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philipp-zettl
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MiniLM-amazon_massive_intent-similarity

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:1027471
loss:CoSENTLoss
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
philipp-zettl commited on
Commit
6a2981e
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1 Parent(s): 2467532

Add new SentenceTransformer model.

Browse files
Files changed (12) hide show
  1. .gitattributes +2 -0
  2. 1_Pooling/config.json +10 -0
  3. README.md +962 -0
  4. config.json +26 -0
  5. config_sentence_transformers.json +10 -0
  6. model.safetensors +3 -0
  7. modules.json +14 -0
  8. sentence_bert_config.json +4 -0
  9. special_tokens_map.json +51 -0
  10. tokenizer.json +3 -0
  11. tokenizer_config.json +64 -0
  12. unigram.json +3 -0
.gitattributes CHANGED
@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
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+ unigram.json filter=lfs diff=lfs merge=lfs -text
1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "word_embedding_dimension": 384,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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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
@@ -0,0 +1,962 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ language: []
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+ library_name: sentence-transformers
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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:1793370
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+ - loss:CoSENTLoss
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+ base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
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+ datasets: []
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+ metrics:
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+ - pearson_cosine
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+ - spearman_cosine
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+ - pearson_manhattan
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+ - spearman_manhattan
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+ - pearson_euclidean
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+ - spearman_euclidean
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+ - pearson_dot
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+ - spearman_dot
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+ - pearson_max
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+ - spearman_max
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+ widget:
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+ - source_sentence: ek wil bietjie moderne rock hoor
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+ sentences:
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+ - request datetime
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+ - turn wemo on
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+ - query cooking
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+ - source_sentence: skakel af die alarm vir woensdag ses v. m.
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+ sentences:
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+ - set alarm
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+ - turn hue light up
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+ - request weather
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+ - source_sentence: speel my top-gegradeerde pop liedjies asseblief
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+ sentences:
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+ - greeting
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+ - request fact
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+ - request datetime
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+ - source_sentence: is dit warm buite
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+ sentences:
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+ - request weather
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+ - play music
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+ - request transport
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+ - source_sentence: maak 'n speellys van al die eminem liedjies en speel dit met skommel
