README.md

---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1615
- loss:TripletLoss
base_model: cl-nagoya/ruri-large
widget:
- source_sentence: 工事キャンセル日を変更したい
  sentences:
  - 工事予定キャンセルしたため日程変更手続き希望
  - 予定キャンセルした工事日を再調整希望
  - 新規工事日を早めてほしい
- source_sentence: 無料体験アンテナマークが30分経っても消えない
  sentences:
  - アンテナ方向狂いでスカパー映像が出ない
  - 体験マークが左下に居座り続ける
  - 有料契約アイコンが表示されない
- source_sentence: 時計表示消失
  sentences:
  - 音声ミュート
  - アンテナ老朽化でプレミアムサービス映像が映らなくなる
  - 液晶表示消灯
- source_sentence: バックアップ後に体験アンテナマークが残る
  sentences:
  - ソフトバックアップ後、左下の無料体験マークが30分経っても消えない
  - 画面右にエラーコードが出る
  - 無料アンテナマーク
- source_sentence: 引越しでアンテナ外して
  sentences:
  - 引っ越しに伴いアンテナ取り外しのみ依頼
  - 引越し先で新規アンテナ設置を依頼
  - 予約取り消し
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
model-index:
- name: SPCC
  results:
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: spcc
      type: spcc
    metrics:
    - type: cosine_accuracy
      value: 0.9876237511634827
      name: Cosine Accuracy
---

# SPCC

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [cl-nagoya/ruri-large](https://huggingface.co/cl-nagoya/ruri-large). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [cl-nagoya/ruri-large](https://huggingface.co/cl-nagoya/ruri-large) <!-- at revision a011c39b13e8bc137ee13c6bc82191ece46c414c -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```

## 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 SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    '引越しでアンテナ外して',
    '引っ越しに伴いアンテナ取り外しのみ依頼',
    '引越し先で新規アンテナ設置を依頼',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

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## Evaluation

### Metrics

#### Triplet

* Dataset: `spcc`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.9876** |

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 1,615 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                          | positive                                                                         | negative                                                                        |
  |:--------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
  | type    | string                                                                          | string                                                                           | string                                                                          |
  | details | <ul><li>min: 3 tokens</li><li>mean: 7.8 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.32 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 8.2 tokens</li><li>max: 20 tokens</li></ul> |
* Samples:
  | anchor                           | positive                          | negative                        |
  |:---------------------------------|:----------------------------------|:--------------------------------|
  | <code>アンテナ向きがズレてスカパー映らない</code>  | <code>アンテナ方向が狂い視聴できない</code>      | <code>テレビ本体の電源が落ちて映らない</code>   |
  | <code>ICカード無いせいでプレミアム見れない</code> | <code>ICカード未申請で一部チャンネル視聴不可</code> | <code>チューナー故障で全チャンネル映らない</code> |
  | <code>SONYチューナー壊れて受信不能</code>    | <code>SONY製チューナー不具合で映像来ない</code>  | <code>BSアンテナ設置ミスで映らない</code>    |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
  ```json
  {
      "distance_metric": "TripletDistanceMetric.COSINE",
      "triplet_margin": 0.25
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `warmup_ratio`: 0.1
- `fp16`: True
- `dataloader_drop_last`: True
- `remove_unused_columns`: False
- `batch_sampler`: no_duplicates

#### 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`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `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`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `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`: True
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: False
- `label_names`: None
- `load_best_model_at_end`: False
- `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}
- `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
- `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
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | spcc_cosine_accuracy |
|:------:|:----:|:-------------:|:--------------------:|
| -1     | -1   | -             | 0.9059               |
| 0.2    | 10   | 0.1661        | -                    |
| 0.4    | 20   | 0.0568        | -                    |
| 0.6    | 30   | 0.0299        | -                    |
| 0.8    | 40   | 0.022         | -                    |
| 1.02   | 50   | 0.0249        | 0.9851               |
| 1.22   | 60   | 0.0081        | -                    |
| 1.42   | 70   | 0.0072        | -                    |
| 1.62   | 80   | 0.0074        | -                    |
| 1.8200 | 90   | 0.0071        | -                    |
| 2.04   | 100  | 0.0062        | 0.9851               |
| 2.24   | 110  | 0.0084        | -                    |
| 2.44   | 120  | 0.0035        | -                    |
| 2.64   | 130  | 0.0034        | -                    |
| 2.84   | 140  | 0.0018        | -                    |
| 0.2    | 10   | 0.0023        | -                    |
| 0.4    | 20   | 0.0007        | -                    |
| 0.6    | 30   | 0.0012        | -                    |
| 0.8    | 40   | 0.0043        | -                    |
| 1.02   | 50   | 0.0058        | 0.9876               |
| 1.22   | 60   | 0.0005        | -                    |
| 1.42   | 70   | 0.0025        | -                    |
| 1.62   | 80   | 0.0011        | -                    |
| 1.8200 | 90   | 0.0026        | -                    |
| 2.04   | 100  | 0.0026        | 0.9876               |
| 2.24   | 110  | 0.0021        | -                    |
| 2.44   | 120  | 0.0015        | -                    |
| 2.64   | 130  | 0.0019        | -                    |
| 2.84   | 140  | 0.0           | -                    |
| 0.2    | 10   | 0.0003        | -                    |
| 0.4    | 20   | 0.0001        | -                    |
| 0.6    | 30   | 0.0006        | -                    |
| 0.8    | 40   | 0.0026        | -                    |
| 1.02   | 50   | 0.0018        | 0.9876               |
| 1.22   | 60   | 0.0007        | -                    |
| 1.42   | 70   | 0.0019        | -                    |
| 1.62   | 80   | 0.0006        | -                    |
| 1.8200 | 90   | 0.0011        | -                    |
| 2.04   | 100  | 0.0012        | 0.9876               |
| 2.24   | 110  | 0.0003        | -                    |
| 2.44   | 120  | 0.0           | -                    |
| 2.64   | 130  | 0.0014        | -                    |
| 2.84   | 140  | 0.0           | -                    |
| -1     | -1   | -             | 0.9876               |


### Framework Versions
- Python: 3.11.13
- Sentence Transformers: 4.1.0
- Transformers: 4.52.4
- PyTorch: 2.6.0+cu124
- Accelerate: 1.8.1
- Datasets: 3.6.0
- Tokenizers: 0.21.2

## 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",
}
```

#### TripletLoss
```bibtex
@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}
```

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