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youssefkhalil320
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gte-small-pairscore

Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:510287
loss:CoSENTLoss
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
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  1. 1_Pooling/config.json +10 -0
  2. README.md +794 -0
  3. config.json +25 -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
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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 +1,794 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ base_model: thenlper/gte-small
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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: gte-small-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: -0.17233834277204704
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: -0.2198666606268324
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+ name: Spearman Cosine
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+ - type: pearson_manhattan
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+ value: -0.18762372004757433
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+ name: Pearson Manhattan
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+ - type: spearman_manhattan
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+ value: -0.2263824285497944
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+ name: Spearman Manhattan
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+ - type: pearson_euclidean
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+ value: -0.1815229012953811
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+ name: Pearson Euclidean
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+ - type: spearman_euclidean
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+ value: -0.21986651824620543
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+ name: Spearman Euclidean
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+ - type: pearson_dot
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+ value: -0.17233841453151344
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+ name: Pearson Dot
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+ - type: spearman_dot
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+ value: -0.21986648743251272
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+ name: Spearman Dot
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+ - type: pearson_max
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+ value: -0.17233834277204704
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+ name: Pearson Max
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+ - type: spearman_max
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+ value: -0.21986648743251272
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+ name: Spearman Max
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+ ---
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+
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+ # gte-small-pair_score
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [thenlper/gte-small](https://huggingface.co/thenlper/gte-small). 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:** [thenlper/gte-small](https://huggingface.co/thenlper/gte-small) <!-- at revision 50c7dd33df1027ef560fd504d95e277948c3c886 -->
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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:** 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': 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})
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+ (2): Normalize()
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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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+
