This is a Cross Encoder model finetuned from microsoft/MiniLM-L12-H384-uncased on the ms-marco-shuffled dataset using the sentence-transformers library. It computes scores for pairs of texts, which can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

Model Type: Cross Encoder
Base model: microsoft/MiniLM-L12-H384-uncased
Maximum Sequence Length: 512 tokens
Number of Output Labels: 1 label
Training Dataset:
- ms-marco-shuffled

Model Sources

Documentation: Sentence Transformers Documentation
Documentation: Cross Encoder Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Cross Encoders on Hugging Face

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 CrossEncoder

# Download from the 🤗 Hub
model = CrossEncoder("tomaarsen/reranker-modernbert-base-msmarco-mse")
# Get scores for pairs of texts
pairs = [
    ['what is a electrophoresis apparatus', 'Gel electrophoresis is a method for separation and analysis of macromolecules (DNA, RNA and proteins) and their fragments, based on their size and charge.el electrophoresis of large DNA or RNA is usually done by agarose gel electrophoresis. See the  Chain termination method  page for an example of a polyacrylamide DNA sequencing gel. Characterization through ligand interaction of nucleic acids or fragments may be performed by mobility shift affinity electrophoresis.'],
    ['does creatine elevate creatinine levels', "Creatinine is produced from creatine, a molecule of major importance for energy production in muscles. Approximately 2% of the body's creatine is converted to creatinine every day. Creatinine is transported through the bloodstream to the kidneys."],
    ['how to get rid of caffeine in the body', 'In addition to quickly curing caffeine withdrawal headaches, caffeine may help cure regular headaches and even migraines. Some studies have shown that small doses of caffeine taken in conjunction with pain killers may help the body absorb the medication more quickly and cure the headache in a shorter period of time.'],
    ['define splanchnopleure', 'delineated, represented, delineate(verb) represented accurately or precisely. define, delineate(verb) show the form or outline of. The tree was clearly defined by the light; The camera could define the smallest object. specify, define, delineate, delimit, delimitate(verb) determine the essential quality of.'],
    ['how many calories does a glass of wine', 'A large glass of wine contains as many calories as an ice cream. We often drink wine with a meal. But did you know that a large glass of wine (250ml) with 13% ABV can add 228 calories to your dinner? Thatâ\x80\x99s similar to an ice cream or two fish fingers. A standard glass of red or white wine (175ml) with 13% ABV could also contain up to 160 calories, similar to a slice of Madeira cake.'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'what is a electrophoresis apparatus',
    [
        'Gel electrophoresis is a method for separation and analysis of macromolecules (DNA, RNA and proteins) and their fragments, based on their size and charge.el electrophoresis of large DNA or RNA is usually done by agarose gel electrophoresis. See the  Chain termination method  page for an example of a polyacrylamide DNA sequencing gel. Characterization through ligand interaction of nucleic acids or fragments may be performed by mobility shift affinity electrophoresis.',
        "Creatinine is produced from creatine, a molecule of major importance for energy production in muscles. Approximately 2% of the body's creatine is converted to creatinine every day. Creatinine is transported through the bloodstream to the kidneys.",
        'In addition to quickly curing caffeine withdrawal headaches, caffeine may help cure regular headaches and even migraines. Some studies have shown that small doses of caffeine taken in conjunction with pain killers may help the body absorb the medication more quickly and cure the headache in a shorter period of time.',
        'delineated, represented, delineate(verb) represented accurately or precisely. define, delineate(verb) show the form or outline of. The tree was clearly defined by the light; The camera could define the smallest object. specify, define, delineate, delimit, delimitate(verb) determine the essential quality of.',
        'A large glass of wine contains as many calories as an ice cream. We often drink wine with a meal. But did you know that a large glass of wine (250ml) with 13% ABV can add 228 calories to your dinner? Thatâ\x80\x99s similar to an ice cream or two fish fingers. A standard glass of red or white wine (175ml) with 13% ABV could also contain up to 160 calories, similar to a slice of Madeira cake.',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

