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ManishThota
/
QueryRouter

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:724
loss:CoSENTLoss
Eval Results
text-embeddings-inference
Model card Files
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Community
ManishThota commited on
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Add new SentenceTransformer model.

Browse files
Files changed (11) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +911 -0
  3. config.json +26 -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.json +0 -0
  10. tokenizer_config.json +64 -0
  11. vocab.txt +0 -0
1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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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,
5
+ "pooling_mode_max_tokens": false,
6
+ "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 +1,911 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ base_model: sentence-transformers/all-MiniLM-L6-v2
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+ datasets: []
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+ language: []
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+ library_name: sentence-transformers
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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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+ pipeline_tag: sentence-similarity
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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:724
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+ - loss:CoSENTLoss
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+ widget:
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+ - source_sentence: Financials
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+ sentences:
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+ - What is the financial performance of ABC?
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+ - What companies operate in the same space as ABC?
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+ - What standards are used to evaluate the industry?
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+ - source_sentence: Research
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+ sentences:
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+ - What recent studies have been conducted on ABC?
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+ - What are the key factors considered in rating ABC?
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+ - How is the rating framework applied to the sector?
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+ - source_sentence: Criteria
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+ sentences:
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+ - What are the projected economic impacts of inflation on the technology industry?
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+ - What is the process for assessing the creditworthiness of ABC?
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+ - What are the primary ESG challenges faced by ABC?
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+ - source_sentence: Financials
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+ sentences:
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+ - Can you list the strengths and weaknesses of ABC?
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+ - What is understood by the term sovereign risk?
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+ - Can you provide the financial history of ABC?
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+ - source_sentence: Research
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+ sentences:
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+ - What macroeconomic trends are influencing the credit ratings of the automotive
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+ industry?
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+ - Who are the main rivals of ABC?
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+ - Can you provide the latest research insights on ABC?
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+ model-index:
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+ - name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-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: sts dev
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+ type: sts-dev
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+ metrics:
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+ - type: pearson_cosine
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+ value: .nan
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: .nan
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+ name: Spearman Cosine
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+ - type: pearson_manhattan
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+ value: .nan
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+ name: Pearson Manhattan
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+ - type: spearman_manhattan
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+ value: .nan
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+ name: Spearman Manhattan
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+ - type: pearson_euclidean
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+ value: .nan
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+ name: Pearson Euclidean
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+ - type: spearman_euclidean
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+ value: .nan
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+ name: Spearman Euclidean
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+ - type: pearson_dot
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+ value: .nan
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+ name: Pearson Dot
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+ - type: spearman_dot
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+ value: .nan
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+ name: Spearman Dot
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+ - type: pearson_max
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+ value: .nan
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+ name: Pearson Max
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+ - type: spearman_max
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+ value: .nan
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+ name: Spearman Max
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+ ---
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+
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+ # SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-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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+
100
+ ### Model Description
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+ - **Model Type:** Sentence Transformer
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+ - **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision 8b3219a92973c328a8e22fadcfa821b5dc75636a -->
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+ - **Maximum Sequence Length:** 512 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': 512, '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})
122
+ )
123
+ ```
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+
125
+ ## Usage
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+
127
+ ### Direct Usage (Sentence Transformers)
128
+
129
+ First install the Sentence Transformers library:
130
+
131
+ ```bash
132
+ pip install -U sentence-transformers
133
+ ```
134
+
135
+ Then you can load this model and run inference.