130
+ First install the Sentence Transformers library:
131
+
132
+ ```bash
133
+ pip install -U sentence-transformers
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+ ```
135
+
136
+ Then you can load this model and run inference.
137
+ ```python
138
+ from sentence_transformers import SentenceTransformer
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+
140
+ # Download from the 🤗 Hub
141
+ model = SentenceTransformer("sentence_transformers_model_id")
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+ # Run inference
143
+ sentences = [
144
+ 'bean bag',
145
+ 'bag',
146
+ 'v-neck dress',
147
+ ]
148
+ embeddings = model.encode(sentences)
149
+ print(embeddings.shape)
150
+ # [3, 384]
151
+
152
+ # Get the similarity scores for the embeddings
153
+ similarities = model.similarity(embeddings, embeddings)
154
+ print(similarities.shape)
155
+ # [3, 3]
156
+ ```
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+
158
+ <!--
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+ ### Direct Usage (Transformers)
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+
161
+ <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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+
169
+ 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: `sts-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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+ |:--------------------|:------------|
192
+ | pearson_cosine | -0.1723 |
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+ | **spearman_cosine** | **-0.2199** |
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+ | pearson_manhattan | -0.1876 |
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+ | spearman_manhattan | -0.2264 |
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+ | pearson_euclidean | -0.1815 |
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+ | spearman_euclidean | -0.2199 |
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+ | pearson_dot | -0.1723 |
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+ | spearman_dot | -0.2199 |
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+ | pearson_max | -0.1723 |
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+ | spearman_max | -0.2199 |
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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`: 4
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+ - `warmup_ratio`: 0.1
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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
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+ <details><summary>Click to expand</summary>
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+
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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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+ - `torch_empty_cache_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`: 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
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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
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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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+ - `eval_on_start`: False
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+ - `use_liger_kernel`: False
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+ - `eval_use_gather_object`: False
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+ - `batch_sampler`: batch_sampler
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+ - `multi_dataset_batch_sampler`: proportional
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+
345
+ </details>
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+
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+ ### Training Logs
