenciessha eryourpeaceofmind.[object Object]LIC'sJeevanArogyagivesyou:[object Object]•[object Object]Valuableﬁnancialproteconincaseofhospitalisaon,surgeryetc[object Object]•[object Object]IncreasingHealthcovereveryyear[object Object]•[object Object]Lumpsumbeneﬁtirrespecveofactualmedicalcosts[object Object]•[object Object]Noclaimbeneﬁt[object Object]•[object Object]Flexiblebeneﬁtlimittochoosefrom[object Object]•[object Object]Flexiblepremiumpaymentopons[object Object]•[object Object]Veryeasytochooseyourplan[object Object]Step 1[object Object]2[object Object]Step[object Object]Choose the level of Health cover you need[object Object]Work out the premium payable along with our Representave[object Object]Step 1: Choose the level of Health cover you need:[object Object]You can choose the amount of Inial Daily Beneﬁt (i.e. the daily Hospital Cash Beneﬁt[object Object]applicableintheﬁrstyearofthepolicy)asperyourneedfromoutofthefollowingchoices:[object Object][object Object] 2000 per day[object Object][object Object] 4000 per day
`If a policyholder selects a daily Hospital Cash Benefit of 3000 per day, what will be the Initial Major Surgical Benefit sum assured?`	`[object Object] 3000 per day[object Object][object Object][object Object]such as Day Care Procedure Beneﬁt, Other Surgical Beneﬁt and Premium waiver[object Object]Beneﬁt (PWB) menoned below shall also be payable depending upon the daily[object Object]HospitalCashBeneﬁtchosen.[object Object]Step 2: Work out the premium payable along with our representave[object Object]Your premium will depend on your age, gender, the Health cover opon you have`

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

per_device_train_batch_size: 10
per_device_eval_batch_size: 10
num_train_epochs: 5
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

do_predict: False
prediction_loss_only: True
per_device_train_batch_size: 10
per_device_eval_batch_size: 10
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: None
warmup_ratio: None
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
enable_jit_checkpoint: False
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
use_cpu: False
seed: 42
data_seed: None
bf16: False
fp16: False
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: -1
ddp_backend: None
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
ignore_data_skip: False
fsdp: []
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
group_by_length: False
length_column_name: length
project: huggingface
trackio_space_id: trackio
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
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
hub_revision: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_for_metrics: []
eval_do_concat_batches: True
auto_find_batch_size: False
full_determinism: False
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
include_num_input_tokens_seen: no
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: True
use_cache: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	cosine_ndcg@10
1.0	2	0.7731

Training Time

Training: 1.8 minutes

Framework Versions

Python: 3.12.13
Sentence Transformers: 5.4.1
Transformers: 5.0.0
PyTorch: 2.10.0+cpu
Accelerate: 1.13.0
Datasets: 4.8.5
Tokenizers: 0.22.2

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{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}

	sentence_0	sentence_1
type	string	string
details	min: 19 tokens mean: 29.65 tokens max: 56 tokens	min: 44 tokens mean: 173.0 tokens max: 226 tokens

justOneMoreTestCase/insurance-rag-embeddings

SentenceTransformer based on nomic-ai/nomic-embed-text-v1.5

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Information Retrieval

Training Details

Training Dataset

Unnamed Dataset

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Training Time

Framework Versions

Citation

BibTeX

Sentence Transformers

MatryoshkaLoss

MultipleNegativesRankingLoss

justOneMoreTestCase/
insurance-rag-embeddings