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
Supported Modality: Text

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

Full Model Architecture

CrossEncoder(
  (0): Transformer({'transformer_task': 'sequence-classification', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'logits'}}, 'module_output_name': 'scores', 'architecture': 'ModernBertForSequenceClassification'})
)

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("kwondw/reranker-ModernBERT-base-gooaq-bce")
# Get scores for pairs of inputs
pairs = [
    ['what to spend my nectar points on?', "There are lots of different places and ways you can spend your points. You can spend straight from your card by swiping it at Sainsbury's, Vue Cinemas and Argos or you can spend them online at nectar.com on great things like holidays, hotels and days out."],
    ['what to spend my nectar points on?', "If you have already registered for Online Services, just log in to see your points balance. For detailed points transactions, go to Membership Rewards page and click on 'Your points Account' and then 'My Points' at the top of the page."],
    ['what to spend my nectar points on?', 'The SDIP authorized insurance rate increase are as follows: 1 point=25%, 2 points=45%, 3 points=65%, 4 points=90%, 5 points=120%, 6 points=150%, 7 points=180%, 8 points=220%, 9 points=260%, 10 points=300%, 11 points=350%, and 12 insurance points equals a 400% increase in your insurance premiums, ouch!'],
    ['what to spend my nectar points on?', 'To redeem your Amex Blue Sky points, log in to your account via the Amex travel portal. At the checkout, there will be an option to pay with points. Points are worth 1 cent each toward flights and 0.7 cents each toward other travel costs.'],
    ['what to spend my nectar points on?', 'Yes, TD rewards points are worth $0.005 each, and must be redeemed in increments of 10,000, at a value of $50 per 10,000 points.'],
]
scores = model.predict(pairs)
print(scores)
# [9.7463e-01 2.7169e-02 1.4214e-03 5.0407e-03 4.4422e-04]

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'what to spend my nectar points on?',
    [
        "There are lots of different places and ways you can spend your points. You can spend straight from your card by swiping it at Sainsbury's, Vue Cinemas and Argos or you can spend them online at nectar.com on great things like holidays, hotels and days out.",
        "If you have already registered for Online Services, just log in to see your points balance. For detailed points transactions, go to Membership Rewards page and click on 'Your points Account' and then 'My Points' at the top of the page.",
        'The SDIP authorized insurance rate increase are as follows: 1 point=25%, 2 points=45%, 3 points=65%, 4 points=90%, 5 points=120%, 6 points=150%, 7 points=180%, 8 points=220%, 9 points=260%, 10 points=300%, 11 points=350%, and 12 insurance points equals a 400% increase in your insurance premiums, ouch!',
        'To redeem your Amex Blue Sky points, log in to your account via the Amex travel portal. At the checkout, there will be an option to pay with points. Points are worth 1 cent each toward flights and 0.7 cents each toward other travel costs.',
        'Yes, TD rewards points are worth $0.005 each, and must be redeemed in increments of 10,000, at a value of $50 per 10,000 points.',
    ]
)
# [{'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	0.7842 (+0.1739)
mrr@10	0.7827 (+0.1773)
ndcg@10	0.8182 (+0.1499)

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	0.4839 (-0.0056)	0.3422 (+0.0812)	0.6052 (+0.1856)
mrr@10	0.4753 (-0.0022)	0.5542 (+0.0544)	0.6189 (+0.1922)
ndcg@10	0.5681 (+0.0277)	0.3908 (+0.0658)	0.6657 (+0.1651)

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 "dataset_id": "sentence-transformers/NanoBEIR-en", 8 "rerank_k": 100, 9 "at_k": 10, 10 "always_rerank_positives": true 11}

Metric	Value
map	0.4771 (+0.0870)
mrr@10	0.5495 (+0.0814)
ndcg@10	0.5415 (+0.0862)

