This is a Cross Encoder model trained 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

Maximum Sequence Length: 512 tokens
Number of Output Labels: 1 label
Supported Modality: Text

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': 'BertForSequenceClassification'})
)

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("cross_encoder_model_id")
# Get scores for pairs of inputs
pairs = [
    ['How do you control your horniness?', 'How do I control my horny emotions?'],
    ['What do i do after my MBBS ?', 'What can one do after MBBS?'],
    ['What is the county of Edgware and how does the lifestyle compare to the London Borough of Enfield?', 'What is the district of Edgware and how does the lifestyle compare to the London Borough of Islington?'],
    ['What is a qualified SAP ERP key user?', 'What is the responsibility of SAP ERP key user?'],
    ['Which is the best book for tensor calculus?', 'Which is the best book to study TENSOR for general relativity from basic?'],
]
scores = model.predict(pairs)
print(scores)
# [ 0.0335  0.6294 -2.3788 -0.096  -0.4309]

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'How do you control your horniness?',
    [
        'How do I control my horny emotions?',
        'What can one do after MBBS?',
        'What is the district of Edgware and how does the lifestyle compare to the London Borough of Islington?',
        'What is the responsibility of SAP ERP key user?',
        'Which is the best book to study TENSOR for general relativity from basic?',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Training Details

Training Dataset

Unnamed Dataset

Size: 11,928 training samples
Columns: query, response, and label
Approximate statistics based on the first 100 samples:
query response label
type string string float
modality text text
details
min: 7 tokens
mean: 14.07 tokens
max: 24 tokens
min: 8 tokens
mean: 14.39 tokens
max: 31 tokens
min: 0.0
mean: 0.62
max: 1.0

Samples:

query	response	label
`How do you control your horniness?`	`How do I control my horny emotions?`	`1.0`
`What do i do after my MBBS ?`	`What can one do after MBBS?`	`1.0`
`What is the county of Edgware and how does the lifestyle compare to the London Borough of Enfield?`	`What is the district of Edgware and how does the lifestyle compare to the London Borough of Islington?`	`0.0`

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

per_device_train_batch_size: 16
num_train_epochs: 1
disable_tqdm: True

All Hyperparameters

Click to expand

per_device_train_batch_size: 16
num_train_epochs: 1
max_steps: -1
learning_rate: 5e-05
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_steps: 0
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: False
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: True
project: huggingface
trackio_space_id: None
trackio_bucket_id: None
trackio_static_space_id: None
per_device_eval_batch_size: 8
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: False
ignore_data_skip: False
restore_callback_states_from_checkpoint: False
full_determinism: False
seed: 42
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: 0
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
0.0013	1	2.9614
0.0134	10	0.8912
0.0268	20	0.8821
0.0402	30	0.6854
0.0536	40	0.7558
0.0670	50	0.6960
0.0804	60	0.6753
0.0938	70	0.6979
0.1072	80	0.6919
0.1206	90	0.6373
0.1340	100	0.6750
0.1475	110	0.7235
0.1609	120	0.6508
0.1743	130	0.6698
0.1877	140	0.6603
0.2011	150	0.6601
0.2145	160	0.6269
0.2279	170	0.6568
0.2413	180	0.5662
0.2547	190	0.6341
0.2681	200	0.6649
0.2815	210	0.6582
0.2949	220	0.6966
0.3083	230	0.5850
0.3217	240	0.5919
0.3351	250	0.6952
0.3485	260	0.6682
0.3619	270	0.6402
0.3753	280	0.6923
0.3887	290	0.5896
0.4021	300	0.6448
0.4155	310	0.6208
0.4290	320	0.6557
0.4424	330	0.6780
0.4558	340	0.6057
0.4692	350	0.6660
0.4826	360	0.6834
0.4960	370	0.6351
0.5094	380	0.6442
0.5228	390	0.6002
0.5362	400	0.6454
0.5496	410	0.6431
0.5630	420	0.6146
0.5764	430	0.5826
0.5898	440	0.6906
0.6032	450	0.6260
0.6166	460	0.6390
0.6300	470	0.6107
0.6434	480	0.6381
0.6568	490	0.6296
0.6702	500	0.6163
0.6836	510	0.5750
0.6971	520	0.6387
0.7105	530	0.6353
0.7239	540	0.5639
0.7373	550	0.5501
0.7507	560	0.6608
0.7641	570	0.6868
0.7775	580	0.5937
0.7909	590	0.6198
0.8043	600	0.6683
0.8177	610	0.6228
0.8311	620	0.5776
0.8445	630	0.6115
0.8579	640	0.6536
0.8713	650	0.6366
0.8847	660	0.6278
0.8981	670	0.6331
0.9115	680	0.5928
0.9249	690	0.6246
0.9383	700	0.6273
0.9517	710	0.6254
0.9651	720	0.5991
0.9786	730	0.6309
0.9920	740	0.5972

Training Time

Training: 19.5 seconds

Framework Versions

Python: 3.11.6
Sentence Transformers: 5.6.1
Transformers: 5.14.1
PyTorch: 2.13.0+cu130
Accelerate: 1.14.0
Datasets: 5.0.0
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",
}

	query	response	label
type	string	string	float
modality	text	text
details	min: 7 tokens mean: 14.07 tokens max: 24 tokens	min: 8 tokens mean: 14.39 tokens max: 31 tokens	min: 0.0 mean: 0.62 max: 1.0

ChengyouXin/cacheverifier-quora

CrossEncoder