| --- |
| tags: |
| - sentence-transformers |
| - cross-encoder |
| - reranker |
| - generated_from_trainer |
| - dataset_size:3190 |
| - loss:ListNetLoss |
| base_model: ProsusAI/finbert |
| pipeline_tag: text-ranking |
| library_name: sentence-transformers |
| --- |
| |
| # CrossEncoder based on ProsusAI/finbert |
|
|
| This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert) 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:** [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert) <!-- at revision 4556d13015211d73dccd3fdd39d39232506f3e43 --> |
| - **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("Pranjal2002/finbert_new_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>](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 |
| } |
| ``` |
|
|
| ### 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>](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 |
|
|
| - `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 |
| | Epoch | Step | Training Loss | Validation Loss | |
| |:---------:|:-------:|:-------------:|:---------------:| |
| | 0.1253 | 50 | 1.5705 | - | |
| | 0.2506 | 100 | 1.4843 | - | |
| | 0.3759 | 150 | 1.441 | - | |
| | 0.5013 | 200 | 1.3953 | 1.4013 | |
| | 0.6266 | 250 | 1.3738 | - | |
| | 0.7519 | 300 | 1.3781 | - | |
| | 0.8772 | 350 | 1.4106 | - | |
| | 1.0025 | 400 | 1.3318 | 1.4033 | |
| | 1.1278 | 450 | 1.3641 | - | |
| | 1.2531 | 500 | 1.3413 | - | |
| | 1.3784 | 550 | 1.3485 | - | |
| | 1.5038 | 600 | 1.3096 | 1.3498 | |
| | 1.6291 | 650 | 1.3473 | - | |
| | 1.7544 | 700 | 1.3594 | - | |
| | 1.8797 | 750 | 1.3418 | - | |
| | **2.005** | **800** | **1.3479** | **1.3386** | |
| | 2.1303 | 850 | 1.3276 | - | |
| | 2.2556 | 900 | 1.3361 | - | |
| | 2.3810 | 950 | 1.3086 | - | |
| | 2.5063 | 1000 | 1.3005 | 1.3472 | |
| | 2.6316 | 1050 | 1.3195 | - | |
| | 2.7569 | 1100 | 1.3199 | - | |
| | 2.8822 | 1150 | 1.3207 | - | |
| | 3.0075 | 1200 | 1.3216 | 1.3496 | |
| | 3.1328 | 1250 | 1.2914 | - | |
| | 3.2581 | 1300 | 1.3086 | - | |
| | 3.3835 | 1350 | 1.2737 | - | |
| | 3.5088 | 1400 | 1.3238 | 1.3380 | |
| | 3.6341 | 1450 | 1.3041 | - | |
| | 3.7594 | 1500 | 1.3069 | - | |
| | 3.8847 | 1550 | 1.2787 | - | |
| | 4.0100 | 1600 | 1.2927 | 1.3569 | |
| | 4.1353 | 1650 | 1.2927 | - | |
| | 4.2607 | 1700 | 1.2703 | - | |
| | 4.3860 | 1750 | 1.2783 | - | |
| | 4.5113 | 1800 | 1.2924 | 1.3532 | |
| | 4.6366 | 1850 | 1.2693 | - | |
| | 4.7619 | 1900 | 1.2819 | - | |
| | 4.8872 | 1950 | 1.2753 | - | |
|
|
| * 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 |
| ```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} |
| } |
| ``` |
|
|
| <!-- |
| ## Glossary |
|
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| *Clearly define terms in order to be accessible across audiences.* |
| --> |
|
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| <!-- |
| ## Model Card Authors |
|
|
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| --> |
|
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| <!-- |
| ## Model Card Contact |
|
|
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
| --> |
