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cahya
/
last-sts

Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
feature-extraction
Generated from Trainer
dataset_size:353831
loss:CosineSimilarityLoss
Eval Results
Inference Endpoints
Model card Files Community
cahya commited on
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Add new SentenceTransformer model

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Files changed (10) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +702 -0
  3. config.json +44 -0
  4. config_sentence_transformers.json +10 -0
  5. model.safetensors +3 -0
  6. modules.json +14 -0
  7. sentence_bert_config.json +4 -0
  8. special_tokens_map.json +37 -0
  9. tokenizer.json +0 -0
  10. tokenizer_config.json +880 -0
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": 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
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+ ---
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+ language:
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+ - en
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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:353831
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+ - loss:CosineSimilarityLoss
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+ widget:
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+ - source_sentence: A chef is preparing some food.
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+ sentences:
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+ - Five birds stand on the snow.
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+ - A chef prepared a meal.
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+ - There is no 'still' that is not relative to some other object.
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+ - source_sentence: A woman is adding oil on fishes.
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+ sentences:
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+ - Large cruise ship floating on the water.
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+ - It refers to the maximum f-stop (which is defined as the ratio of focal length
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+ to effective aperture diameter).
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+ - The woman is cutting potatoes.
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+ - source_sentence: The player shoots the winning points.
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+ sentences:
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+ - Minimum wage laws hurt the least skilled, least productive the most.
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+ - The basketball player is about to score points for his team.
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+ - Three televisions, on on the floor, the other two on a box.
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+ - source_sentence: Stars form in star-formation regions, which itself develop from
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+ molecular clouds.
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+ sentences:
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+ - Although I believe Searle is mistaken, I don't think you have found the problem.
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+ - It may be possible for a solar system like ours to exist outside of a galaxy.
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+ - A blond-haired child performing on the trumpet in front of a house while his younger
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+ brother watches.
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+ - source_sentence: While Queen may refer to both Queen regent (sovereign) or Queen
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+ consort, the King has always been the sovereign.
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+ sentences:
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+ - At first, I thought this is a bit of a tricky question.
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+ - A man plays the guitar.
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+ - There is a very good reason not to refer to the Queen's spouse as "King" - because
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+ they aren't the King.
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+ datasets:
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+ - sentence-transformers/stsb
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+ pipeline_tag: sentence-similarity
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+ library_name: sentence-transformers
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+ metrics:
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+ - pearson_cosine
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+ - spearman_cosine
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+ model-index:
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+ - name: SentenceTransformer
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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: 0.7982244251277283
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.8130492542348773
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+ name: Spearman Cosine
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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 test
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+ type: sts-test
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+ metrics:
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+ - type: pearson_cosine
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+ value: 0.7554305375132837
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.7644057551801444
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+ name: Spearman Cosine
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+ ---
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+
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+ # SentenceTransformer
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** Sentence Transformer
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+ <!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
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+ - **Maximum Sequence Length:** 8192 tokens
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+ - **Output Dimensionality:** 768 dimensions
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+ - **Similarity Function:** Cosine Similarity
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+ <!-- - **Training Dataset:** Unknown -->
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+ - **Language:** en
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
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+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
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+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
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+
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+ ### Full Model Architecture
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+
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+ ```
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+ SentenceTransformer(
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+ (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel
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+ (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
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+ )
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+ ```
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+
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+ ## Usage
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+
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+ ### Direct Usage (Sentence Transformers)
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+
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+ First install the Sentence Transformers library:
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+
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+ ```bash
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+ pip install -U sentence-transformers
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+ ```
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+
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+ Then you can load this model and run inference.
