Add new SentenceTransformer model with an openvino backend

#1
by vumichien - opened
1_Pooling/config.json CHANGED
@@ -1,10 +1,10 @@
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- {
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- "word_embedding_dimension": 768,
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- "pooling_mode_cls_token": true,
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- "pooling_mode_mean_tokens": false,
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- "pooling_mode_max_tokens": false,
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- "pooling_mode_mean_sqrt_len_tokens": false,
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- "pooling_mode_weightedmean_tokens": false,
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- "pooling_mode_lasttoken": false,
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- "include_prompt": true
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  }
 
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": true,
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+ "pooling_mode_mean_tokens": false,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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  }
README.md CHANGED
@@ -1,417 +1,417 @@
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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 スランプフロー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 スランプ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:コンクリートポンプ圧送。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 スランプフロー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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-
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- # SentenceTransformer
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-
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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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-
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- ## Model Details
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-
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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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-
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- ### Model Sources
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-
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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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-
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- ### Full Model Architecture
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-
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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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-
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- ## Usage
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-
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- ### Direct Usage (Sentence Transformers)
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-
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- First install the Sentence Transformers library:
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-
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- ```bash
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- pip install -U sentence-transformers
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- ```
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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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-
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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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-
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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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- <!--
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- ### Direct Usage (Transformers)
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-
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- <details><summary>Click to see the direct usage in Transformers</summary>
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-
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- </details>
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- -->
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-
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- <!--
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- ### Downstream Usage (Sentence Transformers)
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-
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- You can finetune this model on your own dataset.
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-
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- <details><summary>Click to expand</summary>
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-
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- </details>
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- -->
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-
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- <!--
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- ### Out-of-Scope Use
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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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- -->
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-
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- <!--
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- ## Bias, Risks and Limitations
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-
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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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- <!--
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- ### Recommendations
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-
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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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-
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- ## Training Details
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-
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- ### Training Dataset
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-
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- #### Unnamed Dataset
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-
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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:コンクリートポンプ圧送。</code> | <code>科目:コンクリート。名称:ポンプ圧送。</code> | <code>1</code> |
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- | <code>科目:コンクリート。名称:コンクリートポンプ圧送。</code> | <code>科目:コンクリート。名称:コンクリートポンプ圧送。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:コンクリートポンプ圧送。</code> | <code>科目:コンクリート。名称:コンクリートポンプ圧送。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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-
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- ### Training Hyperparameters
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- #### Non-Default Hyperparameters
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-
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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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-
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- #### All Hyperparameters
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- <details><summary>Click to expand</summary>
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-
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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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-
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- </details>
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-
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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 |
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- | 2.3142 | 1650 | 0.016 |
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- | 2.3843 | 1700 | 0.0169 |
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- | 2.4544 | 1750 | 0.0171 |
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- | 2.5245 | 1800 | 0.0146 |
