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 text reranking and semantic search.

Model Details

Model Description

Model Type: Cross Encoder
Base model: answerdotai/ModernBERT-base
Maximum Sequence Length: 8192 tokens
Number of Output Labels: 1 label

Language: en
License: apache-2.0

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-gooaq-lambda")
# Get scores for pairs of texts
pairs = [
    ['How many calories in an egg', 'There are on average between 55 and 80 calories in an egg depending on its size.'],
    ['How many calories in an egg', 'Egg whites are very low in calories, have no fat, no cholesterol, and are loaded with protein.'],
    ['How many calories in an egg', 'Most of the calories in an egg come from the yellow yolk in the center.'],
]
scores = model.predict(pairs)
print(scores.shape)
# (3,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'How many calories in an egg',
    [
        'There are on average between 55 and 80 calories in an egg depending on its size.',
        'Egg whites are very low in calories, have no fat, no cholesterol, and are loaded with protein.',
        'Most of the calories in an egg come from the yellow yolk in the center.',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

Cross Encoder Reranking

Dataset: gooaq-dev
Evaluated with CrossEncoderRerankingEvaluator with these parameters:
json
1{ 2 "at_k": 10, 3 "always_rerank_positives": false 4}

Metric	Value
map	0.7164 (+0.1853)
mrr@10	0.7148 (+0.1908)
ndcg@10	0.7601 (+0.1689)

Cross Encoder Reranking

Datasets: NanoMSMARCO_R100, NanoNFCorpus_R100 and NanoNQ_R100
Evaluated with CrossEncoderRerankingEvaluator with these parameters:
json
1{ 2 "at_k": 10, 3 "always_rerank_positives": true 4}

Metric	NanoMSMARCO_R100	NanoNFCorpus_R100	NanoNQ_R100
map	0.4853 (-0.0042)	0.3379 (+0.0769)	0.5390 (+0.1194)
mrr@10	0.4772 (-0.0003)	0.5293 (+0.0294)	0.5479 (+0.1212)
ndcg@10	0.5514 (+0.0110)	0.3714 (+0.0464)	0.5941 (+0.0934)

Cross Encoder Nano BEIR

Dataset: NanoBEIR_R100_mean
Evaluated with CrossEncoderNanoBEIREvaluator with these parameters:
json
1{ 2 "dataset_names": [ 3 "msmarco", 4 "nfcorpus", 5 "nq" 6 ], 7 "rerank_k": 100, 8 "at_k": 10, 9 "always_rerank_positives": true 10}

Metric	Value
map	0.4541 (+0.0640)
mrr@10	0.5181 (+0.0501)
ndcg@10	0.5056 (+0.0503)

Training Details

Training Dataset

Unnamed Dataset

Size: 95,939 training samples
Columns: question, answer, and labels
Approximate statistics based on the first 1000 samples:
question answer labels
type string list list
details
min: 18 characters
mean: 43.5 characters
max: 101 characters
size: 6 elements
size: 6 elements

Samples:

question	answer	labels
`can u get ip banned from discord?`	['Yes you very much can, infact its already done. When you ban a person its an IP ban (also an account ban) There are no ways to bypass it without a new account.', 'Yes, your account is banned if you see the “Your account has been suspended/terminated for violating the Terms of Service” message when logging in to Pokémon GO.', 'This means that Snap is identifying devices and not users. So if a user, after getting banned, tries to access Snapchat from a different account but the same device, then that account also gets banned automatically. “The jailbreaking ban is apparently actually a device ban.', "When you block someone on Discord, they won't be able to send you private messages, and will servers you share will hide their messages. If the person you blocked was on your Friends list, they'll be removed immediately.", "You will for sure get an e-mail telling you that you were banned. That error happens quite often to me. Just login again from the title screen and game on. It's a commo...	`[1, 0, 0, 0, 0, ...]`
`what is the difference between methylphenidate cd and er?`	['Metadate CD is a once-a-day capsule with biphasic release; initially there is a rapid release of methylphenidate, then a continuous-release phase. Metadate ER, on the other hand, is a tablet given two to three times per day.', 'Irregular Heartbeat Risk Associated with Common ADHD Med. Children who take a common drug to treat attention-deficit/hyperactivity disorder may be at an increased risk for developing an irregular heartbeat. The drug, methylphenidate, is the active ingredient in Concerta, Daytrana and Ritalin.', "Vyvanse contains the drug lisdexamfetamine dimesylate, while Ritalin contains the drug methylphenidate. Both Vyvanse and Ritalin are used to treat ADHD symptoms such as poor focus, reduced impulse control, and hyperactivity. However, they're also prescribed to treat other conditions.", 'Tolerance develops to the side effects of Adderall IR and XR in five to seven days. Side effects that persist longer than one week can be quickly managed by lowering the dose or changin...	`[1, 0, 0, 0, 0, ...]`
`who has the most championships in hockey?`	['Having lifted the trophy a total of 24 times, the Montreal Canadiens are the team with more Stanley Cup titles than any other franchise.', "['Ivy League – 46 National Championships.', 'Big Ten – 39 National Championships. ... ', 'SEC – 29 National Championships. ... ', 'ACC – 18 National Championships. ... ', 'Independents – 17 National Championships. ... ', 'Pac-12 – 15 National Championships. ... ', 'Big 12 – 11 National Championships. ... ']", 'Boston Celtics center Bill Russell holds the record for the most NBA championships won with 11 titles during his 13-year playing career.', 'Alabama can claim the most NCAA titles in the poll era, with only three of its 15 coming prior. With the 15th title — a win in the College Football Playoff in 2017, coach Nick Saban tied the legendary Bear Bryant with five championships recognized by the NCAA.', 'American football is the most popular sport to watch in the United States, followed by baseball, basketball, and ice hockey, which makes up th...	`[1, 0, 0, 0, 0, ...]`