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+ <details><summary>Click to expand</summary>
349
+
350
+ | Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine |
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+ |:------:|:-----:|:-------------:|:------:|:-----------------------:|
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+ | 0 | 0 | - | - | -0.2199 |
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+ | 0.0063 | 100 | 6.3669 | 6.6513 | - |
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+ | 0.0125 | 200 | 6.1795 | 6.2541 | - |
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+ | 0.0188 | 300 | 5.893 | 5.7733 | - |
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+ | 0.0251 | 400 | 5.5099 | 5.3626 | - |
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+ | 0.0314 | 500 | 5.1589 | 4.9902 | - |
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+ | 0.0376 | 600 | 4.8599 | 4.6523 | - |
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+ | 0.0439 | 700 | 4.6075 | 4.4233 | - |
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+ | 0.0502 | 800 | 4.3831 | 4.2431 | - |
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+ | 0.0564 | 900 | 4.1737 | 4.1350 | - |
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+ | 0.0627 | 1000 | 4.0266 | 4.0327 | - |
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+ | 0.0690 | 1100 | 3.9526 | 3.9281 | - |
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+ | 0.0752 | 1200 | 3.8773 | 3.8735 | - |
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+ | 0.0815 | 1300 | 3.7856 | 3.7779 | - |
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+ | 0.0878 | 1400 | 3.5994 | 3.7054 | - |
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+ | 0.0941 | 1500 | 3.7067 | 3.6155 | - |
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+ | 0.1003 | 1600 | 3.5471 | 3.5798 | - |
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+ | 0.1066 | 1700 | 3.6679 | 3.4654 | - |
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+ | 0.1129 | 1800 | 3.4484 | 3.4175 | - |
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+ | 0.1191 | 1900 | 3.377 | 3.4129 | - |
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+ | 0.1254 | 2000 | 3.4259 | 3.3347 | - |
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+ | 0.1317 | 2100 | 3.4832 | 3.2113 | - |
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+ | 0.1380 | 2200 | 3.3043 | 3.1641 | - |
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+ | 0.1442 | 2300 | 3.2344 | 3.1529 | - |
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+ | 0.1505 | 2400 | 3.1238 | 3.2577 | - |
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+ | 0.1568 | 2500 | 3.1456 | 3.0678 | - |
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+ | 0.1630 | 2600 | 3.0223 | 3.0006 | - |
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+ | 0.1693 | 2700 | 3.2046 | 2.9682 | - |
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+ | 0.1756 | 2800 | 3.0866 | 3.0524 | - |
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+ | 0.1819 | 2900 | 2.9271 | 3.0573 | - |
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+ | 0.1881 | 3000 | 2.7692 | 3.0558 | - |
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+ | 0.1944 | 3100 | 3.1498 | 2.7866 | - |
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+ | 0.2007 | 3200 | 3.0683 | 2.8478 | - |
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+ | 0.2069 | 3300 | 2.5776 | 2.9459 | - |
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+ | 0.2132 | 3400 | 2.9394 | 2.7133 | - |
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+ | 0.2195 | 3500 | 2.6996 | 2.8582 | - |
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+ | 0.2257 | 3600 | 2.569 | 2.8092 | - |
