README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:8690
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 1位側開戸排水ﾊﾟﾝ内 切粉(最大3mm×5ヶ)
  sentences:
  - 切粉・素線など伝導性の高い異物。この場合はサイズおよび個数を記録する。
  - ひっかき傷に類するｷｽﾞがあるもの
  - 塗装の色むら・色違い等。ただし、塗装自体が未了の場合は「503作業漏れ」に分類する。
- source_sentence: 2位車販準備室ｶﾓｲ横ｽﾎﾟﾝｼﾞ破れ
  sentences:
  - 整線作業で対処可能な干渉・近接・見栄え不良等。
  - 部品同士が干渉しているが、走行時の異音には至っていないもの
  - 破れ・欠け・割れなどの破損があるもの
- source_sentence: 列車無線装置上部異物
  sentences:
  - 異物(通常ではそこには見られないはずのモノ)があるもの、ビス等の残留も含む。
  - 調整不良(501)や干渉(104)、締結不良(401)の結果、走行時や動作確認で異音(ビビり音、キシミ音、セリ音、ｶﾞﾀﾂｷ音)が確認されたもの。
  - 切粉・素線など伝導性の高い異物。この場合はサイズおよび個数を記録する。
- source_sentence: 3位飾りﾙｰﾊﾞｰ塗装剥がれ
  sentences:
  - 調整不良(501)や干渉(104)、締結不良(401)の結果、走行時や動作確認で異音(ビビり音、キシミ音、セリ音、ｶﾞﾀﾂｷ音)が確認されたもの。
  - 艤装作業時の調整不足による不良、例えばスポンジ、断熱材位置など。ただし、シール切れなどは「111 シール不良」に分類する。また可動部の調整不良は「502可動部の調整不良」に分類する。
  - 塗装の色むら・色違い等。ただし、塗装自体が未了の場合は「503作業漏れ」に分類する。
- source_sentence: 無線機器室扉下部 巾木 取付不良
  sentences:
  - 段付きが発生しているもの。段付きとスキマが同時発生しているもの、ﾊﾟﾈﾙ等貼付物以外の浮きも該当とする。
  - 切粉・素線など伝導性の高い異物。この場合はサイズおよび個数を記録する。
  - 調整不良(501)や干渉(104)、締結不良(401)の結果、走行時や動作確認で異音(ビビり音、キシミ音、セリ音、ｶﾞﾀﾂｷ音)が確認されたもの。
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# SentenceTransformer

This is a [sentence-transformers](https://www.SBERT.net) model trained on the all-nli-pair dataset. It maps sentences & paragraphs to a 768-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:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - all-nli-pair
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/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': 128, 'do_lower_case': False, 'architecture': 'XLMRobertaModel'})
  (1): Pooling({'word_embedding_dimension': 768, '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("vany02/hitachi-defect-classifier")
# Run inference
sentences = [
    '無線機器室扉下部 巾木 取付不良',
    '段付きが発生しているもの。段付きとスキマが同時発生しているもの、ﾊﾟﾈﾙ等貼付物以外の浮きも該当とする。',
    '調整不良(501)や干渉(104)、締結不良(401)の結果、走行時や動作確認で異音(ビビり音、キシミ音、セリ音、ｶﾞﾀﾂｷ音)が確認されたもの。',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.6393, 0.2329],
#         [0.6393, 1.0000, 0.2656],
#         [0.2329, 0.2656, 1.0000]])
```

<!--
### 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

#### all-nli-pair

* Dataset: all-nli-pair
* Size: 8,690 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                           |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             |
  | details | <ul><li>min: 5 tokens</li><li>mean: 16.27 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 33.58 tokens</li><li>max: 94 tokens</li></ul> |
* Samples:
  | anchor                                      | positive                                                                                                 |
  |:--------------------------------------------|:---------------------------------------------------------------------------------------------------------|
  | <code>ATC監視部点検蓋内下部 ｽﾎﾟﾝｼﾞはみ出し</code>        | <code>艤装作業時の調整不足による不良、例えばスポンジ、断熱材位置など。ただし、シール切れなどは「111 シール不良」に分類する。また可動部の調整不良は「502可動部の調整不良」に分類する。</code> |
  | <code>2位空き缶ｺﾞﾐ箱扉内 ﾋﾞｽ締結点のｺﾞﾑ板切欠していない</code> | <code>作業漏れ、取付もれがあった場合。ただし間違った作業を行っている場合は「X02作業間違い」に分類する。</code>                                          |
  | <code>汚物配電盤扉ｽﾄｯﾊﾟｰ裏ｺﾞﾑ上 切粉（2㎜×1ヶ）</code>    | <code>切粉・素線など伝導性の高い異物。この場合はサイズおよび個数を記録する。</code>                                                         |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }
  ```

### Evaluation Dataset

#### all-nli-pair

* Dataset: all-nli-pair
* Size: 966 evaluation samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 966 samples:
  |         | anchor                                                                            | positive                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            |
  | details | <ul><li>min: 5 tokens</li><li>mean: 15.85 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 34.0 tokens</li><li>max: 94 tokens</li></ul> |
* Samples:
  | anchor                         | positive                                                                               |
  |:-------------------------------|:---------------------------------------------------------------------------------------|
  | <code>運転席用CAﾀﾞｸﾄ取付部歪み</code>   | <code>通りが湾曲していたり、ゆがみが発生しているもの</code>                                                   |
  | <code>4位戸閉機ｷｾ 走行時異音</code>     | <code>調整不良(501)や干渉(104)、締結不良(401)の結果、走行時や動作確認で異音(ビビり音、キシミ音、セリ音、ｶﾞﾀﾂｷ音)が確認されたもの。</code> |
  | <code>ｻｰﾋﾞｽ機器情報伝送扉下部 凹み</code> | <code>打痕・凹凸が許容できないサイズのもの。ただし、メーカ要因の場合は「701メーカ/部品不良」に分類する。</code>                       |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }
  ```

### Training Hyperparameters

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

- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `per_device_eval_batch_size`: 8
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-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`: 3.0
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: None
- `warmup_ratio`: None
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `enable_jit_checkpoint`: False
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `use_cpu`: False
- `seed`: 42
- `data_seed`: None
- `bf16`: False
- `fp16`: False
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: -1
- `ddp_backend`: None
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': 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
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `group_by_length`: False
- `length_column_name`: length
- `project`: huggingface
- `trackio_space_id`: trackio
- `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
- `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_for_metrics`: []
- `eval_do_concat_batches`: True
- `auto_find_batch_size`: False
- `full_determinism`: False
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_num_input_tokens_seen`: no
- `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`: True
- `use_cache`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}

</details>

### Training Logs
| Epoch  | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.4600 | 500  | 0.7279        |
| 0.9200 | 1000 | 0.6938        |
| 1.3799 | 1500 | 0.6359        |
| 1.8399 | 2000 | 0.5952        |
| 2.2999 | 2500 | 0.5535        |
| 2.7599 | 3000 | 0.5392        |


### Framework Versions
- Python: 3.12.12
- Sentence Transformers: 5.2.2
- Transformers: 5.0.0
- PyTorch: 2.9.0+cu126
- 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",
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->