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youssefkhalil320
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all-MiniLM-L6-v2-pairscore

Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:510287
loss:CoSENTLoss
Eval Results
text-embeddings-inference
Model card Files
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Community
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  1. 1_Pooling/config.json +10 -0
  2. README.md +1045 -0
  3. config.json +26 -0
  4. config_sentence_transformers.json +10 -0
  5. model.safetensors +3 -0
  6. modules.json +20 -0
  7. optimizer.pt +3 -0
  8. rng_state.pth +3 -0
  9. scheduler.pt +3 -0
  10. sentence_bert_config.json +4 -0
  11. special_tokens_map.json +37 -0
  12. tokenizer.json +0 -0
  13. tokenizer_config.json +64 -0
  14. trainer_state.json +0 -0
  15. training_args.bin +3 -0
  16. vocab.txt +0 -0
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,
4
+ "pooling_mode_mean_tokens": true,
5
+ "pooling_mode_max_tokens": false,
6
+ "pooling_mode_mean_sqrt_len_tokens": false,
7
+ "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 +1,1045 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ base_model: sentence-transformers/all-MiniLM-L6-v2
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+ language:
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+ - en
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+ library_name: sentence-transformers
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+ license: apache-2.0
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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:510287
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+ - loss:CoSENTLoss
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+ widget:
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+ - source_sentence: bag
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+ sentences:
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+ - bag
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+ - summer colors bag
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+ - carry all bag
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+ - source_sentence: bean bag
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+ sentences:
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+ - bag
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+ - havan bag
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+ - black yellow shoes
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+ - source_sentence: pyramid shaped cushion mattress
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+ sentences:
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+ - dress
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+ - silver bag
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+ - women shoes
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+ - source_sentence: handcrafted rug
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+ sentences:
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+ - amaga cross bag - white
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+ - handcrafted boots
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+ - polyester top
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+ - source_sentence: bean bag
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+ sentences:
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+ - bag
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+ - v-neck dress
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+ - bag
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+ model-index:
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+ - name: all-MiniLM-L6-v2-pair_score
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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: -0.13726370961372045
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: -0.16645918619928507
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+ name: Spearman Cosine
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+ - type: pearson_manhattan
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+ value: -0.1405300294713842
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+ name: Pearson Manhattan
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+ - type: spearman_manhattan
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+ value: -0.16334559546016153
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+ name: Spearman Manhattan
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+ - type: pearson_euclidean
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+ value: -0.1432496898556385
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+ name: Pearson Euclidean
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+ - type: spearman_euclidean
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+ value: -0.16645904911745338
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+ name: Spearman Euclidean
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+ - type: pearson_dot
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+ value: -0.13726370008450378
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+ name: Pearson Dot
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+ - type: spearman_dot
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+ value: -0.1664594964294906
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+ name: Spearman Dot
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+ - type: pearson_max
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+ value: -0.13726370008450378
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+ name: Pearson Max
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+ - type: spearman_max
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+ value: -0.16334559546016153
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+ name: Spearman Max
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+ ---
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+
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+ # all-MiniLM-L6-v2-pair_score
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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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+
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+ ### 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:** 256 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:** en
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+ - **License:** apache-2.0
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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': 256, '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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+ (2): Normalize()
123
+ )
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+ ```
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+
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+ ## Usage
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+
128
+ ### Direct Usage (Sentence Transformers)
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+
130
+ First install the Sentence Transformers library:
131
+
132
+ ```bash
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+ pip install -U sentence-transformers
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+ ```
135
+
136
+ Then you can load this model and run inference.