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+ sentences:
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+ - search recipe
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+ - recommend movie
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+ - play music
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+ pipeline_tag: sentence-similarity
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+ model-index:
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+ - name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
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+ results:
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+ - task:
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+ type: semantic-similarity
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+ name: Semantic Similarity
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+ dataset:
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+ name: MiniLM dev
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+ type: MiniLM-dev
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+ metrics:
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+ - type: pearson_cosine
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+ value: 0.807743120621169
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.8111451989044506
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+ name: Spearman Cosine
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+ - type: pearson_manhattan
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+ value: 0.8090992313100879
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+ name: Pearson Manhattan
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+ - type: spearman_manhattan
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+ value: 0.8112673840020295
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+ name: Spearman Manhattan
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+ - type: pearson_euclidean
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+ value: 0.8107892143621067
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+ name: Pearson Euclidean
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+ - type: spearman_euclidean
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+ value: 0.8137277702128023
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+ name: Spearman Euclidean
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+ - type: pearson_dot
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+ value: 0.7013144883870261
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+ name: Pearson Dot
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+ - type: spearman_dot
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+ value: 0.7113684320495312
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+ name: Spearman Dot
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+ - type: pearson_max
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+ value: 0.8107892143621067
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+ name: Pearson Max
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+ - type: spearman_max
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+ value: 0.8137277702128023
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+ name: Spearman Max
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+ ---
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+
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+ # SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2). It maps sentences & paragraphs to a 384-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:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision bf3bf13ab40c3157080a7ab344c831b9ad18b5eb -->
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+ - **Maximum Sequence Length:** 128 tokens
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+ - **Output Dimensionality:** 384 tokens
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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': 128, 'do_lower_case': False}) with Transformer model: BertModel
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+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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("philipp-zettl/MiniLM-amazon_massive_intent-similarity")
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+ # Run inference
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+ sentences = [
