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cl-nagoya-sup-simcse-ja-for-standard-name-v0_9_8

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:8301
loss:BatchAllTripletLoss
Model card Files
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dtm-hoinv commited on
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Add new SentenceTransformer model

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Files changed (10) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +403 -0
  3. config.json +25 -0
  4. config_sentence_transformers.json +10 -0
  5. model.safetensors +3 -0
  6. modules.json +14 -0
  7. sentence_bert_config.json +4 -0
  8. special_tokens_map.json +37 -0
  9. tokenizer_config.json +64 -0
  10. vocab.txt +0 -0
1_Pooling/config.json ADDED
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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 ADDED
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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:8301
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+ - loss:BatchAllTripletLoss
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+ widget:
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+ - source_sentence: 科目：タイル。名称：ﾃﾞｯｷ床ﾀｲﾙ。
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+ sentences:
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+ - 科目：ユニット及びその他。名称：ﾌﾟｰﾙ廻りｸｯｼｮﾝ。
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+ - 科目：ユニット及びその他。名称：P-#ﾋﾟｸﾄｻｲﾝB。
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+ - 科目：ユニット及びその他。名称：秘書室ｶｳﾝﾀｰ。
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+ - source_sentence: 科目：ユニット及びその他。名称：SKﾌｯｸ。
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+ sentences:
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+ - 科目：ユニット及びその他。名称：多目的ﾎｰﾙ入口ｻｲﾝ。
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+ - 科目：ユニット及びその他。名称：免震層ﾒｯｼｭﾌｪﾝｽ片開き扉。
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+ - 科目：ユニット及びその他。名称：#ﾌﾛｱﾏｯﾌﾟ（壁付）。
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+ - source_sentence: 科目：ユニット及びその他。名称：ｶｰﾃﾝﾚｰﾙ。
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+ sentences:
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+ - 科目：コンクリート。名称：多目的ﾎｰﾙ機械式移動座席基礎ｺﾝｸﾘｰﾄ。
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+ - 科目：ユニット及びその他。名称：立下り腰壁ｳｯﾄﾞﾃﾞｯｷ。
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+ - 科目：ユニット及びその他。名称：便所SKﾌｯｸ。
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+ - source_sentence: 科目：ユニット及びその他。名称：守衛室･議会事務局ｶｳﾝﾀｰ上ｶﾞﾗｽ台。
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+ sentences:
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+ - 科目：ユニット及びその他。名称：屋上校庭壁ｺﾞﾑﾁｯﾌﾟ。
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+ - 科目：ユニット及びその他。名称：#F救急外来受付ｶｳﾝﾀｰ。
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+ - 科目：ユニット及びその他。名称：議会事務局ｶｳﾝﾀｰ。
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+ - source_sentence: 科目：ユニット及びその他。名称：#Fｽﾀｯﾌｽﾃｰｼｮﾝｶｳﾝﾀｰ。
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+ sentences:
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+ - 科目：ユニット及びその他。名称：La-#触知案内図ｻｲﾝ。
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+ - 科目：ユニット及びその他。名称：ﾃﾞｼﾞﾀﾙｻｲﾈｰｼﾞ。
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+ - 科目：ユニット及びその他。名称：誘導ｻｲﾝ（自立）。
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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-for-standard-name-v0_9_8")
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+ # Run inference
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+ sentences = [
