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---
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base_model: intfloat/multilingual-e5-small
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datasets: []
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language: []
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library_name: sentence-transformers
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- sentence-similarity
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- feature-extraction
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- generated_from_trainer
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- dataset_size:1446
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- loss:MultipleNegativesRankingLoss
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widget:
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- source_sentence: 圧縮機の回転部分の総称であって、動翼を介して気体に仕事を付加する機能をもつ。動翼を植えてないも のを圧縮機ロータという場合がある。
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sentences:
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- 圧縮機ロータ〔コンプレッサロータ〕はどのように定義されていますか？
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- 溶存水素の定義を教えてください。
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- 中圧圧縮機〔中圧コンプレッサ〕の定義は何ですか？
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- source_sentence: 気体を圧縮、加熱し連続的に膨張させて、その保有する熱エネルギーを機械的エネルギーとして取り出 す熱機関。原則として圧縮機、加熱器、タービン、制御装置のほか必要に応じて熱交換器その他の補機
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からなっている。
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sentences:
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- What is the definition of gas turbine?
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- What is the definition of low fuel pressure tripping device?
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- 微分動作〔Ｄ動作〕の機能は何ですか？
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- source_sentence: 光の透過または散乱を利用して水のにごりの度合を示す計器。
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sentences:
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- 第３リン酸ソーダの機能は何ですか？
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- 濁度計をどのように定義しますか？
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- How is 〔ＤＥＢ〕 direct energy balance coordinated control〔ＡＰＣ〕 automatic power control、automatic plant control
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defined?
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- source_sentence: ボイラ用水として使用している水の不純物の除去および軟化、ｐＨの調整等の処理をいう。
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sentences:
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- 給水処理の機能は何ですか？
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- 除湿器の機能は何ですか？
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- 薬液貯蔵タンクの意味を説明してください。
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- source_sentence: 再生時アニオン樹脂とカチオン樹脂は別々に再生される。再生後両樹脂の混合に用いる送風機をいう。
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sentences:
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- 中和装置の定義は何ですか？
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- 混床塔ブロワをどのように定義しますか？
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- How is raw water defined?
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---
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# SentenceTransformer based on intfloat/multilingual-e5-small
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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.
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## Model Details
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### Model Description
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- **Model Type:** Sentence Transformer
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- **Base model:** [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) <!-- at revision 0a68dcd3dad5b4962a78daa930087728292b241d -->
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- **Maximum Sequence Length:** 512 tokens
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- **Output Dimensionality:** 384 tokens
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- **Similarity Function:** Cosine Similarity
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<!-- - **Training Dataset:** Unknown -->
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<!-- - **Language:** Unknown -->
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<!-- - **License:** Unknown -->
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### Model Sources
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
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### Full Model Architecture
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```
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SentenceTransformer(
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
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(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})
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(2): Normalize()
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)
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```
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## Usage
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### Direct Usage (Sentence Transformers)
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First install the Sentence Transformers library:
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```bash
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pip install -U sentence-transformers
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```
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Then you can load this model and run inference.
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```python
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from sentence_transformers import SentenceTransformer
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# Download from the 🤗 Hub
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model = SentenceTransformer("Nada-10/f-jp-multilingual-e5-small")
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# Run inference
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sentences = [
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'再生時アニオン樹脂とカチオン樹脂は別々に再生される。再生後両樹脂の混合に用いる送風機をいう。',
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'混床塔ブロワをどのように定義しますか？',
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'中和装置の定義は何ですか？',
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]
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embeddings = model.encode(sentences)
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print(embeddings.shape)
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# [3, 384]
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# Get the similarity scores for the embeddings
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similarities = model.similarity(embeddings, embeddings)
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print(similarities.shape)
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# [3, 3]
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```
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<!--
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### Direct Usage (Transformers)
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<details><summary>Click to see the direct usage in Transformers</summary>
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</details>
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-->
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<!--
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### Downstream Usage (Sentence Transformers)
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You can finetune this model on your own dataset.