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- | 2.5947 | 1850 | 0.0145 |
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- | 2.6648 | 1900 | 0.0146 |
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- | 2.7349 | 1950 | 0.0139 |
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- | 2.8050 | 2000 | 0.0119 |
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- | 2.8752 | 2050 | 0.0131 |
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- | 2.9453 | 2100 | 0.0124 |
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- | 3.0154 | 2150 | 0.011 |
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- | 3.0856 | 2200 | 0.0109 |
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- | 3.1557 | 2250 | 0.0103 |
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- | 3.2258 | 2300 | 0.0102 |
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- | 3.2959 | 2350 | 0.0089 |
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- | 3.3661 | 2400 | 0.0083 |
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- | 3.4362 | 2450 | 0.0095 |
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- | 3.5063 | 2500 | 0.0085 |
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- | 3.5764 | 2550 | 0.009 |
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- | 3.6466 | 2600 | 0.0083 |
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- | 3.7167 | 2650 | 0.0093 |
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- | 3.7868 | 2700 | 0.0084 |
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- | 3.8569 | 2750 | 0.0084 |
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- | 3.9271 | 2800 | 0.0088 |
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- | 3.9972 | 2850 | 0.0086 |
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- | 4.0673 | 2900 | 0.0057 |
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- | 4.1374 | 2950 | 0.0078 |
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- | 4.2076 | 3000 | 0.0062 |
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- | 4.2777 | 3050 | 0.0066 |
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- | 4.3478 | 3100 | 0.006 |
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- | 4.4180 | 3150 | 0.0078 |
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- | 4.4881 | 3200 | 0.0056 |
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- | 4.5582 | 3250 | 0.0064 |
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- | 4.6283 | 3300 | 0.0063 |
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- | 4.6985 | 3350 | 0.0058 |
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- | 4.7686 | 3400 | 0.005 |
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- | 4.8387 | 3450 | 0.0057 |
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- | 4.9088 | 3500 | 0.0059 |
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- | 4.9790 | 3550 | 0.0063 |
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- | 5.0491 | 3600 | 0.0046 |
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- | 5.1192 | 3650 | 0.0041 |
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- | 5.1893 | 3700 | 0.005 |
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- | 5.2595 | 3750 | 0.0043 |
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- | 5.3296 | 3800 | 0.0046 |
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- | 5.3997 | 3850 | 0.0041 |
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- | 5.4698 | 3900 | 0.006 |
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- | 5.5400 | 3950 | 0.0052 |
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- | 5.6101 | 4000 | 0.0043 |
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-
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-
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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: 4.51.3
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- - PyTorch: 2.6.0+cu124
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- - Accelerate: 1.6.0
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- - Datasets: 2.14.4
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- - Tokenizers: 0.21.1
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-
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- ## Citation
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-
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- ### BibTeX
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-
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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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-
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- <!--
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- ## Glossary
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-
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- *Clearly define terms in order to be accessible across audiences.*
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- -->
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-
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- <!--
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- ## Model Card Authors
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-
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- *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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- -->
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-
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- <!--
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- ## Model Card Contact
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-
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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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  -->
 
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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
9
+ widget:
10
+ - source_sentence: 科目:コンクリート。名称:浮き床コンクリート。
11
+ sentences:
12
+ - 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。
13
+ - 科目:コンクリート。名称:充填コンクリート(EXP_J内)。
14
+ - 科目:コンクリート。名称:免震上部コンクリート打設手間。
15
+ - 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。
16
+ sentences:
17
+ - 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 スランプフロー55~65高性能AE減水剤。備考:代価表 0059。
18
+ - 科目:コンクリート。名称:基礎部コンクリート。摘要:FC24N/mm2 スランプ15。備考:代価表 0040。
19
+ - 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。
20
+ - source_sentence: 科目:コンクリート。名称:浮き床コンクリート。
21
+ sentences:
22
+ - 科目:コンクリート。名称:コンクリートポンプ圧送。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)。
23
+ - 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。
24
+ - 科目:コンクリート。名称:防水押えコンクリート。
25
+ - source_sentence: 科目:コンクリート。名称:基礎コンクリート。
26
+ sentences:
27
+ - 科目:コンクリート。名称:均しコンクリート。
28
+ - 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。
29
+ - 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 スランプフロー55~65高性能AE減水剤。備考:代価表 0059。
30
+ - source_sentence: 科目:コンクリート。名称:コンクリート打設手間。
31
+ sentences:
32
+ - 科目:コンクリート。名称:免震上部コンクリート打設手間。
33
+ - 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。
34
+ - 科目:コンクリート。名称:コンクリート打設手間。
35
+ pipeline_tag: sentence-similarity
36
+ library_name: sentence-transformers
37
+ ---
38
+
39
+ # SentenceTransformer
40
+
41
+ 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.
42
+
43
+ ## Model Details
44
+
45
+ ### Model Description
46
+ - **Model Type:** Sentence Transformer
47
+ <!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
48
+ - **Maximum Sequence Length:** 512 tokens
49
+ - **Output Dimensionality:** 768 dimensions
50
+ - **Similarity Function:** Cosine Similarity
51
+ <!-- - **Training Dataset:** Unknown -->
52
+ <!-- - **Language:** Unknown -->
53
+ <!-- - **License:** Unknown -->
54
+
55
+ ### Model Sources
56
+
57
+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
58
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
59
+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
60
+
61
+ ### Full Model Architecture
62
+
63
+ ```
64
+ SentenceTransformer(
65
+ (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
66
+ (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})
67
+ )
68
+ ```
69
+
70
+ ## Usage
71
+
72
+ ### Direct Usage (Sentence Transformers)
73
+
74
+ First install the Sentence Transformers library:
75
+
76
+ ```bash
77
+ pip install -U sentence-transformers
78
+ ```
79
+
80
+ Then you can load this model and run inference.