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+ "maak 'n speellys van al die eminem liedjies en speel dit met skommel",
143
+ 'play music',
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+ 'recommend movie',
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+ ]
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+ embeddings = model.encode(sentences)
147
+ print(embeddings.shape)
148
+ # [3, 384]
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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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+ ## Evaluation
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+
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+ ### Metrics
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+
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+ #### Semantic Similarity
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+ * Dataset: `MiniLM-dev`
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+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
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+
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+ | Metric | Value |
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+ |:--------------------|:-----------|
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+ | pearson_cosine | 0.8077 |
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+ | **spearman_cosine** | **0.8111** |
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+ | pearson_manhattan | 0.8091 |
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+ | spearman_manhattan | 0.8113 |
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+ | pearson_euclidean | 0.8108 |
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+ | spearman_euclidean | 0.8137 |
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+ | pearson_dot | 0.7013 |
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+ | spearman_dot | 0.7114 |
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+ | pearson_max | 0.8108 |
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+ | spearman_max | 0.8137 |
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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 Hyperparameters
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+ #### Non-Default Hyperparameters
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+
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+ - `eval_strategy`: steps
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+ - `per_device_train_batch_size`: 32
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+ - `per_device_eval_batch_size`: 32
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+ - `learning_rate`: 2e-05
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+ - `num_train_epochs`: 1
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+ - `warmup_ratio`: 0.1
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+ - `fp16`: True
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+ - `batch_sampler`: no_duplicates
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+
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+ #### All Hyperparameters
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+ <details><summary>Click to expand</summary>
229
+
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+ - `overwrite_output_dir`: False
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+ - `do_predict`: False
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+ - `eval_strategy`: steps
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+ - `prediction_loss_only`: True
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+ - `per_device_train_batch_size`: 32
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+ - `per_device_eval_batch_size`: 32
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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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+ - `learning_rate`: 2e-05
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+ - `weight_decay`: 0.0
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+ - `adam_beta1`: 0.9
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+ - `adam_beta2`: 0.999
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+ - `adam_epsilon`: 1e-08
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+ - `max_grad_norm`: 1.0
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+ - `num_train_epochs`: 1
