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Detomo
/
cl-nagoya-sup-simcse-ja-nss-v1_0_8_5

Sentence Similarity
sentence-transformers
Safetensors
OpenVINO
bert
feature-extraction
Generated from Trainer
dataset_size:356381
loss:CategoricalContrastiveLoss
Model card Files Community
2
vumichien commited on
Commit
7d446ad
·
verified ·
1 Parent(s): 4f567d6

Add new SentenceTransformer model with an openvino backend

Browse files

Hello!

*This pull request has been automatically generated from the [`push_to_hub`](https://sbert.net/docs/package_reference/sentence_transformer/SentenceTransformer.html#sentence_transformers.SentenceTransformer.push_to_hub) method from the Sentence Transformers library.*

## Full Model Architecture:
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(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})
)
```

## Tip:
Consider testing this pull request before merging by loading the model from this PR with the `revision` argument:
```python
from sentence_transformers import SentenceTransformer

# TODO: Fill in the PR number
pr_number = 2
model = SentenceTransformer(
"Detomo/cl-nagoya-sup-simcse-ja-nss-v1_0_8_5",
revision=f"refs/pr/{pr_number}",
backend="openvino",
)

# Verify that everything works as expected
embeddings = model.encode(["The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium."])
print(embeddings.shape)

similarities = model.similarity(embeddings, embeddings)
print(similarities)
```

Files changed (9) hide show
  1. 1_Pooling/config.json +9 -9
  2. README.md +838 -838
  3. config.json +24 -24
  4. config_sentence_transformers.json +9 -9
  5. modules.json +13 -13
  6. sentence_bert_config.json +3 -3
  7. special_tokens_map.json +37 -37
  8. tokenizer_config.json +64 -64
  9. vocab.txt +0 -0
1_Pooling/config.json CHANGED
@@ -1,10 +1,10 @@
1
- {
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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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  }
 
1
+ {
2
+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": true,
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+ "pooling_mode_mean_tokens": false,
5
+ "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,
8
+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
10
  }
README.md CHANGED
@@ -1,839 +1,839 @@
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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:356381
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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：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：免震上部ｺﾝｸﾘｰﾄ。
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- - 科目：コンクリート。名称：立上り壁ｺﾝｸﾘｰﾄ。
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- - 科目：コンクリート。名称：地上部ｺﾝｸﾘｰﾄ。
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- - source_sentence: 科目：コンクリート。名称：高流動ｺﾝｸﾘｰﾄ。
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- sentences:
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- - 科目：タイル。名称：踊場床ﾀｲﾙ張り。
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- - 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。
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- - 科目：タイル。名称：海街ﾃﾞｯｷ床ﾀｲﾙ。
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- - source_sentence: 科目：コンクリート。名称：免震下部鉄筋ｺﾝｸﾘｰﾄ。
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- sentences:
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- - 科目：コンクリート。名称：捨てｺﾝｸﾘｰﾄ。
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- - 科目：コンクリート。名称：基礎ｺﾝｸﾘｰﾄ。
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- - 科目：コンクリート。名称：地上部ｺﾝｸﾘｰﾄ。
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- - source_sentence: 科目：タイル。名称：汚垂ﾀｲﾙ。
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- sentences:
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- - 科目：コンクリート。名称：構造体強度補正。
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- - 科目：タイル。名称：屋外階段踊場ﾀｲﾙ張り。
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- - 科目：タイル。名称：段鼻磁器質ﾀｲﾙ。
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- - source_sentence: 科目：タイル。名称：ﾄﾞﾗｲｴﾘｱ床ﾀｲﾙ。
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- sentences:
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- - 科目：タイル。名称：段鼻ﾀｲﾙ。
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- - 科目：タイル。名称：屋外階段踊場ﾀｲﾙ張り。
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- - 科目：タイル。名称：風除床磁器質ﾀｲﾙ。
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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
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})
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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_8_5")
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- # Run inference
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- sentences = [
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- '科目：タイル。名称：ﾄﾞﾗｲｴﾘｱ床ﾀｲﾙ。',
89
- '科目：タイル。名称：屋外階段踊場ﾀｲﾙ張り。',
90
- '科目：タイル。名称：段鼻ﾀｲﾙ。',
91
- ]
92
- embeddings = model.encode(sentences)
93
- print(embeddings.shape)
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- # [3, 768]
95
-
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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.*
136
- -->
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-
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- ## Training Details
139
-
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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: 356,381 training samples
145
- * 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: 13.78 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 14.8 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>0: ~74.10%</li><li>1: ~2.60%</li><li>2: ~23.30%</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：免震基礎天端ｸﾞﾗｳﾄ注入。</code> | <code>科目：コンクリート。名称：免震BPL下部充填ｺﾝｸﾘｰﾄ打設手間。</code> | <code>0</code> |
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- | <code>科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。</code> | <code>科目：コンクリート。名称：免震下部ｺﾝｸﾘｰﾄ打設手間。</code> | <code>0</code> |
156
- | <code>科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。</code> | <code>科目：コンクリート。名称：免震下部（外周基礎梁）ｺﾝｸﾘｰﾄ打設手間。</code> | <code>0</code> |
157
- * Loss: <code>sentence_transformer_lib.categorical_constrastive_loss.CategoricalContrastiveLoss</code>
158
-
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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`: 4
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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>
172
-
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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`: 4
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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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-
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- </details>
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-
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- ### Training Logs
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- <details><summary>Click to expand</summary>
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-
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- | Epoch | Step | Training Loss |