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+ | 0.2320 | 3700 | 2.6535 | 2.7977 | - |
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+ | 0.2383 | 3800 | 2.6679 | 2.8578 | - |
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+ | 0.2446 | 3900 | 2.592 | 2.8251 | - |
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+ | 0.2508 | 4000 | 2.4931 | 2.5976 | - |
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+ | 0.2571 | 4100 | 2.3012 | 2.9260 | - |
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+ | 0.2634 | 4200 | 2.4728 | 2.7869 | - |
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+ | 0.2696 | 4300 | 2.4391 | 2.8987 | - |
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+ | 0.2759 | 4400 | 2.3825 | 2.7804 | - |
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+ | 0.2822 | 4500 | 2.6257 | 2.8309 | - |
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+ | 0.2885 | 4600 | 2.4304 | 3.2419 | - |
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+ | 0.2947 | 4700 | 3.0246 | 2.5732 | - |
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+ | 0.3010 | 4800 | 2.6894 | 2.8058 | - |
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+ | 0.3073 | 4900 | 2.5333 | 2.4582 | - |
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+ | 0.3135 | 5000 | 2.3268 | 2.8622 | - |
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+ | 0.3198 | 5100 | 2.6996 | 2.7515 | - |
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+ | 0.3261 | 5200 | 2.8175 | 2.5842 | - |
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+ | 0.3324 | 5300 | 2.1244 | 2.7252 | - |
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+ | 0.3386 | 5400 | 2.7331 | 2.5053 | - |
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+ | 0.3449 | 5500 | 2.3226 | 2.2430 | - |
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+ | 0.3512 | 5600 | 2.0706 | 2.6055 | - |
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+ | 0.3574 | 5700 | 2.2461 | 2.8949 | - |
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+ | 0.3637 | 5800 | 2.6365 | 2.5272 | - |
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+ | 0.3700 | 5900 | 2.7119 | 2.4331 | - |
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+ | 0.3762 | 6000 | 2.6146 | 2.3858 | - |
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+ | 0.3825 | 6100 | 2.1998 | 2.6891 | - |
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+ | 0.3888 | 6200 | 2.5076 | 2.3827 | - |
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+ | 0.3951 | 6300 | 2.5244 | 2.6522 | - |
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+ | 0.4013 | 6400 | 2.0613 | 2.4750 | - |
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+ | 0.4076 | 6500 | 2.465 | 2.5254 | - |
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+ | 0.4139 | 6600 | 2.3201 | 2.2249 | - |
419
+ | 0.4201 | 6700 | 2.234 | 2.5168 | - |
420
+ | 0.4264 | 6800 | 2.1277 | 2.5358 | - |
421
+ | 0.4327 | 6900 | 2.3801 | 2.4992 | - |
422
+ | 0.4390 | 7000 | 2.1443 | 2.4043 | - |
423
+ | 0.4452 | 7100 | 1.9136 | 2.3874 | - |
424
+ | 0.4515 | 7200 | 2.3067 | 2.6475 | - |
425
+ | 0.4578 | 7300 | 2.1464 | 2.4704 | - |
426
+ | 0.4640 | 7400 | 2.2151 | 2.5199 | - |
427
+ | 0.4703 | 7500 | 2.4653 | 2.5293 | - |
428
+ | 0.4766 | 7600 | 2.4425 | 2.1264 | - |
429
+ | 0.4828 | 7700 | 2.3138 | 2.1810 | - |
430
+ | 0.4891 | 7800 | 2.247 | 2.1404 | - |
431
+ | 0.4954 | 7900 | 2.1621 | 2.2123 | - |
432
+ | 0.5017 | 8000 | 2.1338 | 2.5108 | - |
433
+ | 0.5079 | 8100 | 2.1846 | 2.1493 | - |
434
+ | 0.5142 | 8200 | 2.1167 | 2.2879 | - |
435
+ | 0.5205 | 8300 | 2.2143 | 2.1664 | - |
436
+ | 0.5267 | 8400 | 2.3152 | 2.1072 | - |
437
+ | 0.5330 | 8500 | 1.7618 | 2.0324 | - |
438
+ | 0.5393 | 8600 | 2.0777 | 2.4468 | - |
439
+ | 0.5456 | 8700 | 2.1573 | 2.2053 | - |
440
+ | 0.5518 | 8800 | 1.9831 | 2.3277 | - |
441