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+ '科目：ユニット及びその他。名称：#Fｽﾀｯﾌｽﾃｰｼｮﾝｶｳﾝﾀｰ。',
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+ '科目：ユニット及びその他。名称：誘導ｻｲﾝ（自立）。',
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+ '科目：ユニット及びその他。名称：ﾃﾞｼﾞﾀﾙｻｲﾈｰｼﾞ。',
91
+ ]
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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: 8,301 training samples
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+ * Columns: <code>sentence</code> and <code>label</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | sentence | label |
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+ |:--------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | type | string | int |
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+ | details | <ul><li>min: 11 tokens</li><li>mean: 17.76 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>0: ~0.10%</li><li>1: ~0.20%</li><li>2: ~0.10%</li><li>3: ~0.10%</li><li>4: ~0.20%</li><li>5: ~0.10%</li><li>6: ~0.10%</li><li>7: ~0.10%</li><li>8: ~0.20%</li><li>9: ~0.10%</li><li>10: ~0.10%</li><li>11: ~0.40%</li><li>12: ~0.10%</li><li>13: ~0.10%</li><li>14: ~0.10%</li><li>15: ~0.10%</li><li>16: ~0.10%</li><li>17: ~0.10%</li><li>18: ~0.50%</li><li>19: ~0.20%</li><li>20: ~0.20%</li><li>21: ~0.10%</li><li>22: ~0.10%</li><li>23: ~0.10%</li><li>24: ~0.30%</li><li>25: ~0.10%</li><li>26: ~0.20%</li><li>27: ~0.20%</li><li>28: ~0.20%</li><li>29: ~0.20%</li><li>30: ~0.10%</li><li>31: ~0.10%</li><li>32: ~0.20%</li><li>33: ~0.20%</li><li>34: ~0.10%</li><li>35: ~0.20%</li><li>36: ~0.20%</li><li>37: ~0.20%</li><li>38: ~0.20%</li><li>39: ~0.20%</li><li>40: ~0.40%</li><li>41: ~0.20%</li><li>42: ~0.20%</li><li>43: ~0.20%</li><li>44: ~0.60%</li><li>45: ~0.70%</li><li>46: ~0.20%</li><li>47: ~0.20%</li><li>48: ~0.10%</li><li>49: ~0.20%</li><li>50: ~0.10%</li><li>51: ~0.20%</li><li>52: ~0.10%</li><li>53: ~0.10%</li><li>54: ~0.20%</li><li>55: ~0.20%</li><li>56: ~0.30%</li><li>57: ~0.80%</li><li>58: ~0.30%</li><li>59: ~0.10%</li><li>60: ~0.70%</li><li>61: ~0.30%</li><li>62: ~0.20%</li><li>63: ~0.20%</li><li>64: ~0.50%</li><li>65: ~0.10%</li><li>66: ~0.20%</li><li>67: ~0.20%</li><li>68: ~0.20%</li><li>69: ~0.30%</li><li>70: ~0.30%</li><li>71: ~0.20%</li><li>72: ~0.20%</li><li>73: ~0.20%</li><li>74: ~0.20%</li><li>75: ~0.20%</li><li>76: ~0.10%</li><li>77: ~0.20%</li><li>78: ~0.30%</li><li>79: ~0.20%</li><li>80: ~0.20%</li><li>81: ~0.10%</li><li>82: ~0.20%</li><li>83: ~0.50%</li><li>84: ~0.30%</li><li>85: ~0.60%</li><li>86: ~0.20%</li><li>87: ~0.30%</li><li>88: ~0.20%</li><li>89: ~0.20%</li><li>90: ~0.20%</li><li>91: ~0.20%</li><li>92: ~1.00%</li><li>93: ~1.70%</li><li>94: ~3.70%</li><li>95: ~0.50%</li><li>96: ~0.20%</li><li>97: ~0.20%</li><li>98: ~0.80%</li><li>99: ~0.20%</li><li>100: ~0.20%</li><li>101: ~0.20%</li><li>102: ~0.20%</li><li>103: ~0.30%</li><li>104: ~1.20%</li><li>105: ~0.20%</li><li>106: ~0.20%</li><li>107: ~0.40%</li><li>108: ~0.30%</li><li>109: ~0.20%</li><li>110: ~0.20%</li><li>111: ~0.20%</li><li>112: ~0.30%</li><li>113: ~0.20%</li><li>114: ~0.20%</li><li>115: ~0.10%</li><li>116: ~0.30%</li><li>117: ~0.40%</li><li>118: ~0.20%</li><li>119: ~0.20%</li><li>120: ~0.20%</li><li>121: ~0.20%</li><li>122: ~0.30%</li><li>123: ~0.20%</li><li>124: ~0.20%</li><li>125: ~0.20%</li><li>126: ~0.10%</li><li>127: ~0.20%</li><li>128: ~0.10%</li><li>129: ~0.30%</li><li>130: ~0.20%</li><li>131: ~0.20%</li><li>132: ~0.10%</li><li>133: ~0.50%</li><li>134: ~0.20%</li><li>135: ~0.20%</li><li>136: ~0.20%</li><li>137: ~0.20%</li><li>138: ~0.20%</li><li>139: ~0.10%</li><li>140: ~0.10%</li><li>141: ~0.40%</li><li>142: ~0.70%</li><li>143: ~0.20%</li><li>144: ~3.10%</li><li>145: ~0.20%</li><li>146: ~2.30%</li><li>147: ~0.30%</li><li>148: ~0.30%</li><li>149: ~0.50%</li><li>150: ~0.50%</li><li>151: ~0.50%</li><li>152: ~0.20%</li><li>153: ~0.20%</li><li>154: ~0.20%</li><li>155: ~0.20%</li><li>156: ~0.30%</li><li>157: ~0.30%</li><li>158: ~0.30%</li><li>159: ~0.20%</li><li>160: ~0.30%</li><li>161: ~0.20%</li><li>162: ~0.20%</li><li>163: ~0.10%</li><li>164: ~0.20%</li><li>165: ~0.20%</li><li>166: ~0.30%</li><li>167: ~0.20%</li><li>168: ~0.20%</li><li>169: ~0.20%</li><li>170: ~0.20%</li><li>171: ~0.20%</li><li>172: ~0.20%</li><li>173: ~0.20%</li><li>174: ~0.30%</li><li>175: ~0.30%</li><li>176: ~0.20%</li><li>177: ~0.20%</li><li>178: ~0.20%</li><li>179: ~0.20%</li><li>180: ~0.30%</li><li>181: ~0.60%</li><li>182: ~0.20%</li><li>183: ~0.20%</li><li>184: ~0.20%</li><li>185: ~0.20%</li><li>186: ~0.20%</li><li>187: ~0.70%</li><li>188: ~0.20%</li><li>189: ~0.20%</li><li>190: ~0.30%</li><li>191: ~0.20%</li><li>192: ~1.30%</li><li>193: ~0.20%</li><li>194: ~0.30%</li><li>195: ~0.30%</li><li>196: ~0.20%</li><li>197: ~0.30%</li><li>198: ~0.10%</li><li>199: ~1.10%</li><li>200: ~0.20%</li><li>201: ~0.20%</li><li>202: ~0.20%</li><li>203: ~0.10%</li><li>204: ~0.10%</li><li>205: ~0.20%</li><li>206: ~0.20%</li><li>207: ~0.10%</li><li>208: ~1.10%</li><li>209: ~0.40%</li><li>210: ~0.10%</li><li>211: ~0.20%</li><li>212: ~0.20%</li><li>213: ~0.10%</li><li>214: ~1.00%</li><li>215: ~0.20%</li><li>216: ~0.30%</li><li>217: ~0.10%</li><li>218: ~1.80%</li><li>219: ~0.30%</li><li>220: ~0.50%</li><li>221: ~0.20%</li><li>222: ~0.20%</li><li>223: ~0.10%</li><li>224: ~0.20%</li><li>225: ~0.10%</li><li>226: ~0.20%</li><li>227: ~0.20%</li><li>228: ~0.10%</li><li>229: ~0.30%</li><li>230: ~4.00%</li><li>231: ~0.20%</li><li>232: ~0.20%</li><li>233: ~0.10%</li><li>234: ~0.60%</li><li>235: ~0.20%</li><li>236: ~0.30%</li><li>237: ~0.70%</li><li>238: ~0.20%</li><li>239: ~0.30%</li><li>240: ~0.30%</li><li>241: ~0.40%</li><li>242: ~0.30%</li><li>243: ~0.10%</li><li>244: ~0.20%</li><li>245: ~0.30%</li><li>246: ~0.20%</li><li>247: ~0.10%</li><li>248: ~0.10%</li><li>249: ~0.30%</li><li>250: ~0.30%</li><li>251: ~0.30%</li><li>252: ~0.60%</li><li>253: ~0.20%</li><li>254: ~0.20%</li><li>255: ~0.20%</li><li>256: ~0.30%</li><li>257: ~0.20%</li><li>258: ~2.20%</li><li>259: ~0.30%</li><li>260: ~0.20%</li><li>261: ~0.20%</li><li>262: ~0.30%</li><li>263: ~0.10%</li><li>264: ~0.10%</li><li>265: ~0.50%</li><li>266: ~0.10%</li><li>267: ~0.10%</li><li>268: ~0.10%</li><li>269: ~0.10%</li><li>270: ~0.20%</li><li>271: ~0.90%</li><li>272: ~0.20%</li><li>273: ~0.20%</li><li>274: ~0.10%</li><li>275: ~0.40%</li><li>276: ~0.20%</li><li>277: ~0.20%</li><li>278: ~0.10%</li><li>279: ~0.10%</li><li>280: ~0.20%</li><li>281: ~0.10%</li><li>282: ~0.20%</li><li>283: ~2.90%</li><li>284: ~0.20%</li><li>285: ~0.20%</li><li>286: ~0.30%</li><li>287: ~0.20%</li><li>288: ~0.20%</li><li>289: ~0.80%</li><li>290: ~0.20%</li><li>291: ~0.20%</li><li>292: ~3.90%</li><li>293: ~0.30%</li><li>294: ~0.10%</li><li>295: ~0.20%</li><li>296: ~0.70%</li><li>297: ~0.40%</li><li>298: ~0.20%</li><li>299: ~0.20%</li></ul> |
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+ * Samples:
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+ | sentence | 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>0</code> |
