| --- |
| 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': ...}, ...] |
| ``` |
|
|
| <!-- |
| ### 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: 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", |
| } |
| ``` |
|
|
| <!-- |
| ## Glossary |
|
|
| *Clearly define terms in order to be accessible across audiences.* |
| --> |
|
|
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| ## Model Card Authors |
|
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| *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 |
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| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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