136
+ ```python
137
+ from sentence_transformers import SentenceTransformer
138
+
139
+ # Download from the 🤗 Hub
140
+ model = SentenceTransformer("ManishThota/QueryRouter")
141
+ # Run inference
142
+ sentences = [
143
+ 'Research',
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+ 'Can you provide the latest research insights on ABC?',
145
+ 'Who are the main rivals of ABC?',
146
+ ]
147
+ embeddings = model.encode(sentences)
148
+ print(embeddings.shape)
149
+ # [3, 384]
150
+
151
+ # Get the similarity scores for the embeddings
152
+ similarities = model.similarity(embeddings, embeddings)
153
+ print(similarities.shape)
154
+ # [3, 3]
155
+ ```
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+
157
+ <!--
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+ ### Direct Usage (Transformers)
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+
160
+ <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>
173
+ -->
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+
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+ <!--
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+ ### Out-of-Scope Use
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+
178
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
179
+ -->
180
+
181
+ ## Evaluation
182
+
183
+ ### Metrics
184
+
185
+ #### Semantic Similarity
186
+ * Dataset: `sts-dev`
187
+ * 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 |
190
+ |:--------------------|:--------|
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+ | pearson_cosine | nan |
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+ | **spearman_cosine** | **nan** |
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+ | pearson_manhattan | nan |
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+ | spearman_manhattan | nan |
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+ | pearson_euclidean | nan |
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+ | spearman_euclidean | nan |
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+ | pearson_dot | nan |
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+ | spearman_dot | nan |
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+ | pearson_max | nan |
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+ | spearman_max | nan |
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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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+
214
+ ## 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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+
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+ * Size: 724 training samples
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+ * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | sentence1 | sentence2 | score |
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+ |:--------|:--------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------|
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+ | type | string | string | float |
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+ | details | <ul><li>min: 3 tokens</li><li>mean: 3.27 tokens</li><li>max: 4 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 14.23 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
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+ * Samples:
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+ | sentence1 | sentence2 | score |
230
+ |:--------------------|:-------------------------------------------------|:-----------------|
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+ | <code>Rating</code> | <code>What rating does XYZ have?</code> | <code>1.0</code> |
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+ | <code>Rating</code> | <code>Can you provide the rating for XYZ?</code> | <code>1.0</code> |
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+ | <code>Rating</code> | <code>How is XYZ rated?</code> | <code>1.0</code> |
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+ * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
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+ ```json
236
+ {
237
+ "scale": 20.0,
238
+ "similarity_fct": "pairwise_cos_sim"
239
+ }
240
+ ```
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+
242
+ ### Evaluation Dataset
243
+
244
+ #### Unnamed Dataset
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+
246
+
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+ * Size: 60 evaluation samples
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+ * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
249
+ * Approximate statistics based on the first 1000 samples:
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+ | | sentence1 | sentence2 | score |
251
+ |:--------|:--------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------|
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+ | type | string | string | float |
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+ | details | <ul><li>min: 3 tokens</li><li>mean: 3.25 tokens</li><li>max: 4 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 12.48 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
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+ * Samples:
255
+ | sentence1 | sentence2 | score |
256
+ |:--------------------|:-------------------------------------------------|:-----------------|
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+ | <code>Rating</code> | <code>What is the current rating of ABC?</code> | <code>1.0</code> |
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+ | <code>Rating</code> | <code>Can you tell me the rating for ABC?</code> | <code>1.0</code> |
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+ | <code>Rating</code> | <code>What rating has ABC been assigned?</code> | <code>1.0</code> |
260
+ * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
261
+ ```json
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+ {
263
+ "scale": 20.0,
264
+ "similarity_fct": "pairwise_cos_sim"
265
+ }
266
+ ```
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+
268
+ ### Training Hyperparameters
269
+ #### Non-Default Hyperparameters
270
+
271
+ - `eval_strategy`: steps
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+ - `learning_rate`: 2e-05
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+ - `num_train_epochs`: 10
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+ - `warmup_ratio`: 0.1
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+ - `save_only_model`: True
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+ - `seed`: 33
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+ - `fp16`: True
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+ - `load_best_model_at_end`: True