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- |:------:|:----:|:-------------:|
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- | 0.0072 | 10 | 0.2157 |
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- | 0.0144 | 20 | 0.1965 |
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- | 0.0215 | 30 | 0.164 |
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- | 0.0287 | 40 | 0.1199 |
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- | 0.0359 | 50 | 0.0913 |
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- | 0.0431 | 60 | 0.0687 |
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- | 0.0503 | 70 | 0.0462 |
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- | 0.0574 | 80 | 0.0459 |
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- | 0.0646 | 90 | 0.0424 |
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- | 0.0718 | 100 | 0.0416 |
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- | 0.0790 | 110 | 0.0377 |
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- | 0.0861 | 120 | 0.0472 |
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- | 0.0933 | 130 | 0.0437 |
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- | 0.1005 | 140 | 0.0332 |
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- | 0.1077 | 150 | 0.0411 |
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- | 0.1149 | 160 | 0.0361 |
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- | 0.1220 | 170 | 0.037 |
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- | 0.1292 | 180 | 0.0325 |
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- | 0.1364 | 190 | 0.0386 |
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- | 0.1436 | 200 | 0.0398 |
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- | 0.1508 | 210 | 0.0415 |
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- | 0.1579 | 220 | 0.0327 |
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- | 0.1651 | 230 | 0.0425 |
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- | 0.1723 | 240 | 0.0437 |
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- | 0.1795 | 250 | 0.0365 |
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- | 0.1866 | 260 | 0.028 |
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- | 0.1938 | 270 | 0.0412 |
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- | 0.2010 | 280 | 0.0424 |
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- | 0.2082 | 290 | 0.0382 |
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- | 0.2154 | 300 | 0.0282 |
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- | 0.2225 | 310 | 0.0358 |
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- | 0.2297 | 320 | 0.0311 |
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- | 0.2369 | 330 | 0.0339 |
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- | 0.2441 | 340 | 0.0313 |
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- | 0.2513 | 350 | 0.0333 |
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- | 0.2584 | 360 | 0.0238 |
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- | 0.2656 | 370 | 0.0367 |
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- | 0.2728 | 380 | 0.0295 |
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- | 0.2800 | 390 | 0.0286 |
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- | 0.2872 | 400 | 0.0358 |
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- | 0.2943 | 410 | 0.0288 |
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- | 0.3015 | 420 | 0.032 |
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- | 0.3087 | 430 | 0.0323 |
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- | 0.3159 | 440 | 0.0284 |
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- | 0.3230 | 450 | 0.0297 |
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- | 0.3302 | 460 | 0.0266 |
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- | 0.3374 | 470 | 0.0317 |
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- | 0.3446 | 480 | 0.0298 |
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- | 0.3518 | 490 | 0.0272 |
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- | 0.3589 | 500 | 0.0307 |
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- | 0.3661 | 510 | 0.0337 |
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- | 0.3733 | 520 | 0.0268 |
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- | 0.3805 | 530 | 0.0286 |
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- | 0.3877 | 540 | 0.0283 |
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- | 0.3948 | 550 | 0.0293 |
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- | 0.4020 | 560 | 0.0299 |
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- | 0.4092 | 570 | 0.0231 |
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- | 0.4164 | 580 | 0.0308 |
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- | 0.4235 | 590 | 0.0294 |
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- | 0.4307 | 600 | 0.0309 |
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- | 0.4379 | 610 | 0.0255 |
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- | 0.4451 | 620 | 0.0269 |
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- | 0.4523 | 630 | 0.0226 |
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- | 0.4594 | 640 | 0.028 |
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- | 0.4666 | 650 | 0.027 |
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- | 0.4738 | 660 | 0.0365 |
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- | 0.4810 | 670 | 0.0264 |
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- | 0.4882 | 680 | 0.0212 |
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- | 0.4953 | 690 | 0.0311 |
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- | 0.5025 | 700 | 0.0266 |
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- | 0.5097 | 710 | 0.0203 |
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- | 0.5169 | 720 | 0.0207 |
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- | 0.5240 | 730 | 0.0348 |
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- | 0.5312 | 740 | 0.0227 |
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- | 0.5384 | 750 | 0.0237 |
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- | 0.5456 | 760 | 0.0201 |
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- | 0.5528 | 770 | 0.0257 |
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- | 0.5599 | 780 | 0.0266 |
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- | 0.5671 | 790 | 0.0276 |
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- | 0.5743 | 800 | 0.0271 |
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- | 0.5815 | 810 | 0.0238 |
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- | 0.5887 | 820 | 0.0217 |
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- | 0.5958 | 830 | 0.018 |
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- | 0.6030 | 840 | 0.0223 |
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- | 0.6102 | 850 | 0.0208 |
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- | 0.6174 | 860 | 0.0248 |
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- | 0.6246 | 870 | 0.0264 |
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- | 0.6317 | 880 | 0.0198 |