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+ - `max_steps`: -1
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+ - `lr_scheduler_type`: linear
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+ - `lr_scheduler_kwargs`: {}
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+ - `warmup_ratio`: 0.1
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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`: False
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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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+ - `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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+ - `dispatch_batches`: None
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+ - `split_batches`: 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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+ - `batch_sampler`: no_duplicates
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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 | loss | MiniLM-dev_spearman_cosine |
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+ |:------:|:-----:|:-------------:|:------:|:--------------------------:|
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+ | 0.0018 | 100 | 10.7509 | - | - |
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+ | 0.0036 | 200 | 9.8726 | - | - |
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+ | 0.0054 | 300 | 8.9837 | - | - |
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+ | 0.0071 | 400 | 7.3162 | - | - |
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+ | 0.0089 | 500 | 8.2842 | - | - |
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+ | 0.0107 | 600 | 6.2254 | - | - |
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+ | 0.0125 | 700 | 6.1004 | - | - |
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+ | 0.0143 | 800 | 5.8583 | - | - |
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+ | 0.0161 | 900 | 6.3118 | - | - |
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+ | 0.0178 | 1000 | 5.7908 | 2.6141 | 0.4045 |
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+ | 0.0196 | 1100 | 5.6907 | - | - |
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+ | 0.0214 | 1200 | 5.6743 | - | - |
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+ | 0.0232 | 1300 | 5.5022 | - | - |
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+ | 0.0250 | 1400 | 5.0283 | - | - |
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+ | 0.0268 | 1500 | 5.2936 | - | - |
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+ | 0.0285 | 1600 | 5.2928 | - | - |
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+ | 0.0303 | 1700 | 5.5088 | - | - |
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+ | 0.0321 | 1800 | 5.3125 | - | - |
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+ | 0.0339 | 1900 | 5.7931 | - | - |
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+ | 0.0357 | 2000 | 5.5979 | 2.3256 | 0.5075 |
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+ | 0.0375 | 2100 | 5.3222 | - | - |
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+ | 0.0393 | 2200 | 5.268 | - | - |
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+ | 0.0410 | 2300 | 5.264 | - | - |
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+ | 0.0428 | 2400 | 4.9437 | - | - |
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+ | 0.0446 | 2500 | 4.9219 | - | - |
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+ | 0.0464 | 2600 | 4.8656 | - | - |
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+ | 0.0482 | 2700 | 5.2733 | - | - |
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+ | 0.0500 | 2800 | 5.0311 | - | - |
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+ | 0.0517 | 2900 | 5.302 | - | - |
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+ | 0.0535 | 3000 | 5.3347 | 2.1545 | 0.6496 |
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+ | 0.0553 | 3100 | 5.1241 | - | - |
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+ | 0.0571 | 3200 | 5.0232 | - | - |
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+ | 0.0589 | 3300 | 4.9932 | - | - |
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+ | 0.0607 | 3400 | 4.9651 | - | - |
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+ | 0.0625 | 3500 | 4.5226 | - | - |