137
+ ```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("sentence_transformers_model_id")
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+ # Run inference
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+ sentences = [
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+ 'bean bag',
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+ 'bag',
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+ 'v-neck dress',
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+ ]
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+ embeddings = model.encode(sentences)
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+ print(embeddings.shape)
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+ # [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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+
184
+ ### Metrics
185
+
186
+ #### Semantic Similarity
187
+ * Dataset: `sts-dev`
188
+ * 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 | -0.1373 |
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+ | **spearman_cosine** | **-0.1665** |
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+ | pearson_manhattan | -0.1405 |
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+ | spearman_manhattan | -0.1633 |
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+ | pearson_euclidean | -0.1432 |
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+ | spearman_euclidean | -0.1665 |
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+ | pearson_dot | -0.1373 |
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+ | spearman_dot | -0.1665 |
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+ | pearson_max | -0.1373 |
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+ | spearman_max | -0.1633 |
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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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+
209
+ <!--
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+ ### Recommendations
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+
212
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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+
215
+ ## Training Details
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+
217
+ ### Training Hyperparameters
218
+ #### Non-Default Hyperparameters
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+
220
+ - `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`: 4
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+ - `warmup_ratio`: 0.1
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+ - `fp16`: True
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+
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+ #### All Hyperparameters
229
+ <details><summary>Click to expand</summary>
230
+
231
+ - `overwrite_output_dir`: False
232
+ - `do_predict`: False
233
+ - `eval_strategy`: steps
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+ - `prediction_loss_only`: True
235
+ - `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
238
+ - `per_gpu_eval_batch_size`: None
239
+ - `gradient_accumulation_steps`: 1
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+ - `eval_accumulation_steps`: None
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+ - `torch_empty_cache_steps`: None
242
+ - `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
246
+ - `adam_epsilon`: 1e-08
247
+ - `max_grad_norm`: 1.0
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+ - `num_train_epochs`: 4
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+ - `max_steps`: -1
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+ - `lr_scheduler_type`: linear
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+ - `lr_scheduler_kwargs`: {}
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+ - `warmup_ratio`: 0.1
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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
268
+ - `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
278
+ - `tpu_num_cores`: None
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+ - `tpu_metrics_debug`: False
