---
tags:
- sentence-transformers
- cross-encoder
- reranker
- generated_from_trainer
- dataset_size:3988
- loss:ListNetLoss
base_model: colbert-ir/colbertv2.0
pipeline_tag: text-ranking
library_name: sentence-transformers
---

# CrossEncoder based on colbert-ir/colbertv2.0

This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [colbert-ir/colbertv2.0](https://huggingface.co/colbert-ir/colbertv2.0) 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:** [colbert-ir/colbertv2.0](https://huggingface.co/colbert-ir/colbertv2.0) <!-- at revision c1e84128e85ef755c096a95bdb06b47793b13acf -->
- **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/finetuned_colbert_finance_v2")
# Get scores for pairs of texts
pairs = [
    ['What guidance was offered on The Estée Lauder Companies Inc.’s inventory management or supply chain efficiency targets?', 'Earnings'],
    ['What guidance was offered on The Estée Lauder Companies Inc.’s inventory management or supply chain efficiency targets?', '8-K'],
    ['What guidance was offered on The Estée Lauder Companies Inc.’s inventory management or supply chain efficiency targets?', 'DEF14A'],
    ['What guidance was offered on The Estée Lauder Companies Inc.’s inventory management or supply chain efficiency targets?', '10-K'],
    ['What guidance was offered on The Estée Lauder Companies Inc.’s inventory management or supply chain efficiency targets?', '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 guidance was offered on The Estée Lauder Companies Inc.’s inventory management or supply chain efficiency targets?',
    [
        'Earnings',
        '8-K',
        'DEF14A',
        '10-K',
        '10-Q',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
```

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 3,988 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: 53 characters</li><li>mean: 101.87 characters</li><li>max: 197 characters</li></ul> | <ul><li>size: 5 elements</li></ul> | <ul><li>size: 5 elements</li></ul> |
* Samples:
  | query                                                                                                    | docs                                                       | labels                       |
  |:---------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------|:-----------------------------|
  | <code>How has Keurig Dr Pepper’s beverage segment profitability trended over recent periods?</code>      | <code>['10-Q', '10-K', 'Earnings', '8-K', 'DEF14A']</code> | <code>[4, 3, 2, 1, 0]</code> |
  | <code>How does management describe competitive advantages in generative AI developer tooling</code>      | <code>['Earnings', '10-K', 'DEF14A', '8-K', '10-Q']</code> | <code>[4, 3, 2, 1, 0]</code> |
  | <code>What did Mohawk Industries’ leadership say about Mohawk Industries’ share repurchase plans?</code> | <code>['10-K', '10-Q', 'Earnings', 'DEF14A', '8-K']</code> | <code>[2, 2, 1, 0, 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: 998 evaluation samples
* Columns: <code>query</code>, <code>docs</code>, and <code>labels</code>
* Approximate statistics based on the first 998 samples:
  |         | query                                                                                            | docs                               | labels                             |
  |:--------|:-------------------------------------------------------------------------------------------------|:-----------------------------------|:-----------------------------------|
  | type    | string                                                                                           | list                               | list                               |
  | details | <ul><li>min: 43 characters</li><li>mean: 102.97 characters</li><li>max: 203 characters</li></ul> | <ul><li>size: 5 elements</li></ul> | <ul><li>size: 5 elements</li></ul> |
* Samples:
  | query                                                                                                                                | docs                                                       | labels                       |
  |:-------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------|:-----------------------------|
  | <code>What guidance was offered on The Estée Lauder Companies Inc.’s inventory management or supply chain efficiency targets?</code> | <code>['Earnings', '8-K', 'DEF14A', '10-K', '10-Q']</code> | <code>[4, 3, 2, 1, 0]</code> |
  | <code>What questions were asked about Live Nation Entertainment’s concert attendance and ticket sales engagement metrics?</code>     | <code>['Earnings', '10-K', '8-K', '10-Q', 'DEF14A']</code> | <code>[4, 3, 2, 1, 0]</code> |
  | <code>How has the ratio of AvalonBay Communities’ recurring to one-time rental income evolved in the latest reporting period?</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
- `fp16`: True
- `load_best_model_at_end`: True

#### 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`: 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`: 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_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 | Validation Loss |
|:----------:|:--------:|:-------------:|:---------------:|
| 0.1003     | 50       | 1.5717        | -               |
| 0.2006     | 100      | 1.4575        | -               |
| 0.3009     | 150      | 1.4404        | -               |
| 0.4012     | 200      | 1.408         | 1.3705          |
| 0.5015     | 250      | 1.3936        | -               |
| 0.6018     | 300      | 1.3719        | -               |
| 0.7021     | 350      | 1.3777        | -               |
| 0.8024     | 400      | 1.3689        | 1.3444          |
| 0.9027     | 450      | 1.3612        | -               |
| 1.0020     | 500      | 1.3263        | -               |
| 1.1023     | 550      | 1.3493        | -               |
| 1.2026     | 600      | 1.3602        | 1.3374          |
| 1.3029     | 650      | 1.3181        | -               |
| 1.4032     | 700      | 1.3217        | -               |
| 1.5035     | 750      | 1.3431        | -               |
| 1.6038     | 800      | 1.3234        | 1.3374          |
| 1.7041     | 850      | 1.3317        | -               |
| 1.8044     | 900      | 1.34          | -               |
| 1.9047     | 950      | 1.3467        | -               |
| 2.0040     | 1000     | 1.3236        | 1.3325          |
| 2.1043     | 1050     | 1.2743        | -               |
| 2.2046     | 1100     | 1.3177        | -               |
| 2.3049     | 1150     | 1.3004        | -               |
| **2.4052** | **1200** | **1.3114**    | **1.3274**      |
| 2.5055     | 1250     | 1.3138        | -               |
| 2.6058     | 1300     | 1.3263        | -               |
| 2.7061     | 1350     | 1.3175        | -               |
| 2.8064     | 1400     | 1.3033        | 1.3462          |
| 2.9067     | 1450     | 1.3112        | -               |
| 3.0060     | 1500     | 1.3025        | -               |
| 3.1063     | 1550     | 1.2818        | -               |
| 3.2066     | 1600     | 1.2768        | 1.3426          |
| 3.3069     | 1650     | 1.275         | -               |
| 3.4072     | 1700     | 1.3024        | -               |
| 3.5075     | 1750     | 1.2765        | -               |
| 3.6078     | 1800     | 1.2932        | 1.3467          |
| 3.7081     | 1850     | 1.2774        | -               |
| 3.8084     | 1900     | 1.2759        | -               |
| 3.9087     | 1950     | 1.2991        | -               |
| 4.0080     | 2000     | 1.2763        | 1.3368          |
| 4.1083     | 2050     | 1.253         | -               |
| 4.2086     | 2100     | 1.243         | -               |
| 4.3089     | 2150     | 1.2719        | -               |
| 4.4092     | 2200     | 1.256         | 1.3448          |
| 4.5095     | 2250     | 1.2718        | -               |
| 4.6098     | 2300     | 1.2536        | -               |
| 4.7101     | 2350     | 1.2696        | -               |
| 4.8104     | 2400     | 1.2626        | 1.3456          |
| 4.9107     | 2450     | 1.2736        | -               |

* 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}
}
```

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