This is a Cross Encoder model finetuned from sentence-transformers/all-mpnet-base-v2 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: sentence-transformers/all-mpnet-base-v2
Maximum Sequence Length: 512 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.

python

from sentence_transformers import CrossEncoder

# Download from the 🤗 Hub
model = CrossEncoder("varadsrivastava/findocranker-mpnet-base-v2")
# Get scores for pairs of texts
pairs = [
    ['What did Fifth Third Bancorp’s leadership say about Fifth Third Bancorp’s dividend policy?', '[DOC=10-K | annual report | comprehensive business overview, risks, financials | 100-300 pages]'],
    ['What did Fifth Third Bancorp’s leadership say about Fifth Third Bancorp’s dividend policy?', '[DOC=10-Q | quarterly report | interim financials, MD&A updates | 30-60 pages]'],
    ['What did Fifth Third Bancorp’s leadership say about Fifth Third Bancorp’s dividend policy?', '[DOC=DEF-14A | proxy statement | governance, compensation, shareholder voting matters | annual filing]'],
    ['What did Fifth Third Bancorp’s leadership say about Fifth Third Bancorp’s dividend policy?', '[DOC=8-K | current report | material events, timely disclosures | ad-hoc filing]'],
    ['What did Fifth Third Bancorp’s leadership say about Fifth Third Bancorp’s dividend policy?', '[DOC=Earnings | earnings call transcript | forward guidance, Q&A, management commentary | quarterly]'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'What did Fifth Third Bancorp’s leadership say about Fifth Third Bancorp’s dividend policy?',
    [
        '[DOC=10-K | annual report | comprehensive business overview, risks, financials | 100-300 pages]',
        '[DOC=10-Q | quarterly report | interim financials, MD&A updates | 30-60 pages]',
        '[DOC=DEF-14A | proxy statement | governance, compensation, shareholder voting matters | annual filing]',
        '[DOC=8-K | current report | material events, timely disclosures | ad-hoc filing]',
        '[DOC=Earnings | earnings call transcript | forward guidance, Q&A, management commentary | quarterly]',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Training Details

Training Dataset

Unnamed Dataset

Size: 3,943 training samples
Columns: query, docs, and labels
Approximate statistics based on the first 1000 samples:
query docs labels
type string list list
details
min: 59 characters
mean: 104.63 characters
max: 181 characters
size: 5 elements
size: 5 elements

Samples:

query	docs	labels
`What did Fifth Third Bancorp’s leadership say about Fifth Third Bancorp’s dividend policy?`	`['[DOC=10-K \| annual report \| comprehensive business overview, risks, financials \| 100-300 pages]', '[DOC=10-Q \| quarterly report \| interim financials, MD&A updates \| 30-60 pages]', '[DOC=DEF-14A \| proxy statement \| governance, compensation, shareholder voting matters \| annual filing]', '[DOC=8-K \| current report \| material events, timely disclosures \| ad-hoc filing]', '[DOC=Earnings \| earnings call transcript \| forward guidance, Q&A, management commentary \| quarterly]']`	`[4, 3, 2, 1, 0]`
`How did Qualcomm’s management describe forecasted capital allocation between developing new semiconductor technologies and potential acquisitions?`	`['[DOC=10-K \| annual report \| comprehensive business overview, risks, financials \| 100-300 pages]', '[DOC=10-Q \| quarterly report \| interim financials, MD&A updates \| 30-60 pages]', '[DOC=8-K \| current report \| material events, timely disclosures \| ad-hoc filing]', '[DOC=DEF-14A \| proxy statement \| governance, compensation, shareholder voting matters \| annual filing]', '[DOC=Earnings \| earnings call transcript \| forward guidance, Q&A, management commentary \| quarterly]']`	`[4, 3, 2, 1, 0]`
`What did GE HealthCare Technologies Inc.’s leadership say about GE HealthCare Technologies Inc.’s dividend policy?`	`['[DOC=10-K \| annual report \| comprehensive business overview, risks, financials \| 100-300 pages]', '[DOC=8-K \| current report \| material events, timely disclosures \| ad-hoc filing]', '[DOC=Earnings \| earnings call transcript \| forward guidance, Q&A, management commentary \| quarterly]', '[DOC=10-Q \| quarterly report \| interim financials, MD&A updates \| 30-60 pages]', '[DOC=DEF-14A \| proxy statement \| governance, compensation, shareholder voting matters \| annual filing]']`	`[4, 3, 2, 1, 0]`

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

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 4
gradient_accumulation_steps: 4
learning_rate: 2e-05
weight_decay: 0.01
num_train_epochs: 5
lr_scheduler_type: cosine
warmup_ratio: 0.1
data_seed: 42
fp16: True
dataloader_num_workers: 2

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 4
per_device_eval_batch_size: 8
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 4
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 2e-05
weight_decay: 0.01
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 5
max_steps: -1
lr_scheduler_type: cosine
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: 42
data_seed: 42
jit_mode_eval: False
use_ipex: False
bf16: False
fp16: True
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: 2
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
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}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
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
hub_revision: None
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
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
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss
0.1014	25	1.6085
0.2028	50	1.5942
0.3043	75	1.4848
0.4057	100	1.405
0.5071	125	1.4059
0.6085	150	1.3635
0.7099	175	1.3535
0.8114	200	1.3472
0.9128	225	1.3368
1.0122	250	1.3291
1.1136	275	1.2947
1.2150	300	1.3202
1.3164	325	1.3245
1.4178	350	1.321
1.5193	375	1.298
1.6207	400	1.307
1.7221	425	1.325
1.8235	450	1.3332
1.9249	475	1.301
2.0243	500	1.3106
2.1258	525	1.2973
2.2272	550	1.2995
2.3286	575	1.2978
2.4300	600	1.3109
2.5314	625	1.298
2.6329	650	1.307
2.7343	675	1.2969
2.8357	700	1.2762
2.9371	725	1.2917
3.0365	750	1.2545
3.1379	775	1.271
3.2394	800	1.2609
3.3408	825	1.2694
3.4422	850	1.2906
3.5436	875	1.2951
3.6450	900	1.2852
3.7465	925	1.2788
3.8479	950	1.283
3.9493	975	1.2727
4.0487	1000	1.263
4.1501	1025	1.2662
4.2515	1050	1.2628
4.3529	1075	1.2511
4.4544	1100	1.2788
4.5558	1125	1.2671
4.6572	1150	1.2648
4.7586	1175	1.2694
4.8600	1200	1.2648
4.9615	1225	1.2678

Framework Versions

Python: 3.12.11
Sentence Transformers: 5.1.0
Transformers: 4.56.1
PyTorch: 2.8.0+cu126
Accelerate: 1.10.1
Datasets: 4.0.0
Tokenizers: 0.22.0

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",
}

ListNetLoss

bibtex

@inproceedings{cao2007learning,
    title={Learning to Rank: From Pairwise Approach to Listwise Approach},
    author={Cao, Zhe and Qin, Tao and Liu, Tie-Yan and Tsai, Ming-Feng and Li, Hang},
    booktitle={Proceedings of the 24th international conference on Machine learning},
    pages={129--136},
    year={2007}
}

	query	docs	labels
type	string	list	list
details	min: 59 characters mean: 104.63 characters max: 181 characters	size: 5 elements	size: 5 elements