81
+ ```python
82
+ from sentence_transformers import SentenceTransformer
83
+
84
+ # Download from the 🤗 Hub
85
+ model = SentenceTransformer("Detomo/cl-nagoya-sup-simcse-ja-nss-v1_0_7_8")
86
+ # Run inference
87
+ sentences = [
88
+ '科目:コンクリート。名称:コンクリート打設手間。',
89
+ '科目:コンクリート。名称:免震上部コンクリート打設手間。',
90
+ '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。',
91
+ ]
92
+ embeddings = model.encode(sentences)
93
+ print(embeddings.shape)
94
+ # [3, 768]
95
+
96
+ # Get the similarity scores for the embeddings
97
+ similarities = model.similarity(embeddings, embeddings)
98
+ print(similarities.shape)
99
+ # [3, 3]
100
+ ```
101
+
102
+ <!--
103
+ ### Direct Usage (Transformers)
104
+
105
+ <details><summary>Click to see the direct usage in Transformers</summary>
106
+
107
+ </details>
108
+ -->
109
+
110
+ <!--
111
+ ### Downstream Usage (Sentence Transformers)
112
+
113
+ You can finetune this model on your own dataset.
114
+
115
+ <details><summary>Click to expand</summary>
116
+
117
+ </details>
118
+ -->
119
+
120
+ <!--
121
+ ### Out-of-Scope Use
122
+
123
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
124
+ -->
125
+
126
+ <!--
127
+ ## Bias, Risks and Limitations
128
+
129
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
130
+ -->
131
+
132
+ <!--
133
+ ### Recommendations
134
+
135
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
136
+ -->
137
+
138
+ ## Training Details
139
+
140
+ ### Training Dataset
141
+
142
+ #### Unnamed Dataset
143
+
144
+ * Size: 182,343 training samples
145
+ * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
146
+ * Approximate statistics based on the first 1000 samples:
147
+ | | sentence1 | sentence2 | label |
148
+ |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------|
149
+ | type | string | string | int |
150
+ | 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> |
151
+ * Samples:
152
+ | sentence1 | sentence2 | label |
153
+ |:-----------------------------------------|:-------------------------------------------------------------------------------------------------|:---------------|
154
+ | <code>科目:コンクリート。名称:コンクリートポンプ圧送。</code> | <code>科目:コンクリート。名称:ポンプ圧送。</code> | <code>1</code> |
155
+ | <code>科目:コンクリート。名称:コンクリートポンプ圧送。</code> | <code>科目:コンクリート。名称:コンクリートポンプ圧送。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> |
156
+ | <code>科目:コンクリート。名称:コンクリートポンプ圧送。</code> | <code>科目:コンクリート。名称:コンクリートポンプ圧送。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> |
157
+ * Loss: <code>sentence_transformer_lib.categorical_constrastive_loss.CategoricalContrastiveLoss</code>
158
+
159
+ ### Training Hyperparameters
160
+ #### Non-Default Hyperparameters
161
+
162
+ - `per_device_train_batch_size`: 256
163
+ - `per_device_eval_batch_size`: 256
164
+ - `learning_rate`: 1e-05
165
+ - `weight_decay`: 0.01
166
+ - `num_train_epochs`: 10
167
+ - `warmup_ratio`: 0.2
168
+ - `fp16`: True
169
+
170
+ #### All Hyperparameters
171
