jina_finance_v2

CrossEncoder based on jinaai/jina-reranker-v2-base-multilingual

This is a Cross Encoder model finetuned from jinaai/jina-reranker-v2-base-multilingual 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: jinaai/jina-reranker-v2-base-multilingual <!-- at revision 2f894e63642a95228da19cdd583cd2309983c867 -->
• Maximum Sequence Length: 1024 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("Pranjal2002/jina_finance_v2")
# Get scores for pairs of texts
pairs = [
    ['What consolidation trends among competitors are highlighted in disclosures affecting Regions Financial Corporation’s regional banking operations?', '10-K'],
    ['What consolidation trends among competitors are highlighted in disclosures affecting Regions Financial Corporation’s regional banking operations?', 'Earnings'],
    ['What consolidation trends among competitors are highlighted in disclosures affecting Regions Financial Corporation’s regional banking operations?', 'DEF14A'],
    ['What consolidation trends among competitors are highlighted in disclosures affecting Regions Financial Corporation’s regional banking operations?', '8-K'],
    ['What consolidation trends among competitors are highlighted in disclosures affecting Regions Financial Corporation’s regional banking operations?', '10-Q'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'What consolidation trends among competitors are highlighted in disclosures affecting Regions Financial Corporation’s regional banking operations?',
    [
        '10-K',
        'Earnings',
        'DEF14A',
        '8-K',
        '10-Q',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

Training Details

Training Dataset

Unnamed Dataset

• Size: 3,190 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: 55 characters</li><li>mean: 103.12 characters</li><li>max: 180 characters</li></ul>	<ul><li>size: 5 elements</li></ul>	<ul><li>size: 5 elements</li></ul>

• Samples:

  query	docs	labels
  <code>What year over year growth rate was shown for paid memberships in the same table</code>	<code>['10-Q', '10-K', '8-K', 'Earnings', 'DEF14A']</code>	<code>[4, 3, 2, 1, 0]</code>
  <code>How did non‑GAAP EPS growth align with the incentive metrics set for management?</code>	<code>['DEF14A', '8-K', '10-K', '10-Q', 'Earnings']</code>	<code>[2, 1, 0, 0, 0]</code>
  <code>What questions were raised regarding Xcel Energy Inc.’s risk factors and mitigation plans related to the integration of renewable energy sources into their grid?</code>	<code>['10-K', 'Earnings', '8-K', '10-Q', 'DEF14A']</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
  }
  

Evaluation Dataset

Unnamed Dataset

• Size: 798 evaluation samples
• Columns: <code>query</code>, <code>docs</code>, and <code>labels</code>
• Approximate statistics based on the first 798 samples:

  	query	docs	labels
  type	string	list	list
  details	<ul><li>min: 53 characters</li><li>mean: 102.91 characters</li><li>max: 179 characters</li></ul>	<ul><li>size: 5 elements</li></ul>	<ul><li>size: 5 elements</li></ul>

• Samples:

  query	docs	labels
  <code>What consolidation trends among competitors are highlighted in disclosures affecting Regions Financial Corporation’s regional banking operations?</code>	<code>['10-K', 'Earnings', 'DEF14A', '8-K', '10-Q']</code>	<code>[4, 3, 2, 1, 0]</code>
  <code>How does Pentair manage equity award burn rate or share pool availability?</code>	<code>['10-K', 'DEF14A', '10-Q', 'Earnings', '8-K']</code>	<code>[4, 3, 2, 1, 0]</code>
  <code>What key takeaways emerged from Valero Energy Corporation’s most recent earnings announcement?</code>	<code>['10-Q', '10-K', 'Earnings', '8-K', 'DEF14A']</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

• eval_strategy: steps
• per_device_train_batch_size: 4
• per_device_eval_batch_size: 4
• gradient_accumulation_steps: 2
• learning_rate: 2e-05
• num_train_epochs: 5
• warmup_steps: 100
• bf16: True
• load_best_model_at_end: True
• optim: adamw_torch

All Hyperparameters

<details><summary>Click to expand</summary>

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 4
• per_device_eval_batch_size: 4
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 2
• 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: 5
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.0
• warmup_steps: 100
• 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: 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: 0
• 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}
• parallelism_config: 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
• 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

• The bold row denotes the saved checkpoint.

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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