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<details><summary>Click to expand</summary>
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</details>
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-->
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<!--
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### Out-of-Scope Use
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*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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-->
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<!--
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## Bias, Risks and Limitations
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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-->
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<!--
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### Recommendations
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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-->
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## Training Details
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### Training Dataset
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#### Unnamed Dataset
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* Size: 1,446 training samples
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* Columns: <code>positive</code> and <code>anchor</code>
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* Approximate statistics based on the first 1000 samples:
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| | positive | anchor |
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|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
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| type | string | string |
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| details | <ul><li>min: 8 tokens</li><li>mean: 26.96 tokens</li><li>max: 123 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 13.06 tokens</li><li>max: 29 tokens</li></ul> |
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* Samples:
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| positive | anchor |
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|:------------------------------------------------------|:------------------------------------------------------------|
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| <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>火力発電所の定義を説明してください。</code> |
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| <code>蒸気タービンにより発電するプラント。</code> | <code>What does the term steam power plant refer to?</code> |
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| <code>ガス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> |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
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```json
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{
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"scale": 20.0,
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"similarity_fct": "cos_sim"
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}
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```
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### Evaluation Dataset
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#### Unnamed Dataset
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* Size: 362 evaluation samples
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* Columns: <code>positive</code> and <code>anchor</code>
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* Approximate statistics based on the first 1000 samples:
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| | positive | anchor |
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|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
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| type | string | string |
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| details | <ul><li>min: 8 tokens</li><li>mean: 30.27 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 13.23 tokens</li><li>max: 30 tokens</li></ul> |
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* Samples:
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| positive | anchor |
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|:--------------------------------------------------------------------------|:-------------------------------------------------------------|
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| <code>半導体といわれる４価の原子であるゲルマニウムとかシリコンを主原料とした真空管に代わる部品。</code> | <code>What is the function of transistor?</code> |
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| <code>シリコンを主原料とした整流子で、そのゲートに信号を加えて導通を制御する。又消イオン時間（ターンオフ時間）も格段に小さい。</code> | <code>ＳＣＲの説明は何ですか？</code> |
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| <code>絶縁板に配線回路を印刷し、回路部品を取り付けたもので電子機器に使われコンパクトで互換性がある。</code> | <code>What is the explanation of print circuit board?</code> |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
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```json
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{
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"scale": 20.0,
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"similarity_fct": "cos_sim"
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}
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```
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### Training Hyperparameters
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#### Non-Default Hyperparameters
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- `eval_strategy`: steps
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- `per_device_train_batch_size`: 16
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- `per_device_eval_batch_size`: 16
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- `num_train_epochs`: 1
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- `warmup_ratio`: 0.1
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- `fp16`: True
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- `batch_sampler`: no_duplicates
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#### All Hyperparameters
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<details><summary>Click to expand</summary>
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- `overwrite_output_dir`: False
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- `do_predict`: False
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- `eval_strategy`: steps
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- `prediction_loss_only`: True
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- `per_device_train_batch_size`: 16
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- `per_device_eval_batch_size`: 16
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- `per_gpu_train_batch_size`: None
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- `per_gpu_eval_batch_size`: None
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- `gradient_accumulation_steps`: 1
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- `eval_accumulation_steps`: None
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- `learning_rate`: 5e-05
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- `weight_decay`: 0.0
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- `adam_beta1`: 0.9
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- `adam_beta2`: 0.999
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- `adam_epsilon`: 1e-08
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- `max_grad_norm`: 1.0
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- `num_train_epochs`: 1
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- `max_steps`: -1
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- `lr_scheduler_type`: linear
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- `lr_scheduler_kwargs`: {}
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- `warmup_ratio`: 0.1
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- `warmup_steps`: 0
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- `log_level`: passive
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- `log_level_replica`: warning
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- `log_on_each_node`: True
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- `logging_nan_inf_filter`: True
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- `save_safetensors`: True
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- `save_on_each_node`: False
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- `save_only_model`: False
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- `restore_callback_states_from_checkpoint`: False
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- `no_cuda`: False
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- `use_cpu`: False
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- `use_mps_device`: False
