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-bce-no-pos-weight")
# Get scores for pairs of texts
pairs = [
    ['what is a default final judgment?', 'Default judgment is a binding judgment in favor of either party based on some failure to take action by the other party. Most often, it is a judgment in favor of a plaintiff when the defendant has not responded to a summons or has failed to appear before a court of law. The failure to take action is the default.'],
    ['what is a default final judgment?', "A default judgment is a judgment issued against a party that doesn't bother to defend itself at all. ... A summary judgment is a judgment issued against a party that doesn't have any evidence to support its claims. Summary judgment means: “You can't prove it; therefore you lose.”"],
    ['what is a default final judgment?', 'This judgment is seen as being mentioned in Hebrews 9:27, which states that "it is appointed unto men once to die, but after this the judgment".'],
    ['what is a default final judgment?', "If you don't file an answer or go to court, your landlord can ask the judge to find you in default. Then the judge may let your landlord show there is reason for you to be evicted. If the landlord does that, the judge can enter a default judgment against you."],
    ['what is a default final judgment?', 'What can High Court Enforcement Officers do to enforce judgment? HCEOs can take control of goods or possessions to the value of the unpaid judgment, and may also attempt to take goods to cover the costs of enforcement, court costs, and interest on the debt.'],
]
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 default final judgment?',
    [
        'Default judgment is a binding judgment in favor of either party based on some failure to take action by the other party. Most often, it is a judgment in favor of a plaintiff when the defendant has not responded to a summons or has failed to appear before a court of law. The failure to take action is the default.',
        "A default judgment is a judgment issued against a party that doesn't bother to defend itself at all. ... A summary judgment is a judgment issued against a party that doesn't have any evidence to support its claims. Summary judgment means: “You can't prove it; therefore you lose.”",
        'This judgment is seen as being mentioned in Hebrews 9:27, which states that "it is appointed unto men once to die, but after this the judgment".',
        "If you don't file an answer or go to court, your landlord can ask the judge to find you in default. Then the judge may let your landlord show there is reason for you to be evicted. If the landlord does that, the judge can enter a default judgment against you.",
        'What can High Court Enforcement Officers do to enforce judgment? HCEOs can take control of goods or possessions to the value of the unpaid judgment, and may also attempt to take goods to cover the costs of enforcement, court costs, and interest on the debt.',
    ]
)
# [{'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	0.7323 (+0.2012)
mrr@10	0.7309 (+0.2069)
ndcg@10	0.7731 (+0.1818)

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	0.4464 (-0.0431)	0.3794 (+0.1184)	0.5135 (+0.0939)
mrr@10	0.4352 (-0.0423)	0.5704 (+0.0706)	0.5180 (+0.0913)
ndcg@10	0.5250 (-0.0154)	0.4269 (+0.1018)	0.5685 (+0.0679)

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	0.4464 (+0.0564)
mrr@10	0.5079 (+0.0399)
ndcg@10	0.5068 (+0.0514)

Training Details

Training Dataset

Unnamed Dataset

Size: 578,402 training samples
Columns: question, answer, and label
Approximate statistics based on the first 1000 samples:
question answer label
type string string int
details
min: 19 characters
mean: 45.16 characters
max: 84 characters
min: 51 characters
mean: 252.6 characters
max: 361 characters
0: ~82.80%
1: ~17.20%

Samples:

question	answer	label
`what is a default final judgment?`	`Default judgment is a binding judgment in favor of either party based on some failure to take action by the other party. Most often, it is a judgment in favor of a plaintiff when the defendant has not responded to a summons or has failed to appear before a court of law. The failure to take action is the default.`	`1`
`what is a default final judgment?`	`A default judgment is a judgment issued against a party that doesn't bother to defend itself at all. ... A summary judgment is a judgment issued against a party that doesn't have any evidence to support its claims. Summary judgment means: “You can't prove it; therefore you lose.”`	`0`
`what is a default final judgment?`	`This judgment is seen as being mentioned in Hebrews 9:27, which states that "it is appointed unto men once to die, but after this the judgment".`	`0`