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+ ```python
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+ from sentence_transformers import SentenceTransformer
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+
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+ # Download from the 🤗 Hub
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+ model = SentenceTransformer("cahya/last-sts")
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+ # Run inference
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+ sentences = [
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+ 'While Queen may refer to both Queen regent (sovereign) or Queen consort, the King has always been the sovereign.',
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+ 'There is a very good reason not to refer to the Queen\'s spouse as "King" - because they aren\'t the King.',
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+ 'A man plays the guitar.',
132
+ ]
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+ embeddings = model.encode(sentences)
134
+ print(embeddings.shape)
135
+ # [3, 768]
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+
137
+ # Get the similarity scores for the embeddings
138
+ similarities = model.similarity(embeddings, embeddings)
139
+ print(similarities.shape)
140
+ # [3, 3]
141
+ ```
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+
143
+ <!--
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+ ### Direct Usage (Transformers)
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+
146
+ <details><summary>Click to see the direct usage in Transformers</summary>
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+
148
+ </details>
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+ -->
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+
151
+ <!--
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+ ### Downstream Usage (Sentence Transformers)
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+
154
+ You can finetune this model on your own dataset.
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+
156
+ <details><summary>Click to expand</summary>
157
+
158
+ </details>
159
+ -->
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+
161
+ <!--
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+ ### Out-of-Scope Use
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+
164
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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+ -->
166
+
167
+ ## Evaluation
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+
169
+ ### Metrics
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+
171
+ #### Semantic Similarity
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+
173
+ * Datasets: `sts-dev` and `sts-test`
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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 | sts-dev | sts-test |
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+ |:--------------------|:----------|:-----------|
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+ | pearson_cosine | 0.7982 | 0.7554 |
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+ | **spearman_cosine** | **0.813** | **0.7644** |
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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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+
187
+ <!--
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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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+
193
+ ## Training Details
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+
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+ ### Training Dataset
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+
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+ #### Unnamed Dataset