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- `seed`: 42
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- `data_seed`: None
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- `jit_mode_eval`: False
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- `use_ipex`: False
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- `bf16`: False
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- `fp16`: True
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- `fp16_opt_level`: O1
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- `half_precision_backend`: auto
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- `bf16_full_eval`: False
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- `fp16_full_eval`: False
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- `tf32`: None
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- `local_rank`: 0
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- `ddp_backend`: None
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- `tpu_num_cores`: None
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- `tpu_metrics_debug`: False
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- `debug`: []
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- `dataloader_drop_last`: False
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- `dataloader_num_workers`: 0
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- `dataloader_prefetch_factor`: None
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- `past_index`: -1
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- `disable_tqdm`: False
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- `remove_unused_columns`: True
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- `label_names`: None
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- `load_best_model_at_end`: False
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- `ignore_data_skip`: False
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- `fsdp`: []
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- `fsdp_min_num_params`: 0
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
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- `fsdp_transformer_layer_cls_to_wrap`: None
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
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- `deepspeed`: None
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- `label_smoothing_factor`: 0.0
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- `optim`: adamw_torch
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- `optim_args`: None
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- `adafactor`: False
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- `group_by_length`: False
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- `length_column_name`: length
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- `ddp_find_unused_parameters`: None
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- `ddp_bucket_cap_mb`: None
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- `ddp_broadcast_buffers`: False
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- `dataloader_pin_memory`: True
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- `dataloader_persistent_workers`: False
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- `skip_memory_metrics`: True
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- `use_legacy_prediction_loop`: False
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- `push_to_hub`: False
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- `resume_from_checkpoint`: None
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- `hub_model_id`: None
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- `hub_strategy`: every_save
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- `hub_private_repo`: False
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- `hub_always_push`: False
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- `gradient_checkpointing`: False
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- `gradient_checkpointing_kwargs`: None
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- `include_inputs_for_metrics`: False
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- `eval_do_concat_batches`: True
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- `fp16_backend`: auto
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- `push_to_hub_model_id`: None
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- `push_to_hub_organization`: None
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- `mp_parameters`:
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- `auto_find_batch_size`: False
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- `full_determinism`: False
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- `torchdynamo`: None
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- `ray_scope`: last
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- `ddp_timeout`: 1800
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- `torch_compile`: False
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- `torch_compile_backend`: None
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- `torch_compile_mode`: None
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- `dispatch_batches`: None
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- `split_batches`: None
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- `include_tokens_per_second`: False
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- `include_num_input_tokens_seen`: False
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- `neftune_noise_alpha`: None
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- `optim_target_modules`: None
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- `batch_eval_metrics`: False
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- `eval_on_start`: False
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- `batch_sampler`: no_duplicates
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- `multi_dataset_batch_sampler`: proportional
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</details>
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### Framework Versions
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- Python: 3.10.0
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- Sentence Transformers: 3.0.1
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- Transformers: 4.42.4
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- PyTorch: 2.3.1+cpu
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- Accelerate: 0.32.1
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- Datasets: 2.20.0
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- Tokenizers: 0.19.1
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## Citation
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### BibTeX
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#### Sentence Transformers
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```bibtex
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@inproceedings{reimers-2019-sentence-bert,
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
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author = "Reimers, Nils and Gurevych, Iryna",
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
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month = "11",
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year = "2019",
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publisher = "Association for Computational Linguistics",
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url = "https://arxiv.org/abs/1908.10084",
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}
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```
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#### MultipleNegativesRankingLoss
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```bibtex
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@misc{henderson2017efficient,
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title={Efficient Natural Language Response Suggestion for Smart Reply},
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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},
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year={2017},
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eprint={1705.00652},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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<!--
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## Glossary
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*Clearly define terms in order to be accessible across audiences.*
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-->
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## Model Card Authors
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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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