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+ - `debug`: []
281
+ - `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`: []
291
+ - `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}
295
+ - `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
301
+ - `length_column_name`: length
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+ - `ddp_find_unused_parameters`: None
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+ - `ddp_bucket_cap_mb`: None
304
+ - `ddp_broadcast_buffers`: False
305
+ - `dataloader_pin_memory`: True
306
+ - `dataloader_persistent_workers`: False
307
+ - `skip_memory_metrics`: True
308
+ - `use_legacy_prediction_loop`: False
309
+ - `push_to_hub`: False
310
+ - `resume_from_checkpoint`: None
311
+ - `hub_model_id`: None
312
+ - `hub_strategy`: every_save
313
+ - `hub_private_repo`: False
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+ - `hub_always_push`: False
315
+ - `gradient_checkpointing`: False
316
+ - `gradient_checkpointing_kwargs`: None
317
+ - `include_inputs_for_metrics`: False
318
+ - `eval_do_concat_batches`: True
319
+ - `fp16_backend`: auto
320
+ - `push_to_hub_model_id`: None
321
+ - `push_to_hub_organization`: None
322
+ - `mp_parameters`:
323
+ - `auto_find_batch_size`: False
324
+ - `full_determinism`: False
325
+ - `torchdynamo`: None
326
+ - `ray_scope`: last
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+ - `ddp_timeout`: 1800
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+ - `torch_compile`: False
329
+ - `torch_compile_backend`: None
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+ - `torch_compile_mode`: None
331
+ - `dispatch_batches`: None
332
+ - `split_batches`: None
333
+ - `include_tokens_per_second`: False
334
+ - `include_num_input_tokens_seen`: False
335
+ - `neftune_noise_alpha`: None
336
+ - `optim_target_modules`: None
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+ - `batch_eval_metrics`: False
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+ - `eval_on_start`: False
339
+ - `use_liger_kernel`: False
340
+ - `eval_use_gather_object`: False
341
+ - `batch_sampler`: batch_sampler
342
+ - `multi_dataset_batch_sampler`: proportional
343
+
344
+ </details>
345
+
346
+ ### Training Logs
347
+ <details><summary>Click to expand</summary>
348
+
349
+ | Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine |
350
+ |:------:|:-----:|:-------------:|:------:|:-----------------------:|
351
+ | 0 | 0 | - | - | -0.1665 |
352
+ | 0.0063 | 100 | 11.9622 | - | - |
353
+ | 0.0125 | 200 | 11.265 | - | - |
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+ | 0.0188 | 300 | 10.5195 | - | - |
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+ | 0.0251 | 400 | 9.4744 | - | - |
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+ | 0.0314 | 500 | 8.4815 | 8.6217 | - |
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+ | 0.0376 | 600 | 7.6105 | - | - |
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+ | 0.0439 | 700 | 6.8023 | - | - |
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+ | 0.0502 | 800 | 6.1258 | - | - |
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+ | 0.0564 | 900 | 5.5032 | - | - |
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+ | 0.0627 | 1000 | 5.0397 | 5.1949 | - |
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+ | 0.0690 | 1100 | 4.6909 | - | - |
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+ | 0.0752 | 1200 | 4.5716 | - | - |
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+ | 0.0815 | 1300 | 4.3983 | - | - |
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+ | 0.0878 | 1400 | 4.2073 | - | - |
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+ | 0.0941 | 1500 | 4.2164 | 4.1422 | - |
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+ | 0.1003 | 1600 | 4.0921 | - | - |
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+ | 0.1066 | 1700 | 4.1785 | - | - |
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+ | 0.1129 | 1800 | 4.0503 | - | - |