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- | 0.6389 | 890 | 0.0215 |
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- | 0.6461 | 900 | 0.0193 |
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- | 0.6533 | 910 | 0.0191 |
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- | 0.6604 | 920 | 0.0205 |
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- | 0.6676 | 930 | 0.0219 |
387
- | 0.6748 | 940 | 0.0229 |
388
- | 0.6820 | 950 | 0.0234 |
389
- | 0.6892 | 960 | 0.0225 |
390
- | 0.6963 | 970 | 0.0185 |
391
- | 0.7035 | 980 | 0.0174 |
392
- | 0.7107 | 990 | 0.0169 |
393
- | 0.7179 | 1000 | 0.0218 |
394
- | 0.7251 | 1010 | 0.0141 |
395
- | 0.7322 | 1020 | 0.0221 |
396
- | 0.7394 | 1030 | 0.0185 |
397
- | 0.7466 | 1040 | 0.0219 |
398
- | 0.7538 | 1050 | 0.0183 |
399
- | 0.7609 | 1060 | 0.0153 |
400
- | 0.7681 | 1070 | 0.0168 |
401
- | 0.7753 | 1080 | 0.0177 |
402
- | 0.7825 | 1090 | 0.0177 |
403
- | 0.7897 | 1100 | 0.0179 |
404
- | 0.7968 | 1110 | 0.0181 |
405
- | 0.8040 | 1120 | 0.02 |
406
- | 0.8112 | 1130 | 0.0186 |
407
- | 0.8184 | 1140 | 0.0185 |
408
- | 0.8256 | 1150 | 0.0162 |
409
- | 0.8327 | 1160 | 0.0156 |
410
- | 0.8399 | 1170 | 0.0141 |
411
- | 0.8471 | 1180 | 0.0152 |
412
- | 0.8543 | 1190 | 0.0146 |
413
- | 0.8615 | 1200 | 0.018 |
414
- | 0.8686 | 1210 | 0.0194 |
415
- | 0.8758 | 1220 | 0.0148 |
416
- | 0.8830 | 1230 | 0.0183 |
417
- | 0.8902 | 1240 | 0.0124 |
418
- | 0.8973 | 1250 | 0.0141 |
419
- | 0.9045 | 1260 | 0.0193 |
420
- | 0.9117 | 1270 | 0.0169 |
421
- | 0.9189 | 1280 | 0.0165 |
422
- | 0.9261 | 1290 | 0.0101 |
423
- | 0.9332 | 1300 | 0.0195 |
424
- | 0.9404 | 1310 | 0.0168 |
425
- | 0.9476 | 1320 | 0.0207 |
426
- | 0.9548 | 1330 | 0.018 |
427
- | 0.9620 | 1340 | 0.0116 |
428
- | 0.9691 | 1350 | 0.0175 |
429
- | 0.9763 | 1360 | 0.0138 |
430
- | 0.9835 | 1370 | 0.0209 |
431
- | 0.9907 | 1380 | 0.0145 |
432
- | 0.9978 | 1390 | 0.0138 |
433
- | 1.0050 | 1400 | 0.0123 |
434
- | 1.0122 | 1410 | 0.0145 |
435
- | 1.0194 | 1420 | 0.0135 |
436
- | 1.0266 | 1430 | 0.0115 |
437
- | 1.0337 | 1440 | 0.014 |
438
- | 1.0409 | 1450 | 0.0106 |
439
- | 1.0481 | 1460 | 0.0102 |
440
- | 1.0553 | 1470 | 0.0133 |
441
- | 1.0625 | 1480 | 0.008 |
442
- | 1.0696 | 1490 | 0.0134 |
443
- | 1.0768 | 1500 | 0.0106 |
444
- | 1.0840 | 1510 | 0.0151 |
445
- | 1.0912 | 1520 | 0.0168 |
446
- | 1.0983 | 1530 | 0.0093 |
447
- | 1.1055 | 1540 | 0.0132 |
448
- | 1.1127 | 1550 | 0.0115 |
449
- | 1.1199 | 1560 | 0.0096 |
450
- | 1.1271 | 1570 | 0.012 |
451
- | 1.1342 | 1580 | 0.0119 |
452
- | 1.1414 | 1590 | 0.0108 |
453
- | 1.1486 | 1600 | 0.013 |
454
- | 1.1558 | 1610 | 0.0109 |
455
- | 1.1630 | 1620 | 0.0131 |
456
- | 1.1701 | 1630 | 0.0093 |
457
- | 1.1773 | 1640 | 0.0126 |
458
- | 1.1845 | 1650 | 0.009 |
459
- | 1.1917 | 1660 | 0.0106 |
460
- | 1.1989 | 1670 | 0.0102 |
461
- | 1.2060 | 1680 | 0.0089 |
462
- | 1.2132 | 1690 | 0.0096 |
463
- | 1.2204 | 1700 | 0.0084 |
464
- | 1.2276 | 1710 | 0.0099 |
465
- | 1.2347 | 1720 | 0.0074 |
466
- | 1.2419 | 1730 | 0.0131 |
467
- | 1.2491 | 1740 | 0.0125 |
468
- | 1.2563 | 1750 | 0.0102 |
469
- | 1.2635 | 1760 | 0.0117 |
470
- | 1.2706 | 1770 | 0.0099 |
471
- | 1.2778 | 1780 | 0.0078 |
472
- | 1.2850 | 1790 | 0.0095 |
473
- | 1.2922 | 1800 | 0.0079 |
474
- | 1.2994 | 1810 | 0.0069 |
475
- | 1.3065 | 1820 | 0.0121 |
476
- | 1.3137 | 1830 | 0.0101 |
477
- | 1.3209 | 1840 | 0.0151 |
478
- | 1.3281 | 1850 | 0.0107 |
479
- | 1.3352 | 1860 | 0.0125 |
480
- | 1.3424 | 1870 | 0.0111 |
481
- | 1.3496 | 1880 | 0.0091 |
482
- | 1.3568 | 1890 | 0.0082 |
483
- | 1.3640 | 1900 | 0.0092 |
484
- | 1.3711 | 1910 | 0.0107 |
485
- | 1.3783 | 1920 | 0.0066 |
486
- | 1.3855 | 1930 | 0.0141 |
487
- | 1.3927 | 1940 | 0.0126 |
488
- | 1.3999 | 1950 | 0.009 |
489
- | 1.4070 | 1960 | 0.0116 |
490
- | 1.4142 | 1970 | 0.0121 |
491
- | 1.4214 | 1980 | 0.0098 |
492
- | 1.4286 | 1990 | 0.0108 |
493
- | 1.4358 | 2000 | 0.0103 |
494
- | 1.4429 | 2010 | 0.0118 |
495
- | 1.4501 | 2020 | 0.0143 |
496
- | 1.4573 | 2030 | 0.0082 |
497
- | 1.4645 | 2040 | 0.0077 |
498
- | 1.4716 | 2050 | 0.0102 |
499
- | 1.4788 | 2060 | 0.0093 |
500
- | 1.4860 | 2070 | 0.0084 |
501
- | 1.4932 | 2080 | 0.0105 |
502
- | 1.5004 | 2090 | 0.0091 |
503
- | 1.5075 | 2100 | 0.0094 |
504
- | 1.5147 | 2110 | 0.0092 |
505
- | 1.5219 | 2120 | 0.0117 |
506
- | 1.5291 | 2130 | 0.0085 |
507
- | 1.5363 | 2140 | 0.0069 |
508
- | 1.5434 | 2150 | 0.0114 |
509
- | 1.5506 | 2160 | 0.0077 |
510
- | 1.5578 | 2170 | 0.0092 |
511
- | 1.5650 | 2180 | 0.0093 |
512
- | 1.5721 | 2190 | 0.0076 |
513
- | 1.5793 | 2200 | 0.0098 |
514
- | 1.5865 | 2210 | 0.01 |
515
- | 1.5937 | 2220 | 0.01 |
516
- | 1.6009 | 2230 | 0.0092 |
517
- | 1.6080 | 2240 | 0.0096 |
518
- | 1.6152 | 2250 | 0.0077 |
519
- | 1.6224 | 2260 | 0.0147 |
520
- | 1.6296 | 2270 | 0.0087 |
521
- | 1.6368 | 2280 | 0.0106 |
522
- | 1.6439 | 2290 | 0.007 |
523
- | 1.6511 | 2300 | 0.0091 |
524
- | 1.6583 | 2310 | 0.0083 |
525
- | 1.6655 | 2320 | 0.0113 |
526
- | 1.6726 | 2330 | 0.0076 |
527
- | 1.6798 | 2340 | 0.0096 |
528
- | 1.6870 | 2350 | 0.0087 |
529
- | 1.6942 | 2360 | 0.0068 |
530
- | 1.7014 | 2370 | 0.0064 |
531
- | 1.7085 | 2380 | 0.0088 |
532
- | 1.7157 | 2390 | 0.0052 |
533
- | 1.7229 | 2400 | 0.0088 |
534
- | 1.7301 | 2410 | 0.0068 |
535
- | 1.7373 | 2420 | 0.0072 |
536
- | 1.7444 | 2430 | 0.0076 |
537
- | 1.7516 | 2440 | 0.0078 |
538
- | 1.7588 | 2450 | 0.0066 |
539
- | 1.7660 | 2460 | 0.0086 |
540
- | 1.7732 | 2470 | 0.0051 |
541
- | 1.7803 | 2480 | 0.0115 |
542
- | 1.7875 | 2490 | 0.0059 |
543
- | 1.7947 | 2500 | 0.0088 |
544
- | 1.8019 | 2510 | 0.0078 |
545
- | 1.8090 | 2520 | 0.0057 |
546
- | 1.8162 | 2530 | 0.0076 |
547
- | 1.8234 | 2540 | 0.0077 |
548
- | 1.8306 | 2550 | 0.009 |
549
- | 1.8378 | 2560 | 0.0073 |
550
- | 1.8449 | 2570 | 0.009 |
551
- | 1.8521 | 2580 | 0.0094 |
552
- | 1.8593 | 2590 | 0.0068 |
553
- | 1.8665 | 2600 | 0.0081 |
554
- | 1.8737 | 2610 | 0.004 |
555
- | 1.8808 | 2620 | 0.0077 |
556
- | 1.8880 | 2630 | 0.0072 |
557
- | 1.8952 | 2640 | 0.0061 |
558
- | 1.9024 | 2650 | 0.0077 |
559
- | 1.9095 | 2660 | 0.0074 |
560
- | 1.9167 | 2670 | 0.0077 |
561
- | 1.9239 | 2680 | 0.0073 |
562
- | 1.9311 | 2690 | 0.0096 |
563
- | 1.9383 | 2700 | 0.006 |
564
- | 1.9454 | 2710 | 0.0092 |
565
- | 1.9526 | 2720 | 0.005 |
566
- | 1.9598 | 2730 | 0.0045 |
567
- | 1.9670 | 2740 | 0.0071 |
568
- | 1.9742 | 2750 | 0.0061 |
569
- | 1.9813 | 2760 | 0.0073 |
570
- | 1.9885 | 2770 | 0.0073 |
571
- | 1.9957 | 2780 | 0.0067 |
572
- | 2.0029 | 2790 | 0.0054 |
573
- | 2.0101 | 2800 | 0.0044 |
574
- | 2.0172 | 2810 | 0.0045 |
575
- | 2.0244 | 2820 | 0.005 |
576
- | 2.0316 | 2830 | 0.0066 |
577
- | 2.0388 | 2840 | 0.0038 |
578
- | 2.0459 | 2850 | 0.0051 |
579
- | 2.0531 | 2860 | 0.0039 |
580
- | 2.0603 | 2870 | 0.0051 |
581
- | 2.0675 | 2880 | 0.0056 |
582
- | 2.0747 | 2890 | 0.0054 |
583
- | 2.0818 | 2900 | 0.0069 |
584
- | 2.0890 | 2910 | 0.006 |
585
- | 2.0962 | 2920 | 0.0074 |
586
- | 2.1034 | 2930 | 0.0067 |
587
- | 2.1106 | 2940 | 0.0044 |
588
- | 2.1177 | 2950 | 0.0065 |
589
- | 2.1249 | 2960 | 0.0066 |
590
- | 2.1321 | 2970 | 0.0044 |
591
- | 2.1393 | 2980 | 0.0041 |
592
- | 2.1464 | 2990 | 0.0066 |
593
- | 2.1536 | 3000 | 0.0046 |