+ <details><summary>Click to expand</summary>
172
+
173
+ - `overwrite_output_dir`: False
174
+ - `do_predict`: False
175
+ - `eval_strategy`: no
176
+ - `prediction_loss_only`: True
177
+ - `per_device_train_batch_size`: 256
178
+ - `per_device_eval_batch_size`: 256
179
+ - `per_gpu_train_batch_size`: None
180
+ - `per_gpu_eval_batch_size`: None
181
+ - `gradient_accumulation_steps`: 1
182
+ - `eval_accumulation_steps`: None
183
+ - `torch_empty_cache_steps`: None
184
+ - `learning_rate`: 1e-05
185
+ - `weight_decay`: 0.01
186
+ - `adam_beta1`: 0.9
187
+ - `adam_beta2`: 0.999
188
+ - `adam_epsilon`: 1e-08
189
+ - `max_grad_norm`: 1.0
190
+ - `num_train_epochs`: 10
191
+ - `max_steps`: -1
192
+ - `lr_scheduler_type`: linear
193
+ - `lr_scheduler_kwargs`: {}
194
+ - `warmup_ratio`: 0.2
195
+ - `warmup_steps`: 0
196
+ - `log_level`: passive
197
+ - `log_level_replica`: warning
198
+ - `log_on_each_node`: True
199
+ - `logging_nan_inf_filter`: True
200
+ - `save_safetensors`: True
201
+ - `save_on_each_node`: False
202
+ - `save_only_model`: False
203
+ - `restore_callback_states_from_checkpoint`: False
204
+ - `no_cuda`: False
205
+ - `use_cpu`: False
206
+ - `use_mps_device`: False
207
+ - `seed`: 42
208
+ - `data_seed`: None
209
+ - `jit_mode_eval`: False
210
+ - `use_ipex`: False
211
+ - `bf16`: False
212
+ - `fp16`: True
213
+ - `fp16_opt_level`: O1
214
+ - `half_precision_backend`: auto
215
+ - `bf16_full_eval`: False
216
+ - `fp16_full_eval`: False
217
+ - `tf32`: None
218
+ - `local_rank`: 0
219
+ - `ddp_backend`: None
220
+ - `tpu_num_cores`: None
221
+ - `tpu_metrics_debug`: False
222
+ - `debug`: []
223
+ - `dataloader_drop_last`: False
224
+ - `dataloader_num_workers`: 0
225
+ - `dataloader_prefetch_factor`: None
226
+ - `past_index`: -1
227
+ - `disable_tqdm`: False
228
+ - `remove_unused_columns`: True
229
+ - `label_names`: None
230
+ - `load_best_model_at_end`: False
231
+ - `ignore_data_skip`: False
232
+ - `fsdp`: []
233
+ - `fsdp_min_num_params`: 0
234
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
235
+ - `tp_size`: 0
236
+ - `fsdp_transformer_layer_cls_to_wrap`: None
237
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
238
+ - `deepspeed`: None
239
+ - `label_smoothing_factor`: 0.0
240
+ - `optim`: adamw_torch
241
+ - `optim_args`: None
242
+ - `adafactor`: False
243
+ - `group_by_length`: False
244
+ - `length_column_name`: length
245
+ - `ddp_find_unused_parameters`: None
246
+ - `ddp_bucket_cap_mb`: None
247
+ - `ddp_broadcast_buffers`: False
248
+ - `dataloader_pin_memory`: True
249
+ - `dataloader_persistent_workers`: False
250
+ - `skip_memory_metrics`: True
251
+ - `use_legacy_prediction_loop`: False
252
+ - `push_to_hub`: False
253
+ - `resume_from_checkpoint`: None
254
+ - `hub_model_id`: None
255
+ - `hub_strategy`: every_save
256
+ - `hub_private_repo`: None
257
+ - `hub_always_push`: False
258
+ - `gradient_checkpointing`: False
259
+ - `gradient_checkpointing_kwargs`: None