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+ | 0.1191 | 1900 | 3.8969 | - | - |
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+ | 0.1254 | 2000 | 3.8538 | 3.9109 | - |
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+ | 0.1317 | 2100 | 3.872 | - | - |
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+ | 0.1380 | 2200 | 3.851 | - | - |
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+ | 0.1442 | 2300 | 3.6301 | - | - |
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+ | 0.1505 | 2400 | 3.5202 | - | - |
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+ | 0.1568 | 2500 | 3.6759 | 3.6389 | - |
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+ | 0.1630 | 2600 | 3.4106 | - | - |
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+ | 0.1693 | 2700 | 3.69 | - | - |
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+ | 0.1756 | 2800 | 3.6336 | - | - |
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+ | 0.1819 | 2900 | 3.4715 | - | - |
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+ | 0.1881 | 3000 | 3.2166 | 3.2739 | - |
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+ | 0.1944 | 3100 | 3.3844 | - | - |
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+ | 0.2007 | 3200 | 3.4449 | - | - |
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+ | 0.2069 | 3300 | 3.0811 | - | - |
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+ | 0.2132 | 3400 | 3.2777 | - | - |
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+ | 0.2195 | 3500 | 2.9505 | 3.0865 | - |
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+ | 0.2257 | 3600 | 3.1534 | - | - |
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+ | 0.2320 | 3700 | 2.9669 | - | - |
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+ | 0.2383 | 3800 | 2.9416 | - | - |
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+ | 0.2446 | 3900 | 2.9637 | - | - |
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+ | 0.2508 | 4000 | 2.9322 | 2.8447 | - |
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+ | 0.2571 | 4100 | 2.6926 | - | - |
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+ | 0.2634 | 4200 | 2.9353 | - | - |
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+ | 0.2696 | 4300 | 2.635 | - | - |
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+ | 0.2759 | 4400 | 2.5692 | - | - |
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+ | 0.2822 | 4500 | 3.0283 | 2.9033 | - |
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+ | 0.2885 | 4600 | 2.5804 | - | - |
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+ | 0.2947 | 4700 | 3.1374 | - | - |
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+ | 0.3010 | 4800 | 2.8479 | - | - |
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+ | 0.3073 | 4900 | 2.6809 | - | - |
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+ | 0.3135 | 5000 | 2.8267 | 2.6946 | - |
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+ | 0.3198 | 5100 | 2.7341 | - | - |
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+ | 0.3261 | 5200 | 2.8157 | - | - |
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+ | 0.3324 | 5300 | 2.5867 | - | - |
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+ | 0.3386 | 5400 | 2.8622 | - | - |
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+ | 0.3449 | 5500 | 2.9063 | 2.6115 | - |
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+ | 0.3512 | 5600 | 2.1514 | - | - |
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+ | 0.3574 | 5700 | 2.3755 | - | - |
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+ | 0.3637 | 5800 | 2.5055 | - | - |
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+ | 0.3700 | 5900 | 3.3237 | - | - |
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+ | 0.3762 | 6000 | 2.561 | 2.7512 | - |
412
+ | 0.3825 | 6100 | 2.4351 | - | - |
413
+ | 0.3888 | 6200 | 2.8472 | - | - |
414
+ | 0.3951 | 6300 | 2.76 | - | - |
415
+ | 0.4013 | 6400 | 2.1947 | - | - |
416
+ | 0.4076 | 6500 | 2.6409 | 2.5367 | - |
417
+ | 0.4139 | 6600 | 2.7262 | - | - |
418
+ | 0.4201 | 6700 | 2.7781 | - | - |