+ | 0.5581 | 8900 | 1.9083 | 1.9949 | - |
442
+ | 0.5644 | 9000 | 1.932 | 1.9848 | - |
443
+ | 0.5706 | 9100 | 2.3223 | 1.9192 | - |
444
+ | 0.5769 | 9200 | 1.7583 | 2.0066 | - |
445
+ | 0.5832 | 9300 | 1.6394 | 2.0322 | - |
446
+ | 0.5895 | 9400 | 1.973 | 2.1010 | - |
447
+ | 0.5957 | 9500 | 2.2377 | 2.1176 | - |
448
+ | 0.6020 | 9600 | 2.2269 | 2.0027 | - |
449
+ | 0.6083 | 9700 | 1.971 | 1.9329 | - |
450
+ | 0.6145 | 9800 | 1.8982 | 1.9797 | - |
451
+ | 0.6208 | 9900 | 2.2853 | 1.8433 | - |
452
+ | 0.6271 | 10000 | 1.6657 | 2.0091 | - |
453
+ | 0.6333 | 10100 | 2.0732 | 1.7602 | - |
454
+ | 0.6396 | 10200 | 1.6951 | 1.8849 | - |
455
+ | 0.6459 | 10300 | 1.6548 | 2.0066 | - |
456
+ | 0.6522 | 10400 | 1.7187 | 1.9644 | - |
457
+ | 0.6584 | 10500 | 2.1948 | 1.8392 | - |
458
+ | 0.6647 | 10600 | 1.9756 | 1.8404 | - |
459
+ | 0.6710 | 10700 | 1.7644 | 1.9101 | - |
460
+ | 0.6772 | 10800 | 1.6295 | 1.9440 | - |
461
+ | 0.6835 | 10900 | 1.7687 | 1.9031 | - |
462
+ | 0.6898 | 11000 | 1.8203 | 1.9650 | - |
463
+ | 0.6961 | 11100 | 2.3055 | 1.8432 | - |
464
+ | 0.7023 | 11200 | 1.8294 | 1.7364 | - |
465
+ | 0.7086 | 11300 | 2.0026 | 1.7894 | - |
466
+ | 0.7149 | 11400 | 1.9916 | 1.8343 | - |
467
+ | 0.7211 | 11500 | 1.8698 | 1.8079 | - |
468
+ | 0.7274 | 11600 | 1.5213 | 1.6849 | - |
469
+ | 0.7337 | 11700 | 1.7462 | 1.7328 | - |
470
+ | 0.7400 | 11800 | 1.3519 | 1.8370 | - |
471
+ | 0.7462 | 11900 | 1.4935 | 1.7247 | - |
472
+ | 0.7525 | 12000 | 1.1721 | 1.6529 | - |
473
+ | 0.7588 | 12100 | 2.2432 | 1.6329 | - |
474
+ | 0.7650 | 12200 | 1.6931 | 1.6563 | - |
475
+ | 0.7713 | 12300 | 1.2736 | 1.6984 | - |
476
+ | 0.7776 | 12400 | 1.7063 | 1.6574 | - |
477
+ | 0.7838 | 12500 | 1.7921 | 1.7760 | - |
478
+ | 0.7901 | 12600 | 1.875 | 1.7149 | - |
479
+ | 0.7964 | 12700 | 1.4435 | 1.8085 | - |
480
+ | 0.8027 | 12800 | 1.5271 | 1.7247 | - |
481
+ | 0.8089 | 12900 | 1.618 | 1.6542 | - |
482
+ | 0.8152 | 13000 | 1.9788 | 1.5686 | - |
483
+ | 0.8215 | 13100 | 1.8213 | 1.5603 | - |
484
+ | 0.8277 | 13200 | 1.3661 | 1.6376 | - |
485
+ | 0.8340 | 13300 | 1.3852 | 1.5953 | - |
486
+ | 0.8403 | 13400 | 1.4673 | 1.6346 | - |
487
+ | 0.8466 | 13500 | 1.6684 | 1.5818 | - |
488
+ | 0.8528 | 13600 | 1.686 | 1.5840 | - |
489
+ | 0.8591 | 13700 | 1.4397 | 1.5855 | - |
490
+ | 0.8654 | 13800 | 1.5973 | 1.7207 | - |
491
+ | 0.8716 | 13900 | 1.221 | 1.6381 | - |
492
+ | 0.8779 | 14000 | 1.2082 | 1.6335 | - |
493
+ | 0.8842 | 14100 | 1.5399 | 1.6434 | - |
494
+ | 0.8904 | 14200 | 1.5265 | 1.7266 | - |
495
+ | 0.8967 | 14300 | 0.9321 | 1.5981 | - |
496
+ | 0.9030 | 14400 | 1.1133 | 1.6126 | - |
497
+ | 0.9093 | 14500 | 1.0754 | 1.6227 | - |
498
+ | 0.9155 | 14600 | 1.3486 | 1.6143 | - |
499
+ | 0.9218 | 14700 | 1.6338 | 1.5452 | - |
500
+ | 0.9281 | 14800 | 1.389 | 1.6099 | - |
501
+ | 0.9343 | 14900 | 1.3776 | 1.6435 | - |
502
+ | 0.9406 | 15000 | 1.8714 | 1.5377 | - |
503
+ | 0.9469 | 15100 | 1.1286 | 1.6326 | - |
504
+ | 0.9532 | 15200 | 1.4029 | 1.6256 | - |
505
+ | 0.9594 | 15300 | 1.7772 | 1.5221 | - |
506
+ | 0.9657 | 15400 | 1.3415 | 1.5604 | - |
507
+ | 0.9720 | 15500 | 1.1088 | 1.5749 | - |
508
+ | 0.9782 | 15600 | 1.4602 | 1.4941 | - |
509
+ | 0.9845 | 15700 | 1.867 | 1.3731 | - |
510
+ | 0.9908 | 15800 | 1.4541 | 1.4206 | - |
511
+ | 0.9971 | 15900 | 1.1966 | 1.4982 | - |
512
+ | 1.0033 | 16000 | 1.1447 | 1.5121 | - |
513
+ | 1.0096 | 16100 | 1.1266 | 1.4103 | - |
514
+ | 1.0159 | 16200 | 1.1971 | 1.5044 | - |
515
+ | 1.0221 | 16300 | 1.3376 | 1.5337 | - |
516
+ | 1.0284 | 16400 | 1.7977 | 1.5712 | - |
517
+ | 1.0347 | 16500 | 1.6946 | 1.5323 | - |
518
+ | 1.0409 | 16600 | 0.8674 | 1.4462 | - |
519
+ | 1.0472 | 16700 | 1.6447 | 1.4831 | - |
520
+ | 1.0535 | 16800 | 1.2709 | 1.5756 | - |
521
+ | 1.0598 | 16900 | 1.5217 | 1.5060 | - |
522
+ | 1.0660 | 17000 | 1.2986 | 1.4834 | - |
523