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

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	-	0.1386 (-0.4527)	0.0206 (-0.5198)	0.2387 (-0.0863)	0.0515 (-0.4491)	0.1036 (-0.3517)
0.0001	1	1.0425	-	-	-	-	-
0.0221	200	0.5627	-	-	-	-	-
0.0443	400	0.4593	-	-	-	-	-
0.0664	600	0.3714	-	-	-	-	-
0.0885	800	0.2955	-	-	-	-	-
0.1106	1000	0.2829	0.7083 (+0.1171)	0.4992 (-0.0412)	0.3110 (-0.0141)	0.4795 (-0.0211)	0.4299 (-0.0255)
0.1328	1200	0.2696	-	-	-	-	-
0.1549	1400	0.2548	-	-	-	-	-
0.1770	1600	0.2485	-	-	-	-	-
0.1992	1800	0.2326	-	-	-	-	-
0.2213	2000	0.241	0.7461 (+0.1549)	0.5350 (-0.0054)	0.3701 (+0.0451)	0.5339 (+0.0332)	0.4797 (+0.0243)
0.2434	2200	0.2373	-	-	-	-	-
0.2655	2400	0.2356	-	-	-	-	-
0.2877	2600	0.2207	-	-	-	-	-
0.3098	2800	0.222	-	-	-	-	-
0.3319	3000	0.2258	0.7443 (+0.1531)	0.5554 (+0.0150)	0.3921 (+0.0671)	0.5368 (+0.0361)	0.4948 (+0.0394)
0.3541	3200	0.2182	-	-	-	-	-
0.3762	3400	0.215	-	-	-	-	-
0.3983	3600	0.2161	-	-	-	-	-
0.4204	3800	0.2202	-	-	-	-	-
0.4426	4000	0.2147	0.7542 (+0.1629)	0.5465 (+0.0061)	0.4047 (+0.0797)	0.5199 (+0.0193)	0.4904 (+0.0350)
0.4647	4200	0.2177	-	-	-	-	-
0.4868	4400	0.2129	-	-	-	-	-
0.5090	4600	0.2099	-	-	-	-	-
0.5311	4800	0.2105	-	-	-	-	-
0.5532	5000	0.2101	0.7644 (+0.1731)	0.5448 (+0.0044)	0.4157 (+0.0907)	0.5746 (+0.0739)	0.5117 (+0.0563)
0.5753	5200	0.2034	-	-	-	-	-
0.5975	5400	0.2047	-	-	-	-	-
0.6196	5600	0.2043	-	-	-	-	-
0.6417	5800	0.2029	-	-	-	-	-
0.6639	6000	0.2021	0.7699 (+0.1786)	0.5250 (-0.0154)	0.4264 (+0.1013)	0.5491 (+0.0484)	0.5002 (+0.0448)
0.6860	6200	0.2048	-	-	-	-	-
0.7081	6400	0.2033	-	-	-	-	-
0.7303	6600	0.2017	-	-	-	-	-
0.7524	6800	0.1976	-	-	-	-	-
0.7745	7000	0.1989	0.7722 (+0.1810)	0.5732 (+0.0328)	0.4206 (+0.0956)	0.6013 (+0.1007)	0.5317 (+0.0763)
0.7966	7200	0.1925	-	-	-	-	-
0.8188	7400	0.1917	-	-	-	-	-
0.8409	7600	0.2002	-	-	-	-	-
0.8630	7800	0.1913	-	-	-	-	-
0.8852	8000	0.191	0.7707 (+0.1794)	0.5412 (+0.0007)	0.4332 (+0.1082)	0.5508 (+0.0502)	0.5084 (+0.0530)
0.9073	8200	0.1929	-	-	-	-	-
0.9294	8400	0.1989	-	-	-	-	-
0.9515	8600	0.1889	-	-	-	-	-
0.9737	8800	0.1874	-	-	-	-	-
0.9958	9000	0.1863	0.7731 (+0.1818)	0.5250 (-0.0154)	0.4269 (+0.1018)	0.5685 (+0.0679)	0.5068 (+0.0514)
-1	-1	-	0.7731 (+0.1818)	0.5250 (-0.0154)	0.4269 (+0.1018)	0.5685 (+0.0679)	0.5068 (+0.0514)

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: 1.5.2
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",
}

	question	answer	label
type	string	string	int
details	min: 19 characters mean: 45.16 characters max: 84 characters	min: 51 characters mean: 252.6 characters max: 361 characters	0: ~82.80% 1: ~17.20%