README.md

---
tags:
- sentence-transformers
- cross-encoder
- reranker
- generated_from_trainer
- dataset_size:104687
- loss:BinaryCrossEntropyLoss
base_model: hotchpotch/japanese-reranker-cross-encoder-small-v1
pipeline_tag: text-ranking
library_name: sentence-transformers
---

# CrossEncoder based on hotchpotch/japanese-reranker-cross-encoder-small-v1

This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [hotchpotch/japanese-reranker-cross-encoder-small-v1](https://huggingface.co/hotchpotch/japanese-reranker-cross-encoder-small-v1) 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:** [hotchpotch/japanese-reranker-cross-encoder-small-v1](https://huggingface.co/hotchpotch/japanese-reranker-cross-encoder-small-v1) <!-- at revision d7462bce0b065e0624028b412693dec055d719a0 -->
- **Maximum Sequence Length:** 514 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/huggingface/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("Miya67/aiq-scoring-e5-small-wiki-absolute")
# Get scores for pairs of texts
pairs = [
    ['問題: 俳優の哀川翔、女優の小西真奈美、歌手の長渕剛の出身都道府県はどこでしょう? / 想定解: 鹿児島県', '熊毛地域'],
    ['問題: 和名を「トウショウブ」や「オランダショウブ」という、剣のように尖った葉が特徴的なアヤメ科の植物は何でしょう? / 想定解: グラジオラス', 'グラジオラス属'],
    ['問題: 月見そばで、卵の黄身が表しているものは月ですが、白身が表しているものは何でしょう? / 想定解: 雲', '分子雲'],
    ['問題: 1950年に第1回日本シリーズの第1戦が行われた、現在はヤクルトスワローズが本拠地とする野球場はどこでしょう? / 想定解: 明治神宮野球場', '明治神宮野球大会'],
    ['問題: オーストラリアの6つの州の中で、最も面積が大きいのは西オーストラリア州ですが、最も面積が小さいのは何州でしょう? / 想定解: タスマニア州', 'たすまにあしゅうそうとく'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    '問題: 俳優の哀川翔、女優の小西真奈美、歌手の長渕剛の出身都道府県はどこでしょう? / 想定解: 鹿児島県',
    [
        '熊毛地域',
        'グラジオラス属',
        '分子雲',
        '明治神宮野球大会',
        'たすまにあしゅうそうとく',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
```

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### Downstream Usage (Sentence Transformers)

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: 104,687 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                                     | sentence_1                                                                                   | label                                                          |
  |:--------|:-----------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                                         | string                                                                                       | float                                                          |
  | details | <ul><li>min: 29 characters</li><li>mean: 64.2 characters</li><li>max: 108 characters</li></ul> | <ul><li>min: 1 characters</li><li>mean: 7.33 characters</li><li>max: 30 characters</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.59</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence_0                                                                            | sentence_1           | label            |
  |:--------------------------------------------------------------------------------------|:---------------------|:-----------------|
  | <code>問題: 俳優の哀川翔、女優の小西真奈美、歌手の長渕剛の出身都道府県はどこでしょう? / 想定解: 鹿児島県</code>                    | <code>熊毛地域</code>    | <code>1.0</code> |
  | <code>問題: 和名を「トウショウブ」や「オランダショウブ」という、剣のように尖った葉が特徴的なアヤメ科の植物は何でしょう? / 想定解: グラジオラス</code> | <code>グラジオラス属</code> | <code>1.0</code> |
  | <code>問題: 月見そばで、卵の黄身が表しているものは月ですが、白身が表しているものは何でしょう? / 想定解: 雲</code>                   | <code>分子雲</code>     | <code>0.0</code> |
* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
  ```json
  {
      "activation_fn": "torch.nn.modules.linear.Identity",
      "pos_weight": null
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `per_device_train_batch_size`: 32
- `num_train_epochs`: 4
- `per_device_eval_batch_size`: 32

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `per_device_train_batch_size`: 32
- `num_train_epochs`: 4
- `max_steps`: -1
- `learning_rate`: 5e-05
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: None
- `warmup_steps`: 0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `optim_target_modules`: None
- `gradient_accumulation_steps`: 1
- `average_tokens_across_devices`: True
- `max_grad_norm`: 1
- `label_smoothing_factor`: 0.0
- `bf16`: False
- `fp16`: False
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `use_cache`: False
- `neftune_noise_alpha`: None
- `torch_empty_cache_steps`: None
- `auto_find_batch_size`: False
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `include_num_input_tokens_seen`: no
- `log_level`: passive
- `log_level_replica`: warning
- `disable_tqdm`: False
- `project`: huggingface
- `trackio_space_id`: trackio
- `eval_strategy`: no
- `per_device_eval_batch_size`: 32
- `prediction_loss_only`: True
- `eval_on_start`: False
- `eval_do_concat_batches`: True
- `eval_use_gather_object`: False
- `eval_accumulation_steps`: None
- `include_for_metrics`: []
- `batch_eval_metrics`: False
- `save_only_model`: False
- `save_on_each_node`: False
- `enable_jit_checkpoint`: False
- `push_to_hub`: False
- `hub_private_repo`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_always_push`: False
- `hub_revision`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `restore_callback_states_from_checkpoint`: False
- `full_determinism`: False
- `seed`: 42
- `data_seed`: None
- `use_cpu`: False
- `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
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `dataloader_prefetch_factor`: None
- `remove_unused_columns`: True
- `label_names`: None
- `train_sampling_strategy`: random
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `ddp_backend`: None
- `ddp_timeout`: 1800
- `fsdp`: []
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `deepspeed`: None
- `debug`: []
- `skip_memory_metrics`: True
- `do_predict`: False
- `resume_from_checkpoint`: None
- `warmup_ratio`: None
- `local_rank`: -1
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}

</details>

### Training Logs
| Epoch  | Step  | Training Loss |
|:------:|:-----:|:-------------:|
| 0.1528 | 500   | 0.6662        |
| 0.3056 | 1000  | 0.6092        |
| 0.4584 | 1500  | 0.5728        |
| 0.6112 | 2000  | 0.5385        |
| 0.7641 | 2500  | 0.5165        |
| 0.9169 | 3000  | 0.4984        |
| 1.0697 | 3500  | 0.4844        |
| 1.2225 | 4000  | 0.4681        |
| 1.3753 | 4500  | 0.4557        |
| 1.5281 | 5000  | 0.4539        |
| 1.6809 | 5500  | 0.4460        |
| 1.8337 | 6000  | 0.4402        |
| 1.9866 | 6500  | 0.4317        |
| 2.1394 | 7000  | 0.4071        |
| 2.2922 | 7500  | 0.3955        |
| 2.4450 | 8000  | 0.3957        |
| 2.5978 | 8500  | 0.3874        |
| 2.7506 | 9000  | 0.3996        |
| 2.9034 | 9500  | 0.3933        |
| 3.0562 | 10000 | 0.3788        |
| 3.2090 | 10500 | 0.3635        |
| 3.3619 | 11000 | 0.3613        |
| 3.5147 | 11500 | 0.3633        |
| 3.6675 | 12000 | 0.3584        |
| 3.8203 | 12500 | 0.3592        |
| 3.9731 | 13000 | 0.3555        |


### Framework Versions
- Python: 3.12.12
- Sentence Transformers: 5.2.3
- Transformers: 5.3.0
- PyTorch: 2.10.0+cu128
- Accelerate: 1.12.0
- Datasets: 4.0.0
- Tokenizers: 0.22.2

## 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",
}
```

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