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+ | 0.0642 | 3600 | 4.6666 | - | - |
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+ | 0.0660 | 3700 | 4.8979 | - | - |
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+ | 0.0678 | 3800 | 4.9139 | - | - |
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+ | 0.0696 | 3900 | 4.9241 | - | - |
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+ | 0.0714 | 4000 | 5.2878 | 2.1118 | 0.6948 |
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+ | 0.0732 | 4100 | 5.0776 | - | - |
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+ | 0.0749 | 4200 | 4.934 | - | - |
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+ | 0.0767 | 4300 | 4.9012 | - | - |
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+ | 0.0785 | 4400 | 4.8835 | - | - |
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+ | 0.0803 | 4500 | 4.5886 | - | - |
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+ | 0.0821 | 4600 | 4.7829 | - | - |
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+ | 0.0839 | 4700 | 4.8057 | - | - |
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+ | 0.0856 | 4800 | 4.8761 | - | - |
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+ | 0.0874 | 4900 | 4.6787 | - | - |
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+ | 0.0892 | 5000 | 5.313 | 2.1114 | 0.6770 |
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+ | 0.0910 | 5100 | 5.3036 | - | - |
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+ | 0.0928 | 5200 | 5.0731 | - | - |
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+ | 0.0946 | 5300 | 5.0052 | - | - |
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+ | 0.0964 | 5400 | 4.9494 | - | - |
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+ | 0.0981 | 5500 | 4.836 | - | - |
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+ | 0.0999 | 5600 | 4.6319 | - | - |
402
+ | 0.1017 | 5700 | 4.667 | - | - |
403
+ | 0.1035 | 5800 | 4.9578 | - | - |
404
+ | 0.1053 | 5900 | 4.9473 | - | - |
405
+ | 0.1071 | 6000 | 4.9897 | 3.0813 | 0.4424 |
406
+ | 0.1088 | 6100 | 5.1704 | - | - |
407
+ | 0.1106 | 6200 | 4.8472 | - | - |
408
+ | 0.1124 | 6300 | 4.8296 | - | - |
409
+ | 0.1142 | 6400 | 4.8287 | - | - |
410
+ | 0.1160 | 6500 | 4.6539 | - | - |
411
+ | 0.1178 | 6600 | 4.2599 | - | - |
412
+ | 0.1196 | 6700 | 4.5506 | - | - |
413
+ | 0.1213 | 6800 | 4.6585 | - | - |
414
+ | 0.1231 | 6900 | 4.7248 | - | - |
415
+ | 0.1249 | 7000 | 4.6389 | 3.1390 | 0.5199 |
416
+ | 0.1267 | 7100 | 4.8133 | - | - |
417
+ | 0.1285 | 7200 | 4.8838 | - | - |
418
+ | 0.1303 | 7300 | 4.7375 | - | - |
419
+ | 0.1320 | 7400 | 4.6357 | - | - |
420
+ | 0.1338 | 7500 | 4.7807 | - | - |
421
+ | 0.1356 | 7600 | 4.409 | - | - |
422
+ | 0.1374 | 7700 | 4.5612 | - | - |
423
+ | 0.1392 | 7800 | 4.3731 | - | - |
424
+ | 0.1410 | 7900 | 4.622 | - | - |
425
+ | 0.1427 | 8000 | 4.5574 | 2.6558 | 0.5814 |
426
+ | 0.1445 | 8100 | 4.6542 | - | - |
427
+ | 0.1463 | 8200 | 4.7831 | - | - |
428
+ | 0.1481 | 8300 | 4.6775 | - | - |
429
+ | 0.1499 | 8400 | 4.61 | - | - |
430
+ | 0.1517 | 8500 | 4.6416 | - | - |
431
+ | 0.1535 | 8600 | 4.3096 | - | - |
432
+ | 0.1552 | 8700 | 4.2629 | - | - |
433
+ | 0.1570 | 8800 | 4.5151 | - | - |
434
+ | 0.1588 | 8900 | 4.5301 | - | - |
435
+ | 0.1606 | 9000 | 4.5731 | 2.8939 | 0.5675 |
436
+ | 0.1624 | 9100 | 4.4347 | - | - |
437
+ | 0.1642 | 9200 | 4.648 | - | - |
438
+ | 0.1659 | 9300 | 4.6076 | - | - |
439
+ | 0.1677 | 9400 | 4.4229 | - | - |
440
+ | 0.1695 | 9500 | 4.4785 | - | - |
441
+ | 0.1713 | 9600 | 4.4252 | - | - |
442
+ | 0.1731 | 9700 | 4.0223 | - | - |
443
+ | 0.1749 | 9800 | 4.1593 | - | - |
444
+ | 0.1767 | 9900 | 4.2946 | - | - |
445
+ | 0.1784 | 10000 | 4.4888 | 2.7814 | 0.5852 |
446
+ | 0.1802 | 10100 | 4.3605 | - | - |
447
+ | 0.1820 | 10200 | 4.5952 | - | - |
448
+ | 0.1838 | 10300 | 4.709 | - | - |
449
+ | 0.1856 | 10400 | 4.5743 | - | - |
450
+ | 0.1874 | 10500 | 4.5539 | - | - |
451
+ | 0.1891 | 10600 | 4.4427 | - | - |
452
+ | 0.1909 | 10700 | 4.1095 | - | - |
453
+ | 0.1927 | 10800 | 4.4079 | - | - |
454
+ | 0.1945 | 10900 | 4.1667 | - | - |
455
+ | 0.1963 | 11000 | 4.2273 | 3.3803 | 0.5663 |
456
+ | 0.1981 | 11100 | 4.3333 | - | - |
457
+ | 0.1998 | 11200 | 4.5174 | - | - |
458
+ | 0.2016 | 11300 | 4.4961 | - | - |
459
+ | 0.2034 | 11400 | 4.5746 | - | - |
460
+ | 0.2052 | 11500 | 4.731 | - | - |