594
- | 2.1608 | 3010 | 0.0061 |
595
- | 2.1680 | 3020 | 0.0039 |
596
- | 2.1752 | 3030 | 0.0048 |
597
- | 2.1823 | 3040 | 0.0059 |
598
- | 2.1895 | 3050 | 0.0067 |
599
- | 2.1967 | 3060 | 0.005 |
600
- | 2.2039 | 3070 | 0.0028 |
601
- | 2.2111 | 3080 | 0.0055 |
602
- | 2.2182 | 3090 | 0.0032 |
603
- | 2.2254 | 3100 | 0.0074 |
604
- | 2.2326 | 3110 | 0.0052 |
605
- | 2.2398 | 3120 | 0.0058 |
606
- | 2.2469 | 3130 | 0.0067 |
607
- | 2.2541 | 3140 | 0.0065 |
608
- | 2.2613 | 3150 | 0.0036 |
609
- | 2.2685 | 3160 | 0.005 |
610
- | 2.2757 | 3170 | 0.0083 |
611
- | 2.2828 | 3180 | 0.0038 |
612
- | 2.2900 | 3190 | 0.0044 |
613
- | 2.2972 | 3200 | 0.0057 |
614
- | 2.3044 | 3210 | 0.0042 |
615
- | 2.3116 | 3220 | 0.0037 |
616
- | 2.3187 | 3230 | 0.0061 |
617
- | 2.3259 | 3240 | 0.0038 |
618
- | 2.3331 | 3250 | 0.0051 |
619
- | 2.3403 | 3260 | 0.0076 |
620
- | 2.3475 | 3270 | 0.005 |
621
- | 2.3546 | 3280 | 0.0042 |
622
- | 2.3618 | 3290 | 0.005 |
623
- | 2.3690 | 3300 | 0.0077 |
624
- | 2.3762 | 3310 | 0.0067 |
625
- | 2.3833 | 3320 | 0.008 |
626
- | 2.3905 | 3330 | 0.0077 |
627
- | 2.3977 | 3340 | 0.0052 |
628
- | 2.4049 | 3350 | 0.0055 |
629
- | 2.4121 | 3360 | 0.0059 |
630
- | 2.4192 | 3370 | 0.0042 |
631
- | 2.4264 | 3380 | 0.0044 |
632
- | 2.4336 | 3390 | 0.0055 |
633
- | 2.4408 | 3400 | 0.0048 |
634
- | 2.4480 | 3410 | 0.0035 |
635
- | 2.4551 | 3420 | 0.0068 |
636
- | 2.4623 | 3430 | 0.007 |
637
- | 2.4695 | 3440 | 0.0059 |
638
- | 2.4767 | 3450 | 0.0037 |
639
- | 2.4838 | 3460 | 0.0049 |
640
- | 2.4910 | 3470 | 0.0042 |
641
- | 2.4982 | 3480 | 0.004 |
642
- | 2.5054 | 3490 | 0.0033 |
643
- | 2.5126 | 3500 | 0.004 |
644
- | 2.5197 | 3510 | 0.0055 |
645
- | 2.5269 | 3520 | 0.0057 |
646
- | 2.5341 | 3530 | 0.0059 |
647
- | 2.5413 | 3540 | 0.0031 |
648
- | 2.5485 | 3550 | 0.0039 |
649
- | 2.5556 | 3560 | 0.0046 |
650
- | 2.5628 | 3570 | 0.0035 |
651
- | 2.5700 | 3580 | 0.0037 |
652
- | 2.5772 | 3590 | 0.0045 |
653
- | 2.5844 | 3600 | 0.006 |
654
- | 2.5915 | 3610 | 0.0058 |
655
- | 2.5987 | 3620 | 0.0053 |
656
- | 2.6059 | 3630 | 0.0045 |
657
- | 2.6131 | 3640 | 0.0031 |
658
- | 2.6202 | 3650 | 0.0063 |
659
- | 2.6274 | 3660 | 0.004 |
660
- | 2.6346 | 3670 | 0.0043 |
661
- | 2.6418 | 3680 | 0.0055 |
662
- | 2.6490 | 3690 | 0.0044 |
663
- | 2.6561 | 3700 | 0.0025 |
664
- | 2.6633 | 3710 | 0.0047 |
665
- | 2.6705 | 3720 | 0.0043 |
666
- | 2.6777 | 3730 | 0.0041 |
667
- | 2.6849 | 3740 | 0.0064 |
668
- | 2.6920 | 3750 | 0.0055 |
669
- | 2.6992 | 3760 | 0.0038 |
670
- | 2.7064 | 3770 | 0.0059 |
671
- | 2.7136 | 3780 | 0.0059 |
672
- | 2.7207 | 3790 | 0.0039 |
673
- | 2.7279 | 3800 | 0.0051 |
674
- | 2.7351 | 3810 | 0.0061 |
675
- | 2.7423 | 3820 | 0.0029 |
676
- | 2.7495 | 3830 | 0.0043 |
677
- | 2.7566 | 3840 | 0.0044 |
678
- | 2.7638 | 3850 | 0.0047 |
679
- | 2.7710 | 3860 | 0.0041 |
680
- | 2.7782 | 3870 | 0.0033 |
681
- | 2.7854 | 3880 | 0.0028 |
682
- | 2.7925 | 3890 | 0.0049 |
683
- | 2.7997 | 3900 | 0.0048 |
684
- | 2.8069 | 3910 | 0.0042 |
685
- | 2.8141 | 3920 | 0.0047 |
686
- | 2.8212 | 3930 | 0.0043 |
687
- | 2.8284 | 3940 | 0.0034 |
688
- | 2.8356 | 3950 | 0.0034 |
689
- | 2.8428 | 3960 | 0.0036 |
690
- | 2.8500 | 3970 | 0.0057 |
691
- | 2.8571 | 3980 | 0.0067 |
692
- | 2.8643 | 3990 | 0.0053 |
693
- | 2.8715 | 4000 | 0.0045 |
694
- | 2.8787 | 4010 | 0.0044 |
695
- | 2.8859 | 4020 | 0.0045 |
696
- | 2.8930 | 4030 | 0.0028 |
697
- | 2.9002 | 4040 | 0.0032 |
698
- | 2.9074 | 4050 | 0.0054 |
699
- | 2.9146 | 4060 | 0.005 |
700
- | 2.9218 | 4070 | 0.0039 |
701
- | 2.9289 | 4080 | 0.003 |
702
- | 2.9361 | 4090 | 0.0036 |
703
- | 2.9433 | 4100 | 0.003 |
704
- | 2.9505 | 4110 | 0.0052 |
705
- | 2.9576 | 4120 | 0.0029 |
706
- | 2.9648 | 4130 | 0.0038 |
707
- | 2.9720 | 4140 | 0.0048 |
708
- | 2.9792 | 4150 | 0.0046 |
709
- | 2.9864 | 4160 | 0.005 |
710
- | 2.9935 | 4170 | 0.0047 |
711
- | 3.0007 | 4180 | 0.0048 |
712
- | 3.0079 | 4190 | 0.0033 |
713
- | 3.0151 | 4200 | 0.0026 |
714
- | 3.0223 | 4210 | 0.0031 |
715
- | 3.0294 | 4220 | 0.0043 |
716
- | 3.0366 | 4230 | 0.0034 |
717
- | 3.0438 | 4240 | 0.0038 |
718
- | 3.0510 | 4250 | 0.0023 |
719
- | 3.0581 | 4260 | 0.0036 |
720
- | 3.0653 | 4270 | 0.0045 |
721
- | 3.0725 | 4280 | 0.0028 |
722
- | 3.0797 | 4290 | 0.0025 |
723
- | 3.0869 | 4300 | 0.0036 |
724
- | 3.0940 | 4310 | 0.0055 |
725
- | 3.1012 | 4320 | 0.0041 |
726
- | 3.1084 | 4330 | 0.0027 |
727
- | 3.1156 | 4340 | 0.0048 |
728
- | 3.1228 | 4350 | 0.0049 |
729
- | 3.1299 | 4360 | 0.0028 |
730
- | 3.1371 | 4370 | 0.0052 |
731
- | 3.1443 | 4380 | 0.0029 |
732
- | 3.1515 | 4390 | 0.0039 |
733
- | 3.1587 | 4400 | 0.0029 |
734
- | 3.1658 | 4410 | 0.0045 |
735
- | 3.1730 | 4420 | 0.0031 |
736
- | 3.1802 | 4430 | 0.004 |
737
- | 3.1874 | 4440 | 0.0042 |
738
- | 3.1945 | 4450 | 0.0039 |
739
- | 3.2017 | 4460 | 0.0027 |
740
- | 3.2089 | 4470 | 0.0031 |
741
- | 3.2161 | 4480 | 0.0043 |
742
- | 3.2233 | 4490 | 0.0027 |
743
- | 3.2304 | 4500 | 0.0035 |
744
- | 3.2376 | 4510 | 0.0034 |
745
- | 3.2448 | 4520 | 0.0039 |
746
- | 3.2520 | 4530 | 0.0026 |
747
- | 3.2592 | 4540 | 0.0035 |
748
- | 3.2663 | 4550 | 0.0041 |
749
- | 3.2735 | 4560 | 0.0021 |
750
- | 3.2807 | 4570 | 0.0032 |
751
- | 3.2879 | 4580 | 0.0032 |
752
- | 3.2950 | 4590 | 0.0026 |
753
- | 3.3022 | 4600 | 0.0045 |
754
- | 3.3094 | 4610 | 0.0046 |
755
- | 3.3166 | 4620 | 0.0014 |
756
- | 3.3238 | 4630 | 0.0026 |
757
- | 3.3309 | 4640 | 0.0026 |
758
- | 3.3381 | 4650 | 0.002 |
759
- | 3.3453 | 4660 | 0.0043 |
760
- | 3.3525 | 4670 | 0.0051 |
761
- | 3.3597 | 4680 | 0.0041 |
762
- | 3.3668 | 4690 | 0.0021 |
763
- | 3.3740 | 4700 | 0.0059 |
764
- | 3.3812 | 4710 | 0.006 |
765
- | 3.3884 | 4720 | 0.0049 |
766
- | 3.3955 | 4730 | 0.0035 |
767
- | 3.4027 | 4740 | 0.004 |
768
- | 3.4099 | 4750 | 0.0039 |
769
- | 3.4171 | 4760 | 0.0024 |
770
- | 3.4243 | 4770 | 0.0026 |
771
- | 3.4314 | 4780 | 0.0038 |
772
- | 3.4386 | 4790 | 0.0029 |
773
- | 3.4458 | 4800 | 0.0045 |
774
- | 3.4530 | 4810 | 0.0025 |
775
- | 3.4602 | 4820 | 0.0031 |
776
- | 3.4673 | 4830 | 0.0044 |
777
- | 3.4745 | 4840 | 0.0018 |
778
- | 3.4817 | 4850 | 0.0035 |
779
- | 3.4889 | 4860 | 0.0031 |
780
- | 3.4961 | 4870 | 0.0058 |
781
- | 3.5032 | 4880 | 0.0032 |
782
- | 3.5104 | 4890 | 0.0028 |
783
- | 3.5176 | 4900 | 0.0029 |
784
- | 3.5248 | 4910 | 0.0038 |
785
- | 3.5319 | 4920 | 0.0026 |
786
- | 3.5391 | 4930 | 0.0028 |
787
- | 3.5463 | 4940 | 0.0034 |
788
- | 3.5535 | 4950 | 0.0044 |
789
- | 3.5607 | 4960 | 0.003 |
790
- | 3.5678 | 4970 | 0.0028 |
791
- | 3.5750 | 4980 | 0.0031 |
792
- | 3.5822 | 4990 | 0.003 |
793
- | 3.5894 | 5000 | 0.0028 |
794
-
795
- </details>
796
-
797
- ### Framework Versions
798
- - Python: 3.11.13
799
- - Sentence Transformers: 4.1.0
800
- - Transformers: 4.52.4
801
- - PyTorch: 2.6.0+cu124
802
- - Accelerate: 1.8.1
803
- - Datasets: 2.14.4
804
- - Tokenizers: 0.21.1
805
-
806
- ## Citation
807
-
808
- ### BibTeX
809
-
810
- #### Sentence Transformers
811
- ```bibtex
812
- @inproceedings{reimers-2019-sentence-bert,
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- title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
814
- 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",
817
- year = "2019",
818