+ | 1.0723 | 17100 | 0.9976 | 1.4841 | - |
524
+ | 1.0786 | 17200 | 1.3457 | 1.4228 | - |
525
+ | 1.0848 | 17300 | 0.987 | 1.3807 | - |
526
+ | 1.0911 | 17400 | 1.2714 | 1.3471 | - |
527
+ | 1.0974 | 17500 | 1.298 | 1.4134 | - |
528
+ | 1.1037 | 17600 | 0.9522 | 1.4225 | - |
529
+ | 1.1099 | 17700 | 1.0634 | 1.4474 | - |
530
+ | 1.1162 | 17800 | 1.2889 | 1.4679 | - |
531
+ | 1.1225 | 17900 | 1.7532 | 1.3757 | - |
532
+ | 1.1287 | 18000 | 1.6613 | 1.3808 | - |
533
+ | 1.1350 | 18100 | 1.1765 | 1.3903 | - |
534
+ | 1.1413 | 18200 | 1.2787 | 1.3921 | - |
535
+ | 1.1476 | 18300 | 1.2532 | 1.3519 | - |
536
+ | 1.1538 | 18400 | 1.8056 | 1.2984 | - |
537
+ | 1.1601 | 18500 | 1.0985 | 1.3322 | - |
538
+ | 1.1664 | 18600 | 1.8665 | 1.4060 | - |
539
+ | 1.1726 | 18700 | 1.2427 | 1.3775 | - |
540
+ | 1.1789 | 18800 | 1.1241 | 1.3168 | - |
541
+ | 1.1852 | 18900 | 1.2348 | 1.3539 | - |
542
+ | 1.1914 | 19000 | 1.1709 | 1.3540 | - |
543
+ | 1.1977 | 19100 | 0.8844 | 1.3142 | - |
544
+ | 1.2040 | 19200 | 1.0035 | 1.3781 | - |
545
+ | 1.2103 | 19300 | 1.4279 | 1.2615 | - |
546
+ | 1.2165 | 19400 | 1.3327 | 1.2696 | - |
547
+ | 1.2228 | 19500 | 0.993 | 1.3170 | - |
548
+ | 1.2291 | 19600 | 0.7869 | 1.2967 | - |
549
+ | 1.2353 | 19700 | 0.985 | 1.3057 | - |
550
+ | 1.2416 | 19800 | 1.1603 | 1.2797 | - |
551
+ | 1.2479 | 19900 | 1.2469 | 1.2394 | - |
552
+ | 1.2542 | 20000 | 1.521 | 1.2309 | - |
553
+ | 1.2604 | 20100 | 1.2632 | 1.2353 | - |
554
+ | 1.2667 | 20200 | 1.3621 | 1.2433 | - |
555
+ | 1.2730 | 20300 | 1.5145 | 1.3065 | - |
556
+ | 1.2792 | 20400 | 1.3708 | 1.2423 | - |
557
+ | 1.2855 | 20500 | 1.1716 | 1.2923 | - |
558
+ | 1.2918 | 20600 | 1.419 | 1.2194 | - |
559
+ | 1.2980 | 20700 | 1.1644 | 1.2369 | - |
560
+ | 1.3043 | 20800 | 1.6589 | 1.1971 | - |
561
+ | 1.3106 | 20900 | 1.0299 | 1.2343 | - |
562
+ | 1.3169 | 21000 | 1.3452 | 1.2725 | - |
563
+ | 1.3231 | 21100 | 1.4234 | 1.2416 | - |
564
+ | 1.3294 | 21200 | 1.2496 | 1.3609 | - |
565
+ | 1.3357 | 21300 | 1.2133 | 1.2893 | - |
566
+ | 1.3419 | 21400 | 0.8682 | 1.2353 | - |
567
+ | 1.3482 | 21500 | 0.9499 | 1.2423 | - |
568
+ | 1.3545 | 21600 | 1.2896 | 1.1797 | - |
569
+ | 1.3608 | 21700 | 1.2392 | 1.1962 | - |
570
+ | 1.3670 | 21800 | 0.9206 | 1.2483 | - |
571
+ | 1.3733 | 21900 | 1.174 | 1.2328 | - |
572
+ | 1.3796 | 22000 | 1.6361 | 1.1558 | - |
573
+ | 1.3858 | 22100 | 0.8284 | 1.2711 | - |
574
+ | 1.3921 | 22200 | 1.2814 | 1.2462 | - |
575
+ | 1.3984 | 22300 | 1.1595 | 1.2613 | - |
576
+ | 1.4047 | 22400 | 1.3129 | 1.1816 | - |
577
+ | 1.4109 | 22500 | 1.1353 | 1.2454 | - |
578
+ | 1.4172 | 22600 | 1.3302 | 1.1398 | - |
579
+ | 1.4235 | 22700 | 1.1591 | 1.2936 | - |
580
+ | 1.4297 | 22800 | 0.6551 | 1.2345 | - |
581
+ | 1.4360 | 22900 | 1.2884 | 1.1629 | - |
582
+ | 1.4423 | 23000 | 1.1769 | 1.2340 | - |
583
+ | 1.4485 | 23100 | 1.1331 | 1.2036 | - |
584
+ | 1.4548 | 23200 | 1.1008 | 1.1685 | - |
585
+ | 1.4611 | 23300 | 1.1487 | 1.1274 | - |
586
+ | 1.4674 | 23400 | 0.7753 | 1.1738 | - |
587
+ | 1.4736 | 23500 | 1.3236 | 1.2377 | - |
588
+ | 1.4799 | 23600 | 0.919 | 1.2018 | - |
589
+ | 1.4862 | 23700 | 0.8516 | 1.2297 | - |
590
+ | 1.4924 | 23800 | 1.092 | 1.1629 | - |
591
+ | 1.4987 | 23900 | 0.673 | 1.2162 | - |
592
+ | 1.5050 | 24000 | 0.994 | 1.1778 | - |
593
+ | 1.5113 | 24100 | 0.8766 | 1.1902 | - |
594
+ | 1.5175 | 24200 | 1.3818 | 1.1638 | - |
595
+ | 1.5238 | 24300 | 1.1215 | 1.1666 | - |
596
+ | 1.5301 | 24400 | 0.8485 | 1.1907 | - |
597
+ | 1.5363 | 24500 | 1.1033 | 1.2318 | - |
598
+ | 1.5426 | 24600 | 0.9001 | 1.2113 | - |
599
+ | 1.5489 | 24700 | 1.3256 | 1.2309 | - |
600
+ | 1.5552 | 24800 | 0.8162 | 1.2139 | - |
601
+ | 1.5614 | 24900 | 0.5741 | 1.2375 | - |
602
+ | 1.5677 | 25000 | 0.883 | 1.2039 | - |
603
+ | 1.5740 | 25100 | 1.1212 | 1.1428 | - |
604
+ | 1.5802 | 25200 | 0.8229 | 1.2338 | - |