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+
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+ * Size: 353,831 training samples
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+ * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | sentence1 | sentence2 | score |
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+ |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------|
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+ | type | string | string | float |
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+ | details | <ul><li>min: 5 tokens</li><li>mean: 38.53 tokens</li><li>max: 151 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 38.78 tokens</li><li>max: 145 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.91</li><li>max: 1.0</li></ul> |
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+ * Samples:
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+ | sentence1 | sentence2 | score |
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+ |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------|
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+ | <code>A long-term researcher into diabetes, he achieved significant notability with his 1988 Banting Lecture (organized annually by the American Diabetes Association in memory of Frederick Banting).</code> | <code>A renowned expert on diabetes, he gained widespread acclaim for his 1988 Banting Lecture, which is presented annually by the American Diabetes Association to commemorate Frederick Banting.</code> | <code>0.926345705986023</code> |
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+ | <code>investigators claim the british company was a cia cover. </code> | <code>russian investigators stated that the british company was a cia cover.</code> | <code>0.88</code> |
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+ | <code>Albert Weber (21 November 1888, in Berlin – 17 September 1940) was a German amateur football (soccer) player who competed in the 1912 Summer Olympics.</code> | <code>Albert Weber (21 November 1888, in Berlin – 17 September 1940) was a German amateur footballer who participated in the 1912 Summer Olympics.</code> | <code>0.904914379119873</code> |
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+ * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
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+ ```json
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+ {
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+ "loss_fct": "torch.nn.modules.loss.MSELoss"
216
+ }
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+ ```
218
+
219
+ ### Evaluation Dataset
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+
221
+ #### stsb
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+
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+ * Dataset: [stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308)
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+ * Size: 1,500 evaluation samples
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+ * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
226
+ * Approximate statistics based on the first 1000 samples:
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+ | | sentence1 | sentence2 | score |
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+ |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
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+ | type | string | string | float |
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+ | details | <ul><li>min: 5 tokens</li><li>mean: 15.44 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.43 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
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+ * Samples:
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+ | sentence1 | sentence2 | score |