419
+ | 0.4264 | 6800 | 2.4718 | - | - |
420
+ | 0.4327 | 6900 | 2.567 | - | - |
421
+ | 0.4390 | 7000 | 2.4215 | 2.3409 | - |
422
+ | 0.4452 | 7100 | 1.9308 | - | - |
423
+ | 0.4515 | 7200 | 2.1232 | - | - |
424
+ | 0.4578 | 7300 | 2.421 | - | - |
425
+ | 0.4640 | 7400 | 2.3232 | - | - |
426
+ | 0.4703 | 7500 | 2.8543 | 2.3706 | - |
427
+ | 0.4766 | 7600 | 2.4276 | - | - |
428
+ | 0.4828 | 7700 | 2.4507 | - | - |
429
+ | 0.4891 | 7800 | 2.1963 | - | - |
430
+ | 0.4954 | 7900 | 2.4247 | - | - |
431
+ | 0.5017 | 8000 | 2.1948 | 2.5729 | - |
432
+ | 0.5079 | 8100 | 2.4069 | - | - |
433
+ | 0.5142 | 8200 | 2.4328 | - | - |
434
+ | 0.5205 | 8300 | 2.2198 | - | - |
435
+ | 0.5267 | 8400 | 2.1746 | - | - |
436
+ | 0.5330 | 8500 | 2.2618 | 2.3459 | - |
437
+ | 0.5393 | 8600 | 2.3909 | - | - |
438
+ | 0.5456 | 8700 | 2.035 | - | - |
439
+ | 0.5518 | 8800 | 2.2626 | - | - |
440
+ | 0.5581 | 8900 | 2.1541 | - | - |
441
+ | 0.5644 | 9000 | 1.9424 | 2.1625 | - |
442
+ | 0.5706 | 9100 | 2.5152 | - | - |
443
+ | 0.5769 | 9200 | 2.0462 | - | - |
444
+ | 0.5832 | 9300 | 1.6124 | - | - |
445
+ | 0.5895 | 9400 | 2.2236 | - | - |
446
+ | 0.5957 | 9500 | 2.4706 | 2.0569 | - |
447
+ | 0.6020 | 9600 | 2.4612 | - | - |
448
+ | 0.6083 | 9700 | 2.2784 | - | - |
449
+ | 0.6145 | 9800 | 1.9335 | - | - |
450
+ | 0.6208 | 9900 | 2.3779 | - | - |
451
+ | 0.6271 | 10000 | 1.6778 | 2.1123 | - |
452
+ | 0.6333 | 10100 | 2.4721 | - | - |
453
+ | 0.6396 | 10200 | 1.7822 | - | - |
454
+ | 0.6459 | 10300 | 2.077 | - | - |
455
+ | 0.6522 | 10400 | 1.9223 | - | - |
456
+ | 0.6584 | 10500 | 2.3513 | 1.8403 | - |
457
+ | 0.6647 | 10600 | 2.1387 | - | - |
458
+ | 0.6710 | 10700 | 2.1853 | - | - |
459
+ | 0.6772 | 10800 | 1.8715 | - | - |
460
+ | 0.6835 | 10900 | 1.8581 | - | - |
461
+ | 0.6898 | 11000 | 2.0076 | 2.0063 | - |
462
+ | 0.6961 | 11100 | 2.3144 | - | - |
463
+ | 0.7023 | 11200 | 2.0942 | - | - |
464
+ | 0.7086 | 11300 | 1.9117 | - | - |
465
+ | 0.7149 | 11400 | 2.2214 | - | - |
466
+ | 0.7211 | 11500 | 1.9678 | 1.9029 | - |
467
+ | 0.7274 | 11600 | 1.7459 | - | - |
468
+ | 0.7337 | 11700 | 2.0616 | - | - |
469
+ | 0.7400 | 11800 | 1.6169 | - | - |
470
+ | 0.7462 | 11900 | 1.5674 | - | - |
471
+ | 0.7525 | 12000 | 1.4956 | 1.8267 | - |
472
+ | 0.7588 | 12100 | 2.3816 | - | - |
473
+ | 0.7650 | 12200 | 2.2387 | - | - |
474
+ | 0.7713 | 12300 | 1.4625 | - | - |
475
+ | 0.7776 | 12400 | 2.028 | - | - |
476
+ | 0.7838 | 12500 | 2.151 | 1.7581 | - |
477
+ | 0.7901 | 12600 | 1.6896 | - | - |
478
+ | 0.7964 | 12700 | 1.8526 | - | - |
479
+ | 0.8027 | 12800 | 1.9745 | - | - |
480
+ | 0.8089 | 12900 | 2.1042 | - | - |
481
+ | 0.8152 | 13000 | 1.83 | 1.5667 | - |
482
+ | 0.8215 | 13100 | 1.7451 | - | - |
483
+ | 0.8277 | 13200 | 1.568 | - | - |
484
+ | 0.8340 | 13300 | 1.4432 | - | - |
485
+ | 0.8403 | 13400 | 1.9172 | - | - |
486
+ | 0.8466 | 13500 | 1.9438 | 1.6055 | - |
487
+ | 0.8528 | 13600 | 1.6488 | - | - |
488
+ | 0.8591 | 13700 | 1.8166 | - | - |
489
+ | 0.8654 | 13800 | 1.5929 | - | - |
490
+ | 0.8716 | 13900 | 1.2476 | - | - |
491
+ | 0.8779 | 14000 | 1.5236 | 1.8921 | - |
492
+ | 0.8842 | 14100 | 1.6538 | - | - |
493
+ | 0.8904 | 14200 | 1.8689 | - | - |
494
+ | 0.8967 | 14300 | 1.0831 | - | - |
495
+ | 0.9030 | 14400 | 1.7765 | - | - |
496
+ | 0.9093 | 14500 | 1.3548 | 1.6683 | - |
497
+ | 0.9155 | 14600 | 1.7792 | - | - |
498
+ | 0.9218 | 14700 | 1.73 | - | - |
499
+ | 0.9281 | 14800 | 1.5979 | - | - |
500
+ | 0.9343 | 14900 | 1.3678 | - | - |
501
+ | 0.9406 | 15000 | 2.0664 | 1.5161 | - |
502
+ | 0.9469 | 15100 | 1.4472 | - | - |
503
+ | 0.9532 | 15200 | 1.447 | - | - |
504
+ | 0.9594 | 15300 | 1.7261 | - | - |
505
+ | 0.9657 | 15400 | 1.4881 | - | - |
506
+ | 0.9720 | 15500 | 1.313 | 1.6227 | - |
507
+ | 0.9782 | 15600 | 1.4587 | - | - |
508
+ | 0.9845 | 15700 | 2.0982 | - | - |
509
+ | 0.9908 | 15800 | 1.4854 | - | - |
510
+ | 0.9971 | 15900 | 1.343 | - | - |
511
+ | 1.0033 | 16000 | 1.1795 | 1.5639 | - |
512
+ | 1.0096 | 16100 | 1.4001 | - | - |
513
+ | 1.0159 | 16200 | 1.3867 | - | - |
514