Training Details

Training Dataset

Unnamed Dataset

Size: 292,161 training samples
Columns: question, answer, and label
Approximate statistics based on the first 100 samples:
question answer label
type string string int
modality text text
details
min: 8 tokens
mean: 11.17 tokens
max: 14 tokens
min: 23 tokens
mean: 57.12 tokens
max: 115 tokens
0: ~82.69%
1: ~17.31%

Samples:

question	answer	label
`what to spend my nectar points on?`	`There are lots of different places and ways you can spend your points. You can spend straight from your card by swiping it at Sainsbury's, Vue Cinemas and Argos or you can spend them online at nectar.com on great things like holidays, hotels and days out.`	`1`
`what to spend my nectar points on?`	`If you have already registered for Online Services, just log in to see your points balance. For detailed points transactions, go to Membership Rewards page and click on 'Your points Account' and then 'My Points' at the top of the page.`	`0`
`what to spend my nectar points on?`	`The SDIP authorized insurance rate increase are as follows: 1 point=25%, 2 points=45%, 3 points=65%, 4 points=90%, 5 points=120%, 6 points=150%, 7 points=180%, 8 points=220%, 9 points=260%, 10 points=300%, 11 points=350%, and 12 insurance points equals a 400% increase in your insurance premiums, ouch!`	`0`

Loss: BinaryCrossEntropyLoss with these parameters:
json
1{ 2 "activation_fn": "torch.nn.modules.linear.Identity", 3 "pos_weight": 5 4}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 64
num_train_epochs: 1
learning_rate: 2e-05
warmup_steps: 0.1
fp16: True
per_device_eval_batch_size: 64
load_best_model_at_end: True
seed: 12
dataloader_num_workers: 2

All Hyperparameters

Click to expand

per_device_train_batch_size: 64
num_train_epochs: 1
max_steps: -1
learning_rate: 2e-05
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_steps: 0.1
optim: adamw_torch_fused
optim_args: None
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
optim_target_modules: None
gradient_accumulation_steps: 1
average_tokens_across_devices: True
max_grad_norm: 1.0
label_smoothing_factor: 0.0
bf16: False
fp16: True
bf16_full_eval: False
fp16_full_eval: False
tf32: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
use_liger_kernel: False
liger_kernel_config: None
use_cache: False
neftune_noise_alpha: None
torch_empty_cache_steps: None
auto_find_batch_size: False
log_on_each_node: True
logging_nan_inf_filter: True
include_num_input_tokens_seen: no
log_level: passive
log_level_replica: warning
disable_tqdm: False
project: huggingface
trackio_space_id: None
trackio_bucket_id: None
trackio_static_space_id: None
per_device_eval_batch_size: 64
prediction_loss_only: True
eval_on_start: False
eval_do_concat_batches: True
eval_use_gather_object: False
eval_accumulation_steps: None
include_for_metrics: []
batch_eval_metrics: False
save_only_model: False
save_on_each_node: False
enable_jit_checkpoint: False
push_to_hub: False
hub_private_repo: None
hub_model_id: None
hub_strategy: every_save
hub_always_push: False
hub_revision: None
load_best_model_at_end: True
ignore_data_skip: False
restore_callback_states_from_checkpoint: False
full_determinism: False
seed: 12
data_seed: None
use_cpu: 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
dataloader_drop_last: False
dataloader_num_workers: 2
dataloader_pin_memory: True
dataloader_persistent_workers: False
dataloader_prefetch_factor: None
remove_unused_columns: True
label_names: None
train_sampling_strategy: random
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
ddp_static_graph: None
ddp_backend: None
ddp_timeout: 1800
fsdp: None
fsdp_config: None
deepspeed: None
debug: []
skip_memory_metrics: True
do_predict: False
resume_from_checkpoint: None
warmup_ratio: None
local_rank: -1
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