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+ |:--------------------------------------------------|:------------------------------------------------------|:------------------|
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+ | <code>A man with a hard hat is dancing.</code> | <code>A man wearing a hard hat is dancing.</code> | <code>1.0</code> |
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+ | <code>A young child is riding a horse.</code> | <code>A child is riding a horse.</code> | <code>0.95</code> |
236
+ | <code>A man is feeding a mouse to a snake.</code> | <code>The man is feeding a mouse to the snake.</code> | <code>1.0</code> |
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+ * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
238
+ ```json
239
+ {
240
+ "loss_fct": "torch.nn.modules.loss.MSELoss"
241
+ }
242
+ ```
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+
244
+ ### Training Hyperparameters
245
+ #### Non-Default Hyperparameters
246
+
247
+ - `eval_strategy`: steps
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+ - `per_device_train_batch_size`: 64
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+ - `per_device_eval_batch_size`: 64
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+ - `num_train_epochs`: 10
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+ - `warmup_ratio`: 0.1
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+ - `bf16`: True
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+
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+ #### All Hyperparameters
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+ <details><summary>Click to expand</summary>
256
+
257
+ - `overwrite_output_dir`: False
258
+ - `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`: 64
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+ - `per_device_eval_batch_size`: 64
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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`: 5e-05
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+ - `weight_decay`: 0.0
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+ - `adam_beta1`: 0.9
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+ - `adam_beta2`: 0.999
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+ - `adam_epsilon`: 1e-08
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+ - `max_grad_norm`: 1.0
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+ - `num_train_epochs`: 10
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+ - `max_steps`: -1
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+ - `lr_scheduler_type`: linear
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+ - `lr_scheduler_kwargs`: {}
278
+ - `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`: True
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+ - `fp16`: False
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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`: True
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+ - `dataloader_num_workers`: 0
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+ - `dataloader_prefetch_factor`: None
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+ - `past_index`: -1
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+ - `disable_tqdm`: False
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+ - `remove_unused_columns`: True
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+ - `label_names`: None
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+ - `load_best_model_at_end`: False
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+ - `ignore_data_skip`: False
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+ - `fsdp`: []
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+ - `fsdp_min_num_params`: 0
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+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