+ | 1.0221 | 16300 | 1.5191 | - | - |
515
+ | 1.0284 | 16400 | 1.4693 | - | - |
516
+ | 1.0347 | 16500 | 1.628 | 1.4716 | - |
517
+ | 1.0409 | 16600 | 1.0041 | - | - |
518
+ | 1.0472 | 16700 | 1.7728 | - | - |
519
+ | 1.0535 | 16800 | 1.5586 | - | - |
520
+ | 1.0598 | 16900 | 1.7229 | - | - |
521
+ | 1.0660 | 17000 | 1.5556 | 1.4676 | - |
522
+ | 1.0723 | 17100 | 1.2529 | - | - |
523
+ | 1.0786 | 17200 | 1.4787 | - | - |
524
+ | 1.0848 | 17300 | 1.1947 | - | - |
525
+ | 1.0911 | 17400 | 1.3014 | - | - |
526
+ | 1.0974 | 17500 | 1.3743 | 1.4624 | - |
527
+ | 1.1037 | 17600 | 1.3397 | - | - |
528
+ | 1.1099 | 17700 | 1.3062 | - | - |
529
+ | 1.1162 | 17800 | 1.3288 | - | - |
530
+ | 1.1225 | 17900 | 2.0002 | - | - |
531
+ | 1.1287 | 18000 | 2.0294 | 1.4185 | - |
532
+ | 1.1350 | 18100 | 1.5053 | - | - |
533
+ | 1.1413 | 18200 | 1.3657 | - | - |
534
+ | 1.1476 | 18300 | 1.3877 | - | - |
535
+ | 1.1538 | 18400 | 1.9034 | - | - |
536
+ | 1.1601 | 18500 | 1.4001 | 1.3813 | - |
537
+ | 1.1664 | 18600 | 1.7503 | - | - |
538
+ | 1.1726 | 18700 | 1.1482 | - | - |
539
+ | 1.1789 | 18800 | 1.0958 | - | - |
540
+ | 1.1852 | 18900 | 1.2657 | - | - |
541
+ | 1.1914 | 19000 | 1.3721 | 1.4702 | - |
542
+ | 1.1977 | 19100 | 1.2361 | - | - |
543
+ | 1.2040 | 19200 | 1.003 | - | - |
544
+ | 1.2103 | 19300 | 1.3677 | - | - |
545
+ | 1.2165 | 19400 | 1.668 | - | - |
546
+ | 1.2228 | 19500 | 1.2026 | 1.3641 | - |
547
+ | 1.2291 | 19600 | 1.1754 | - | - |
548
+ | 1.2353 | 19700 | 1.3196 | - | - |
549
+ | 1.2416 | 19800 | 1.4766 | - | - |
550
+ | 1.2479 | 19900 | 1.389 | - | - |
551
+ | 1.2542 | 20000 | 1.6974 | 1.3344 | - |
552
+ | 1.2604 | 20100 | 1.5036 | - | - |
553
+ | 1.2667 | 20200 | 1.1728 | - | - |
554
+ | 1.2730 | 20300 | 1.6058 | - | - |
555
+ | 1.2792 | 20400 | 1.5191 | - | - |
556
+ | 1.2855 | 20500 | 1.4516 | 1.3210 | - |
557
+ | 1.2918 | 20600 | 1.3485 | - | - |
558
+ | 1.2980 | 20700 | 1.2598 | - | - |
559
+ | 1.3043 | 20800 | 1.5871 | - | - |
560
+ | 1.3106 | 20900 | 1.1965 | - | - |
561
+ | 1.3169 | 21000 | 1.3983 | 1.2517 | - |
562
+ | 1.3231 | 21100 | 1.2605 | - | - |
563
+ | 1.3294 | 21200 | 1.5629 | - | - |
564
+ | 1.3357 | 21300 | 1.0668 | - | - |
565
+ | 1.3419 | 21400 | 1.1879 | - | - |
566
+ | 1.3482 | 21500 | 1.132 | 1.3881 | - |
567
+ | 1.3545 | 21600 | 1.7231 | - | - |
568
+ | 1.3608 | 21700 | 1.7636 | - | - |
569
+ | 1.3670 | 21800 | 1.1193 | - | - |
570
+ | 1.3733 | 21900 | 1.4662 | - | - |
571
+ | 1.3796 | 22000 | 2.0394 | 1.1927 | - |
572
+ | 1.3858 | 22100 | 1.1535 | - | - |
573
+ | 1.3921 | 22200 | 1.4592 | - | - |
574
+ | 1.3984 | 22300 | 1.276 | - | - |
575
+ | 1.4047 | 22400 | 1.2984 | - | - |
576
+ | 1.4109 | 22500 | 0.9741 | 1.2707 | - |
577
+ | 1.4172 | 22600 | 1.4253 | - | - |
578
+ | 1.4235 | 22700 | 1.0769 | - | - |
579
+ | 1.4297 | 22800 | 0.8276 | - | - |
580
+ | 1.4360 | 22900 | 1.2689 | - | - |
581
+ | 1.4423 | 23000 | 1.4817 | 1.2095 | - |
582
+ | 1.4485 | 23100 | 1.1522 | - | - |
583
+ | 1.4548 | 23200 | 0.8978 | - | - |
584
+ | 1.4611 | 23300 | 1.015 | - | - |
585
+ | 1.4674 | 23400 | 1.0351 | - | - |
586
+ | 1.4736 | 23500 | 1.3959 | 1.1969 | - |
587
+ | 1.4799 | 23600 | 1.2879 | - | - |
588
+ | 1.4862 | 23700 | 1.0651 | - | - |
589
+ | 1.4924 | 23800 | 1.1601 | - | - |
590
+ | 1.4987 | 23900 | 1.0034 | - | - |
591
+ | 1.5050 | 24000 | 1.3386 | 1.1590 | - |
592
+ | 1.5113 | 24100 | 1.142 | - | - |
593
+ | 1.5175 | 24200 | 1.3495 | - | - |
594
+ | 1.5238 | 24300 | 0.9993 | - | - |
595
+ | 1.5301 | 24400 | 0.9363 | - | - |
596
+ | 1.5363 | 24500 | 1.4402 | 1.2178 | - |
597
+ | 1.5426 | 24600 | 1.0648 | - | - |
598
+ | 1.5489 | 24700 | 1.5102 | - | - |
599
+ | 1.5552 | 24800 | 1.3415 | - | - |
600
+ | 1.5614 | 24900 | 0.7441 | - | - |
601
+ | 1.5677 | 25000 | 0.901 | 1.1982 | - |
602
+ | 1.5740 | 25100 | 1.3147 | - | - |
603
+ | 1.5802 | 25200 | 0.971 | - | - |
604
+ | 1.5865 | 25300 | 0.9988 | - | - |
605
+ | 1.5928 | 25400 | 1.1445 | - | - |
606
+ | 1.5990 | 25500 | 1.1018 | 1.1423 | - |
607
+ | 1.6053 | 25600 | 1.0902 | - | - |
608
+ | 1.6116 | 25700 | 1.2577 | - | - |
609