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	-	0.1149 (-0.5534)	0.0212 (-0.5192)	0.2526 (-0.0724)	0.0288 (-0.4719)	0.1009 (-0.3545)
0.0002	1	1.3195	-	-	-	-	-
0.0438	200	1.1767	-	-	-	-	-
0.0876	400	1.0007	-	-	-	-	-
0.1095	500	-	0.7463 (+0.0779)	0.4619 (-0.0785)	0.3117 (-0.0133)	0.5694 (+0.0688)	0.4477 (-0.0077)
0.1314	600	0.6751	-	-	-	-	-
0.1752	800	0.6153	-	-	-	-	-
0.2190	1000	0.5813	0.7963 (+0.1279)	0.5554 (+0.0150)	0.3518 (+0.0267)	0.6018 (+0.1012)	0.5030 (+0.0476)
0.2628	1200	0.5345	-	-	-	-	-
0.3066	1400	0.5544	-	-	-	-	-
0.3285	1500	-	0.8035 (+0.1351)	0.5196 (-0.0208)	0.3772 (+0.0522)	0.6118 (+0.1111)	0.5029 (+0.0475)
0.3504	1600	0.5324	-	-	-	-	-
0.3942	1800	0.5199	-	-	-	-	-
0.4380	2000	0.5015	0.8045 (+0.1362)	0.5731 (+0.0326)	0.3638 (+0.0387)	0.6564 (+0.1558)	0.5311 (+0.0757)
0.4818	2200	0.4899	-	-	-	-	-
0.5256	2400	0.4703	-	-	-	-	-
0.5475	2500	-	0.8030 (+0.1346)	0.5821 (+0.0417)	0.3820 (+0.0569)	0.6695 (+0.1688)	0.5445 (+0.0892)
0.5694	2600	0.4863	-	-	-	-	-
0.6132	2800	0.4823	-	-	-	-	-
0.6570	3000	0.4656	0.8066 (+0.1382)	0.5458 (+0.0054)	0.3882 (+0.0632)	0.6386 (+0.1379)	0.5242 (+0.0688)
0.7008	3200	0.4689	-	-	-	-	-
0.7446	3400	0.4595	-	-	-	-	-
0.7665	3500	-	0.8146 (+0.1462)	0.5861 (+0.0456)	0.3904 (+0.0654)	0.6578 (+0.1572)	0.5448 (+0.0894)
0.7884	3600	0.4484	-	-	-	-	-
0.8322	3800	0.4597	-	-	-	-	-
0.876	4000	0.4482	0.8182 (+0.1499)	0.5681 (+0.0277)	0.3908 (+0.0658)	0.6657 (+0.1651)	0.5415 (+0.0862)
0.9198	4200	0.4488	-	-	-	-	-
0.9636	4400	0.4548	-	-	-	-	-
0.9855	4500	-	0.8168 (+0.1484)	0.5612 (+0.0208)	0.4006 (+0.0755)	0.6864 (+0.1858)	0.5494 (+0.0940)
1.0	4566	-	0.8166 (+0.1482)	0.5640 (+0.0236)	0.4006 (+0.0755)	0.6838 (+0.1832)	0.5495 (+0.0941)
-1	-1	-	0.8182 (+0.1499)	0.5681 (+0.0277)	0.3908 (+0.0658)	0.6657 (+0.1651)	0.5415 (+0.0862)

The bold row denotes the saved checkpoint.

Training Time

Training: 1.5 hours

Framework Versions

Python: 3.12.13
Sentence Transformers: 5.6.0
Transformers: 5.13.1
PyTorch: 2.11.0+cu128
Accelerate: 1.14.0
Datasets: 4.0.0
Tokenizers: 0.22.2

Additional Resources

Training and Finetuning Reranker Models with Sentence Transformers: the end-to-end guide for training or finetuning Cross Encoder (reranker) models.
Multimodal Embedding & Reranker Models with Sentence Transformers: use text, image, audio, and video reranker models through the same API.
Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers: training multimodal Cross Encoders.

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",
}

	question	answer	label
type	string	string	int
modality	text	text
details	min: 8 tokens mean: 11.17 tokens max: 14 tokens	min: 23 tokens mean: 57.12 tokens max: 115 tokens	0: ~82.69% 1: ~17.31%