- publisher = "Association for Computational Linguistics",
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- url = "https://arxiv.org/abs/1908.10084",
820
- }
821
- ```
822
-
823
- <!--
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- ## Glossary
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-
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- *Clearly define terms in order to be accessible across audiences.*
827
- -->
828
-
829
- <!--
830
- ## Model Card Authors
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-
832
- *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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- -->
834
-
835
- <!--
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- ## Model Card Contact
837
-
838
- *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
839
  -->
 
1
+ ---
2
+ tags:
3
+ - 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:356381
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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：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：免震上部ｺﾝｸﾘｰﾄ。
13
+ - 科目：コンクリート。名称：立上り壁ｺﾝｸﾘｰﾄ。
14
+ - 科目：コンクリート。名称：地上部ｺﾝｸﾘｰﾄ。
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+ - source_sentence: 科目：コンクリート。名称：高流動ｺﾝｸﾘｰﾄ。
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+ sentences:
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+ - 科目：タイル。名称：踊場床ﾀｲﾙ張り。
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+ - 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。
19
+ - 科目：タイル。名称：海街ﾃﾞｯｷ床ﾀｲﾙ。
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+ - source_sentence: 科目：コンクリート。名称：免震下部鉄筋ｺﾝｸﾘｰﾄ。
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+ sentences:
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+ - 科目：コンクリート。名称：捨てｺﾝｸﾘｰﾄ。
23
+ - 科目：コンクリート。名称：基礎ｺﾝｸﾘｰﾄ。
24
+ - 科目：コンクリート。名称：地上部ｺﾝｸﾘｰﾄ。
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+ - source_sentence: 科目：タイル。名称：汚垂ﾀｲﾙ。
26
+ sentences:
27
+ - 科目：コンクリート。名称：構造体強度補正。
28
+ - 科目：タイル。名称：屋外階段踊場ﾀｲﾙ張り。
29
+ - 科目：タイル。名称：段鼻磁器質ﾀｲﾙ。
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+ - source_sentence: 科目：タイル。名称：ﾄﾞﾗｲｴﾘｱ床ﾀｲﾙ。
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+ sentences:
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+ - 科目：タイル。名称：段鼻ﾀｲﾙ。
33
+ - 科目：タイル。名称：屋外階段踊場ﾀｲﾙ張り。
34
+ - 科目：タイル。名称：風除床磁器質ﾀｲﾙ。
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+ pipeline_tag: sentence-similarity
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+ library_name: sentence-transformers
37
+ ---
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+
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+ # SentenceTransformer
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+
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
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+
45
+ ### 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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+
57
+ - **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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+
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_8_5")
86
+ # Run inference
87
+ sentences = [
88
+ '科目：タイル。名称：ﾄﾞﾗｲｴﾘｱ床ﾀｲﾙ。',
89
+ '科目：タイル。名称：屋外階段踊場ﾀｲﾙ張り。',
90
+ '科目：タイル。名称：段鼻ﾀｲﾙ。',
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: 356,381 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: 13.78 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 14.8 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>0: ~74.10%</li><li>1: ~2.60%</li><li>2: ~23.30%</li></ul> |
151
+ * Samples:
152
+ | sentence1 | sentence2 | label |
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+ |:-----------------------------------------|:-------------------------------------------------|:---------------|
154
+ | <code>科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。</code> | <code>科目：コンクリート。名称：免震BPL下部充填ｺﾝｸﾘｰﾄ打設手間。</code> | <code>0</code> |
155
+ | <code>科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。</code> | <code>科目：コンクリート。名称：免震下部ｺﾝｸﾘｰﾄ打設手間。</code> | <code>0</code> |
156
+ | <code>科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。</code> | <code>科目：コンクリート。名称：免震下部（外周基礎梁）ｺﾝｸﾘｰﾄ打設手間。</code> | <code>0</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`: 4
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`: 4
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
+ - `fsdp_transformer_layer_cls_to_wrap`: None
236
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
237
+ - `deepspeed`: None
238
+ - `label_smoothing_factor`: 0.0
239
+ - `optim`: adamw_torch
240
+ - `optim_args`: None
241
+ - `adafactor`: False
242
+ - `group_by_length`: False
243
+ - `length_column_name`: length
244
+ - `ddp_find_unused_parameters`: None
245
+ - `ddp_bucket_cap_mb`: None
246
+ - `ddp_broadcast_buffers`: False
247
+ - `dataloader_pin_memory`: True
248
+ - `dataloader_persistent_workers`: False
249
+ - `skip_memory_metrics`: True
250
+ - `use_legacy_prediction_loop`: False
251
+ - `push_to_hub`: False
252
+ - `resume_from_checkpoint`: None
253
+ - `hub_model_id`: None
254
+ - `hub_strategy`: every_save
255
+ - `hub_private_repo`: None
256
+ - `hub_always_push`: False
257
+ - `gradient_checkpointing`: False
258
+ - `gradient_checkpointing_kwargs`: None
259
+ - `include_inputs_for_metrics`: False
260
+ - `include_for_metrics`: []
261
+ - `eval_do_concat_batches`: True
262
+ - `fp16_backend`: auto
263
+ - `push_to_hub_model_id`: None
264
+ - `push_to_hub_organization`: None
265
+ - `mp_parameters`:
266
+ - `auto_find_batch_size`: False
267
+ - `full_determinism`: False
268
+ - `torchdynamo`: None
269
+ - `ray_scope`: last
270
+ - `ddp_timeout`: 1800
271
+ - `torch_compile`: False
272
+ - `torch_compile_backend`: None
273
+ - `torch_compile_mode`: None
274
+ - `include_tokens_per_second`: False
275
+ - `include_num_input_tokens_seen`: False
276
+ - `neftune_noise_alpha`: None
277
+ - `optim_target_modules`: None
278
+ - `batch_eval_metrics`: False
279
+ - `eval_on_start`: False
280
+ - `use_liger_kernel`: False
281
+ - `eval_use_gather_object`: False
282
+ - `average_tokens_across_devices`: False
283
+ - `prompts`: None
284
+ - `batch_sampler`: batch_sampler
285
+ - `multi_dataset_batch_sampler`: proportional
286
+
287
+ </details>
288
+
289
+ ### Training Logs
290
+ <details><summary>Click to expand</summary>
291
+
292
+ | Epoch | Step | Training Loss |
293
+ |:------:|:----:|:-------------:|
294
+ | 0.0072 | 10 | 0.2157 |
295
+ | 0.0144 | 20 | 0.1965 |
296
+ | 0.0215 | 30 | 0.164 |
297
+ | 0.0287 | 40 | 0.1199 |
298
+ | 0.0359 | 50 | 0.0913 |
299
+ | 0.0431 | 60 | 0.0687 |
300
+ | 0.0503 | 70 | 0.0462 |
301
+ | 0.0574 | 80 | 0.0459 |
302
+ | 0.0646 | 90 | 0.0424 |
303
+ | 0.0718 | 100 | 0.0416 |
304
+ | 0.0790 | 110 | 0.0377 |
305
+ | 0.0861 | 120 | 0.0472 |
306
+ | 0.0933 | 130 | 0.0437 |
307
+ | 0.1005 | 140 | 0.0332 |
308
+ | 0.1077 | 150 | 0.0411 |
309
+ | 0.1149 | 160 | 0.0361 |
310
+ | 0.1220 | 170 | 0.037 |
311
+ | 0.1292 | 180 | 0.0325 |
312
+ | 0.1364 | 190 | 0.0386 |
313
+ | 0.1436 | 200 | 0.0398 |
314
+ | 0.1508 | 210 | 0.0415 |
315
+ | 0.1579 | 220 | 0.0327 |
316
+ | 0.1651 | 230 | 0.0425 |
317
+ | 0.1723 | 240 | 0.0437 |
318
+ | 0.1795 | 250 | 0.0365 |
319
+ | 0.1866 | 260 | 0.028 |
320
