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---
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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:182343
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- loss:CategoricalContrastiveLoss
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widget:
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- source_sentence: 科目：コンクリート。名称：浮き床ｺﾝｸﾘｰﾄ。
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sentences:
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- 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308 FC21 S18粗骨材20。備考：刊-ｺﾝ 2118EXP.J面ｺﾝ。
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- 科目：コンクリート。名称：充填ｺﾝｸﾘｰﾄ（EXP_J内）。
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- 科目：コンクリート。名称：免震上部ｺﾝｸﾘｰﾄ打設手間。
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- source_sentence: 科目：コンクリート。名称：基礎部ｺﾝｸﾘｰﾄ打設手間。
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sentences:
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- 科目：コンクリート。名称：基礎部高流動ｺﾝｸﾘｰﾄ。摘要：FC36N/mm2 ｽﾗﾝﾌﾟﾌﾛｰ55～65高性能AE減水剤。備考：代価表 0059。
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- 科目：コンクリート。名称：基礎部ｺﾝｸﾘｰﾄ。摘要：FC24N/mm2 ｽﾗﾝﾌﾟ15。備考：代価表 0040。
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- 科目：コンクリート。名称：ｺﾝｸﾘｰﾄ（個別）。摘要：Ｆ0＝18N/mm2 Ｓ＝１８ 徳島１。備考：B1-111111 H2906BD 個別嵩上げｺﾝｸﾘｰﾄ。
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- source_sentence: 科目：コンクリート。名称：浮き床ｺﾝｸﾘｰﾄ。
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sentences:
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- 科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。摘要：100m3/回以上基本料金別途加算。備考：B0-434226 No.1 市場地上部ｺﾝ（5F）。
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- 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308 FC48 ﾌﾛｰ60粗骨材20高性能AE減水剤。備考：刊-ｺﾝ 4860K免震装置下部ｺﾝ。
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- 科目：コンクリート。名称：防水押えｺﾝｸﾘｰﾄ。
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- source_sentence: 科目：コンクリート。名称：基礎ｺﾝｸﾘｰﾄ。
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sentences:
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- 科目：コンクリート。名称：均しｺﾝｸﾘｰﾄ。
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- 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：FC=24 S18粗骨材地上部。備考：代価表 0060。
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- 科目：コンクリート。名称：基礎部高流動ｺﾝｸﾘｰﾄ。摘要：FC36N/mm2 ｽﾗﾝﾌﾟﾌﾛｰ55～65高性能AE減水剤。備考：代価表 0059。
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- source_sentence: 科目：コンクリート。名称：ｺﾝｸﾘｰﾄ打設手間。
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sentences:
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- 科目：コンクリート。名称：免震上部ｺﾝｸﾘｰﾄ打設手間。
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- 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308 FC=18 S15粗骨材20。備考：B0-114112 H22.11 協議防水保護ｺﾝｸﾘｰﾄ。
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- 科目：コンクリート。名称：ｺﾝｸﾘｰﾄ打設手間。
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pipeline_tag: sentence-similarity
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library_name: sentence-transformers
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---
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# SentenceTransformer
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This is a [sentence-transformers](https://www.SBERT.net) model trained. 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.
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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:** [Unknown](https://huggingface.co/unknown) -->
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- **Maximum Sequence Length:** 512 tokens
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- **Output Dimensionality:** 768 dimensions
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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': 768, '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})
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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("Detomo/cl-nagoya-sup-simcse-ja-nss-v1_0_7_8")
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# Run inference
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sentences = [
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'科目：コンクリート。名称：ｺﾝｸﾘｰﾄ打設手間。',
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'科目：コンクリート。名称：免震上部ｺﾝｸﾘｰﾄ打設手間。',
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'科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308 FC=18 S15粗骨材20。備考：B0-114112 H22.11 協議防水保護ｺﾝｸﾘｰﾄ。',
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]
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embeddings = model.encode(sentences)
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print(embeddings.shape)
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# [3, 768]
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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: 182,343 training samples
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
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* Approximate statistics based on the first 1000 samples:
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| | sentence1 | sentence2 | label |
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|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------|
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| type | string | string | int |
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| details | <ul><li>min: 11 tokens</li><li>mean: 13.32 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 34.8 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>0: ~68.50%</li><li>1: ~4.50%</li><li>2: ~27.00%</li></ul> |
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* Samples:
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| sentence1 | sentence2 | label |
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|:-----------------------------------------|:-------------------------------------------------------------------------------------------------|:---------------|
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| <code>科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。</code> | <code>科目：コンクリート。名称：ﾎﾟﾝﾌﾟ圧送。</code> | <code>1</code> |
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| <code>科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。</code> | <code>科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。摘要：100m3/回以上基本料金別途加算。備考：B0-434226 No.1 市場免震層下部ｺﾝ。</code> | <code>2</code> |
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| <code>科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。</code> | <code>科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。摘要：100m3/回以上基本料金別途加算。備考：B0-434226 No.1 市場基礎部ﾏｽｺﾝ。</code> | <code>2</code> |
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* Loss: <code>sentence_transformer_lib.categorical_constrastive_loss.CategoricalContrastiveLoss</code>
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### Training Hyperparameters
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#### Non-Default Hyperparameters
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- `per_device_train_batch_size`: 256
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- `per_device_eval_batch_size`: 256
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- `learning_rate`: 1e-05
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- `weight_decay`: 0.01
