CrossEncoder based on answerdotai/ModernBERT-base

This is a Cross Encoder model finetuned from answerdotai/ModernBERT-base 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: answerdotai/ModernBERT-base
Maximum Sequence Length: 8192 tokens
Number of Output Labels: 1 label

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.

from sentence_transformers import CrossEncoder

# Download from the 🤗 Hub
model = CrossEncoder("tomaarsen/reranker-modernbert-base-msmarco-bce")
# Get scores for pairs of texts
pairs = [
    ['what gb sp model is bright', 'From the list above it is easy to understand that Gegabyte is bigger than Megabyte. Or GB is bigger between MB and GB. Thanks.'],
    ['does immunotherapy work', 'The US and Drug Administration (FDA) this week convened a panel of outside experts to weigh in on the readiness of a first-of-its-kind cancer therapy. The treatment, which works by tweaking a patientâ\x80\x99s own cells, is a type of immunotherapy called CAR T-cell therapy and has been in clinical trials for several years. One drug maker is now seeking FDA approval to use the treatment in pediatric and young adult patients ages 3 to 25 with B-cell acute lymphoblastic leukemia (ALL) that has not responded to standard care.'],
    ['how long to wear oasis contacts', 'There is something wrong with my Xperia Z last week, and I did a resetting to make the phone become original, but I forgot backing up some important contacts, so I have to find the way for Sony Xperia Z contacts recovery, finanlly, I got this Android Data Recovery software to recover lost contacts from my phone.here is something wrong with my Xperia Z last week, and I did a resetting to make the phone become original, but I forgot backing up some important contacts, so I have to find the way for Sony Xperia Z contacts recovery, finanlly, I got this Android Data Recovery software to recover lost contacts from my phone.'],
    ['water baby definition', "Someone very comfortable in the water, Good swimmers, and never scared while in bodies of water. There's Jadine, back in the lake. She's such a water baby. #water #aqua #babies #water babies #water kids."],
    ['youngest suicide case', 'Samantha Kuberskki was found hanging by a belt at her home in Oregon after being sent to her room for arguing with her mother. A six-year-old girl who was sent to her room for punishment is feared to be one of the youngest people to have ever committed suicide in the U.S. Samantha Kuberskki was found hanging by a belt at her home in Oregon after being sent to her room for arguing with her mother. Her death was ruled as suicide by the coroner - sparking a bitter row with police who investigated her death and insist it was an accident.'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'what gb sp model is bright',
    [
        'From the list above it is easy to understand that Gegabyte is bigger than Megabyte. Or GB is bigger between MB and GB. Thanks.',
        'The US and Drug Administration (FDA) this week convened a panel of outside experts to weigh in on the readiness of a first-of-its-kind cancer therapy. The treatment, which works by tweaking a patientâ\x80\x99s own cells, is a type of immunotherapy called CAR T-cell therapy and has been in clinical trials for several years. One drug maker is now seeking FDA approval to use the treatment in pediatric and young adult patients ages 3 to 25 with B-cell acute lymphoblastic leukemia (ALL) that has not responded to standard care.',
        'There is something wrong with my Xperia Z last week, and I did a resetting to make the phone become original, but I forgot backing up some important contacts, so I have to find the way for Sony Xperia Z contacts recovery, finanlly, I got this Android Data Recovery software to recover lost contacts from my phone.here is something wrong with my Xperia Z last week, and I did a resetting to make the phone become original, but I forgot backing up some important contacts, so I have to find the way for Sony Xperia Z contacts recovery, finanlly, I got this Android Data Recovery software to recover lost contacts from my phone.',
        "Someone very comfortable in the water, Good swimmers, and never scared while in bodies of water. There's Jadine, back in the lake. She's such a water baby. #water #aqua #babies #water babies #water kids.",
        'Samantha Kuberskki was found hanging by a belt at her home in Oregon after being sent to her room for arguing with her mother. A six-year-old girl who was sent to her room for punishment is feared to be one of the youngest people to have ever committed suicide in the U.S. Samantha Kuberskki was found hanging by a belt at her home in Oregon after being sent to her room for arguing with her mother. Her death was ruled as suicide by the coroner - sparking a bitter row with police who investigated her death and insist it was an accident.',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

Cross Encoder Reranking

Datasets: NanoMSMARCO, NanoNFCorpus and NanoNQ
Evaluated with CERerankingEvaluator