605
+ | 1.5865 | 25300 | 0.8856 | 1.1461 | - |
606
+ | 1.5928 | 25400 | 1.2323 | 1.1569 | - |
607
+ | 1.5990 | 25500 | 0.9724 | 1.1549 | - |
608
+ | 1.6053 | 25600 | 1.0791 | 1.1161 | - |
609
+ | 1.6116 | 25700 | 0.9845 | 1.1061 | - |
610
+ | 1.6179 | 25800 | 1.1591 | 1.1103 | - |
611
+ | 1.6241 | 25900 | 1.116 | 1.1405 | - |
612
+ | 1.6304 | 26000 | 1.2221 | 1.1528 | - |
613
+ | 1.6367 | 26100 | 0.9085 | 1.1396 | - |
614
+ | 1.6429 | 26200 | 0.9543 | 1.1953 | - |
615
+ | 1.6492 | 26300 | 1.1855 | 1.1792 | - |
616
+ | 1.6555 | 26400 | 1.0583 | 1.1666 | - |
617
+ | 1.6618 | 26500 | 0.6583 | 1.1152 | - |
618
+ | 1.6680 | 26600 | 1.3067 | 1.0397 | - |
619
+ | 1.6743 | 26700 | 1.5336 | 1.1244 | - |
620
+ | 1.6806 | 26800 | 0.614 | 1.1273 | - |
621
+ | 1.6868 | 26900 | 1.0336 | 1.0680 | - |
622
+ | 1.6931 | 27000 | 1.462 | 1.0983 | - |
623
+ | 1.6994 | 27100 | 0.8858 | 1.0672 | - |
624
+ | 1.7056 | 27200 | 0.7494 | 1.1624 | - |
625
+ | 1.7119 | 27300 | 0.8152 | 1.0928 | - |
626
+ | 1.7182 | 27400 | 0.7785 | 1.0952 | - |
627
+ | 1.7245 | 27500 | 1.0471 | 1.0999 | - |
628
+ | 1.7307 | 27600 | 1.0994 | 0.9880 | - |
629
+ | 1.7370 | 27700 | 1.0706 | 1.0416 | - |
630
+ | 1.7433 | 27800 | 1.1158 | 1.0676 | - |
631
+ | 1.7495 | 27900 | 0.9893 | 1.0289 | - |
632
+ | 1.7558 | 28000 | 1.2939 | 1.0150 | - |
633
+ | 1.7621 | 28100 | 0.9543 | 1.0767 | - |
634
+ | 1.7684 | 28200 | 0.7907 | 1.0717 | - |
635
+ | 1.7746 | 28300 | 0.92 | 1.1133 | - |
636
+ | 1.7809 | 28400 | 0.8636 | 1.0702 | - |
637
+ | 1.7872 | 28500 | 0.9118 | 1.0536 | - |
638
+ | 1.7934 | 28600 | 1.2643 | 1.1354 | - |
639
+ | 1.7997 | 28700 | 0.8284 | 1.0715 | - |
640
+ | 1.8060 | 28800 | 0.8447 | 1.0458 | - |
641
+ | 1.8123 | 28900 | 1.2102 | 1.1001 | - |
642
+ | 1.8185 | 29000 | 1.1042 | 1.0364 | - |
643
+ | 1.8248 | 29100 | 0.9638 | 1.0947 | - |
644
+ | 1.8311 | 29200 | 0.6847 | 1.0312 | - |
645
+ | 1.8373 | 29300 | 1.7671 | 1.0470 | - |
646
+ | 1.8436 | 29400 | 0.7525 | 1.1158 | - |
647
+ | 1.8499 | 29500 | 1.2843 | 1.0140 | - |
648
+ | 1.8561 | 29600 | 0.6844 | 1.1604 | - |
649
+ | 1.8624 | 29700 | 1.2824 | 1.0052 | - |
650
+ | 1.8687 | 29800 | 1.314 | 1.0323 | - |
651
+ | 1.8750 | 29900 | 1.0796 | 1.0885 | - |
652
+ | 1.8812 | 30000 | 1.0012 | 1.0267 | - |
653
+ | 1.8875 | 30100 | 1.4932 | 1.0438 | - |
654
+ | 1.8938 | 30200 | 1.0404 | 1.0163 | - |
655
+ | 1.9000 | 30300 | 0.614 | 1.0367 | - |
656
+ | 1.9063 | 30400 | 1.2676 | 1.0803 | - |
657
+ | 1.9126 | 30500 | 1.2431 | 1.0428 | - |
658
+ | 1.9189 | 30600 | 1.4063 | 1.0319 | - |
659
+ | 1.9251 | 30700 | 0.7787 | 0.9666 | - |
660
+ | 1.9314 | 30800 | 1.0311 | 1.0376 | - |
661
+ | 1.9377 | 30900 | 1.0353 | 0.9869 | - |
662
+ | 1.9439 | 31000 | 1.2221 | 0.9686 | - |
663
+ | 1.9502 | 31100 | 0.5806 | 0.9663 | - |
664
+ | 1.9565 | 31200 | 0.6919 | 0.9838 | - |
665
+ | 1.9628 | 31300 | 0.8028 | 0.9759 | - |
666
+ | 1.9690 | 31400 | 0.8365 | 0.9641 | - |
667
+ | 1.9753 | 31500 | 0.7518 | 1.0081 | - |
668
+ | 1.9816 | 31600 | 1.0654 | 0.9843 | - |
669
+ | 1.9878 | 31700 | 0.8637 | 0.9635 | - |
670
+ | 1.9941 | 31800 | 0.8663 | 0.9538 | - |
671
+ | 2.0004 | 31900 | 0.8524 | 0.9628 | - |
672
+ | 2.0066 | 32000 | 1.2748 | 0.9382 | - |
673
+ | 2.0129 | 32100 | 0.8138 | 0.9461 | - |
674
+ | 2.0192 | 32200 | 0.4484 | 0.9221 | - |
675
+ | 2.0255 | 32300 | 0.8839 | 0.9567 | - |
676
+ | 2.0317 | 32400 | 0.7599 | 0.9440 | - |
677
+ | 2.0380 | 32500 | 0.8665 | 0.9652 | - |
678
+ | 2.0443 | 32600 | 0.5802 | 0.9475 | - |
679
+ | 2.0505 | 32700 | 0.7731 | 0.9197 | - |
680
+ | 2.0568 | 32800 | 0.7913 | 1.0024 | - |
681
+ | 2.0631 | 32900 | 0.7758 | 0.9257 | - |
682
+ | 2.0694 | 33000 | 0.7468 | 0.9663 | - |
683
+ | 2.0756 | 33100 | 0.9947 | 0.9788 | - |
684
+ | 2.0819 | 33200 | 0.5618 | 0.9480 | - |
685
+ | 2.0882 | 33300 | 0.8805 | 0.9520 | - |
686
+ | 2.0944 | 33400 | 0.9755 | 0.9288 | - |