Cross Encoder Reranking

Datasets: NanoMSMARCO, NanoNFCorpus and NanoNQ
Evaluated with CERerankingEvaluator

Metric	NanoMSMARCO	NanoNFCorpus	NanoNQ
map	0.5979 (+0.1083)	0.3464 (+0.0760)	0.6886 (+0.2679)
mrr@10	0.5893 (+0.1118)	0.6264 (+0.1266)	0.6962 (+0.2695)
ndcg@10	0.6585 (+0.1181)	0.3864 (+0.0613)	0.7366 (+0.2359)

Cross Encoder Nano BEIR

Dataset: NanoBEIR_mean
Evaluated with CENanoBEIREvaluator

Metric	Value
map	0.5443 (+0.1507)
mrr@10	0.6373 (+0.1693)
ndcg@10	0.5938 (+0.1385)

Training Details

Training Dataset

ms-marco-shuffled

Dataset: ms-marco-shuffled at 0e80192
Size: 1,990,000 training samples
Columns: score, query, and passage
Approximate statistics based on the first 1000 samples:
score query passage
type float string string
details
min: -11.8
mean: 0.75
max: 11.16
min: 9 characters
mean: 33.33 characters
max: 123 characters
min: 53 characters
mean: 348.8 characters
max: 1016 characters

Samples:

score	query	passage
`6.732539335886638`	`what is shielding in welding`	`A benefit in using a shielding gas when welding is that there is no slag left on the weld that requires chipping and cleaning like that which is found on an arc weld. When a new wire welding machine is purchased, it does not come with a shielding gas tank. This must be purchased or rented from a gas supplier. Most welding supply stores also sell welding gasses and will be able to assist the buyer in a tank purchase.`
`-5.769245758652687`	`what degree do you need for physical therapy`	`E. Medicaid covers occupational therapy, physical therapy and speech therapy services when provided to eligible Medicaid beneficiaries under age 21 in the Child Health Services (EPSDT) Program by qualified occupational, physical or speech therapy providers.`
`9.033631960550943`	`cascade effect definition`	`In medicine, cascade effect may also refer to a chain of events initiated by an unnecessary test, an unexpected result, or patient or physician anxiety, which results in ill-advised tests or treatments that may cause harm to patients as the results are pursued.`

Loss: MSELoss

Evaluation Dataset

ms-marco-shuffled

Dataset: ms-marco-shuffled at 0e80192
Size: 10,000 evaluation samples
Columns: score, query, and passage
Approximate statistics based on the first 1000 samples:
score query passage
type float string string
details
min: -11.86
mean: 0.72
max: 11.07
min: 10 characters
mean: 33.83 characters
max: 101 characters
min: 50 characters
mean: 343.73 characters
max: 929 characters

Samples:

score	query	passage
`4.691008905569713`	`what is a electrophoresis apparatus`	`Gel electrophoresis is a method for separation and analysis of macromolecules (DNA, RNA and proteins) and their fragments, based on their size and charge.el electrophoresis of large DNA or RNA is usually done by agarose gel electrophoresis. See the Chain termination method page for an example of a polyacrylamide DNA sequencing gel. Characterization through ligand interaction of nucleic acids or fragments may be performed by mobility shift affinity electrophoresis.`
`0.7860534191131592`	`does creatine elevate creatinine levels`	`Creatinine is produced from creatine, a molecule of major importance for energy production in muscles. Approximately 2% of the body's creatine is converted to creatinine every day. Creatinine is transported through the bloodstream to the kidneys.`
`-1.2669222354888916`	`how to get rid of caffeine in the body`	`In addition to quickly curing caffeine withdrawal headaches, caffeine may help cure regular headaches and even migraines. Some studies have shown that small doses of caffeine taken in conjunction with pain killers may help the body absorb the medication more quickly and cure the headache in a shorter period of time.`