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+ | <code>科目：コンクリート。名称：ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送。</code> | <code>1</code> |
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+ | <code>科目：コンクリート。名称：ﾎﾟﾝﾌﾟ圧送。</code> | <code>1</code> |
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+ * Loss: [<code>BatchAllTripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchalltripletloss)
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+
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+ ### Training Hyperparameters
160
+ #### Non-Default Hyperparameters
161
+
162
+ - `per_device_train_batch_size`: 512
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+ - `per_device_eval_batch_size`: 512
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+ - `learning_rate`: 1e-05
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+ - `weight_decay`: 0.01
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+ - `num_train_epochs`: 200
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+ - `warmup_ratio`: 0.1
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+ - `fp16`: True
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+ - `batch_sampler`: group_by_label
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+
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+ #### All Hyperparameters
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+ <details><summary>Click to expand</summary>
173
+
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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`: 512
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+ - `per_device_eval_batch_size`: 512
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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`: 200
192
+ - `max_steps`: -1
193
+ - `lr_scheduler_type`: linear
194
+ - `lr_scheduler_kwargs`: {}
195
+ - `warmup_ratio`: 0.1
196
+ - `warmup_steps`: 0
197
+ - `log_level`: passive
198
+ - `log_level_replica`: warning
199
+ - `log_on_each_node`: True
200
+ - `logging_nan_inf_filter`: True
201
+ - `save_safetensors`: True
202
+ - `save_on_each_node`: False
203
+ - `save_only_model`: False
204
+ - `restore_callback_states_from_checkpoint`: False
205
+ - `no_cuda`: False
206
+ - `use_cpu`: False
207
+ - `use_mps_device`: False
208
+ - `seed`: 42
209
+ - `data_seed`: None
210
+ - `jit_mode_eval`: False
211
+ - `use_ipex`: False
212
+ - `bf16`: False
213
+ - `fp16`: True
214
+ - `fp16_opt_level`: O1
215
+ - `half_precision_backend`: auto
216
+ - `bf16_full_eval`: False
217
+ - `fp16_full_eval`: False
218
+ - `tf32`: None
219
+ - `local_rank`: 0
220
+ - `ddp_backend`: None
221
+ - `tpu_num_cores`: None
222
+ - `tpu_metrics_debug`: False
223
+ - `debug`: []
224
+ - `dataloader_drop_last`: False
225
+ - `dataloader_num_workers`: 0
226
+ - `dataloader_prefetch_factor`: None
227
+ - `past_index`: -1
228
+ - `disable_tqdm`: False
229
+ - `remove_unused_columns`: True
230
+ - `label_names`: None
231
+ - `load_best_model_at_end`: False
232
+ - `ignore_data_skip`: False
233
+ - `fsdp`: []
234
+ - `fsdp_min_num_params`: 0
235
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
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
+ - `dispatch_batches`: None
276
+ - `split_batches`: None
277
+ - `include_tokens_per_second`: False
278
+ - `include_num_input_tokens_seen`: False
279
+ - `neftune_noise_alpha`: None
280
+ - `optim_target_modules`: None
281
+ - `batch_eval_metrics`: False
282
+ - `eval_on_start`: False
283
+ - `use_liger_kernel`: False
284
+ - `eval_use_gather_object`: False
285