Metric	NanoMSMARCO	NanoNFCorpus	NanoNQ
map	0.6519 (+0.1623)	0.3432 (+0.0728)	0.6951 (+0.2744)
mrr@10	0.6449 (+0.1674)	0.5016 (+0.0017)	0.7152 (+0.2885)
ndcg@10	0.7069 (+0.1665)	0.3801 (+0.0550)	0.7469 (+0.2462)

Cross Encoder Nano BEIR

Dataset: NanoBEIR_mean
Evaluated with CENanoBEIREvaluator

Metric	Value
map	0.5634 (+0.1698)
mrr@10	0.6206 (+0.1525)
ndcg@10	0.6113 (+0.1559)

Training Details

Training Dataset

Unnamed Dataset

Size: 19,990,000 training samples
Columns: query, answer, and label

Approximate statistics based on the first 1000 samples:

	query	answer	label
type	string	string	int
details	min: 10 characters mean: 34.21 characters max: 197 characters	min: 82 characters mean: 350.38 characters max: 860 characters	0: ~73.10% 1: ~26.90%

Samples:

query	answer	label
`who plays the trickster on flash`	`The Flash (2014 TV series) The Flash is a TV show based on the fictional character Flash, a costumed superhero crime-fighter who appears in comic books published by DC Comics.`	`0`
`what type of business is plastics engineering company`	Plastics Engineering Company is a leading North American manufacturer of phenolic resins and thermoset molding materials, selling products under its trademark Plenco. If you have a phenolic resin or thermoset molding material project, chances are, the Plenco team can make it work. We've been doing it for over 80 years. Come and benefit from the Plenco difference. Plastics Engineering Company, a family owned and managed business founded in 1934, established as its corporate mission a sincere desire to respond efficiently to the needs of our customers through development, manufacture, and servicing of useful, high-value products.	`1`
`what is allianz global assistance`	`Please choose 'Allianz Direct Customers' for Car, Home, Pet, Boat and Horse & Rider Insurance. Allianz Direct Customers Allianz Direct Customers Car, Home, Pet, Boat and Horse & Rider Insurance. Phone. In the Republic of Ireland: 01 448 48 48. Outside Republic of Ireland: 00 353 1 448 48 48. Opening Hours: Monday to Friday 8am - 6pm and Saturday 9am - 1pm.`	`0`

Loss: BinaryCrossEntropyLoss with these parameters:

{
    "activation_fct": "Identity",
    "pos_weight": 4
}

Evaluation Dataset

Unnamed Dataset

Size: 10,000 evaluation samples
Columns: query, answer, and label

Approximate statistics based on the first 1000 samples:

	query	answer	label
type	string	string	int
details	min: 11 characters mean: 33.77 characters max: 215 characters	min: 73 characters mean: 351.17 characters max: 935 characters	0: ~75.80% 1: ~24.20%

Samples:

query	answer	label
`what gb sp model is bright`	`From the list above it is easy to understand that Gegabyte is bigger than Megabyte. Or GB is bigger between MB and GB. Thanks.`	`0`
`does immunotherapy work`	The US and Drug Administration (FDA) this week convened a panel of outside experts to weigh in on the readiness of a first-of-its-kind cancer therapy. The treatment, which works by tweaking a patientâs own cells, is a type of immunotherapy called CAR T-cell therapy and has been in clinical trials for several years. One drug maker is now seeking FDA approval to use the treatment in pediatric and young adult patients ages 3 to 25 with B-cell acute lymphoblastic leukemia (ALL) that has not responded to standard care.	`0`
`how long to wear oasis contacts`	There is something wrong with my Xperia Z last week, and I did a resetting to make the phone become original, but I forgot backing up some important contacts, so I have to find the way for Sony Xperia Z contacts recovery, finanlly, I got this Android Data Recovery software to recover lost contacts from my phone.here is something wrong with my Xperia Z last week, and I did a resetting to make the phone become original, but I forgot backing up some important contacts, so I have to find the way for Sony Xperia Z contacts recovery, finanlly, I got this Android Data Recovery software to recover lost contacts from my phone.	`0`

Loss: BinaryCrossEntropyLoss with these parameters:

{
    "activation_fct": "Identity",
    "pos_weight": 4
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 128
per_device_eval_batch_size: 128
learning_rate: 8e-05
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: 128
per_device_eval_batch_size: 128
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-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: 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