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+ - `fsdp_transformer_layer_cls_to_wrap`: None
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+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
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+ - `deepspeed`: None
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+ - `label_smoothing_factor`: 0.0
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+ - `optim`: adamw_torch
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+ - `optim_args`: None
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+ - `adafactor`: False
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+ - `group_by_length`: False
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+ - `length_column_name`: length
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+ - `ddp_find_unused_parameters`: None
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+ - `ddp_bucket_cap_mb`: None
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+ - `ddp_broadcast_buffers`: False
331
+ - `dataloader_pin_memory`: True
332
+ - `dataloader_persistent_workers`: False
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+ - `skip_memory_metrics`: True
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+ - `use_legacy_prediction_loop`: False
335
+ - `push_to_hub`: False
336
+ - `resume_from_checkpoint`: None
337
+ - `hub_model_id`: None
338
+ - `hub_strategy`: every_save
339
+ - `hub_private_repo`: None
340
+ - `hub_always_push`: False
341
+ - `gradient_checkpointing`: False
342
+ - `gradient_checkpointing_kwargs`: None
343
+ - `include_inputs_for_metrics`: False
344
+ - `include_for_metrics`: []
345
+ - `eval_do_concat_batches`: True
346
+ - `fp16_backend`: auto
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+ - `push_to_hub_model_id`: None
348
+ - `push_to_hub_organization`: None
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+ - `mp_parameters`:
350
+ - `auto_find_batch_size`: False
351
+ - `full_determinism`: False
352
+ - `torchdynamo`: None
353
+ - `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
362
+ - `neftune_noise_alpha`: None
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+ - `optim_target_modules`: None
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+ - `batch_eval_metrics`: False
365
+ - `eval_on_start`: False
366
+ - `use_liger_kernel`: False
367
+ - `eval_use_gather_object`: False
368
+ - `average_tokens_across_devices`: False
369
+ - `prompts`: None
370
+ - `batch_sampler`: batch_sampler
371
+ - `multi_dataset_batch_sampler`: proportional
372
+
373
+ </details>
374
+
375
+ ### Training Logs
376
+ <details><summary>Click to expand</summary>
377
+
378
+ | Epoch | Step | Training Loss | Validation Loss | sts-dev_spearman_cosine | sts-test_spearman_cosine |
379
+ |:------:|:-----:|:-------------:|:---------------:|:-----------------------:|:------------------------:|
380
+ | 0.0362 | 100 | 0.0019 | 0.1114 | 0.8115 | - |
381
+ | 0.0724 | 200 | 0.0021 | 0.0882 | 0.8177 | - |
382
+ | 0.1085 | 300 | 0.0015 | 0.0748 | 0.8125 | - |
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+ | 0.1447 | 400 | 0.0012 | 0.0679 | 0.8086 | - |
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+ | 0.1809 | 500 | 0.0012 | 0.0608 | 0.8069 | - |
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+ | 0.2171 | 600 | 0.001 | 0.0596 | 0.7986 | - |
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+ | 0.2533 | 700 | 0.0011 | 0.0547 | 0.7946 | - |
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+ | 0.2894 | 800 | 0.0011 | 0.0492 | 0.7870 | - |
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+ | 0.3256 | 900 | 0.0009 | 0.0522 | 0.7862 | - |
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+ | 0.3618 | 1000 | 0.0008 | 0.0519 | 0.7880 | - |
390
+ | 0.3980 | 1100 | 0.0009 | 0.0529 | 0.7962 | - |