461
+ | 0.2070 | 11600 | 4.4485 | - | - |
462
+ | 0.2088 | 11700 | 4.4099 | - | - |
463
+ | 0.2106 | 11800 | 3.8921 | - | - |
464
+ | 0.2123 | 11900 | 4.2423 | - | - |
465
+ | 0.2141 | 12000 | 4.2641 | 3.0230 | 0.6300 |
466
+ | 0.2159 | 12100 | 4.2052 | - | - |
467
+ | 0.2177 | 12200 | 4.2757 | - | - |
468
+ | 0.2195 | 12300 | 4.8586 | - | - |
469
+ | 0.2213 | 12400 | 4.5872 | - | - |
470
+ | 0.2230 | 12500 | 4.4273 | - | - |
471
+ | 0.2248 | 12600 | 4.5728 | - | - |
472
+ | 0.2266 | 12700 | 4.4607 | - | - |
473
+ | 0.2284 | 12800 | 4.1361 | - | - |
474
+ | 0.2302 | 12900 | 4.4781 | - | - |
475
+ | 0.2320 | 13000 | 4.145 | 2.7088 | 0.6617 |
476
+ | 0.2337 | 13100 | 4.3366 | - | - |
477
+ | 0.2355 | 13200 | 4.2699 | - | - |
478
+ | 0.2373 | 13300 | 4.3397 | - | - |
479
+ | 0.2391 | 13400 | 4.6033 | - | - |
480
+ | 0.2409 | 13500 | 4.2292 | - | - |
481
+ | 0.2427 | 13600 | 4.3399 | - | - |
482
+ | 0.2445 | 13700 | 4.5222 | - | - |
483
+ | 0.2462 | 13800 | 4.2185 | - | - |
484
+ | 0.2480 | 13900 | 3.9426 | - | - |
485
+ | 0.2498 | 14000 | 4.2146 | 2.6014 | 0.6724 |
486
+ | 0.2516 | 14100 | 4.2534 | - | - |
487
+ | 0.2534 | 14200 | 4.1765 | - | - |
488
+ | 0.2552 | 14300 | 4.117 | - | - |
489
+ | 0.2569 | 14400 | 5.0908 | - | - |
490
+ | 0.2587 | 14500 | 4.488 | - | - |
491
+ | 0.2605 | 14600 | 4.4429 | - | - |
492
+ | 0.2623 | 14700 | 4.3688 | - | - |
493
+ | 0.2641 | 14800 | 4.4857 | - | - |
494
+ | 0.2659 | 14900 | 4.1763 | - | - |
495
+ | 0.2677 | 15000 | 4.4425 | 2.6388 | 0.6842 |
496
+ | 0.2694 | 15100 | 4.4277 | - | - |
497
+ | 0.2712 | 15200 | 4.3841 | - | - |
498
+ | 0.2730 | 15300 | 4.4 | - | - |
499
+ | 0.2748 | 15400 | 4.55 | - | - |
500
+ | 0.2766 | 15500 | 4.4769 | - | - |
501
+ | 0.2784 | 15600 | 4.3918 | - | - |
502
+ | 0.2801 | 15700 | 4.554 | - | - |
503
+ | 0.2819 | 15800 | 4.406 | - | - |
504
+ | 0.2837 | 15900 | 4.0593 | - | - |
505
+ | 0.2855 | 16000 | 4.3586 | 2.5251 | 0.7238 |
506
+ | 0.2873 | 16100 | 4.2308 | - | - |
507
+ | 0.2891 | 16200 | 4.469 | - | - |
508
+ | 0.2908 | 16300 | 4.2312 | - | - |
509
+ | 0.2926 | 16400 | 4.2695 | - | - |
510
+ | 0.2944 | 16500 | 4.5821 | - | - |
511
+ | 0.2962 | 16600 | 4.5623 | - | - |
512
+ | 0.2980 | 16700 | 4.1865 | - | - |
513
+ | 0.2998 | 16800 | 4.4228 | - | - |
514
+ | 0.3016 | 16900 | 4.0553 | - | - |
515
+ | 0.3033 | 17000 | 3.7183 | 2.6050 | 0.7319 |
516
+ | 0.3051 | 17100 | 4.1849 | - | - |
517
+ | 0.3069 | 17200 | 4.2975 | - | - |
518
+ | 0.3087 | 17300 | 4.4272 | - | - |
519
+ | 0.3105 | 17400 | 4.0634 | - | - |
520
+ | 0.3123 | 17500 | 4.8608 | - | - |
521
+ | 0.3140 | 17600 | 4.4146 | - | - |
522
+ | 0.3158 | 17700 | 4.2655 | - | - |
523
+ | 0.3176 | 17800 | 4.3814 | - | - |
524
+ | 0.3194 | 17900 | 4.3972 | - | - |
525
+ | 0.3212 | 18000 | 3.8868 | 2.4737 | 0.7500 |
526
+ | 0.3230 | 18100 | 4.434 | - | - |
527
+ | 0.3248 | 18200 | 4.2213 | - | - |
528
+ | 0.3265 | 18300 | 4.4632 | - | - |
529
+ | 0.3283 | 18400 | 4.4001 | - | - |
530
+ | 0.3301 | 18500 | 4.8262 | - | - |
531
+ | 0.3319 | 18600 | 4.5022 | - | - |
532
+ | 0.3337 | 18700 | 4.4148 | - | - |
533
+ | 0.3355 | 18800 | 4.2182 | - | - |
534
+ | 0.3372 | 18900 | 4.2127 | - | - |
535
+ | 0.3390 | 19000 | 4.051 | 2.4633 | 0.7575 |
536
+ | 0.3408 | 19100 | 3.655 | - | - |
537
+ | 0.3426 | 19200 | 4.2441 | - | - |
538
+ | 0.3444 | 19300 | 4.3494 | - | - |
539
+ | 0.3462 | 19400 | 4.1824 | - | - |
540
+ | 0.3479 | 19500 | 4.3528 | - | - |
541
+ | 0.3497 | 19600 | 5.6073 | - | - |
542
+ | 0.3515 | 19700 | 4.8231 | - | - |
543
+ | 0.3533 | 19800 | 4.5816 | - | - |
544
+ | 0.3551 | 19900 | 4.5812 | - | - |
545
+ | 0.3569 | 20000 | 4.637 | 2.1229 | 0.7945 |
546
+ | 0.3587 | 20100 | 4.2619 | - | - |
547
+ | 0.3604 | 20200 | 4.5645 | - | - |
548
+ | 0.3622 | 20300 | 4.7248 | - | - |
549
+ | 0.3640 | 20400 | 4.5665 | - | - |
550
+ | 0.3658 | 20500 | 4.5628 | - | - |
551
+ | 0.3676 | 20600 | 4.8494 | - | - |
552
+ | 0.3694 | 20700 | 4.4338 | - | - |
553
+ | 0.3711 | 20800 | 4.3256 | - | - |
554
+ | 0.3729 | 20900 | 4.4388 | - | - |
555
+ | 0.3747 | 21000 | 4.158 | 2.3475 | 0.7732 |
556
+ | 0.3765 | 21100 | 3.962 | - | - |
557
+ | 0.3783 | 21200 | 3.931 | - | - |
558
+ | 0.3801 | 21300 | 4.0345 | - | - |
559
+ | 0.3818 | 21400 | 4.319 | - | - |
560
+ | 0.3836 | 21500 | 4.1329 | - | - |
561
+ | 0.3854 | 21600 | 4.245 | - | - |
562
+ | 0.3872 | 21700 | 4.518 | - | - |
563
+ | 0.3890 | 21800 | 4.4653 | - | - |