Click to expand

Epoch	Step	Training Loss	Validation Loss	NanoMSMARCO_ndcg@10	NanoNFCorpus_ndcg@10	NanoNQ_ndcg@10	NanoBEIR_mean_ndcg@10
-1	-1	-	-	0.0245 (-0.5159)	0.2709 (-0.0541)	0.0858 (-0.4148)	0.1271 (-0.3283)
0.0000	1	1.1359	-	-	-	-	-
0.0064	1000	0.9962	-	-	-	-	-
0.0128	2000	0.3958	-	-	-	-	-
0.0192	3000	0.3292	-	-	-	-	-
0.0256	4000	0.3023	-	-	-	-	-
0.0320	5000	0.2839	0.2495	0.6231 (+0.0827)	0.3748 (+0.0498)	0.7295 (+0.2288)	0.5758 (+0.1204)
0.0384	6000	0.2812	-	-	-	-	-
0.0448	7000	0.2755	-	-	-	-	-
0.0512	8000	0.2672	-	-	-	-	-
0.0576	9000	0.2624	-	-	-	-	-
0.0640	10000	0.2627	0.2368	0.6976 (+0.1572)	0.4094 (+0.0844)	0.7139 (+0.2133)	0.6070 (+0.1516)
0.0704	11000	0.2586	-	-	-	-	-
0.0768	12000	0.253	-	-	-	-	-
0.0832	13000	0.25	-	-	-	-	-
0.0896	14000	0.2545	-	-	-	-	-
0.0960	15000	0.2516	0.2297	0.6671 (+0.1267)	0.3685 (+0.0434)	0.7301 (+0.2295)	0.5886 (+0.1332)
0.1025	16000	0.241	-	-	-	-	-
0.1089	17000	0.2459	-	-	-	-	-
0.1153	18000	0.2371	-	-	-	-	-
0.1217	19000	0.2374	-	-	-	-	-
0.1281	20000	0.234	0.2226	0.6377 (+0.0973)	0.3988 (+0.0737)	0.7002 (+0.1996)	0.5789 (+0.1235)
0.1345	21000	0.2293	-	-	-	-	-
0.1409	22000	0.2222	-	-	-	-	-
0.1473	23000	0.2231	-	-	-	-	-
0.1537	24000	0.2212	-	-	-	-	-
0.1601	25000	0.2165	0.2266	0.7114 (+0.1710)	0.3775 (+0.0524)	0.7314 (+0.2308)	0.6068 (+0.1514)
0.1665	26000	0.2119	-	-	-	-	-
0.1729	27000	0.2086	-	-	-	-	-
0.1793	28000	0.204	-	-	-	-	-
0.1857	29000	0.204	-	-	-	-	-
0.1921	30000	0.1959	0.1913	0.6630 (+0.1225)	0.3962 (+0.0712)	0.7027 (+0.2020)	0.5873 (+0.1319)
0.1985	31000	0.195	-	-	-	-	-
0.2049	32000	0.1899	-	-	-	-	-
0.2113	33000	0.1887	-	-	-	-	-
0.2177	34000	0.1865	-	-	-	-	-
0.2241	35000	0.1878	0.1765	0.6709 (+0.1304)	0.3858 (+0.0607)	0.7060 (+0.2053)	0.5875 (+0.1322)
0.2305	36000	0.1822	-	-	-	-	-
0.2369	37000	0.1795	-	-	-	-	-
0.2433	38000	0.1802	-	-	-	-	-
0.2497	39000	0.1762	-	-	-	-	-
0.2561	40000	0.1694	0.1739	0.6902 (+0.1498)	0.3771 (+0.0521)	0.7198 (+0.2192)	0.5957 (+0.1403)
0.2625	41000	0.1718	-	-	-	-	-
0.2689	42000	0.1706	-	-	-	-	-
0.2753	43000	0.1659	-	-	-	-	-
0.2817	44000	0.1593	-	-	-	-	-
0.2881	45000	0.1608	0.1532	0.7132 (+0.1728)	0.3606 (+0.0356)	0.7393 (+0.2386)	0.6044 (+0.1490)
0.2945	46000	0.1589	-	-	-	-	-
0.3010	47000	0.1563	-	-	-	-	-
0.3074	48000	0.1553	-	-	-	-	-
0.3138	49000	0.155	-	-	-	-	-
0.3202	50000	0.1501	0.1373	0.7168 (+0.1764)	0.3830 (+0.0579)	0.6954 (+0.1948)	0.5984 (+0.1430)
0.3266	51000	0.1508	-	-	-	-	-
0.3330	52000	0.1497	-	-	-	-	-
0.3394	53000	0.1478	-	-	-	-	-
0.3458	54000	0.1445	-	-	-	-	-
0.3522	55000	0.1468	0.1403	0.6828 (+0.1424)	0.3780 (+0.0530)	0.7147 (+0.2141)	0.5919 (+0.1365)