+ | 0.1938 | 270 | 0.0412 |
321
+ | 0.2010 | 280 | 0.0424 |
322
+ | 0.2082 | 290 | 0.0382 |
323
+ | 0.2154 | 300 | 0.0282 |
324
+ | 0.2225 | 310 | 0.0358 |
325
+ | 0.2297 | 320 | 0.0311 |
326
+ | 0.2369 | 330 | 0.0339 |
327
+ | 0.2441 | 340 | 0.0313 |
328
+ | 0.2513 | 350 | 0.0333 |
329
+ | 0.2584 | 360 | 0.0238 |
330
+ | 0.2656 | 370 | 0.0367 |
331
+ | 0.2728 | 380 | 0.0295 |
332
+ | 0.2800 | 390 | 0.0286 |
333
+ | 0.2872 | 400 | 0.0358 |
334
+ | 0.2943 | 410 | 0.0288 |
335
+ | 0.3015 | 420 | 0.032 |
336
+ | 0.3087 | 430 | 0.0323 |
337
+ | 0.3159 | 440 | 0.0284 |
338
+ | 0.3230 | 450 | 0.0297 |
339
+ | 0.3302 | 460 | 0.0266 |
340
+ | 0.3374 | 470 | 0.0317 |
341
+ | 0.3446 | 480 | 0.0298 |
342
+ | 0.3518 | 490 | 0.0272 |
343
+ | 0.3589 | 500 | 0.0307 |
344
+ | 0.3661 | 510 | 0.0337 |
345
+ | 0.3733 | 520 | 0.0268 |
346
+ | 0.3805 | 530 | 0.0286 |
347
+ | 0.3877 | 540 | 0.0283 |
348
+ | 0.3948 | 550 | 0.0293 |
349
+ | 0.4020 | 560 | 0.0299 |
350
+ | 0.4092 | 570 | 0.0231 |
351
+ | 0.4164 | 580 | 0.0308 |
352
+ | 0.4235 | 590 | 0.0294 |
353
+ | 0.4307 | 600 | 0.0309 |
354
+ | 0.4379 | 610 | 0.0255 |
355
+ | 0.4451 | 620 | 0.0269 |
356
+ | 0.4523 | 630 | 0.0226 |
357
+ | 0.4594 | 640 | 0.028 |
358
+ | 0.4666 | 650 | 0.027 |
359
+ | 0.4738 | 660 | 0.0365 |
360
+ | 0.4810 | 670 | 0.0264 |
361
+ | 0.4882 | 680 | 0.0212 |
362
+ | 0.4953 | 690 | 0.0311 |
363
+ | 0.5025 | 700 | 0.0266 |
364
+ | 0.5097 | 710 | 0.0203 |
365
+ | 0.5169 | 720 | 0.0207 |
366
+ | 0.5240 | 730 | 0.0348 |
367
+ | 0.5312 | 740 | 0.0227 |
368
+ | 0.5384 | 750 | 0.0237 |
369
+ | 0.5456 | 760 | 0.0201 |
370
+ | 0.5528 | 770 | 0.0257 |
371
+ | 0.5599 | 780 | 0.0266 |
372
+ | 0.5671 | 790 | 0.0276 |
373
+ | 0.5743 | 800 | 0.0271 |
374
+ | 0.5815 | 810 | 0.0238 |
375
+ | 0.5887 | 820 | 0.0217 |
376
+ | 0.5958 | 830 | 0.018 |
377
+ | 0.6030 | 840 | 0.0223 |
378
+ | 0.6102 | 850 | 0.0208 |
379
+ | 0.6174 | 860 | 0.0248 |
380
+ | 0.6246 | 870 | 0.0264 |
381
+ | 0.6317 | 880 | 0.0198 |
382
+ | 0.6389 | 890 | 0.0215 |
383
+ | 0.6461 | 900 | 0.0193 |
384
+ | 0.6533 | 910 | 0.0191 |
385
+ | 0.6604 | 920 | 0.0205 |
386
+ | 0.6676 | 930 | 0.0219 |
387
+ | 0.6748 | 940 | 0.0229 |
388
+ | 0.6820 | 950 | 0.0234 |
389
+ | 0.6892 | 960 | 0.0225 |
390
+ | 0.6963 | 970 | 0.0185 |
391
+ | 0.7035 | 980 | 0.0174 |
392
+ | 0.7107 | 990 | 0.0169 |
393
+ | 0.7179 | 1000 | 0.0218 |
394
+ | 0.7251 | 1010 | 0.0141 |
395
+ | 0.7322 | 1020 | 0.0221 |
396
+ | 0.7394 | 1030 | 0.0185 |
397
+ | 0.7466 | 1040 | 0.0219 |
398
+ | 0.7538 | 1050 | 0.0183 |
399
+ | 0.7609 | 1060 | 0.0153 |
400
+ | 0.7681 | 1070 | 0.0168 |
401
+ | 0.7753 | 1080 | 0.0177 |
402
+ | 0.7825 | 1090 | 0.0177 |
403
+ | 0.7897 | 1100 | 0.0179 |
404
+ | 0.7968 | 1110 | 0.0181 |
405
+ | 0.8040 | 1120 | 0.02 |
406
+ | 0.8112 | 1130 | 0.0186 |
407
+ | 0.8184 | 1140 | 0.0185 |
408
+ | 0.8256 | 1150 | 0.0162 |
409
+ | 0.8327 | 1160 | 0.0156 |
410
+ | 0.8399 | 1170 | 0.0141 |
411
+ | 0.8471 | 1180 | 0.0152 |
412
+ | 0.8543 | 1190 | 0.0146 |
413
+ | 0.8615 | 1200 | 0.018 |
414
+ | 0.8686 | 1210 | 0.0194 |
415
+ | 0.8758 | 1220 | 0.0148 |
416
+ | 0.8830 | 1230 | 0.0183 |
417
+ | 0.8902 | 1240 | 0.0124 |
418
+ | 0.8973 | 1250 | 0.0141 |
419
+ | 0.9045 | 1260 | 0.0193 |
420
+ | 0.9117 | 1270 | 0.0169 |
421
+ | 0.9189 | 1280 | 0.0165 |
422
+ | 0.9261 | 1290 | 0.0101 |
423
+ | 0.9332 | 1300 | 0.0195 |
424
+ | 0.9404 | 1310 | 0.0168 |
425
+ | 0.9476 | 1320 | 0.0207 |
426
+ | 0.9548 | 1330 | 0.018 |
427
+ | 0.9620 | 1340 | 0.0116 |
428
+ | 0.9691 | 1350 | 0.0175 |
429
+ | 0.9763 | 1360 | 0.0138 |
430
+ | 0.9835 | 1370 | 0.0209 |
431
+ | 0.9907 | 1380 | 0.0145 |
432
+ | 0.9978 | 1390 | 0.0138 |
433
+ | 1.0050 | 1400 | 0.0123 |
434
+ | 1.0122 | 1410 | 0.0145 |
435
+ | 1.0194 | 1420 | 0.0135 |
436
+ | 1.0266 | 1430 | 0.0115 |
437
+ | 1.0337 | 1440 | 0.014 |
438
+ | 1.0409 | 1450 | 0.0106 |
439
+ | 1.0481 | 1460 | 0.0102 |
440
+ | 1.0553 | 1470 | 0.0133 |
441
+ | 1.0625 | 1480 | 0.008 |
442
+ | 1.0696 | 1490 | 0.0134 |
443
+ | 1.0768 | 1500 | 0.0106 |
444
+ | 1.0840 | 1510 | 0.0151 |
445
+ | 1.0912 | 1520 | 0.0168 |
446
+ | 1.0983 | 1530 | 0.0093 |
447
+ | 1.1055 | 1540 | 0.0132 |
448
+ | 1.1127 | 1550 | 0.0115 |
449
+ | 1.1199 | 1560 | 0.0096 |
450
+ | 1.1271 | 1570 | 0.012 |
451
+ | 1.1342 | 1580 | 0.0119 |
452
+ | 1.1414 | 1590 | 0.0108 |
453
+ | 1.1486 | 1600 | 0.013 |
454
+ | 1.1558 | 1610 | 0.0109 |
455
+ | 1.1630 | 1620 | 0.0131 |
456
+ | 1.1701 | 1630 | 0.0093 |
457
+ | 1.1773 | 1640 | 0.0126 |
458
+ | 1.1845 | 1650 | 0.009 |
459
+ | 1.1917 | 1660 | 0.0106 |
460
+ | 1.1989 | 1670 | 0.0102 |
461
+ | 1.2060 | 1680 | 0.0089 |
462
+ | 1.2132 | 1690 | 0.0096 |
463
+ | 1.2204 | 1700 | 0.0084 |
464
+ | 1.2276 | 1710 | 0.0099 |
465
+ | 1.2347 | 1720 | 0.0074 |
466
+ | 1.2419 | 1730 | 0.0131 |
467
+ | 1.2491 | 1740 | 0.0125 |
468
+ | 1.2563 | 1750 | 0.0102 |
469
+ | 1.2635 | 1760 | 0.0117 |
470
+ | 1.2706 | 1770 | 0.0099 |
471
+ | 1.2778 | 1780 | 0.0078 |
472
+ | 1.2850 | 1790 | 0.0095 |
473
+ | 1.2922 | 1800 | 0.0079 |
474
+ | 1.2994 | 1810 | 0.0069 |
475
+ | 1.3065 | 1820 | 0.0121 |
476
+ | 1.3137 | 1830 | 0.0101 |
477
+ | 1.3209 | 1840 | 0.0151 |
478
+ | 1.3281 | 1850 | 0.0107 |
479
+ | 1.3352 | 1860 | 0.0125 |
480
+ | 1.3424 | 1870 | 0.0111 |
481
+ | 1.3496 | 1880 | 0.0091 |
482
+ | 1.3568 | 1890 | 0.0082 |
483
+ | 1.3640 | 1900 | 0.0092 |
484
+ | 1.3711 | 1910 | 0.0107 |
485
+ | 1.3783 | 1920 | 0.0066 |
486
+ | 1.3855 | 1930 | 0.0141 |
487
+ | 1.3927 | 1940 | 0.0126 |
488
+ | 1.3999 | 1950 | 0.009 |
489
+ | 1.4070 | 1960 | 0.0116 |
490
+ | 1.4142 | 1970 | 0.0121 |
491
+ | 1.4214 | 1980 | 0.0098 |
492
+ | 1.4286 | 1990 | 0.0108 |
493
+ | 1.4358 | 2000 | 0.0103 |
494
+ | 1.4429 | 2010 | 0.0118 |
495
+ | 1.4501 | 2020 | 0.0143 |
496
+ | 1.4573 | 2030 | 0.0082 |
497
+ | 1.4645 | 2040 | 0.0077 |
498
+ | 1.4716 | 2050 | 0.0102 |
499
+ | 1.4788 | 2060 | 0.0093 |
500
+ | 1.4860 | 2070 | 0.0084 |
501
+ | 1.4932 | 2080 | 0.0105 |
502
+ | 1.5004 | 2090 | 0.0091 |
503
+ | 1.5075 | 2100 | 0.0094 |
504
+ | 1.5147 | 2110 | 0.0092 |
505
+ | 1.5219 | 2120 | 0.0117 |
506
+ | 1.5291 | 2130 | 0.0085 |
507
+ | 1.5363 | 2140 | 0.0069 |
508
+ | 1.5434 | 2150 | 0.0114 |
509
+ | 1.5506 | 2160 | 0.0077 |
510
+ | 1.5578 | 2170 | 0.0092 |
511
+ | 1.5650 | 2180 | 0.0093 |
512
+ | 1.5721 | 2190 | 0.0076 |
513
+ | 1.5793 | 2200 | 0.0098 |
514
+ | 1.5865 | 2210 | 0.01 |
515
+ | 1.5937 | 2220 | 0.01 |
516
+ | 1.6009 | 2230 | 0.0092 |
517
+ | 1.6080 | 2240 | 0.0096 |
518
+ | 1.6152 | 2250 | 0.0077 |
519
+ | 1.6224 | 2260 | 0.0147 |
520
+ | 1.6296 | 2270 | 0.0087 |
521
+ | 1.6368 | 2280 | 0.0106 |
522
+ | 1.6439 | 2290 | 0.007 |
523
+ | 1.6511 | 2300 | 0.0091 |
524
+ | 1.6583 | 2310 | 0.0083 |
525
+ | 1.6655 | 2320 | 0.0113 |
526
+ | 1.6726 | 2330 | 0.0076 |
527
+ | 1.6798 | 2340 | 0.0096 |
528
+ | 1.6870 | 2350 | 0.0087 |
529
+ | 1.6942 | 2360 | 0.0068 |
530
+ | 1.7014 | 2370 | 0.0064 |
531
+ | 1.7085 | 2380 | 0.0088 |
532
+ | 1.7157 | 2390 | 0.0052 |
533
+ | 1.7229 | 2400 | 0.0088 |
534
+ | 1.7301 | 2410 | 0.0068 |
535
+ | 1.7373 | 2420 | 0.0072 |
536
+ | 1.7444 | 2430 | 0.0076 |
537
+ | 1.7516 | 2440 | 0.0078 |
538
+ | 1.7588 | 2450 | 0.0066 |
539
+ | 1.7660 | 2460 | 0.0086 |
540
+ | 1.7732 | 2470 | 0.0051 |
541
+ | 1.7803 | 2480 | 0.0115 |