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- `num_train_epochs`: 10
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- `warmup_ratio`: 0.2
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- `fp16`: True
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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`: no
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- `prediction_loss_only`: True
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- `per_device_train_batch_size`: 256
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- `per_device_eval_batch_size`: 256
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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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- `torch_empty_cache_steps`: None
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- `learning_rate`: 1e-05
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- `weight_decay`: 0.01
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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`: 10
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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`: 0.2
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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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- `tp_size`: 0
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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`: None
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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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- `include_for_metrics`: []
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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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- `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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- `use_liger_kernel`: False
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- `eval_use_gather_object`: False
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- `average_tokens_across_devices`: False
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- `prompts`: None
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- `batch_sampler`: batch_sampler
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- `multi_dataset_batch_sampler`: proportional
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</details>
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### Training Logs
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| Epoch | Step | Training Loss |
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|:------:|:----:|:-------------:|
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| 0.0701 | 50 | 0.2825 |
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| 0.1403 | 100 | 0.1467 |
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| 0.2104 | 150 | 0.0947 |
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| 0.2805 | 200 | 0.0839 |
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| 0.3506 | 250 | 0.0769 |
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| 0.4208 | 300 | 0.0684 |
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| 0.4909 | 350 | 0.0625 |
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| 0.5610 | 400 | 0.0582 |
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| 0.6311 | 450 | 0.0579 |
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| 0.7013 | 500 | 0.0514 |
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| 0.7714 | 550 | 0.0514 |
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| 0.8415 | 600 | 0.0448 |
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| 0.9116 | 650 | 0.0436 |
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| 0.9818 | 700 | 0.0422 |
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| 1.0519 | 750 | 0.0371 |
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| 1.1220 | 800 | 0.0377 |
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| 1.1921 | 850 | 0.0353 |
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| 1.2623 | 900 | 0.0354 |
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| 1.3324 | 950 | 0.0325 |
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| 1.4025 | 1000 | 0.0328 |
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| 1.4727 | 1050 | 0.0302 |
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| 1.5428 | 1100 | 0.0259 |
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| 1.6129 | 1150 | 0.0267 |
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| 1.6830 | 1200 | 0.0274 |
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| 1.7532 | 1250 | 0.0262 |
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| 1.8233 | 1300 | 0.0234 |
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| 1.8934 | 1350 | 0.0244 |
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| 1.9635 | 1400 | 0.0238 |
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| 2.0337 | 1450 | 0.02 |
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| 2.1038 | 1500 | 0.0187 |
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| 2.1739 | 1550 | 0.0185 |
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|
| 2.2440 | 1600 | 0.0178 |
|
|
|
| 2.3142 | 1650 | 0.016 |
|
|
|
| 2.3843 | 1700 | 0.0169 |
|
|
|
| 2.4544 | 1750 | 0.0171 |
|
|
|
| 2.5245 | 1800 | 0.0146 |
|
|
|
| 2.5947 | 1850 | 0.0145 |
|
|
|
| 2.6648 | 1900 | 0.0146 |
|
|
|
| 2.7349 | 1950 | 0.0139 |
|
|
|
| 2.8050 | 2000 | 0.0119 |
|
|
|
| 2.8752 | 2050 | 0.0131 |
|
|
|
| 2.9453 | 2100 | 0.0124 |
|
|
|
| 3.0154 | 2150 | 0.011 |
|
|
|
| 3.0856 | 2200 | 0.0109 |
|
|
|
| 3.1557 | 2250 | 0.0103 |
|
|
|
| 3.2258 | 2300 | 0.0102 |
|
|
|
| 3.2959 | 2350 | 0.0089 |
|
|
|
| 3.3661 | 2400 | 0.0083 |
|
|
|
| 3.4362 | 2450 | 0.0095 |
|
|
|
| 3.5063 | 2500 | 0.0085 |
|
|
|
| 3.5764 | 2550 | 0.009 |
|
|
|
| 3.6466 | 2600 | 0.0083 |
|
|
|
| 3.7167 | 2650 | 0.0093 |
|
|
|
| 3.7868 | 2700 | 0.0084 |
|
|
|
| 3.8569 | 2750 | 0.0084 |
|
|
|
| 3.9271 | 2800 | 0.0088 |
|
|
|
| 3.9972 | 2850 | 0.0086 |
|
|
|
| 4.0673 | 2900 | 0.0057 |
|
|
|
| 4.1374 | 2950 | 0.0078 |
|
|
|
| 4.2076 | 3000 | 0.0062 |
|
|
|
| 4.2777 | 3050 | 0.0066 |
|
|
|
| 4.3478 | 3100 | 0.006 |
|
|
|
| 4.4180 | 3150 | 0.0078 |
|
|
|
| 4.4881 | 3200 | 0.0056 |
|
|
|
| 4.5582 | 3250 | 0.0064 |
|
|
|
| 4.6283 | 3300 | 0.0063 |
|
|
|
| 4.6985 | 3350 | 0.0058 |
|
|
|
| 4.7686 | 3400 | 0.005 |
|
|
|
| 4.8387 | 3450 | 0.0057 |
|
|
|
| 4.9088 | 3500 | 0.0059 |
|
|
|
| 4.9790 | 3550 | 0.0063 |
|
|
|
| 5.0491 | 3600 | 0.0046 |
|
|
|
| 5.1192 | 3650 | 0.0041 |
|
|
|
| 5.1893 | 3700 | 0.005 |
|
|
|
| 5.2595 | 3750 | 0.0043 |
|
|
|
| 5.3296 | 3800 | 0.0046 |
|
|
|
| 5.3997 | 3850 | 0.0041 |
|
|
|
| 5.4698 | 3900 | 0.006 |
|
|
|
| 5.5400 | 3950 | 0.0052 |
|
|
|
| 5.6101 | 4000 | 0.0043 |
|
|
|
|
|
|
|
|
|
### 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: 2.14.4
|
|
|
- Tokenizers: 0.21.1
|
|
|
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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",
|
|
|
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",
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|
|
url = "https://arxiv.org/abs/1908.10084",
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}
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|
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```
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