varadsrivastava/findocranker-mpnet-base-v2

CrossEncoder based on sentence-transformers/all-mpnet-base-v2

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 <!-- 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
• —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.

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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Downstream Usage (Sentence Transformers)

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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> with these parameters:

  {
      "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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —fsdp_transformer_layer_cls_to_wrap: None
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
• —parallelism_config: None
• —deepspeed: None
• —label_smoothing_factor: 0.0
• —optim: adamwtorchfused
• —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

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

@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

@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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