README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:164
- loss:MultipleNegativesRankingLoss
- loss:CosineSimilarityLoss
base_model: BAAI/bge-small-zh-v1.5
widget:
- source_sentence: qa_234
  sentences:
  - 1客戶主軸馬達編碼器異常主軸馬達送修拿備品安裝聯軸器廠商安裝並校正動平衡我司協助裝回
  - 故障狀況 追加皮帶式油水分離機 處理狀況 備料為客戶追加
  - 追加皮帶式油水分離機
- source_sentence: qa_97
  sentences:
  - 故障狀況 1C軸轉盤整修 處理狀況 1XYZC軸伺服濾波及增益調整 2主軸測試棒精度確認發現主軸偏擺過大4條半主軸需檢修 3角尺精度調整及確認XYXZYZ符合精度要求1條內
    4確認C軸盤面偏擺符合精度要求1條內 5確認工作台平面精度需再處理將工作台墊片拆回座精度上的調整X軸光學尺關閉
  - 1C軸轉盤整修
  - 1客戶機台移廠房協助定位校正水平精度
- source_sentence: qa_202
  sentences:
  - 1客戶反應油冷機跳EX1038 OIL COOLER ALARM EX1014 OIL COOLER OVERLOAD
  - 故障狀況 1客戶要求刀臂sensor異常時需動作停止避免刀臂一直揮造成人員受傷 處理狀況 1修改PLC並測試所有sensor異常時需刀臂停止測試給用戶確認ok
  - 1客戶要求刀臂sensor異常時需動作停止避免刀臂一直揮造成人員受傷
- source_sentence: qa_60
  sentences:
  - 1客戶反應吊桿太矮要求更換 2切削液馬達有異音
  - 上滑軌有磨損以及塊的C釦及內部零件已掉落
  - 故障狀況 上滑軌有磨損以及塊的C釦及內部零件已掉落 處理狀況 備料為客戶更換
- source_sentence: qa_217
  sentences:
  - 1客戶反應跳主軸異警協助西門子檢修
  - 油壓箱table spin clamp油管壓接不良有漏油現象
  - 故障狀況 油壓箱table spin clamp油管壓接不良有漏油現象 處理狀況 備油管為客戶更換
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# SentenceTransformer based on BAAI/bge-small-zh-v1.5

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) on the train dataset. It maps sentences & paragraphs to a 512-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:** [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) <!-- at revision 7999e1d3359715c523056ef9478215996d62a620 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 512 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - train
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### 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': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 512, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)
```

## 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 = [
    'qa_217',
    '油壓箱table spin clamp油管壓接不良有漏油現象',
    '故障狀況 油壓箱table spin clamp油管壓接不良有漏油現象 處理狀況 備油管為客戶更換',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 512]

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

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## Training Details

### Training Dataset

#### train

* Dataset: train
* Size: 164 training samples
* Columns: <code>question</code>, <code>chunk</code>, and <code>label</code>
* Approximate statistics based on the first 164 samples:
  |         | question                                                                          | chunk                                                                               | label                                                         |
  |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------|
  | type    | string                                                                            | string                                                                              | float                                                         |
  | details | <ul><li>min: 6 tokens</li><li>mean: 23.19 tokens</li><li>max: 86 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 79.21 tokens</li><li>max: 176 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
* Samples:
  | question                  | chunk                                                                                   | label            |
  |:--------------------------|:----------------------------------------------------------------------------------------|:-----------------|
  | <code>1中噴箱體壓力表異常</code>   | <code>故障狀況 1中噴箱體壓力表異常 處理狀況 1依照廠商檢查方案過濾灌乾淨未阻塞濾心乾淨壓力表洩氣未改善 2更換壓力表安裝測試中噴壓力已改善客戶確認OK</code> | <code>1.0</code> |
  | <code>1用戶反應機台有漏水現象</code> | <code>故障狀況 1用戶反應機台有漏水現象 處理狀況 1查修後危機台左後立柱位置漏出拆開Y後伸縮護罩鈑金重新填上矽利康測試確認已無漏水</code>            | <code>1.0</code> |
  | <code>風槍的管路破裂會漏風</code>   | <code>故障狀況 風槍的管路破裂會漏風 處理狀況 備風槍管為客戶更換</code>                                             | <code>1.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
  ```json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  ```

### Evaluation Dataset

#### train

* Dataset: train
* Size: 40 evaluation samples
* Columns: <code>question</code>, <code>chunk</code>, and <code>label</code>
* Approximate statistics based on the first 40 samples:
  |         | question                                                                         | chunk                                                                               | label                                                         |
  |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------|
  | type    | string                                                                           | string                                                                              | float                                                         |
  | details | <ul><li>min: 7 tokens</li><li>mean: 22.3 tokens</li><li>max: 90 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 69.75 tokens</li><li>max: 144 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
* Samples:
  | question                                        | chunk                                                                                        | label            |
  |:------------------------------------------------|:---------------------------------------------------------------------------------------------|:-----------------|
  | <code>冷氣機結冰</code>                              | <code>故障狀況 冷氣機結冰 處理狀況 經威士頓評估後 同意保固提供一片冷氣控制板給客戶更換</code>                                      | <code>1.0</code> |
  | <code>1客戶要求刀臂sensor異常時需動作停止避免刀臂一直揮造成人員受傷</code> | <code>故障狀況 1客戶要求刀臂sensor異常時需動作停止避免刀臂一直揮造成人員受傷 處理狀況 1修改PLC並測試所有sensor異常時需刀臂停止測試給用戶確認ok</code> | <code>1.0</code> |
  | <code>更換鏈條以及鏈條軸承</code>                         | <code>故障狀況 更換鏈條以及鏈條軸承 處理狀況 備料為客戶更換</code>                                                    | <code>1.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
  ```json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 1
- `max_steps`: 500
- `warmup_ratio`: 0.1
- `fp16`: 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`: 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`: 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`: 1
- `max_steps`: 500
- `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`: 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`: 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}
- `tp_size`: 0
- `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`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch   | Step | Training Loss | train loss |
|:-------:|:----:|:-------------:|:----------:|
| 9.0909  | 100  | 2.3557        | 2.8228     |
| 18.1818 | 200  | 0.3241        | 2.9318     |
| 27.2727 | 300  | 0.0786        | 3.0996     |
| 36.3636 | 400  | 0.0408        | 3.1550     |
| 45.4545 | 500  | 0.0328        | 3.1758     |
| 9.0909  | 100  | 0.2424        | 0.0369     |
| 18.1818 | 200  | 0.0199        | 0.0374     |
| 27.2727 | 300  | 0.0231        | 0.0395     |
| 36.3636 | 400  | 0.0178        | 0.0387     |
| 45.4545 | 500  | 0.0157        | 0.0385     |
| 9.0909  | 100  | 0.0172        | 0.0000     |
| 18.1818 | 200  | 0.002         | 0.0000     |
| 27.2727 | 300  | 0.0016        | 0.0000     |
| 36.3636 | 400  | 0.0014        | 0.0000     |
| 45.4545 | 500  | 0.0013        | 0.0000     |


### Framework Versions
- Python: 3.11.12
- Sentence Transformers: 4.1.0
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1

## 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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