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tags:
- sentence-transformers
- cross-encoder
- reranker
- generated_from_trainer
- dataset_size:3943
- loss:ListNetLoss
base_model: sentence-transformers/all-mpnet-base-v2
pipeline_tag: text-ranking
library_name: sentence-transformers
---
# CrossEncoder based on sentence-transformers/all-mpnet-base-v2
This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) using the [sentence-transformers](https://www.SBERT.net) 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](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision e8c3b32edf5434bc2275fc9bab85f82640a19130 -->
- **Maximum Sequence Length:** 512 tokens
- **Number of Output Labels:** 1 label
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
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': ...}, ...]
```
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You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 3,943 training samples
* Columns: <code>query</code>, <code>docs</code>, and <code>labels</code>
* Approximate statistics based on the first 1000 samples:
| | query | docs | labels |
|:--------|:-------------------------------------------------------------------------------------------------|:-----------------------------------|:-----------------------------------|
| type | string | list | list |
| details | <ul><li>min: 59 characters</li><li>mean: 104.63 characters</li><li>max: 181 characters</li></ul> | <ul><li>size: 5 elements</li></ul> | <ul><li>size: 5 elements</li></ul> |
* Samples:
| query | docs | labels |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------|
| <code>What did Fifth Third Bancorp’s leadership say about Fifth Third Bancorp’s dividend policy?</code> | <code>['[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]']</code> | <code>[4, 3, 2, 1, 0]</code> |
| <code>How did Qualcomm’s management describe forecasted capital allocation between developing new semiconductor technologies and potential acquisitions?</code> | <code>['[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]']</code> | <code>[4, 3, 2, 1, 0]</code> |
| <code>What did GE HealthCare Technologies Inc.’s leadership say about GE HealthCare Technologies Inc.’s dividend policy?</code> | <code>['[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]']</code> | <code>[4, 3, 2, 1, 0]</code> |
* Loss: [<code>ListNetLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#listnetloss) with these parameters:
```json
{
"activation_fn": "torch.nn.modules.linear.Identity",
"mini_batch_size": null
}
```
### 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
<details><summary>Click to expand</summary>
- `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`: {}
</details>
### 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}
}
```
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