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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:193915 |
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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：均しｺﾝｸﾘｰﾄ。 |
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sentences: |
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- 科目：コンクリート。名称：設備基礎ｺﾝｸﾘｰﾄ。摘要：FC21N/mm2 ｽﾗﾝﾌﾟ18。備考：代価表 0036。 |
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- 科目：コンクリート。名称：機械基礎ｺﾝｸﾘｰﾄ。摘要：FC21N/mm2 ｽﾗﾝﾌﾟ15。備考：代価表 0045。 |
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- 科目：コンクリート。名称：ｺﾝｸﾘｰﾄ打設手間。摘要：防水保護ｺﾝ 人力打設工作物の基礎等 S15～S18- -。備考：代価表 0013。 |
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- source_sentence: 科目：コンクリート。名称：均しｺﾝｸﾘｰﾄ。 |
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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 FC=21 S15粗骨材20膨張剤 ﾃﾞﾝｶﾊﾟﾜｰCSA20㎏/m2。備考：代価表 0061屋内土間ｺﾝｸﾘｰﾄ。 |
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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 S15粗骨材基礎部。備考：代価表 0065。 |
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- 科目：タイル。名称：段床ﾀｲﾙ張り。 |
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- source_sentence: 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。 |
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sentences: |
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- 科目：コンクリート。名称：ｺﾝｸﾘｰﾄ打設手間。摘要：捨てｺﾝｸﾘｰﾄ ﾎﾟﾝﾌﾟ打設30m3/回程度 S15～S18- 圧送費､基本料別途。備考：B0-434215 |
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No.1 市場。 |
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- 科目：コンクリート。名称：ｺﾝｸﾘｰﾄ打設手間。摘要：躯体 ﾎﾟﾝﾌﾟ打設50m3/回未満 S15～S18標準階高 圧送費､基本料別途。備考：B0-434215 |
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3101新営地上部ｺﾝｸﾘｰﾄ。 |
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- 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308 FC=21 S15粗骨材20。備考：B0-114112 No.1 参資基礎部ｺﾝｸﾘｰﾄ。 |
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- source_sentence: 科目：コンクリート。名称：擁壁部ｺﾝｸﾘｰﾄ。 |
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sentences: |
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- 科目：コンクリート。名称：擁壁部ｺﾝｸﾘｰﾄ。摘要：FC36 S15粗骨材20 高性能AE減水剤躯体防水材 ﾍﾞｽﾄﾝA同等品以上。備考：代価表 0105。 |
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- 科目：コンクリート。名称：ｺﾝｸﾘｰﾄ打設手間。摘要：躯体 ﾎﾟﾝﾌﾟ打設50m3/回未満 S15～S18標準階高 圧送費､基本料別途。備考：B0-434215 |
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3101新営高流動ｺﾝｸﾘｰﾄ（免震下部基礎）。 |
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- 科目：コンクリート。名称：免震下部ｺﾝｸﾘｰﾄ打設手間。摘要：基礎部 ﾎﾟﾝﾌﾟ打設100m3/回以上 S15～S18- 圧送費､基本料別途。備考：代価表 0115。 |
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- source_sentence: 科目：タイル。名称：階段蹴上ﾀｲﾙ。 |
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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（個別）。摘要：Ｆ0＝24N/mm2 Ｓ＝１５ 徳島１。備考：B1-111111 H2906BD 個別基礎部躯体ｺﾝｸﾘｰﾄ。 |
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- 科目：コンクリート。名称：免震EXP_J用充填ｺﾝｸﾘｰﾄ。摘要：FC18N/mm2 ｽﾗﾝﾌﾟ18。備考：代価表 0064。 |
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- 科目：タイル。名称：屋外階段A段床床ﾀｲﾙA。 |
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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-v_1_0_7_13") |
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# Run inference |
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sentences = [ |
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'科目：タイル。名称：階段蹴上ﾀｲﾙ。', |
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'科目：タイル。名称：屋外階段A段床床ﾀｲﾙA。', |
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'科目：コンクリート。名称：免震EXP_J用充填ｺﾝｸﾘｰﾄ。摘要：FC18N/mm2 ｽﾗﾝﾌﾟ18。備考：代価表 0064。', |
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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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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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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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### 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: 193,915 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: 13.74 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 34.76 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>0: ~57.20%</li><li>1: ~5.00%</li><li>2: ~37.80%</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：30m3以上 50m3/回未満基本料金別途加算。備考：B0-434226 No.1 市場免震装置下部ｺﾝ。</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基本料金。</code> | <code>0</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`: 1 |
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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`: 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`: 0.2 |
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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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- `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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| 0.0660 | 50 | 0.0048 | |
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| 0.1319 | 100 | 0.0049 | |
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| 0.1979 | 150 | 0.0042 | |
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| 0.2639 | 200 | 0.0062 | |
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| 0.3298 | 250 | 0.0056 | |
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| 0.3958 | 300 | 0.005 | |
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| 0.4617 | 350 | 0.0048 | |
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| 0.5277 | 400 | 0.0049 | |
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| 0.5937 | 450 | 0.0043 | |
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| 0.6596 | 500 | 0.0051 | |
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| 0.7256 | 550 | 0.0046 | |
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| 0.7916 | 600 | 0.0045 | |
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| 0.8575 | 650 | 0.0043 | |
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| 0.9235 | 700 | 0.0049 | |
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| 0.9894 | 750 | 0.0038 | |
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### Framework Versions |
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- Python: 3.11.12 |
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- Sentence Transformers: 4.1.0 |
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- Transformers: 4.52.2 |
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- PyTorch: 2.6.0+cu124 |
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- Accelerate: 1.7.0 |
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- Datasets: 2.14.4 |
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- 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", |
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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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