260
+ - `include_inputs_for_metrics`: False
261
+ - `include_for_metrics`: []
262
+ - `eval_do_concat_batches`: True
263
+ - `fp16_backend`: auto
264
+ - `push_to_hub_model_id`: None
265
+ - `push_to_hub_organization`: None
266
+ - `mp_parameters`:
267
+ - `auto_find_batch_size`: False
268
+ - `full_determinism`: False
269
+ - `torchdynamo`: None
270
+ - `ray_scope`: last
271
+ - `ddp_timeout`: 1800
272
+ - `torch_compile`: False
273
+ - `torch_compile_backend`: None
274
+ - `torch_compile_mode`: None
275
+ - `include_tokens_per_second`: False
276
+ - `include_num_input_tokens_seen`: False
277
+ - `neftune_noise_alpha`: None
278
+ - `optim_target_modules`: None
279
+ - `batch_eval_metrics`: False
280
+ - `eval_on_start`: False
281
+ - `use_liger_kernel`: False
282
+ - `eval_use_gather_object`: False
283
+ - `average_tokens_across_devices`: False
284
+ - `prompts`: None
285
+ - `batch_sampler`: batch_sampler
286
+ - `multi_dataset_batch_sampler`: proportional
287
+
288
+ </details>
289
+
290
+ ### Training Logs
291
+ | Epoch | Step | Training Loss |
292
+ |:------:|:----:|:-------------:|
293
+ | 0.0701 | 50 | 0.2825 |
294
+ | 0.1403 | 100 | 0.1467 |
295
+ | 0.2104 | 150 | 0.0947 |
296
+ | 0.2805 | 200 | 0.0839 |
297
+ | 0.3506 | 250 | 0.0769 |
298
+ | 0.4208 | 300 | 0.0684 |
299
+ | 0.4909 | 350 | 0.0625 |
300
+ | 0.5610 | 400 | 0.0582 |
301
+ | 0.6311 | 450 | 0.0579 |
302
+ | 0.7013 | 500 | 0.0514 |
303
+ | 0.7714 | 550 | 0.0514 |
304
+ | 0.8415 | 600 | 0.0448 |
305
+ | 0.9116 | 650 | 0.0436 |
306
+ | 0.9818 | 700 | 0.0422 |
307
+ | 1.0519 | 750 | 0.0371 |
308
+ | 1.1220 | 800 | 0.0377 |
309
+ | 1.1921 | 850 | 0.0353 |
310
+ | 1.2623 | 900 | 0.0354 |
311
+ | 1.3324 | 950 | 0.0325 |
312
+ | 1.4025 | 1000 | 0.0328 |
313
+ | 1.4727 | 1050 | 0.0302 |
314
+ | 1.5428 | 1100 | 0.0259 |
315
+ | 1.6129 | 1150 | 0.0267 |
316
+ | 1.6830 | 1200 | 0.0274 |
317
+ | 1.7532 | 1250 | 0.0262 |
318
+ | 1.8233 | 1300 | 0.0234 |
319
+ | 1.8934 | 1350 | 0.0244 |
320
+ | 1.9635 | 1400 | 0.0238 |
321
+ | 2.0337 | 1450 | 0.02 |
322
+ | 2.1038 | 1500 | 0.0187 |
323
+ | 2.1739 | 1550 | 0.0185 |
324
+ | 2.2440 | 1600 | 0.0178 |
325
+ | 2.3142 | 1650 | 0.016 |
326
+ | 2.3843 | 1700 | 0.0169 |
327
+ | 2.4544 | 1750 | 0.0171 |
328
+ | 2.5245 | 1800 | 0.0146 |
329
+ | 2.5947 | 1850 | 0.0145 |
330
+ | 2.6648 | 1900 | 0.0146 |
331
+ | 2.7349 | 1950 | 0.0139 |
332
+ | 2.8050 | 2000 | 0.0119 |
333
+ | 2.8752 | 2050 | 0.0131 |
334
+ | 2.9453 | 2100 | 0.0124 |
335
+ | 3.0154 | 2150 | 0.011 |
336
+ | 3.0856 | 2200 | 0.0109 |
337
+ | 3.1557 | 2250 | 0.0103 |
338
+ | 3.2258 | 2300 | 0.0102 |
339
+ | 3.2959 | 2350 | 0.0089 |
340
+ | 3.3661 | 2400 | 0.0083 |
341
+ | 3.4362 | 2450 | 0.0095 |
342
+ | 3.5063 | 2500 | 0.0085 |
343
+ | 3.5764 | 2550 | 0.009 |
344
+ | 3.6466 | 2600 | 0.0083 |
345
+ | 3.7167 | 2650 | 0.0093 |
346
+ | 3.7868 | 2700 | 0.0084 |
347
+ | 3.8569 | 2750 | 0.0084 |
348
+ | 3.9271 | 2800 | 0.0088 |