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+ | 0.4342 | 1200 | 0.0008 | 0.0469 | 0.7954 | - |
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+ | 0.4703 | 1300 | 0.0009 | 0.0506 | 0.7928 | - |
393
+ | 0.5065 | 1400 | 0.0009 | 0.0466 | 0.7873 | - |
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+ | 0.5427 | 1500 | 0.001 | 0.0495 | 0.7999 | - |
395
+ | 0.5789 | 1600 | 0.0008 | 0.0506 | 0.7861 | - |
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+ | 0.6151 | 1700 | 0.0008 | 0.0522 | 0.7873 | - |
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+ | 0.6512 | 1800 | 0.0009 | 0.0582 | 0.7843 | - |
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+ | 0.6874 | 1900 | 0.0009 | 0.0585 | 0.7888 | - |
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+ | 0.7236 | 2000 | 0.001 | 0.0508 | 0.8040 | - |
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+ | 0.7598 | 2100 | 0.001 | 0.0483 | 0.8018 | - |
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+ | 0.7959 | 2200 | 0.0008 | 0.0520 | 0.7841 | - |
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+ | 0.8321 | 2300 | 0.0009 | 0.0519 | 0.7896 | - |
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+ | 0.8683 | 2400 | 0.001 | 0.0514 | 0.7906 | - |
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+ | 0.9045 | 2500 | 0.0009 | 0.0521 | 0.7946 | - |
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+ | 0.9407 | 2600 | 0.0009 | 0.0496 | 0.7920 | - |
406
+ | 0.9768 | 2700 | 0.001 | 0.0566 | 0.7956 | - |
407
+ | 1.0130 | 2800 | 0.0009 | 0.0511 | 0.8044 | - |
408
+ | 1.0492 | 2900 | 0.0009 | 0.0622 | 0.8197 | - |
409
+ | 1.0854 | 3000 | 0.001 | 0.0504 | 0.8113 | - |
410
+ | 1.1216 | 3100 | 0.001 | 0.0550 | 0.8005 | - |
411
+ | 1.1577 | 3200 | 0.001 | 0.0549 | 0.7821 | - |
412
+ | 1.1939 | 3300 | 0.0009 | 0.0578 | 0.7758 | - |
413
+ | 1.2301 | 3400 | 0.0009 | 0.0543 | 0.7860 | - |
414
+ | 1.2663 | 3500 | 0.0008 | 0.0575 | 0.7891 | - |
415
+ | 1.3025 | 3600 | 0.0009 | 0.0567 | 0.7995 | - |
416
+ | 1.3386 | 3700 | 0.001 | 0.0488 | 0.7985 | - |
417
+ | 1.3748 | 3800 | 0.0009 | 0.0514 | 0.7789 | - |
418
+ | 1.4110 | 3900 | 0.001 | 0.0584 | 0.7765 | - |
419
+ | 1.4472 | 4000 | 0.001 | 0.0554 | 0.7888 | - |
420
+ | 1.4834 | 4100 | 0.001 | 0.0659 | 0.7959 | - |
421
+ | 1.5195 | 4200 | 0.0009 | 0.0511 | 0.7816 | - |
422
+ | 1.5557 | 4300 | 0.0009 | 0.0555 | 0.7826 | - |
423
+ | 1.5919 | 4400 | 0.001 | 0.0525 | 0.7944 | - |
424
+ | 1.6281 | 4500 | 0.0009 | 0.0553 | 0.7941 | - |
425
+ | 1.6643 | 4600 | 0.001 | 0.0588 | 0.7984 | - |
426
+ | 1.7004 | 4700 | 0.001 | 0.0579 | 0.8004 | - |
427
+ | 1.7366 | 4800 | 0.0009 | 0.0540 | 0.7916 | - |
428
+ | 1.7728 | 4900 | 0.0009 | 0.0557 | 0.7963 | - |
429
+ | 1.8090 | 5000 | 0.0008 | 0.0536 | 0.8044 | - |
430
+ | 1.8452 | 5100 | 0.0009 | 0.0541 | 0.7870 | - |
431
+ | 1.8813 | 5200 | 0.0009 | 0.0594 | 0.7989 | - |
432
+ | 1.9175 | 5300 | 0.001 | 0.0558 | 0.8000 | - |
433
+ | 1.9537 | 5400 | 0.0009 | 0.0538 | 0.7905 | - |
434
+ | 1.9899 | 5500 | 0.0008 | 0.0555 | 0.7944 | - |
435
+ | 2.0260 | 5600 | 0.0009 | 0.0557 | 0.8127 | - |
436
+ | 2.0622 | 5700 | 0.0007 | 0.0542 | 0.8146 | - |
437
+ | 2.0984 | 5800 | 0.0008 | 0.0517 | 0.7990 | - |
438
+ | 2.1346 | 5900 | 0.0009 | 0.0500 | 0.8051 | - |
439
+ | 2.1708 | 6000 | 0.0009 | 0.0521 | 0.8019 | - |
440
+ | 2.2069 | 6100 | 0.0009 | 0.0511 | 0.8101 | - |
441
+ | 2.2431 | 6200 | 0.0008 | 0.0578 | 0.8087 | - |
442
+ | 2.2793 | 6300 | 0.0008 | 0.0585 | 0.8012 | - |
443
+ | 2.3155 | 6400 | 0.0008 | 0.0566 | 0.8083 | - |
444
+ | 2.3517 | 6500 | 0.0007 | 0.0535 | 0.8036 | - |
445
+ | 2.3878 | 6600 | 0.0008 | 0.0531 | 0.7988 | - |
446
+ | 2.4240 | 6700 | 0.0007 | 0.0574 | 0.8102 | - |
447
+ | 2.4602 | 6800 | 0.0007 | 0.0566 | 0.7944 | - |
448
+ | 2.4964 | 6900 | 0.0008 | 0.0528 | 0.8058 | - |
449
+ | 2.5326 | 7000 | 0.0007 | 0.0528 | 0.8056 | - |