+ | 1.6179 | 25800 | 1.2005 | - | - |
610
+ | 1.6241 | 25900 | 1.2839 | - | - |
611
+ | 1.6304 | 26000 | 1.4122 | 1.1125 | - |
612
+ | 1.6367 | 26100 | 0.7832 | - | - |
613
+ | 1.6429 | 26200 | 1.3278 | - | - |
614
+ | 1.6492 | 26300 | 1.2055 | - | - |
615
+ | 1.6555 | 26400 | 1.5814 | - | - |
616
+ | 1.6618 | 26500 | 1.0393 | 1.0946 | - |
617
+ | 1.6680 | 26600 | 1.4531 | - | - |
618
+ | 1.6743 | 26700 | 1.4162 | - | - |
619
+ | 1.6806 | 26800 | 0.8498 | - | - |
620
+ | 1.6868 | 26900 | 1.1318 | - | - |
621
+ | 1.6931 | 27000 | 1.3287 | 1.0439 | - |
622
+ | 1.6994 | 27100 | 1.0886 | - | - |
623
+ | 1.7056 | 27200 | 0.8991 | - | - |
624
+ | 1.7119 | 27300 | 0.7563 | - | - |
625
+ | 1.7182 | 27400 | 0.9284 | - | - |
626
+ | 1.7245 | 27500 | 1.3388 | 1.0940 | - |
627
+ | 1.7307 | 27600 | 1.2951 | - | - |
628
+ | 1.7370 | 27700 | 0.9789 | - | - |
629
+ | 1.7433 | 27800 | 1.2898 | - | - |
630
+ | 1.7495 | 27900 | 0.9915 | - | - |
631
+ | 1.7558 | 28000 | 1.5349 | 1.0266 | - |
632
+ | 1.7621 | 28100 | 1.124 | - | - |
633
+ | 1.7684 | 28200 | 0.809 | - | - |
634
+ | 1.7746 | 28300 | 0.9617 | - | - |
635
+ | 1.7809 | 28400 | 1.3061 | - | - |
636
+ | 1.7872 | 28500 | 1.1323 | 1.0488 | - |
637
+ | 1.7934 | 28600 | 1.2991 | - | - |
638
+ | 1.7997 | 28700 | 0.8708 | - | - |
639
+ | 1.8060 | 28800 | 0.7493 | - | - |
640
+ | 1.8123 | 28900 | 1.004 | - | - |
641
+ | 1.8185 | 29000 | 1.1477 | 1.0206 | - |
642
+ | 1.8248 | 29100 | 1.1826 | - | - |
643
+ | 1.8311 | 29200 | 1.0961 | - | - |
644
+ | 1.8373 | 29300 | 1.4743 | - | - |
645
+ | 1.8436 | 29400 | 0.8413 | - | - |
646
+ | 1.8499 | 29500 | 1.2623 | 1.0047 | - |
647
+ | 1.8561 | 29600 | 0.8486 | - | - |
648
+ | 1.8624 | 29700 | 1.4481 | - | - |
649
+ | 1.8687 | 29800 | 1.2704 | - | - |
650
+ | 1.8750 | 29900 | 1.1913 | - | - |
651
+ | 1.8812 | 30000 | 0.9369 | 1.0277 | - |
652
+ | 1.8875 | 30100 | 1.2427 | - | - |
653
+ | 1.8938 | 30200 | 1.0576 | - | - |
654
+ | 1.9000 | 30300 | 0.9188 | - | - |
655
+ | 1.9063 | 30400 | 1.3227 | - | - |
656
+ | 1.9126 | 30500 | 1.4614 | 1.0550 | - |
657
+ | 1.9189 | 30600 | 1.2316 | - | - |
658
+ | 1.9251 | 30700 | 0.9487 | - | - |
659
+ | 1.9314 | 30800 | 1.1651 | - | - |
660
+ | 1.9377 | 30900 | 1.1622 | - | - |
661
+ | 1.9439 | 31000 | 1.1801 | 0.9981 | - |
662
+ | 1.9502 | 31100 | 0.8798 | - | - |
663
+ | 1.9565 | 31200 | 0.7196 | - | - |
664
+ | 1.9628 | 31300 | 1.2003 | - | - |
665
+ | 1.9690 | 31400 | 1.1823 | - | - |
666
+ | 1.9753 | 31500 | 1.1453 | 1.0320 | - |
667
+ | 1.9816 | 31600 | 1.4751 | - | - |
668
+ | 1.9878 | 31700 | 0.8502 | - | - |
669
+ | 1.9941 | 31800 | 0.8757 | - | - |
670
+ | 2.0004 | 31900 | 1.0489 | - | - |
671
+ | 2.0066 | 32000 | 1.4672 | 1.0571 | - |
672
+ | 2.0129 | 32100 | 0.9474 | - | - |
673
+ | 2.0192 | 32200 | 0.8037 | - | - |
674
+ | 2.0255 | 32300 | 0.9782 | - | - |
675
+ | 2.0317 | 32400 | 0.6943 | - | - |
676
+ | 2.0380 | 32500 | 1.0097 | 0.9797 | - |
677
+ | 2.0443 | 32600 | 0.9067 | - | - |
678
+ | 2.0505 | 32700 | 1.09 | - | - |
679
+ | 2.0568 | 32800 | 0.8464 | - | - |
680
+ | 2.0631 | 32900 | 0.9359 | - | - |
681
+ | 2.0694 | 33000 | 0.813 | 0.9907 | - |
682
+ | 2.0756 | 33100 | 0.8738 | - | - |
683
+ | 2.0819 | 33200 | 0.8178 | - | - |
684
+ | 2.0882 | 33300 | 1.1704 | - | - |
685
+ | 2.0944 | 33400 | 1.0073 | - | - |
686
+ | 2.1007 | 33500 | 1.1849 | 0.9582 | - |
687
+ | 2.1070 | 33600 | 0.7795 | - | - |
688
+ | 2.1133 | 33700 | 0.7688 | - | - |
689
+ | 2.1195 | 33800 | 0.9465 | - | - |
690
+ | 2.1258 | 33900 | 1.0883 | - | - |
691
+ | 2.1321 | 34000 | 0.7711 | 0.9557 | - |
692
+ | 2.1383 | 34100 | 0.9767 | - | - |
693
+ | 2.1446 | 34200 | 0.6702 | - | - |
694
+ | 2.1509 | 34300 | 0.9444 | - | - |
695
+ | 2.1571 | 34400 | 0.8741 | - | - |
696
+ | 2.1634 | 34500 | 1.0717 | 0.9526 | - |
697
+ | 2.1697 | 34600 | 0.8584 | - | - |
698
+ | 2.1760 | 34700 | 0.8926 | - | - |
699
+ | 2.1822 | 34800 | 0.8567 | - | - |
700
+ | 2.1885 | 34900 | 0.71 | - | - |
701
+ | 2.1948 | 35000 | 1.1285 | 0.9589 | - |
702
+ | 2.2010 | 35100 | 0.8999 | - | - |
703
+ | 2.2073 | 35200 | 0.8459 | - | - |