542
+ | 1.7875 | 2490 | 0.0059 |
543
+ | 1.7947 | 2500 | 0.0088 |
544
+ | 1.8019 | 2510 | 0.0078 |
545
+ | 1.8090 | 2520 | 0.0057 |
546
+ | 1.8162 | 2530 | 0.0076 |
547
+ | 1.8234 | 2540 | 0.0077 |
548
+ | 1.8306 | 2550 | 0.009 |
549
+ | 1.8378 | 2560 | 0.0073 |
550
+ | 1.8449 | 2570 | 0.009 |
551
+ | 1.8521 | 2580 | 0.0094 |
552
+ | 1.8593 | 2590 | 0.0068 |
553
+ | 1.8665 | 2600 | 0.0081 |
554
+ | 1.8737 | 2610 | 0.004 |
555
+ | 1.8808 | 2620 | 0.0077 |
556
+ | 1.8880 | 2630 | 0.0072 |
557
+ | 1.8952 | 2640 | 0.0061 |
558
+ | 1.9024 | 2650 | 0.0077 |
559
+ | 1.9095 | 2660 | 0.0074 |
560
+ | 1.9167 | 2670 | 0.0077 |
561
+ | 1.9239 | 2680 | 0.0073 |
562
+ | 1.9311 | 2690 | 0.0096 |
563
+ | 1.9383 | 2700 | 0.006 |
564
+ | 1.9454 | 2710 | 0.0092 |
565
+ | 1.9526 | 2720 | 0.005 |
566
+ | 1.9598 | 2730 | 0.0045 |
567
+ | 1.9670 | 2740 | 0.0071 |
568
+ | 1.9742 | 2750 | 0.0061 |
569
+ | 1.9813 | 2760 | 0.0073 |
570
+ | 1.9885 | 2770 | 0.0073 |
571
+ | 1.9957 | 2780 | 0.0067 |
572
+ | 2.0029 | 2790 | 0.0054 |
573
+ | 2.0101 | 2800 | 0.0044 |
574
+ | 2.0172 | 2810 | 0.0045 |
575
+ | 2.0244 | 2820 | 0.005 |
576
+ | 2.0316 | 2830 | 0.0066 |
577
+ | 2.0388 | 2840 | 0.0038 |
578
+ | 2.0459 | 2850 | 0.0051 |
579
+ | 2.0531 | 2860 | 0.0039 |
580
+ | 2.0603 | 2870 | 0.0051 |
581
+ | 2.0675 | 2880 | 0.0056 |
582
+ | 2.0747 | 2890 | 0.0054 |
583
+ | 2.0818 | 2900 | 0.0069 |
584
+ | 2.0890 | 2910 | 0.006 |
585
+ | 2.0962 | 2920 | 0.0074 |
586
+ | 2.1034 | 2930 | 0.0067 |
587
+ | 2.1106 | 2940 | 0.0044 |
588
+ | 2.1177 | 2950 | 0.0065 |
589
+ | 2.1249 | 2960 | 0.0066 |
590
+ | 2.1321 | 2970 | 0.0044 |
591
+ | 2.1393 | 2980 | 0.0041 |
592
+ | 2.1464 | 2990 | 0.0066 |
593
+ | 2.1536 | 3000 | 0.0046 |
594
+ | 2.1608 | 3010 | 0.0061 |
595
+ | 2.1680 | 3020 | 0.0039 |
596
+ | 2.1752 | 3030 | 0.0048 |
597
+ | 2.1823 | 3040 | 0.0059 |
598
+ | 2.1895 | 3050 | 0.0067 |
599
+ | 2.1967 | 3060 | 0.005 |
600
+ | 2.2039 | 3070 | 0.0028 |
601
+ | 2.2111 | 3080 | 0.0055 |
602
+ | 2.2182 | 3090 | 0.0032 |
603
+ | 2.2254 | 3100 | 0.0074 |
604
+ | 2.2326 | 3110 | 0.0052 |
605
+ | 2.2398 | 3120 | 0.0058 |
606
+ | 2.2469 | 3130 | 0.0067 |
607
+ | 2.2541 | 3140 | 0.0065 |
608
+ | 2.2613 | 3150 | 0.0036 |
609
+ | 2.2685 | 3160 | 0.005 |
610
+ | 2.2757 | 3170 | 0.0083 |
611
+ | 2.2828 | 3180 | 0.0038 |
612
+ | 2.2900 | 3190 | 0.0044 |
613
+ | 2.2972 | 3200 | 0.0057 |
614
+ | 2.3044 | 3210 | 0.0042 |
615
+ | 2.3116 | 3220 | 0.0037 |
616
+ | 2.3187 | 3230 | 0.0061 |
617
+ | 2.3259 | 3240 | 0.0038 |
618
+ | 2.3331 | 3250 | 0.0051 |
619
+ | 2.3403 | 3260 | 0.0076 |
620
+ | 2.3475 | 3270 | 0.005 |
621
+ | 2.3546 | 3280 | 0.0042 |
622
+ | 2.3618 | 3290 | 0.005 |
623
+ | 2.3690 | 3300 | 0.0077 |
624
+ | 2.3762 | 3310 | 0.0067 |
625
+ | 2.3833 | 3320 | 0.008 |
626
+ | 2.3905 | 3330 | 0.0077 |
627
+ | 2.3977 | 3340 | 0.0052 |
628
+ | 2.4049 | 3350 | 0.0055 |
629
+ | 2.4121 | 3360 | 0.0059 |
630
+ | 2.4192 | 3370 | 0.0042 |
631
+ | 2.4264 | 3380 | 0.0044 |
632
+ | 2.4336 | 3390 | 0.0055 |
633
+ | 2.4408 | 3400 | 0.0048 |
634
+ | 2.4480 | 3410 | 0.0035 |
635
+ | 2.4551 | 3420 | 0.0068 |
636
+ | 2.4623 | 3430 | 0.007 |
637
+ | 2.4695 | 3440 | 0.0059 |
638
+ | 2.4767 | 3450 | 0.0037 |
639
+ | 2.4838 | 3460 | 0.0049 |
640
+ | 2.4910 | 3470 | 0.0042 |
641
+ | 2.4982 | 3480 | 0.004 |
642
+ | 2.5054 | 3490 | 0.0033 |
643
+ | 2.5126 | 3500 | 0.004 |
644
+ | 2.5197 | 3510 | 0.0055 |
645
+ | 2.5269 | 3520 | 0.0057 |
646
+ | 2.5341 | 3530 | 0.0059 |
647
+ | 2.5413 | 3540 | 0.0031 |
648
+ | 2.5485 | 3550 | 0.0039 |
649
+ | 2.5556 | 3560 | 0.0046 |
650
+ | 2.5628 | 3570 | 0.0035 |
651
+ | 2.5700 | 3580 | 0.0037 |
652
+ | 2.5772 | 3590 | 0.0045 |
653
+ | 2.5844 | 3600 | 0.006 |
654
+ | 2.5915 | 3610 | 0.0058 |
655
+ | 2.5987 | 3620 | 0.0053 |
656
+ | 2.6059 | 3630 | 0.0045 |
657
+ | 2.6131 | 3640 | 0.0031 |
658
+ | 2.6202 | 3650 | 0.0063 |
659
+ | 2.6274 | 3660 | 0.004 |
660
+ | 2.6346 | 3670 | 0.0043 |
661
+ | 2.6418 | 3680 | 0.0055 |
662
+ | 2.6490 | 3690 | 0.0044 |
663
+ | 2.6561 | 3700 | 0.0025 |
664
+ | 2.6633 | 3710 | 0.0047 |
665
+ | 2.6705 | 3720 | 0.0043 |
666
+ | 2.6777 | 3730 | 0.0041 |
667
+ | 2.6849 | 3740 | 0.0064 |
668
+ | 2.6920 | 3750 | 0.0055 |
669
+ | 2.6992 | 3760 | 0.0038 |
670
+ | 2.7064 | 3770 | 0.0059 |
671
+ | 2.7136 | 3780 | 0.0059 |
672
+ | 2.7207 | 3790 | 0.0039 |
673
+ | 2.7279 | 3800 | 0.0051 |
674
+ | 2.7351 | 3810 | 0.0061 |
675
+ | 2.7423 | 3820 | 0.0029 |
676
+ | 2.7495 | 3830 | 0.0043 |
677
+ | 2.7566 | 3840 | 0.0044 |
678
+ | 2.7638 | 3850 | 0.0047 |
679
+ | 2.7710 | 3860 | 0.0041 |
680
+ | 2.7782 | 3870 | 0.0033 |
681
+ | 2.7854 | 3880 | 0.0028 |
682
+ | 2.7925 | 3890 | 0.0049 |
683
+ | 2.7997 | 3900 | 0.0048 |
684
+ | 2.8069 | 3910 | 0.0042 |
685
+ | 2.8141 | 3920 | 0.0047 |
686
+ | 2.8212 | 3930 | 0.0043 |
687
+ | 2.8284 | 3940 | 0.0034 |
688
+ | 2.8356 | 3950 | 0.0034 |
689
+ | 2.8428 | 3960 | 0.0036 |
690
+ | 2.8500 | 3970 | 0.0057 |
691
+ | 2.8571 | 3980 | 0.0067 |
692
+ | 2.8643 | 3990 | 0.0053 |
693
+ | 2.8715 | 4000 | 0.0045 |
694
+ | 2.8787 | 4010 | 0.0044 |
695
+ | 2.8859 | 4020 | 0.0045 |
696
+ | 2.8930 | 4030 | 0.0028 |
697
+ | 2.9002 | 4040 | 0.0032 |
698
+ | 2.9074 | 4050 | 0.0054 |
699
+ | 2.9146 | 4060 | 0.005 |
700
+ | 2.9218 | 4070 | 0.0039 |
701
+ | 2.9289 | 4080 | 0.003 |
702
+ | 2.9361 | 4090 | 0.0036 |
703
+ | 2.9433 | 4100 | 0.003 |
704
+ | 2.9505 | 4110 | 0.0052 |
705
+ | 2.9576 | 4120 | 0.0029 |
706
+ | 2.9648 | 4130 | 0.0038 |
707
+ | 2.9720 | 4140 | 0.0048 |
708
+ | 2.9792 | 4150 | 0.0046 |
709
+ | 2.9864 | 4160 | 0.005 |
710
+ | 2.9935 | 4170 | 0.0047 |
711
+ | 3.0007 | 4180 | 0.0048 |
712
+ | 3.0079 | 4190 | 0.0033 |
713
+ | 3.0151 | 4200 | 0.0026 |
714
+ | 3.0223 | 4210 | 0.0031 |
715
+ | 3.0294 | 4220 | 0.0043 |
716
+ | 3.0366 | 4230 | 0.0034 |
717
+ | 3.0438 | 4240 | 0.0038 |
718
+ | 3.0510 | 4250 | 0.0023 |
719
+ | 3.0581 | 4260 | 0.0036 |
720
+ | 3.0653 | 4270 | 0.0045 |
721
+ | 3.0725 | 4280 | 0.0028 |
722
+ | 3.0797 | 4290 | 0.0025 |
723
+ | 3.0869 | 4300 | 0.0036 |
724
+ | 3.0940 | 4310 | 0.0055 |
725
+ | 3.1012 | 4320 | 0.0041 |
726
+ | 3.1084 | 4330 | 0.0027 |
727
+ | 3.1156 | 4340 | 0.0048 |
728
+ | 3.1228 | 4350 | 0.0049 |
729
+ | 3.1299 | 4360 | 0.0028 |
730
+ | 3.1371 | 4370 | 0.0052 |
731
+ | 3.1443 | 4380 | 0.0029 |
732
+ | 3.1515 | 4390 | 0.0039 |
733
+ | 3.1587 | 4400 | 0.0029 |
734
+ | 3.1658 | 4410 | 0.0045 |
735
+ | 3.1730 | 4420 | 0.0031 |
736
+ | 3.1802 | 4430 | 0.004 |
737
+ | 3.1874 | 4440 | 0.0042 |
738
+ | 3.1945 | 4450 | 0.0039 |
739
+ | 3.2017 | 4460 | 0.0027 |
740
+ | 3.2089 | 4470 | 0.0031 |
741
+ | 3.2161 | 4480 | 0.0043 |
742
+ | 3.2233 | 4490 | 0.0027 |
743
+ | 3.2304 | 4500 | 0.0035 |
744
+ | 3.2376 | 4510 | 0.0034 |
745
+ | 3.2448 | 4520 | 0.0039 |
746
+ | 3.2520 | 4530 | 0.0026 |
747
+ | 3.2592 | 4540 | 0.0035 |
748
+ | 3.2663 | 4550 | 0.0041 |
749
+ | 3.2735 | 4560 | 0.0021 |
750
+ | 3.2807 | 4570 | 0.0032 |
751
+ | 3.2879 | 4580 | 0.0032 |
752
+ | 3.2950 | 4590 | 0.0026 |
753
+ | 3.3022 | 4600 | 0.0045 |
754
+ | 3.3094 | 4610 | 0.0046 |
755
+ | 3.3166 | 4620 | 0.0014 |
756
+ | 3.3238 | 4630 | 0.0026 |
757
+ | 3.3309 | 4640 | 0.0026 |
758
+ | 3.3381 | 4650 | 0.002 |
759
+ | 3.3453 | 4660 | 0.0043 |
760
+ | 3.3525 | 4670 | 0.0051 |