Loss: LambdaLoss with these parameters:
json
1{ 2 "weighting_scheme": "sentence_transformers.cross_encoder.losses.LambdaLoss.NDCGLoss2PPScheme", 3 "k": null, 4 "sigma": 1.0, 5 "eps": 1e-10, 6 "reduction_log": "binary", 7 "activation_fct": "torch.nn.modules.linear.Identity", 8 "mini_batch_size": 16 9}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
learning_rate: 2e-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: 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: 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: 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	gooaq-dev_ndcg@10	NanoMSMARCO_R100_ndcg@10	NanoNFCorpus_R100_ndcg@10	NanoNQ_R100_ndcg@10	NanoBEIR_R100_mean_ndcg@10
-1	-1	-	0.1318 (-0.4594)	0.0314 (-0.5091)	0.3145 (-0.0105)	0.0444 (-0.4562)	0.1301 (-0.3253)
0.0007	1	2.1483	-	-	-	-	-
0.0667	100	2.0302	-	-	-	-	-
0.1333	200	1.0684	-	-	-	-	-
0.1667	250	-	0.7116 (+0.1204)	0.4469 (-0.0935)	0.3483 (+0.0233)	0.6251 (+0.1244)	0.4734 (+0.0181)
0.2	300	0.6541	-	-	-	-	-
0.2667	400	0.5459	-	-	-	-	-
0.3333	500	0.5159	0.7425 (+0.1513)	0.5219 (-0.0186)	0.3722 (+0.0471)	0.6300 (+0.1294)	0.5080 (+0.0526)
0.4	600	0.4852	-	-	-	-	-
0.4667	700	0.4655	-	-	-	-	-
0.5	750	-	0.7545 (+0.1633)	0.5572 (+0.0167)	0.3726 (+0.0476)	0.6188 (+0.1182)	0.5162 (+0.0608)
0.5333	800	0.448	-	-	-	-	-
0.6	900	0.4283	-	-	-	-	-
0.6667	1000	0.4296	0.7582 (+0.1670)	0.5540 (+0.0136)	0.3723 (+0.0473)	0.6142 (+0.1136)	0.5135 (+0.0581)
0.7333	1100	0.4237	-	-	-	-	-
0.8	1200	0.4165	-	-	-	-	-
0.8333	1250	-	0.7600 (+0.1687)	0.5574 (+0.0169)	0.3676 (+0.0426)	0.5671 (+0.0665)	0.4974 (+0.0420)
0.8667	1300	0.4258	-	-	-	-	-
0.9333	1400	0.4192	-	-	-	-	-
1.0	1500	0.425	0.7601 (+0.1689)	0.5514 (+0.0110)	0.3714 (+0.0464)	0.5941 (+0.0934)	0.5056 (+0.0503)
-1	-1	-	0.7601 (+0.1689)	0.5514 (+0.0110)	0.3714 (+0.0464)	0.5941 (+0.0934)	0.5056 (+0.0503)

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.11.10
Sentence Transformers: 3.5.0.dev0
Transformers: 4.49.0
PyTorch: 2.5.1+cu124
Accelerate: 1.2.0
Datasets: 2.21.0
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",
}

LambdaLoss

bibtex

@inproceedings{wang2018lambdaloss,
  title={The lambdaloss framework for ranking metric optimization},
  author={Wang, Xuanhui and Li, Cheng and Golbandi, Nadav and Bendersky, Michael and Najork, Marc},
  booktitle={Proceedings of the 27th ACM international conference on information and knowledge management},
  pages={1313--1322},
  year={2018}
}

	question	answer	labels
type	string	list	list
details	min: 18 characters mean: 43.5 characters max: 101 characters	size: 6 elements	size: 6 elements