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Fine-tuned with QuicKB

Name: modernbert-embed-quickb
Rating: 2.5 (70 reviews)

This is a sentence-transformers model finetuned from nomic-ai/modernbert-embed-base. 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: nomic-ai/modernbert-embed-base
Maximum Sequence Length: 1024 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity

Language: en
License: apache-2.0

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': 1024, 'do_lower_case': False}) with Transformer model: ModernBertModel 
  (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:

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("Mdean77/modernbert-embed-quickb")
# Run inference
sentences = [
    'What age groups will be enrolled in the study?',
    'Subject Population to be Studied Participating sites will enroll infants, children and adoles-\ncent patients who are admitted to a Pediatric or Cardiac Intensive Care Unit with sepsis-induced\nmultiple organ dysfunction syndrome (MODS). The goal is to determine if personalized im-\nmunomodulation is an effective strategy to reduce mortality and morbidity from sepsis-induced',
    'have mild to moderate inflammation (i.e. a serum ferritin level <2,000 ng/ml) from the TRIPS\ntrial. Those subjects will be instead entered into a completely distinct clinical trial of immune\nstimulation with GM-CSF (GRACE-2) that is covered by a separate IND (#112277).\nPRECISE Protocol Version 1.07\nProtocol Version Date: June 16, 2023',
]
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

Datasets: dim_768, dim_512, dim_256, dim_128 and dim_64
Evaluated with InformationRetrievalEvaluator

Metric	dim_768	dim_512	dim_256	dim_128	dim_64
cosine_accuracy@1	0.5714	0.5486	0.5486	0.4914	0.3829
cosine_accuracy@3	0.7829	0.7886	0.76	0.7029	0.5714
cosine_accuracy@5	0.8114	0.8286	0.84	0.7886	0.6571
cosine_accuracy@10	0.8743	0.8686	0.9086	0.8686	0.7886
cosine_precision@1	0.5714	0.5486	0.5486	0.4914	0.3829
cosine_precision@3	0.261	0.2629	0.2533	0.2343	0.1905
cosine_precision@5	0.1623	0.1657	0.168	0.1577	0.1314
cosine_precision@10	0.0874	0.0869	0.0909	0.0869	0.0789
cosine_recall@1	0.5714	0.5486	0.5486	0.4914	0.3829
cosine_recall@3	0.7829	0.7886	0.76	0.7029	0.5714
cosine_recall@5	0.8114	0.8286	0.84	0.7886	0.6571
cosine_recall@10	0.8743	0.8686	0.9086	0.8686	0.7886
cosine_ndcg@10	0.7305	0.7172	0.7269	0.6778	0.5698
cosine_mrr@10	0.6836	0.6676	0.6688	0.6169	0.5015
cosine_map@100	0.6898	0.6742	0.672	0.622	0.5091

Training Details

Training Dataset

Unnamed Dataset

Size: 1,567 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 8 tokens
mean: 15.03 tokens
max: 30 tokens
min: 57 tokens
mean: 90.85 tokens
max: 185 tokens

Samples:

anchor	positive
`How many terabytes of data are referenced?`	`over 125 terabytes of data.[object Object]Information systems are available 24/7/365 unless a scheduled maintenance period or[object Object]mitigation of an unexpected event is required. Critical systems availability has exceeded 99.9%[object Object]for the past 5 years.[object Object]7.2.3 Security, Support, Encryption, and Confidentiality[object Object]The data center coordinates the network infrastructure and security with University Information`
`What regulation allows single parent permission for the study?`	`for their child in the study. Single parent permission is permitted under 45 CFR §46.405. The[object Object]parent or legal guardian will be informed about the objectives of the study and the potential[object Object]risks and benefits of their child’s participation. If the parent or legal guardian refuses permission[object Object]for their child to participate, then all clinical management will continue to be provided by the`
`What is included in the follow-up plan for non-compliant sites?`	`planned site visits, criteria for focused visits, additional visits or remote monitoring, a plan for[object Object]chart review and a follow up plan for non-compliant sites. The monitoring plan also describes[object Object]the type of monitoring that will take place (e.g., sample of all subjects within a site; key data or[object Object]all data), the schedule of visits, how they are reported and a time frame to resolve any issues[object Object]found.`

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: epoch
per_device_train_batch_size: 16
gradient_accumulation_steps: 16
learning_rate: 2e-05
num_train_epochs: 4
lr_scheduler_type: cosine
warmup_ratio: 0.1
tf32: False
load_best_model_at_end: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 16
per_device_eval_batch_size: 8
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 16
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 2e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 4
max_steps: -1
lr_scheduler_type: cosine
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
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: False
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: True
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: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
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
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	dim_768_cosine_ndcg@10	dim_512_cosine_ndcg@10	dim_256_cosine_ndcg@10	dim_128_cosine_ndcg@10	dim_64_cosine_ndcg@10
1.0	7	-	0.6698	0.6606	0.6458	0.6146	0.5049
1.4898	10	55.7211	-	-	-	-	-
2.0	14	-	0.7210	0.7080	0.7183	0.6653	0.5621
2.9796	20	26.9161	-	-	-	-	-
3.0	21	-	0.7309	0.7172	0.7262	0.6762	0.5694
3.4898	24	-	0.7305	0.7172	0.7269	0.6778	0.5698

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.12.3
Sentence Transformers: 3.4.1
Transformers: 4.49.0
PyTorch: 2.6.0
Accelerate: 1.4.0
Datasets: 3.3.2
Tokenizers: 0.21.0

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}
}

	anchor	positive
type	string	string
details	min: 8 tokens mean: 15.03 tokens max: 30 tokens	min: 57 tokens mean: 90.85 tokens max: 185 tokens