0.3586	56000	0.1422	-	-	-	-	-
0.3650	57000	0.1369	-	-	-	-	-
0.3714	58000	0.1364	-	-	-	-	-
0.3778	59000	0.1328	-	-	-	-	-
0.3842	60000	0.1351	0.1448	0.6881 (+0.1477)	0.3430 (+0.0179)	0.7267 (+0.2260)	0.5859 (+0.1306)
0.3906	61000	0.1312	-	-	-	-	-
0.3970	62000	0.1308	-	-	-	-	-
0.4034	63000	0.1289	-	-	-	-	-
0.4098	64000	0.1273	-	-	-	-	-
0.4162	65000	0.1257	0.1290	0.7288 (+0.1883)	0.3830 (+0.0580)	0.7180 (+0.2173)	0.6099 (+0.1545)
0.4226	66000	0.1246	-	-	-	-	-
0.4290	67000	0.1275	-	-	-	-	-
0.4354	68000	0.1246	-	-	-	-	-
0.4418	69000	0.1214	-	-	-	-	-
0.4482	70000	0.115	0.1184	0.6911 (+0.1506)	0.3903 (+0.0652)	0.7189 (+0.2182)	0.6001 (+0.1447)
0.4546	71000	0.113	-	-	-	-	-
0.4610	72000	0.1156	-	-	-	-	-
0.4674	73000	0.1142	-	-	-	-	-
0.4738	74000	0.1133	-	-	-	-	-
0.4802	75000	0.1132	0.1194	0.7069 (+0.1665)	0.3801 (+0.0550)	0.7469 (+0.2462)	0.6113 (+0.1559)
0.4866	76000	0.1085	-	-	-	-	-
0.4930	77000	0.1095	-	-	-	-	-
0.4994	78000	0.1105	-	-	-	-	-
0.5059	79000	0.1068	-	-	-	-	-
0.5123	80000	0.1039	0.1085	0.7017 (+0.1612)	0.3565 (+0.0315)	0.7199 (+0.2192)	0.5927 (+0.1373)
0.5187	81000	0.1059	-	-	-	-	-
0.5251	82000	0.1001	-	-	-	-	-
0.5315	83000	0.1019	-	-	-	-	-
0.5379	84000	0.1021	-	-	-	-	-
0.5443	85000	0.0982	0.0962	0.6842 (+0.1438)	0.3516 (+0.0266)	0.7431 (+0.2425)	0.5930 (+0.1376)
0.5507	86000	0.0967	-	-	-	-	-
0.5571	87000	0.0962	-	-	-	-	-
0.5635	88000	0.098	-	-	-	-	-
0.5699	89000	0.0973	-	-	-	-	-
0.5763	90000	0.0957	0.0863	0.6729 (+0.1325)	0.3852 (+0.0601)	0.7147 (+0.2141)	0.5909 (+0.1356)
0.5827	91000	0.0925	-	-	-	-	-
0.5891	92000	0.0948	-	-	-	-	-
0.5955	93000	0.0887	-	-	-	-	-
0.6019	94000	0.0918	-	-	-	-	-
0.6083	95000	0.0926	0.0846	0.6857 (+0.1453)	0.3503 (+0.0253)	0.7321 (+0.2315)	0.5894 (+0.1340)
0.6147	96000	0.0881	-	-	-	-	-
0.6211	97000	0.0871	-	-	-	-	-
0.6275	98000	0.0867	-	-	-	-	-
0.6339	99000	0.0854	-	-	-	-	-
0.6403	100000	0.0833	0.0790	0.6665 (+0.1261)	0.3415 (+0.0165)	0.6905 (+0.1898)	0.5662 (+0.1108)
0.6467	101000	0.0837	-	-	-	-	-
0.6531	102000	0.0834	-	-	-	-	-
0.6595	103000	0.0798	-	-	-	-	-
0.6659	104000	0.0825	-	-	-	-	-
0.6723	105000	0.0803	0.0750	0.6897 (+0.1493)	0.3415 (+0.0165)	0.7096 (+0.2090)	0.5803 (+0.1249)
0.6787	106000	0.076	-	-	-	-	-
0.6851	107000	0.0782	-	-	-	-	-
0.6915	108000	0.0786	-	-	-	-	-
0.6979	109000	0.075	-	-	-	-	-
0.7044	110000	0.0747	0.0690	0.6665 (+0.1261)	0.3384 (+0.0134)	0.7209 (+0.2202)	0.5753 (+0.1199)
0.7108	111000	0.0728	-	-	-	-	-
0.7172	112000	0.0708	-	-	-	-	-
0.7236	113000	0.0714	-	-	-	-	-
0.7300	114000	0.0725	-	-	-	-	-