704
+ | 2.2136 | 35300 | 1.0608 | - | - |
705
+ | 2.2199 | 35400 | 0.6115 | - | - |
706
+ | 2.2261 | 35500 | 1.2468 | 0.9769 | - |
707
+ | 2.2324 | 35600 | 0.9987 | - | - |
708
+ | 2.2387 | 35700 | 0.9186 | - | - |
709
+ | 2.2449 | 35800 | 1.0505 | - | - |
710
+ | 2.2512 | 35900 | 0.6253 | - | - |
711
+ | 2.2575 | 36000 | 0.6523 | 0.9501 | - |
712
+ | 2.2637 | 36100 | 0.8252 | - | - |
713
+ | 2.2700 | 36200 | 0.9793 | - | - |
714
+ | 2.2763 | 36300 | 0.8845 | - | - |
715
+ | 2.2826 | 36400 | 1.0121 | - | - |
716
+ | 2.2888 | 36500 | 0.9849 | 0.9245 | - |
717
+ | 2.2951 | 36600 | 1.2937 | - | - |
718
+ | 2.3014 | 36700 | 1.0484 | - | - |
719
+ | 2.3076 | 36800 | 0.8801 | - | - |
720
+ | 2.3139 | 36900 | 0.7552 | - | - |
721
+ | 2.3202 | 37000 | 0.7641 | 0.9280 | - |
722
+ | 2.3265 | 37100 | 0.883 | - | - |
723
+ | 2.3327 | 37200 | 0.77 | - | - |
724
+ | 2.3390 | 37300 | 1.2699 | - | - |
725
+ | 2.3453 | 37400 | 0.8766 | - | - |
726
+ | 2.3515 | 37500 | 1.1154 | 0.9623 | - |
727
+ | 2.3578 | 37600 | 1.0634 | - | - |
728
+ | 2.3641 | 37700 | 0.8822 | - | - |
729
+ | 2.3704 | 37800 | 0.839 | - | - |
730
+ | 2.3766 | 37900 | 0.684 | - | - |
731
+ | 2.3829 | 38000 | 0.8051 | 0.9198 | - |
732
+ | 2.3892 | 38100 | 0.9585 | - | - |
733
+ | 2.3954 | 38200 | 0.7156 | - | - |
734
+ | 2.4017 | 38300 | 0.5271 | - | - |
735
+ | 2.4080 | 38400 | 0.805 | - | - |
736
+ | 2.4142 | 38500 | 0.7898 | 0.8785 | - |
737
+ | 2.4205 | 38600 | 0.6935 | - | - |
738
+ | 2.4268 | 38700 | 0.8011 | - | - |
739
+ | 2.4331 | 38800 | 0.9812 | - | - |
740
+ | 2.4393 | 38900 | 0.4427 | - | - |
741
+ | 2.4456 | 39000 | 0.492 | 0.9313 | - |
742
+ | 2.4519 | 39100 | 0.47 | - | - |
743
+ | 2.4581 | 39200 | 1.1876 | - | - |
744
+ | 2.4644 | 39300 | 0.5778 | - | - |
745
+ | 2.4707 | 39400 | 0.6763 | - | - |
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975
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977
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978
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979
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980
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981
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982
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983
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984
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985
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986
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987
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988
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989
+
990
+ </details>
991
+
992
+ ### Framework Versions
993
+ - Python: 3.8.10
994
+ - Sentence Transformers: 3.1.1
995
+ - Transformers: 4.45.1
996
+ - PyTorch: 2.4.0+cu121
997
+ - Accelerate: 0.34.2
998
+ - Datasets: 3.0.1
999
+ - Tokenizers: 0.20.0
1000
+
1001
+ ## Citation
1002
+
1003
+ ### BibTeX
1004
+
1005
+ #### Sentence Transformers
1006
+ ```bibtex
1007
+ @inproceedings{reimers-2019-sentence-bert,
1008
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
1009
+ author = "Reimers, Nils and Gurevych, Iryna",
1010
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
1011
+ month = "11",
1012
+ year = "2019",
1013
+ publisher = "Association for Computational Linguistics",
1014
+ url = "https://arxiv.org/abs/1908.10084",
1015
+ }
1016
+ ```
1017
+
1018
+ #### CoSENTLoss
1019
+ ```bibtex
1020
+ @online{kexuefm-8847,
1021
+ title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
1022
+ author={Su Jianlin},
1023
+ year={2022},
1024
+ month={Jan},
1025
+ url={https://kexue.fm/archives/8847},
1026
+ }
1027
+ ```
1028
+
1029
+ <!--
1030
+ ## Glossary
1031
+
1032
+ *Clearly define terms in order to be accessible across audiences.*
1033
+ -->
1034
+
1035
+ <!--
1036
+ ## Model Card Authors
1037
+
1038
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
1039
+ -->
1040
+
1041
+ <!--
1042
+ ## Model Card Contact
1043
+
1044
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
1045
+ -->
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