761
+ | 3.3597 | 4680 | 0.0041 |
762
+ | 3.3668 | 4690 | 0.0021 |
763
+ | 3.3740 | 4700 | 0.0059 |
764
+ | 3.3812 | 4710 | 0.006 |
765
+ | 3.3884 | 4720 | 0.0049 |
766
+ | 3.3955 | 4730 | 0.0035 |
767
+ | 3.4027 | 4740 | 0.004 |
768
+ | 3.4099 | 4750 | 0.0039 |
769
+ | 3.4171 | 4760 | 0.0024 |
770
+ | 3.4243 | 4770 | 0.0026 |
771
+ | 3.4314 | 4780 | 0.0038 |
772
+ | 3.4386 | 4790 | 0.0029 |
773
+ | 3.4458 | 4800 | 0.0045 |
774
+ | 3.4530 | 4810 | 0.0025 |
775
+ | 3.4602 | 4820 | 0.0031 |
776
+ | 3.4673 | 4830 | 0.0044 |
777
+ | 3.4745 | 4840 | 0.0018 |
778
+ | 3.4817 | 4850 | 0.0035 |
779
+ | 3.4889 | 4860 | 0.0031 |
780
+ | 3.4961 | 4870 | 0.0058 |
781
+ | 3.5032 | 4880 | 0.0032 |
782
+ | 3.5104 | 4890 | 0.0028 |
783
+ | 3.5176 | 4900 | 0.0029 |
784
+ | 3.5248 | 4910 | 0.0038 |
785
+ | 3.5319 | 4920 | 0.0026 |
786
+ | 3.5391 | 4930 | 0.0028 |
787
+ | 3.5463 | 4940 | 0.0034 |
788
+ | 3.5535 | 4950 | 0.0044 |
789
+ | 3.5607 | 4960 | 0.003 |
790
+ | 3.5678 | 4970 | 0.0028 |
791
+ | 3.5750 | 4980 | 0.0031 |
792
+ | 3.5822 | 4990 | 0.003 |
793
+ | 3.5894 | 5000 | 0.0028 |
794
+
795
+ </details>
796
+
797
+ ### Framework Versions
798
+ - Python: 3.11.13
799
+ - Sentence Transformers: 4.1.0
800
+ - Transformers: 4.52.4
801
+ - PyTorch: 2.6.0+cu124
802
+ - Accelerate: 1.8.1
803
+ - Datasets: 2.14.4
804
+ - Tokenizers: 0.21.1
805
+
806
+ ## Citation
807
+
808
+ ### BibTeX
809
+
810
+ #### Sentence Transformers
811
+ ```bibtex
812
+ @inproceedings{reimers-2019-sentence-bert,
813
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
814
+ author = "Reimers, Nils and Gurevych, Iryna",
815
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
816
+ month = "11",
817
+ year = "2019",
818
+ publisher = "Association for Computational Linguistics",
819
+ url = "https://arxiv.org/abs/1908.10084",
820
+ }
821
+ ```
822
+
823
+ <!--
824
+ ## Glossary
825
+
826
+ *Clearly define terms in order to be accessible across audiences.*
827
+ -->
828
+
829
+ <!--
830
+ ## Model Card Authors
831
+
832
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
833
+ -->
834
+
835
+ <!--
836
+ ## Model Card Contact
837
+
838
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
839
  -->
config.json CHANGED
@@ -1,24 +1,24 @@
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": "4.52.4",
21
- "type_vocab_size": 2,
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- "use_cache": true,
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- "vocab_size": 32768
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- }
 
1
+ {
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+ "type_vocab_size": 2,
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+ "use_cache": true,
23
+ "vocab_size": 32768
24
+ }
config_sentence_transformers.json CHANGED
@@ -1,10 +1,10 @@
1
- {
2
- "__version__": {
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- "sentence_transformers": "4.1.0",
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- "transformers": "4.52.4",
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- "pytorch": "2.6.0+cu124"
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- },
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- "prompts": {},
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- "similarity_fn_name": "cosine"
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  }
 
1
+ {
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+ "__version__": {
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+ "transformers": "4.52.4",
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+ "pytorch": "2.5.1+cu121"
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+ },
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+ "prompts": {},
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+ "default_prompt_name": null,
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+ "similarity_fn_name": "cosine"
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  }
modules.json CHANGED
@@ -1,14 +1,14 @@
1
- [
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- {
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- "idx": 0,
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- "name": "0",
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- "path": "",
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- "type": "sentence_transformers.models.Transformer"
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- },
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- "path": "1_Pooling",
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- "type": "sentence_transformers.models.Pooling"
13
- }
14
  ]
 
1
+ [
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+ },
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+ {
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+ "path": "1_Pooling",
12
+ "type": "sentence_transformers.models.Pooling"
13
+ }
14
  ]
sentence_bert_config.json CHANGED
@@ -1,4 +1,4 @@
1
- {
2
- "max_seq_length": 512,
3
- "do_lower_case": false
4
  }
 
1
+ {
2
+ "max_seq_length": 512,
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  }
special_tokens_map.json CHANGED
@@ -1,37 +1,37 @@
1
- {
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- "cls_token": {
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37
- }
 
1
+ {
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37
+ }
tokenizer_config.json CHANGED
@@ -1,64 +1,64 @@
1
- {
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- "added_tokens_decoder": {
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- },
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- "cls_token": "[CLS]",
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- "jumanpp_kwargs": null,
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- "mask_token": "[MASK]",
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- "mecab_kwargs": {
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- "sudachi_kwargs": null,
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- "tokenizer_class": "BertJapaneseTokenizer",
62
- "unk_token": "[UNK]",
63
- "word_tokenizer_type": "mecab"
64
- }
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "0": {
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5
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11
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42
+ }
43
+ },
44
+ "clean_up_tokenization_spaces": true,
45
+ "cls_token": "[CLS]",
46
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47
+ "do_subword_tokenize": true,
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+ "do_word_tokenize": true,
49
+ "extra_special_tokens": {},
50
+ "jumanpp_kwargs": null,
51
+ "mask_token": "[MASK]",
52
+ "mecab_kwargs": {
53
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54
+ },
55
+ "model_max_length": 512,
56
+ "never_split": null,
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58
+ "sep_token": "[SEP]",
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+ "subword_tokenizer_type": "wordpiece",
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+ "sudachi_kwargs": null,
61
+ "tokenizer_class": "BertJapaneseTokenizer",
62
+ "unk_token": "[UNK]",
63
+ "word_tokenizer_type": "mecab"
64
+ }
vocab.txt CHANGED
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