450
+ | 2.5687 | 7100 | 0.0007 | 0.0506 | 0.8002 | - |
451
+ | 2.6049 | 7200 | 0.0007 | 0.0526 | 0.8038 | - |
452
+ | 2.6411 | 7300 | 0.0007 | 0.0554 | 0.8054 | - |
453
+ | 2.6773 | 7400 | 0.0007 | 0.0505 | 0.7928 | - |
454
+ | 2.7135 | 7500 | 0.0007 | 0.0505 | 0.8070 | - |
455
+ | 2.7496 | 7600 | 0.0007 | 0.0535 | 0.7977 | - |
456
+ | 2.7858 | 7700 | 0.0007 | 0.0536 | 0.8019 | - |
457
+ | 2.8220 | 7800 | 0.0006 | 0.0546 | 0.7989 | - |
458
+ | 2.8582 | 7900 | 0.0007 | 0.0543 | 0.8042 | - |
459
+ | 2.8944 | 8000 | 0.0007 | 0.0542 | 0.8105 | - |
460
+ | 2.9305 | 8100 | 0.0007 | 0.0541 | 0.8053 | - |
461
+ | 2.9667 | 8200 | 0.0007 | 0.0545 | 0.8135 | - |
462
+ | 3.0029 | 8300 | 0.0007 | 0.0598 | 0.8201 | - |
463
+ | 3.0391 | 8400 | 0.0008 | 0.0558 | 0.8050 | - |
464
+ | 3.0753 | 8500 | 0.0007 | 0.0510 | 0.7965 | - |
465
+ | 3.1114 | 8600 | 0.0006 | 0.0564 | 0.8042 | - |
466
+ | 3.1476 | 8700 | 0.0006 | 0.0559 | 0.7932 | - |
467
+ | 3.1838 | 8800 | 0.0006 | 0.0529 | 0.8028 | - |
468
+ | 3.2200 | 8900 | 0.0006 | 0.0542 | 0.8142 | - |
469
+ | 3.2562 | 9000 | 0.0006 | 0.0532 | 0.8055 | - |
470
+ | 3.2923 | 9100 | 0.0006 | 0.0506 | 0.7930 | - |
471
+ | 3.3285 | 9200 | 0.0007 | 0.0542 | 0.7927 | - |
472
+ | 3.3647 | 9300 | 0.0006 | 0.0523 | 0.8033 | - |
473
+ | 3.4009 | 9400 | 0.0006 | 0.0530 | 0.8079 | - |
474
+ | 3.4370 | 9500 | 0.0006 | 0.0544 | 0.7977 | - |
475
+ | 3.4732 | 9600 | 0.0005 | 0.0515 | 0.8019 | - |
476
+ | 3.5094 | 9700 | 0.0006 | 0.0481 | 0.8037 | - |
477
+ | 3.5456 | 9800 | 0.0005 | 0.0557 | 0.8007 | - |
478
+ | 3.5818 | 9900 | 0.0006 | 0.0495 | 0.8087 | - |
479
+ | 3.6179 | 10000 | 0.0006 | 0.0555 | 0.7991 | - |
480
+ | 3.6541 | 10100 | 0.0005 | 0.0560 | 0.7973 | - |
481
+ | 3.6903 | 10200 | 0.0007 | 0.0581 | 0.7945 | - |
482
+ | 3.7265 | 10300 | 0.0006 | 0.0546 | 0.8098 | - |
483
+ | 3.7627 | 10400 | 0.0006 | 0.0539 | 0.8074 | - |
484
+ | 3.7988 | 10500 | 0.0005 | 0.0501 | 0.8051 | - |
485
+ | 3.8350 | 10600 | 0.0005 | 0.0531 | 0.8032 | - |
486
+ | 3.8712 | 10700 | 0.0005 | 0.0502 | 0.8077 | - |
487
+ | 3.9074 | 10800 | 0.0006 | 0.0537 | 0.8131 | - |
488
+ | 3.9436 | 10900 | 0.0005 | 0.0510 | 0.8115 | - |
489
+ | 3.9797 | 11000 | 0.0006 | 0.0525 | 0.8173 | - |
490
+ | 4.0159 | 11100 | 0.0005 | 0.0513 | 0.8106 | - |
491
+ | 4.0521 | 11200 | 0.0006 | 0.0594 | 0.8061 | - |
492
+ | 4.0883 | 11300 | 0.0005 | 0.0514 | 0.8150 | - |
493
+ | 4.1245 | 11400 | 0.0005 | 0.0537 | 0.8168 | - |
494
+ | 4.1606 | 11500 | 0.0005 | 0.0571 | 0.8176 | - |
495
+ | 4.1968 | 11600 | 0.0005 | 0.0546 | 0.8159 | - |
496
+ | 4.2330 | 11700 | 0.0005 | 0.0496 | 0.8115 | - |
497
+ | 4.2692 | 11800 | 0.0005 | 0.0526 | 0.8072 | - |
498
+ | 4.3054 | 11900 | 0.0005 | 0.0512 | 0.8081 | - |
499
+ | 4.3415 | 12000 | 0.0005 | 0.0517 | 0.8025 | - |
500
+ | 4.3777 | 12100 | 0.0005 | 0.0533 | 0.8128 | - |
501
+ | 4.4139 | 12200 | 0.0005 | 0.0501 | 0.8121 | - |
502
+ | 4.4501 | 12300 | 0.0005 | 0.0507 | 0.8079 | - |
503
+ | 4.4863 | 12400 | 0.0005 | 0.0501 | 0.8070 | - |
504
+ | 4.5224 | 12500 | 0.0004 | 0.0537 | 0.8019 | - |
505
+ | 4.5586 | 12600 | 0.0004 | 0.0541 | 0.8005 | - |
506
+ | 4.5948 | 12700 | 0.0005 | 0.0525 | 0.8117 | - |
507
+ | 4.6310 | 12800 | 0.0004 | 0.0523 | 0.8070 | - |
508
+ | 4.6671 | 12900 | 0.0005 | 0.0526 | 0.8099 | - |
509
+ | 4.7033 | 13000 | 0.0004 | 0.0518 | 0.8166 | - |
510
+ | 4.7395 | 13100 | 0.0004 | 0.0547 | 0.8129 | - |
511
+ | 4.7757 | 13200 | 0.0005 | 0.0523 | 0.8130 | - |
512
+ | 4.8119 | 13300 | 0.0004 | 0.0504 | 0.8129 | - |
513
+ | 4.8480 | 13400 | 0.0005 | 0.0539 | 0.8113 | - |
514
+ | 4.8842 | 13500 | 0.0004 | 0.0523 | 0.8169 | - |
515
+ | 4.9204 | 13600 | 0.0005 | 0.0521 | 0.8164 | - |
516
+ | 4.9566 | 13700 | 0.0004 | 0.0575 | 0.8115 | - |
517
+ | 4.9928 | 13800 | 0.0004 | 0.0538 | 0.8186 | - |
518
+ | 5.0289 | 13900 | 0.0004 | 0.0530 | 0.8095 | - |