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+
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+ #### All Hyperparameters
281
+ <details><summary>Click to expand</summary>
282
+
283
+ - `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`: 8
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+ - `per_device_eval_batch_size`: 8
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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`: 10
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+ - `max_steps`: -1
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+ - `lr_scheduler_type`: linear
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+ - `lr_scheduler_kwargs`: {}
303
+ - `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`: True
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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`: 33
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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`: True
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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
362
+ - `hub_model_id`: None
363
+ - `hub_strategy`: every_save
364
+ - `hub_private_repo`: False
365
+ - `hub_always_push`: False
366
+ - `gradient_checkpointing`: False
367
+ - `gradient_checkpointing_kwargs`: None
368
+ - `include_inputs_for_metrics`: False
369
+ - `eval_do_concat_batches`: True
370
+ - `fp16_backend`: auto
371
+ - `push_to_hub_model_id`: None
372
+ - `push_to_hub_organization`: None
373
+ - `mp_parameters`:
374
+ - `auto_find_batch_size`: False
375
+ - `full_determinism`: False
376
+ - `torchdynamo`: None
377
+ - `ray_scope`: last
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+ - `ddp_timeout`: 1800
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+ - `torch_compile`: False
380
+ - `torch_compile_backend`: None
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+ - `torch_compile_mode`: None
382
+ - `dispatch_batches`: None
383
+ - `split_batches`: None
384
+ - `include_tokens_per_second`: False
385
+ - `include_num_input_tokens_seen`: False
386
+ - `neftune_noise_alpha`: None
387
+ - `optim_target_modules`: None
388
+ - `batch_eval_metrics`: False
389
+ - `batch_sampler`: batch_sampler
390
+ - `multi_dataset_batch_sampler`: proportional
391
+
392
+ </details>
393
+
394
+ ### Training Logs
395
+ <details><summary>Click to expand</summary>
396
+
397
+ | Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine |
398
+ |:----------:|:-------:|:-------------:|:-------:|:-----------------------:|
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+ | 0.0220 | 2 | - | 0.0 | nan |
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+ | 0.0440 | 4 | - | 0.0 | nan |
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+ | 0.0659 | 6 | - | 0.0 | nan |
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+ | 0.0879 | 8 | - | 0.0 | nan |
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+ | 0.1099 | 10 | - | 0.0 | nan |
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+ | 0.1319 | 12 | - | 0.0 | nan |
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+ | 0.1538 | 14 | - | 0.0 | nan |
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+ | 0.1758 | 16 | - | 0.0 | nan |
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+ | 0.1978 | 18 | - | 0.0 | nan |
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+ | 0.2198 | 20 | - | 0.0 | nan |
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+ | 0.2418 | 22 | - | 0.0 | nan |
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+ | 0.2637 | 24 | - | 0.0 | nan |
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+ | 0.2857 | 26 | - | 0.0 | nan |
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+ | 0.3077 | 28 | - | 0.0 | nan |
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+ | 0.3297 | 30 | - | 0.0 | nan |
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+ | 0.3516 | 32 | - | 0.0 | nan |
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+ | 0.3736 | 34 | - | 0.0 | nan |
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+ | 0.3956 | 36 | - | 0.0 | nan |
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+ | 0.4176 | 38 | - | 0.0 | nan |
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+ | 0.4396 | 40 | - | 0.0 | nan |
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+ | 0.4615 | 42 | - | 0.0 | nan |
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+ | 0.4835 | 44 | - | 0.0 | nan |
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+ | 0.5055 | 46 | - | 0.0 | nan |
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+ | 0.5275 | 48 | - | 0.0 | nan |
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+ | 0.5495 | 50 | - | 0.0 | nan |
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+ | 0.5714 | 52 | - | 0.0 | nan |
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+ | 0.5934 | 54 | - | 0.0 | nan |
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+ | 0.6154 | 56 | - | 0.0 | nan |
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+ | 0.6374 | 58 | - | 0.0 | nan |
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+ | 0.6593 | 60 | - | 0.0 | nan |
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+
855
+ * The bold row denotes the saved checkpoint.
856
+ </details>
857
+
858
+ ### Framework Versions
859
+ - Python: 3.10.12
860
+ - Sentence Transformers: 3.0.1
861
+ - Transformers: 4.41.2
862
+ - PyTorch: 2.0.1+cu118
863
+ - Accelerate: 0.31.0
864
+ - Datasets: 2.20.0
865
+ - Tokenizers: 0.19.1
866
+
867
+ ## Citation
868
+
869
+ ### BibTeX
870
+
871
+ #### Sentence Transformers
872
+ ```bibtex
873
+ @inproceedings{reimers-2019-sentence-bert,
874
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
875
+ author = "Reimers, Nils and Gurevych, Iryna",
876
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
877
+ month = "11",
878
+ year = "2019",
879
+ publisher = "Association for Computational Linguistics",
880
+ url = "https://arxiv.org/abs/1908.10084",
881
+ }
882
+ ```
883
+
884
+ #### CoSENTLoss
885
+ ```bibtex
886
+ @online{kexuefm-8847,
887
+ title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
888
+ author={Su Jianlin},
889
+ year={2022},
890
+ month={Jan},
891
+ url={https://kexue.fm/archives/8847},
892
+ }
893
+ ```
894
+
895
+ <!--
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+ ## Glossary
897
+
898
+ *Clearly define terms in order to be accessible across audiences.*
899
+ -->
900
+
901
+ <!--
902
+ ## Model Card Authors
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+
904
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
905
+ -->
906
+
907
+ <!--
908
+ ## Model Card Contact
909
+
910
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
911
+ -->
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