564
+ | 0.3908 | 21900 | 4.2777 | - | - |
565
+ | 0.3926 | 22000 | 4.3358 | 2.1933 | 0.7845 |
566
+ | 0.3943 | 22100 | 4.2291 | - | - |
567
+ | 0.3961 | 22200 | 3.8067 | - | - |
568
+ | 0.3979 | 22300 | 4.2039 | - | - |
569
+ | 0.3997 | 22400 | 4.0104 | - | - |
570
+ | 0.4015 | 22500 | 4.2346 | - | - |
571
+ | 0.4033 | 22600 | 4.0056 | - | - |
572
+ | 0.4050 | 22700 | 5.6038 | - | - |
573
+ | 0.4068 | 22800 | 5.1185 | - | - |
574
+ | 0.4086 | 22900 | 4.924 | - | - |
575
+ | 0.4104 | 23000 | 4.7841 | 1.9839 | 0.7956 |
576
+ | 0.4122 | 23100 | 4.7953 | - | - |
577
+ | 0.4140 | 23200 | 4.4229 | - | - |
578
+ | 0.4158 | 23300 | 4.6432 | - | - |
579
+ | 0.4175 | 23400 | 4.5284 | - | - |
580
+ | 0.4193 | 23500 | 4.7215 | - | - |
581
+ | 0.4211 | 23600 | 4.7432 | - | - |
582
+ | 0.4229 | 23700 | 5.0136 | - | - |
583
+ | 0.4247 | 23800 | 4.7958 | - | - |
584
+ | 0.4265 | 23900 | 4.6827 | - | - |
585
+ | 0.4282 | 24000 | 4.6665 | 1.9663 | 0.7870 |
586
+ | 0.4300 | 24100 | 4.5074 | - | - |
587
+ | 0.4318 | 24200 | 4.4189 | - | - |
588
+ | 0.4336 | 24300 | 4.4586 | - | - |
589
+ | 0.4354 | 24400 | 4.6421 | - | - |
590
+ | 0.4372 | 24500 | 4.4281 | - | - |
591
+ | 0.4389 | 24600 | 4.5153 | - | - |
592
+ | 0.4407 | 24700 | 4.9942 | - | - |
593
+ | 0.4425 | 24800 | 5.11 | - | - |
594
+ | 0.4443 | 24900 | 4.7071 | - | - |
595
+ | 0.4461 | 25000 | 4.6257 | 1.9461 | 0.7935 |
596
+ | 0.4479 | 25100 | 4.6576 | - | - |
597
+ | 0.4497 | 25200 | 4.6103 | - | - |
598
+ | 0.4514 | 25300 | 4.2066 | - | - |
599
+ | 0.4532 | 25400 | 4.6869 | - | - |
600
+ | 0.4550 | 25500 | 4.7575 | - | - |
601
+ | 0.4568 | 25600 | 4.6081 | - | - |
602
+ | 0.4586 | 25700 | 4.8144 | - | - |
603
+ | 0.4604 | 25800 | 5.2007 | - | - |
604
+ | 0.4621 | 25900 | 4.8367 | - | - |
605
+ | 0.4639 | 26000 | 4.5258 | 1.9131 | 0.7993 |
606
+ | 0.4657 | 26100 | 4.4784 | - | - |
607
+ | 0.4675 | 26200 | 4.5568 | - | - |
608
+ | 0.4693 | 26300 | 4.2591 | - | - |
609
+ | 0.4711 | 26400 | 4.4521 | - | - |
610
+ | 0.4729 | 26500 | 4.4041 | - | - |
611
+ | 0.4746 | 26600 | 4.4926 | - | - |
612
+ | 0.4764 | 26700 | 4.1686 | - | - |
613
+ | 0.4782 | 26800 | 4.6294 | - | - |
614
+ | 0.4800 | 26900 | 4.6889 | - | - |
615
+ | 0.4818 | 27000 | 4.5765 | 1.9539 | 0.7961 |
616
+ | 0.4836 | 27100 | 4.3427 | - | - |
617
+ | 0.4853 | 27200 | 4.5275 | - | - |
618
+ | 0.4871 | 27300 | 4.4186 | - | - |
619
+ | 0.4889 | 27400 | 4.0163 | - | - |
620
+ | 0.4907 | 27500 | 4.3204 | - | - |
621
+ | 0.4925 | 27600 | 4.179 | - | - |
622
+ | 0.4943 | 27700 | 4.3838 | - | - |
623
+ | 0.4960 | 27800 | 4.2631 | - | - |
624
+ | 0.4978 | 27900 | 4.7177 | - | - |
625
+ | 0.4996 | 28000 | 4.5161 | 2.0116 | 0.7935 |
626
+ | 0.5014 | 28100 | 4.2861 | - | - |
627
+ | 0.5032 | 28200 | 4.4123 | - | - |
628
+ | 0.5050 | 28300 | 4.293 | - | - |
629
+ | 0.5068 | 28400 | 4.2346 | - | - |
630
+ | 0.5085 | 28500 | 4.3355 | - | - |
631
+ | 0.5103 | 28600 | 4.4616 | - | - |
632
+ | 0.5121 | 28700 | 4.2409 | - | - |
633
+ | 0.5139 | 28800 | 4.2398 | - | - |
634
+ | 0.5157 | 28900 | 4.7412 | - | - |
635
+ | 0.5175 | 29000 | 4.5044 | 2.1008 | 0.7859 |
636
+ | 0.5192 | 29100 | 4.4556 | - | - |
637
+ | 0.5210 | 29200 | 4.2938 | - | - |
638
+ | 0.5228 | 29300 | 4.4962 | - | - |
639
+ | 0.5246 | 29400 | 4.477 | - | - |
640
+ | 0.5264 | 29500 | 4.2602 | - | - |
641
+ | 0.5282 | 29600 | 4.4231 | - | - |
642
+ | 0.5300 | 29700 | 4.2165 | - | - |
643
+ | 0.5317 | 29800 | 4.3729 | - | - |
644
+ | 0.5335 | 29900 | 4.2414 | - | - |
645
+ | 0.5353 | 30000 | 4.9937 | 2.0884 | 0.7702 |
646
+ | 0.5371 | 30100 | 4.5737 | - | - |
647
+ | 0.5389 | 30200 | 4.4517 | - | - |
648
+ | 0.5407 | 30300 | 4.4178 | - | - |
649
+ | 0.5424 | 30400 | 4.3514 | - | - |
650
+ | 0.5442 | 30500 | 3.9723 | - | - |
651
+ | 0.5460 | 30600 | 4.3707 | - | - |
652
+ | 0.5478 | 30700 | 4.2235 | - | - |
653
+ | 0.5496 | 30800 | 4.4278 | - | - |
654
+ | 0.5514 | 30900 | 4.2914 | - | - |
655
+ | 0.5531 | 31000 | 4.5636 | 2.3277 | 0.7454 |
656
+ | 0.5549 | 31100 | 4.4889 | - | - |
657
+ | 0.5567 | 31200 | 4.3211 | - | - |
658
+ | 0.5585 | 31300 | 4.404 | - | - |
659
+ | 0.5603 | 31400 | 4.2117 | - | - |
660
+ | 0.5621 | 31500 | 4.1126 | - | - |
661
+ | 0.5639 | 31600 | 4.1737 | - | - |
662
+ | 0.5656 | 31700 | 4.203 | - | - |
663
+ | 0.5674 | 31800 | 4.1093 | - | - |
664
+ | 0.5692 | 31900 | 4.0702 | - | - |
665
+ | 0.5710 | 32000 | 4.4189 | 2.6265 | 0.7375 |
666