+ - `average_tokens_across_devices`: False
286
+ - `prompts`: None
287
+ - `batch_sampler`: group_by_label
288
+ - `multi_dataset_batch_sampler`: proportional
289
+
290
+ </details>
291
+
292
+ ### Training Logs
293
+ | Epoch | Step | Training Loss |
294
+ |:--------:|:----:|:-------------:|
295
+ | 3.6471 | 50 | 0.5866 |
296
+ | 7.5294 | 100 | 0.4693 |
297
+ | 11.4118 | 150 | 0.4486 |
298
+ | 15.2941 | 200 | 0.2783 |
299
+ | 19.1765 | 250 | 0.2732 |
300
+ | 23.0588 | 300 | 0.3268 |
301
+ | 26.7059 | 350 | 0.3403 |
302
+ | 30.5882 | 400 | 0.1967 |
303
+ | 34.4706 | 450 | 0.2025 |
304
+ | 38.3529 | 500 | 0.2108 |
305
+ | 42.2353 | 550 | 0.1458 |
306
+ | 46.1176 | 600 | 0.1914 |
307
+ | 49.7647 | 650 | 0.1065 |
308
+ | 53.6471 | 700 | 0.0607 |
309
+ | 57.5294 | 750 | 0.128 |
310
+ | 61.4118 | 800 | 0.0579 |
311
+ | 65.2941 | 850 | 0.1695 |
312
+ | 69.1765 | 900 | 0.1121 |
313
+ | 73.0588 | 950 | 0.1096 |
314
+ | 76.7059 | 1000 | 0.1213 |
315
+ | 80.5882 | 1050 | 0.0485 |
316
+ | 84.4706 | 1100 | 0.0759 |
317
+ | 88.3529 | 1150 | 0.0673 |
318
+ | 92.2353 | 1200 | 0.111 |
319
+ | 96.1176 | 1250 | 0.0159 |
320
+ | 99.7647 | 1300 | 0.1044 |
321
+ | 103.6471 | 1350 | 0.0928 |
322
+ | 107.5294 | 1400 | 0.0712 |
323
+ | 111.4118 | 1450 | 0.096 |
324
+ | 115.2941 | 1500 | 0.0648 |
325
+ | 119.1765 | 1550 | 0.0534 |
326
+ | 123.0588 | 1600 | 0.0071 |
327
+ | 126.7059 | 1650 | 0.0688 |
328
+ | 130.5882 | 1700 | 0.105 |
329
+ | 134.4706 | 1750 | 0.0344 |
330
+ | 138.3529 | 1800 | 0.0543 |
331
+ | 142.2353 | 1850 | 0.0072 |
332
+ | 146.1176 | 1900 | 0.0218 |
333
+ | 149.7647 | 1950 | 0.0203 |
334
+ | 153.6471 | 2000 | 0.0837 |
335
+ | 157.5294 | 2050 | 0.0423 |
336
+ | 161.4118 | 2100 | 0.0457 |
337
+ | 165.2941 | 2150 | 0.0591 |
338
+ | 169.1765 | 2200 | 0.0168 |
339
+ | 173.0588 | 2250 | 0.0234 |
340
+ | 176.7059 | 2300 | 0.0452 |
341
+ | 180.5882 | 2350 | 0.031 |
342
+ | 184.4706 | 2400 | 0.0241 |
343
+ | 188.3529 | 2450 | 0.0001 |
344
+ | 192.2353 | 2500 | 0.0427 |
345
+ | 196.1176 | 2550 | 0.0381 |
346
+ | 199.7647 | 2600 | 0.0203 |
347
+
348
+
349
+ ### Framework Versions
350
+ - Python: 3.11.11
351
+ - Sentence Transformers: 3.4.1
352
+ - Transformers: 4.49.0
353
+ - PyTorch: 2.6.0+cu124
354
+ - Accelerate: 1.5.2
355
+ - Datasets: 3.4.1
356
+ - Tokenizers: 0.21.1
357
+
358
+ ## Citation
359
+
360
+ ### BibTeX
361
+
362
+ #### Sentence Transformers
363
+ ```bibtex
364
+ @inproceedings{reimers-2019-sentence-bert,
365
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
366
+ author = "Reimers, Nils and Gurevych, Iryna",
367
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
368
+ month = "11",
369
+ year = "2019",
370
+ publisher = "Association for Computational Linguistics",
371
+ url = "https://arxiv.org/abs/1908.10084",
372
+ }
373
+ ```
374
+
375
+ #### BatchAllTripletLoss
376
+ ```bibtex
377
+ @misc{hermans2017defense,
378
+ title={In Defense of the Triplet Loss for Person Re-Identification},
379
+ author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
380
+ year={2017},
381
+ eprint={1703.07737},
382
+ archivePrefix={arXiv},
383
+ primaryClass={cs.CV}
384
+ }
385
+ ```
386
+
387
+ <!--
388
+ ## Glossary
389
+
390
+ *Clearly define terms in order to be accessible across audiences.*
391
+ -->
392
+
393
+ <!--
394
+ ## Model Card Authors
395
+
396
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
397
+ -->
398
+
399
+ <!--
400
+ ## Model Card Contact
401
+
402
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
403
+ -->
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