349
+ | 3.9972 | 2850 | 0.0086 |
350
+ | 4.0673 | 2900 | 0.0057 |
351
+ | 4.1374 | 2950 | 0.0078 |
352
+ | 4.2076 | 3000 | 0.0062 |
353
+ | 4.2777 | 3050 | 0.0066 |
354
+ | 4.3478 | 3100 | 0.006 |
355
+ | 4.4180 | 3150 | 0.0078 |
356
+ | 4.4881 | 3200 | 0.0056 |
357
+ | 4.5582 | 3250 | 0.0064 |
358
+ | 4.6283 | 3300 | 0.0063 |
359
+ | 4.6985 | 3350 | 0.0058 |
360
+ | 4.7686 | 3400 | 0.005 |
361
+ | 4.8387 | 3450 | 0.0057 |
362
+ | 4.9088 | 3500 | 0.0059 |
363
+ | 4.9790 | 3550 | 0.0063 |
364
+ | 5.0491 | 3600 | 0.0046 |
365
+ | 5.1192 | 3650 | 0.0041 |
366
+ | 5.1893 | 3700 | 0.005 |
367
+ | 5.2595 | 3750 | 0.0043 |
368
+ | 5.3296 | 3800 | 0.0046 |
369
+ | 5.3997 | 3850 | 0.0041 |
370
+ | 5.4698 | 3900 | 0.006 |
371
+ | 5.5400 | 3950 | 0.0052 |
372
+ | 5.6101 | 4000 | 0.0043 |
373
+
374
+
375
+ ### Framework Versions
376
+ - Python: 3.11.12
377
+ - Sentence Transformers: 4.1.0
378
+ - Transformers: 4.51.3
379
+ - PyTorch: 2.6.0+cu124
380
+ - Accelerate: 1.6.0
381
+ - Datasets: 2.14.4
382
+ - Tokenizers: 0.21.1
383
+
384
+ ## Citation
385
+
386
+ ### BibTeX
387
+
388
+ #### Sentence Transformers
389
+ ```bibtex
390
+ @inproceedings{reimers-2019-sentence-bert,
391
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
392
+ author = "Reimers, Nils and Gurevych, Iryna",
393
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
394
+ month = "11",
395
+ year = "2019",
396
+ publisher = "Association for Computational Linguistics",
397
+ url = "https://arxiv.org/abs/1908.10084",
398
+ }
399
+ ```
400
+
401
+ <!--
402
+ ## Glossary
403
+
404
+ *Clearly define terms in order to be accessible across audiences.*
405
+ -->
406
+
407
+ <!--
408
+ ## Model Card Authors
409
+
410
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
411
+ -->
412
+
413
+ <!--
414
+ ## Model Card Contact
415
+
416
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
417
  -->
config.json CHANGED
@@ -1,24 +1,25 @@
1
- {
2
- "architectures": [
3
- "BertModel"
4
- ],
5
- "attention_probs_dropout_prob": 0.1,
6
- "classifier_dropout": null,
7
- "hidden_act": "gelu",
8
- "hidden_dropout_prob": 0.1,
9
- "hidden_size": 768,
10
- "initializer_range": 0.02,
11
- "intermediate_size": 3072,
12
- "layer_norm_eps": 1e-12,
13
- "max_position_embeddings": 512,
14
- "model_type": "bert",
15
- "num_attention_heads": 12,
16
- "num_hidden_layers": 12,
17
- "pad_token_id": 0,
18
- "position_embedding_type": "absolute",
19
- "torch_dtype": "float32",
20
- "transformers_version": "4.51.3",
21
- "type_vocab_size": 2,
22
- "use_cache": true,
23
- "vocab_size": 32768
24
- }
 
 
1
+ {
2
+ "_name_or_path": "Detomo/cl-nagoya-sup-simcse-ja-nss-v1_0_7_8",
3
+ "architectures": [
4
+ "BertModel"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
7
+ "classifier_dropout": null,
8
+ "hidden_act": "gelu",
9
+ "hidden_dropout_prob": 0.1,
10
+ "hidden_size": 768,
11
+ "initializer_range": 0.02,
12
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