0.7364	115000	0.0708	0.0659	0.6753 (+0.1348)	0.3423 (+0.0172)	0.7093 (+0.2087)	0.5756 (+0.1202)
0.7428	116000	0.0684	-	-	-	-	-
0.7492	117000	0.0709	-	-	-	-	-
0.7556	118000	0.0661	-	-	-	-	-
0.7620	119000	0.0685	-	-	-	-	-
0.7684	120000	0.0655	0.0613	0.6774 (+0.1369)	0.3295 (+0.0044)	0.7244 (+0.2238)	0.5771 (+0.1217)
0.7748	121000	0.0643	-	-	-	-	-
0.7812	122000	0.066	-	-	-	-	-
0.7876	123000	0.0625	-	-	-	-	-
0.7940	124000	0.0653	-	-	-	-	-
0.8004	125000	0.0619	0.0564	0.6797 (+0.1393)	0.3598 (+0.0348)	0.7193 (+0.2187)	0.5863 (+0.1309)
0.8068	126000	0.0616	-	-	-	-	-
0.8132	127000	0.0607	-	-	-	-	-
0.8196	128000	0.0584	-	-	-	-	-
0.8260	129000	0.0609	-	-	-	-	-
0.8324	130000	0.0568	0.0502	0.6855 (+0.1450)	0.3394 (+0.0143)	0.7297 (+0.2291)	0.5849 (+0.1295)
0.8388	131000	0.0577	-	-	-	-	-
0.8452	132000	0.056	-	-	-	-	-
0.8516	133000	0.0556	-	-	-	-	-
0.8580	134000	0.0553	-	-	-	-	-
0.8644	135000	0.0546	0.0471	0.6903 (+0.1499)	0.3404 (+0.0153)	0.7419 (+0.2413)	0.5909 (+0.1355)
0.8708	136000	0.0525	-	-	-	-	-
0.8772	137000	0.0512	-	-	-	-	-
0.8836	138000	0.0528	-	-	-	-	-
0.8900	139000	0.0523	-	-	-	-	-
0.8964	140000	0.0544	0.0442	0.6915 (+0.1511)	0.3507 (+0.0257)	0.7258 (+0.2251)	0.5893 (+0.1340)
0.9029	141000	0.0497	-	-	-	-	-
0.9093	142000	0.0508	-	-	-	-	-
0.9157	143000	0.0485	-	-	-	-	-
0.9221	144000	0.0492	-	-	-	-	-
0.9285	145000	0.0472	0.0442	0.6614 (+0.1210)	0.3394 (+0.0144)	0.7361 (+0.2355)	0.5790 (+0.1236)
0.9349	146000	0.0469	-	-	-	-	-
0.9413	147000	0.0459	-	-	-	-	-
0.9477	148000	0.0471	-	-	-	-	-
0.9541	149000	0.0454	-	-	-	-	-
0.9605	150000	0.0444	0.0429	0.6587 (+0.1183)	0.3311 (+0.0060)	0.7298 (+0.2291)	0.5732 (+0.1178)
0.9669	151000	0.0451	-	-	-	-	-
0.9733	152000	0.0429	-	-	-	-	-
0.9797	153000	0.0448	-	-	-	-	-
0.9861	154000	0.0441	-	-	-	-	-
0.9925	155000	0.0443	0.0418	0.6653 (+0.1249)	0.3335 (+0.0084)	0.7391 (+0.2385)	0.5793 (+0.1239)
0.9989	156000	0.0409	-	-	-	-	-
-1	-1	-	-	0.7069 (+0.1665)	0.3801 (+0.0550)	0.7469 (+0.2462)	0.6113 (+0.1559)

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

@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",
}

tomaarsen
/

reranker-modernbert-base-msmarco-bce

CrossEncoder based on answerdotai/ModernBERT-base

Model Details

Model Description

Model Sources

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Cross Encoder Reranking

Cross Encoder Nano BEIR

Training Details

Training Dataset

Unnamed Dataset

Evaluation Dataset

Unnamed Dataset

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

Model tree for tomaarsen/reranker-modernbert-base-msmarco-bce

Evaluation results