Loss: MSELoss

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
learning_rate: 8e-06
num_train_epochs: 1
warmup_ratio: 0.1
seed: 12
bf16: True
dataloader_num_workers: 4
load_best_model_at_end: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
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: 8e-06
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 1
max_steps: -1
lr_scheduler_type: linear
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: 12
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: True
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: 4
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: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	NanoMSMARCO_ndcg@10	NanoNFCorpus_ndcg@10	NanoNQ_ndcg@10	NanoBEIR_mean_ndcg@10
-1	-1	-	-	0.0219 (-0.5185)	0.2538 (-0.0712)	0.0498 (-0.4509)	0.1085 (-0.3469)
0.0000	1	64.054	-	-	-	-	-
0.0322	1000	55.8586	-	-	-	-	-
0.0643	2000	31.6183	-	-	-	-	-
0.0965	3000	13.1762	-	-	-	-	-
0.1286	4000	6.1773	-	-	-	-	-
0.1608	5000	4.2945	3.4889	0.6180 (+0.0776)	0.3893 (+0.0643)	0.7144 (+0.2137)	0.5739 (+0.1185)
0.1930	6000	3.6451	-	-	-	-	-
0.2251	7000	3.3041	-	-	-	-	-
0.2573	8000	2.9813	-	-	-	-	-
0.2894	9000	2.8473	-	-	-	-	-
0.3216	10000	2.6852	2.6960	0.6124 (+0.0720)	0.3992 (+0.0742)	0.7315 (+0.2309)	0.5811 (+0.1257)
0.3538	11000	2.6128	-	-	-	-	-
0.3859	12000	2.5252	-	-	-	-	-
0.4181	13000	2.461	-	-	-	-	-
0.4502	14000	2.3625	-	-	-	-	-
0.4824	15000	2.2746	2.0279	0.6397 (+0.0993)	0.3963 (+0.0713)	0.7369 (+0.2363)	0.5910 (+0.1356)
0.5146	16000	2.2551	-	-	-	-	-
0.5467	17000	2.2193	-	-	-	-	-
0.5789	18000	2.2099	-	-	-	-	-
0.6111	19000	2.1277	-	-	-	-	-
0.6432	20000	2.0969	1.9564	0.6468 (+0.1063)	0.3936 (+0.0685)	0.7391 (+0.2385)	0.5932 (+0.1378)
0.6754	21000	2.0624	-	-	-	-	-
0.7075	22000	2.0565	-	-	-	-	-
0.7397	23000	2.0226	-	-	-	-	-
0.7719	24000	1.9583	-	-	-	-	-
0.8040	25000	2.0048	1.8239	0.6575 (+0.1171)	0.3884 (+0.0634)	0.7339 (+0.2333)	0.5933 (+0.1379)
0.8362	26000	1.9861	-	-	-	-	-
0.8683	27000	1.9675	-	-	-	-	-
0.9005	28000	1.9531	-	-	-	-	-
0.9327	29000	1.9139	-	-	-	-	-
0.9648	30000	1.9224	1.7848	0.6585 (+0.1181)	0.3864 (+0.0613)	0.7366 (+0.2359)	0.5938 (+0.1385)
0.9970	31000	1.9059	-	-	-	-	-
-1	-1	-	-	0.6585 (+0.1181)	0.3864 (+0.0613)	0.7366 (+0.2359)	0.5938 (+0.1385)

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.11.10
Sentence Transformers: 3.5.0.dev0
Transformers: 4.49.0.dev0
PyTorch: 2.6.0.dev20241112+cu121
Accelerate: 1.2.0
Datasets: 3.2.0
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",
}

	score	query	passage
type	float	string	string
details	min: -11.8 mean: 0.75 max: 11.16	min: 9 characters mean: 33.33 characters max: 123 characters	min: 53 characters mean: 348.8 characters max: 1016 characters

tomaarsen/reranker-MiniLM-L12-H384-msmarco-mse

CrossEncoder based on microsoft/MiniLM-L12-H384-uncased

Model Details

Model Description

Model Sources

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Cross Encoder Reranking

Cross Encoder Nano BEIR

Training Details

Training Dataset

ms-marco-shuffled

Evaluation Dataset

ms-marco-shuffled

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

tomaarsen/
reranker-MiniLM-L12-H384-msmarco-mse