README.md

---
base_model: intfloat/multilingual-e5-small
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:800
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Can you provide the definition of deaerating chamber?
  sentences:
  - 冷却水中に含まれる泥砂を沈澱させる池。
  - 脱気室の上部にあって、溶存ガスを分離する室。
  - 制御系の状態を変えようとする外的作用。
- source_sentence: Explain the definition of sodium phosphate dibasic.
  sentences:
  - 作用は、第３リン酸ソーダと同様であるがアルカリ性は弱い。
  - タービン起動停止時または主油ポンプが異常の場合に自動起動し制御油および軸受油を供給するポンプ。
  - 内筒の外側における空気の流れの方向と燃焼の進行する方向とが逆向きになっている燃焼器。
- source_sentence: 抽気 をどのように定義しますか？
  sentences:
  - 所内電源喪失時に密封油制御装置の電源を確保するための直流電動機駆動の交流発電機。
  - 他に使用する目的で圧縮機の出口側若しくは圧縮過程の途中から気体を抜き出すこと。又はそのようにし て抜き出された気体。
  - 排ガス中の酸素の割合を示すものであり、連続指示ができるためボイラの燃焼管理上重要な計器で、Eco出口のガスO2その他のO２の指示を与える。動作原理は水素との燃焼熱量によって算出される燃焼式、及びO２の磁化率が大きいことを利用した磁気式がある。
- source_sentence: What is the explanation of outer casing?
  sentences:
  - 二重構造のケーシングで、直接高圧蒸気にふれない外側のケーシング。
  - 伸縮自在継手のことで、タービン低圧排気室と復水器との継目に使用してある。これにより膨張収縮を吸収し本体と管束間の不等の膨張歪を減少する。
  - 操作信号を受けて信号に見合った開度に自動操作される弁。火力発電所弁類名称基準参照。
- source_sentence: Describe the meaning of diesel generator panel.
  sentences:
  - ユニットのタイプ別にｐＨ、シリカ、電導率、溶存酸素量等の目標値を定めたもの。
  - 取水口などのスクリーンを制御する盤。
  - ディーゼル発電機の制御、操作、監視などを行う盤。
---

# SentenceTransformer based on intfloat/multilingual-e5-small

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small). It maps sentences & paragraphs to a 384-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:** [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) <!-- at revision 0a68dcd3dad5b4962a78daa930087728292b241d -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **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': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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})
  (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("Nada-10/multilingual-e5-small-finetuned")
# Run inference
sentences = [
    'Describe the meaning of diesel generator panel.',
    'ディーゼル発電機の制御、操作、監視などを行う盤。',
    '取水口などのスクリーンを制御する盤。',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# 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

#### Unnamed Dataset


* Size: 800 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: 7 tokens</li><li>mean: 12.91 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 26.73 tokens</li><li>max: 86 tokens</li></ul> |
* Samples:
  | anchor                                                      | positive                                              |
  |:------------------------------------------------------------|:------------------------------------------------------|
  | <code>火力発電所の定義を説明してください。</code>                             | <code>石油、石炭、天然ガス、高炉ガスなどのもつ熱エネルギーを利用して発電するプラント。</code> |
  | <code>What does the term steam power plant refer to?</code> | <code>蒸気タービンにより発電するプラント。</code>                       |
  | <code>ガスタービン発電所の機能は何ですか？</code>                             | <code>ガスタービンにより発電するプラント。</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"
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset


* Size: 1,008 evaluation 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: 7 tokens</li><li>mean: 13.45 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 28.35 tokens</li><li>max: 123 tokens</li></ul> |
* Samples:
  | anchor                                            | positive                             |
  |:--------------------------------------------------|:-------------------------------------|
  | <code>What is the explanation of ash gate?</code> | <code>アッシュホッパからクリンカを排出するゲート。</code>  |
  | <code>クリンカクラッシャをどのように定義しますか？</code>               | <code>クリンカを適当な大きさに破砕する機械。</code>     |
  | <code>What is meant by jet pump?</code>           | <code>圧力水を噴射させクリンカを水力輸送するポンプ。</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"
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 5
- `warmup_ratio`: 0.2
- `fp16`: True
- `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
- `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`: 5
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.2
- `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}
- `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`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `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
- `dispatch_batches`: None
- `split_batches`: 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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch | Step | Training Loss | loss   |
|:-----:|:----:|:-------------:|:------:|
| 2.0   | 100  | 0.8734        | 1.4281 |
| 4.0   | 200  | 0.3795        | 1.4617 |


### Framework Versions
- Python: 3.11.7
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.3.1+cpu
- Accelerate: 0.32.1
- Datasets: 2.20.0
- Tokenizers: 0.19.1

## 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",
}
```

#### 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}
}
```

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