+ | 0.5728 | 32100 | 4.9817 | - | - |
667
+ | 0.5746 | 32200 | 4.4736 | - | - |
668
+ | 0.5763 | 32300 | 4.348 | - | - |
669
+ | 0.5781 | 32400 | 4.5404 | - | - |
670
+ | 0.5799 | 32500 | 4.2987 | - | - |
671
+ | 0.5817 | 32600 | 4.0725 | - | - |
672
+ | 0.5835 | 32700 | 4.5469 | - | - |
673
+ | 0.5853 | 32800 | 4.4367 | - | - |
674
+ | 0.5870 | 32900 | 4.3369 | - | - |
675
+ | 0.5888 | 33000 | 4.2292 | 2.5687 | 0.7213 |
676
+ | 0.5906 | 33100 | 4.7929 | - | - |
677
+ | 0.5924 | 33200 | 4.4123 | - | - |
678
+ | 0.5942 | 33300 | 4.1699 | - | - |
679
+ | 0.5960 | 33400 | 4.4021 | - | - |
680
+ | 0.5978 | 33500 | 4.5257 | - | - |
681
+ | 0.5995 | 33600 | 3.7222 | - | - |
682
+ | 0.6013 | 33700 | 4.0746 | - | - |
683
+ | 0.6031 | 33800 | 4.1399 | - | - |
684
+ | 0.6049 | 33900 | 3.9957 | - | - |
685
+ | 0.6067 | 34000 | 4.093 | 2.4645 | 0.7524 |
686
+ | 0.6085 | 34100 | 4.2929 | - | - |
687
+ | 0.6102 | 34200 | 4.4765 | - | - |
688
+ | 0.6120 | 34300 | 4.3871 | - | - |
689
+ | 0.6138 | 34400 | 4.385 | - | - |
690
+ | 0.6156 | 34500 | 4.1455 | - | - |
691
+ | 0.6174 | 34600 | 3.7689 | - | - |
692
+ | 0.6192 | 34700 | 3.6574 | - | - |
693
+ | 0.6210 | 34800 | 4.2426 | - | - |
694
+ | 0.6227 | 34900 | 4.293 | - | - |
695
+ | 0.6245 | 35000 | 4.1368 | 2.4370 | 0.7765 |
696
+ | 0.6263 | 35100 | 3.6174 | - | - |
697
+ | 0.6281 | 35200 | 4.7763 | - | - |
698
+ | 0.6299 | 35300 | 4.3121 | - | - |
699
+ | 0.6317 | 35400 | 4.1886 | - | - |
700
+ | 0.6334 | 35500 | 4.3538 | - | - |
701
+ | 0.6352 | 35600 | 4.0285 | - | - |
702
+ | 0.6370 | 35700 | 3.4691 | - | - |
703
+ | 0.6388 | 35800 | 4.2732 | - | - |
704
+ | 0.6406 | 35900 | 4.2052 | - | - |
705
+ | 0.6424 | 36000 | 4.0452 | 2.4680 | 0.7732 |
706
+ | 0.6441 | 36100 | 3.9032 | - | - |
707
+ | 0.6459 | 36200 | 4.2608 | - | - |
708
+ | 0.6477 | 36300 | 4.262 | - | - |
709
+ | 0.6495 | 36400 | 4.1138 | - | - |
710
+ | 0.6513 | 36500 | 4.248 | - | - |
711
+ | 0.6531 | 36600 | 4.1163 | - | - |
712
+ | 0.6549 | 36700 | 3.6375 | - | - |
713
+ | 0.6566 | 36800 | 4.0768 | - | - |
714
+ | 0.6584 | 36900 | 4.0268 | - | - |
715
+ | 0.6602 | 37000 | 4.0129 | 2.6361 | 0.7702 |
716
+ | 0.6620 | 37100 | 3.7976 | - | - |
717
+ | 0.6638 | 37200 | 4.2518 | - | - |
718
+ | 0.6656 | 37300 | 4.5011 | - | - |
719
+ | 0.6673 | 37400 | 4.4488 | - | - |
720
+ | 0.6691 | 37500 | 3.9798 | - | - |
721
+ | 0.6709 | 37600 | 4.027 | - | - |
722
+ | 0.6727 | 37700 | 4.0342 | - | - |
723
+ | 0.6745 | 37800 | 3.8229 | - | - |
724
+ | 0.6763 | 37900 | 4.0573 | - | - |
725
+ | 0.6781 | 38000 | 4.1739 | 2.4511 | 0.7935 |
726
+ | 0.6798 | 38100 | 4.57 | - | - |
727
+ | 0.6816 | 38200 | 3.9108 | - | - |
728
+ | 0.6834 | 38300 | 4.3569 | - | - |
729
+ | 0.6852 | 38400 | 4.3775 | - | - |
730
+ | 0.6870 | 38500 | 4.2887 | - | - |
731
+ | 0.6888 | 38600 | 4.144 | - | - |
732
+ | 0.6905 | 38700 | 4.5112 | - | - |
733
+ | 0.6923 | 38800 | 3.5093 | - | - |
734
+ | 0.6941 | 38900 | 3.9626 | - | - |
735
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+
907
+ </details>
908
+
909
+ ### Framework Versions
910
+ - Python: 3.10.14
911
+ - Sentence Transformers: 3.0.1
912
+ - Transformers: 4.41.2
913
+ - PyTorch: 2.3.1+cu121
914
+ - Accelerate: 0.33.0
915
+ - Datasets: 2.21.0
916
+ - Tokenizers: 0.19.1
917
+
918
+ ## Citation
919
+
920
+ ### BibTeX
921
+
922
+ #### Sentence Transformers
923
+ ```bibtex
924
+ @inproceedings{reimers-2019-sentence-bert,
925
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
926
+ author = "Reimers, Nils and Gurevych, Iryna",
927
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
928
+ month = "11",
929
+ year = "2019",
930
+ publisher = "Association for Computational Linguistics",
931
+ url = "https://arxiv.org/abs/1908.10084",
932
+ }
933
+ ```
934
+
935
+ #### CoSENTLoss
936
+ ```bibtex
937
+ @online{kexuefm-8847,
938
+ title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
939
+ author={Su Jianlin},
940
+ year={2022},
941
+ month={Jan},
942
+ url={https://kexue.fm/archives/8847},
943
+ }
944
+ ```
945
+
946
+ <!--
947
+ ## Glossary
948
+
949
+ *Clearly define terms in order to be accessible across audiences.*
950
+ -->
951
+
952
+ <!--
953
+ ## Model Card Authors
954
+
955
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
956
+ -->
957
+
958
+ <!--
959
+ ## Model Card Contact
960
+
961
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
962
+ -->
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