687
+ | 2.1007 | 33500 | 0.8942 | 0.9234 | - |
688
+ | 2.1070 | 33600 | 0.7242 | 0.9413 | - |
689
+ | 2.1133 | 33700 | 0.6231 | 0.9660 | - |
690
+ | 2.1195 | 33800 | 0.7144 | 0.8901 | - |
691
+ | 2.1258 | 33900 | 0.7139 | 0.9536 | - |
692
+ | 2.1321 | 34000 | 0.6378 | 0.9370 | - |
693
+ | 2.1383 | 34100 | 0.7607 | 0.9209 | - |
694
+ | 2.1446 | 34200 | 0.8667 | 0.9734 | - |
695
+ | 2.1509 | 34300 | 0.8533 | 0.9177 | - |
696
+ | 2.1571 | 34400 | 0.6395 | 0.9285 | - |
697
+ | 2.1634 | 34500 | 0.7377 | 0.9047 | - |
698
+ | 2.1697 | 34600 | 0.7787 | 0.9968 | - |
699
+ | 2.1760 | 34700 | 0.6561 | 0.9653 | - |
700
+ | 2.1822 | 34800 | 0.6169 | 0.9404 | - |
701
+ | 2.1885 | 34900 | 0.7643 | 0.9397 | - |
702
+ | 2.1948 | 35000 | 0.998 | 0.9152 | - |
703
+ | 2.2010 | 35100 | 0.8246 | 0.9513 | - |
704
+ | 2.2073 | 35200 | 0.6655 | 0.9354 | - |
705
+ | 2.2136 | 35300 | 0.9279 | 0.9034 | - |
706
+ | 2.2199 | 35400 | 0.4239 | 0.9607 | - |
707
+ | 2.2261 | 35500 | 1.0023 | 0.8732 | - |
708
+ | 2.2324 | 35600 | 0.7426 | 0.8883 | - |
709
+ | 2.2387 | 35700 | 0.8675 | 0.9296 | - |
710
+ | 2.2449 | 35800 | 0.9226 | 0.9146 | - |
711
+ | 2.2512 | 35900 | 0.4944 | 0.9145 | - |
712
+ | 2.2575 | 36000 | 0.9663 | 0.8893 | - |
713
+ | 2.2637 | 36100 | 0.6455 | 0.9238 | - |
714
+ | 2.2700 | 36200 | 0.9673 | 0.8943 | - |
715
+ | 2.2763 | 36300 | 0.7974 | 0.8620 | - |
716
+ | 2.2826 | 36400 | 0.9777 | 0.8812 | - |
717
+ | 2.2888 | 36500 | 0.8741 | 0.8862 | - |
718
+ | 2.2951 | 36600 | 0.9642 | 0.9157 | - |
719
+ | 2.3014 | 36700 | 0.9225 | 0.8784 | - |
720
+ | 2.3076 | 36800 | 0.6789 | 0.9066 | - |
721
+ | 2.3139 | 36900 | 0.6726 | 0.9091 | - |
722
+ | 2.3202 | 37000 | 0.7326 | 0.9203 | - |
723
+ | 2.3265 | 37100 | 1.007 | 0.9125 | - |
724
+ | 2.3327 | 37200 | 0.6134 | 0.8837 | - |
725
+ | 2.3390 | 37300 | 0.9051 | 0.8945 | - |
726
+ | 2.3453 | 37400 | 0.837 | 0.8740 | - |
727
+ | 2.3515 | 37500 | 0.7615 | 0.9165 | - |
728
+ | 2.3578 | 37600 | 0.8304 | 0.9107 | - |
729
+ | 2.3641 | 37700 | 0.6255 | 0.8891 | - |
730
+ | 2.3704 | 37800 | 0.6775 | 0.8908 | - |
731
+ | 2.3766 | 37900 | 0.7159 | 0.8590 | - |
732
+ | 2.3829 | 38000 | 0.6422 | 0.8559 | - |
733
+ | 2.3892 | 38100 | 0.7773 | 0.8601 | - |
734
+ | 2.3954 | 38200 | 0.5457 | 0.8856 | - |
735
+ | 2.4017 | 38300 | 0.4997 | 0.8785 | - |
736
+ | 2.4080 | 38400 | 0.6319 | 0.8850 | - |
737
+ | 2.4142 | 38500 | 0.7096 | 0.8234 | - |
738
+
739
+ </details>
740
+
741
+ ### Framework Versions
742
+ - Python: 3.8.10
743
+ - Sentence Transformers: 3.1.1
744
+ - Transformers: 4.45.1
745
+ - PyTorch: 2.4.0+cu121
746
+ - Accelerate: 0.34.2
747
+ - Datasets: 3.0.1
748
+ - Tokenizers: 0.20.0
749
+
750
+ ## Citation
751
+
752
+ ### BibTeX
753
+
754
+ #### Sentence Transformers
755
+ ```bibtex
756
+ @inproceedings{reimers-2019-sentence-bert,
757
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
758
+ author = "Reimers, Nils and Gurevych, Iryna",
759
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
760
+ month = "11",
761
+ year = "2019",
762
+ publisher = "Association for Computational Linguistics",
763
+ url = "https://arxiv.org/abs/1908.10084",
764
+ }
765
+ ```
766
+
767
+ #### CoSENTLoss
768
+ ```bibtex
769
+ @online{kexuefm-8847,
770
+ title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
771
+ author={Su Jianlin},
772
+ year={2022},
773
+ month={Jan},
774
+ url={https://kexue.fm/archives/8847},
775
+ }
776
+ ```
777
+
778
+ <!--
779
+ ## Glossary
780
+
781
+ *Clearly define terms in order to be accessible across audiences.*
782
+ -->
783
+
784
+ <!--
785
+ ## Model Card Authors
786
+
787
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
788
+ -->
789
+
790
+ <!--
791
+ ## Model Card Contact
792
+
793
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
794
+ -->
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