519
+ | 5.0651 | 14000 | 0.0003 | 0.0537 | 0.8162 | - |
520
+ | 5.1013 | 14100 | 0.0004 | 0.0560 | 0.8112 | - |
521
+ | 5.1375 | 14200 | 0.0004 | 0.0528 | 0.8125 | - |
522
+ | 5.1737 | 14300 | 0.0004 | 0.0533 | 0.8137 | - |
523
+ | 5.2098 | 14400 | 0.0003 | 0.0537 | 0.8198 | - |
524
+ | 5.2460 | 14500 | 0.0004 | 0.0530 | 0.8102 | - |
525
+ | 5.2822 | 14600 | 0.0004 | 0.0562 | 0.8099 | - |
526
+ | 5.3184 | 14700 | 0.0004 | 0.0522 | 0.8084 | - |
527
+ | 5.3546 | 14800 | 0.0004 | 0.0515 | 0.8128 | - |
528
+ | 5.3907 | 14900 | 0.0004 | 0.0555 | 0.8107 | - |
529
+ | 5.4269 | 15000 | 0.0004 | 0.0533 | 0.8113 | - |
530
+ | 5.4631 | 15100 | 0.0003 | 0.0538 | 0.8135 | - |
531
+ | 5.4993 | 15200 | 0.0004 | 0.0552 | 0.8139 | - |
532
+ | 5.5355 | 15300 | 0.0003 | 0.0513 | 0.8102 | - |
533
+ | 5.5716 | 15400 | 0.0004 | 0.0542 | 0.8108 | - |
534
+ | 5.6078 | 15500 | 0.0003 | 0.0541 | 0.8041 | - |
535
+ | 5.6440 | 15600 | 0.0004 | 0.0512 | 0.8074 | - |
536
+ | 5.6802 | 15700 | 0.0003 | 0.0553 | 0.8100 | - |
537
+ | 5.7164 | 15800 | 0.0003 | 0.0539 | 0.8088 | - |
538
+ | 5.7525 | 15900 | 0.0004 | 0.0527 | 0.8094 | - |
539
+ | 5.7887 | 16000 | 0.0004 | 0.0524 | 0.8080 | - |
540
+ | 5.8249 | 16100 | 0.0003 | 0.0525 | 0.8112 | - |
541
+ | 5.8611 | 16200 | 0.0003 | 0.0537 | 0.8109 | - |
542
+ | 5.8973 | 16300 | 0.0003 | 0.0539 | 0.8129 | - |
543
+ | 5.9334 | 16400 | 0.0003 | 0.0543 | 0.8052 | - |
544
+ | 5.9696 | 16500 | 0.0003 | 0.0544 | 0.8093 | - |
545
+ | 6.0058 | 16600 | 0.0004 | 0.0532 | 0.8109 | - |
546
+ | 6.0420 | 16700 | 0.0002 | 0.0558 | 0.8108 | - |
547
+ | 6.0781 | 16800 | 0.0002 | 0.0529 | 0.8089 | - |
548
+ | 6.1143 | 16900 | 0.0003 | 0.0539 | 0.8074 | - |
549
+ | 6.1505 | 17000 | 0.0003 | 0.0534 | 0.8118 | - |
550
+ | 6.1867 | 17100 | 0.0003 | 0.0539 | 0.8048 | - |
551
+ | 6.2229 | 17200 | 0.0003 | 0.0537 | 0.8049 | - |
552
+ | 6.2590 | 17300 | 0.0003 | 0.0553 | 0.8102 | - |
553
+ | 6.2952 | 17400 | 0.0002 | 0.0533 | 0.8053 | - |
554
+ | 6.3314 | 17500 | 0.0003 | 0.0550 | 0.8071 | - |
555
+ | 6.3676 | 17600 | 0.0002 | 0.0530 | 0.8128 | - |
556
+ | 6.4038 | 17700 | 0.0003 | 0.0547 | 0.8159 | - |
557
+ | 6.4399 | 17800 | 0.0002 | 0.0539 | 0.8120 | - |
558
+ | 6.4761 | 17900 | 0.0003 | 0.0540 | 0.8107 | - |
559
+ | 6.5123 | 18000 | 0.0003 | 0.0535 | 0.8069 | - |
560
+ | 6.5485 | 18100 | 0.0003 | 0.0541 | 0.8129 | - |
561
+ | 6.5847 | 18200 | 0.0003 | 0.0522 | 0.8132 | - |
562
+ | 6.6208 | 18300 | 0.0002 | 0.0539 | 0.8135 | - |
563
+ | 6.6570 | 18400 | 0.0002 | 0.0542 | 0.8142 | - |
564
+ | 6.6932 | 18500 | 0.0003 | 0.0529 | 0.8101 | - |
565
+ | 6.7294 | 18600 | 0.0003 | 0.0533 | 0.8073 | - |
566
+ | 6.7656 | 18700 | 0.0003 | 0.0525 | 0.8095 | - |
567
+ | 6.8017 | 18800 | 0.0003 | 0.0534 | 0.8089 | - |
568
+ | 6.8379 | 18900 | 0.0002 | 0.0519 | 0.8134 | - |
569
+ | 6.8741 | 19000 | 0.0002 | 0.0536 | 0.8141 | - |
570
+ | 6.9103 | 19100 | 0.0002 | 0.0535 | 0.8115 | - |
571
+ | 6.9465 | 19200 | 0.0002 | 0.0519 | 0.8107 | - |
572
+ | 6.9826 | 19300 | 0.0002 | 0.0546 | 0.8093 | - |
573
+ | 7.0188 | 19400 | 0.0002 | 0.0532 | 0.8112 | - |
574
+ | 7.0550 | 19500 | 0.0002 | 0.0526 | 0.8145 | - |
575
+ | 7.0912 | 19600 | 0.0002 | 0.0529 | 0.8111 | - |
576
+ | 7.1274 | 19700 | 0.0002 | 0.0540 | 0.8090 | - |
577
+ | 7.1635 | 19800 | 0.0002 | 0.0525 | 0.8116 | - |
578
+ | 7.1997 | 19900 | 0.0002 | 0.0534 | 0.8115 | - |
579
+ | 7.2359 | 20000 | 0.0002 | 0.0526 | 0.8123 | - |
580
+ | 7.2721 | 20100 | 0.0002 | 0.0524 | 0.8143 | - |
581
+ | 7.3082 | 20200 | 0.0002 | 0.0526 | 0.8059 | - |
582
+ | 7.3444 | 20300 | 0.0002 | 0.0535 | 0.8091 | - |
583
+ | 7.3806 | 20400 | 0.0002 | 0.0532 | 0.8094 | - |
584
+ | 7.4168 | 20500 | 0.0002 | 0.0529 | 0.8108 | - |
585
+ | 7.4530 | 20600 | 0.0002 | 0.0542 | 0.8108 | - |
586
+ | 7.4891 | 20700 | 0.0002 | 0.0525 | 0.8102 | - |
587
+ | 7.5253 | 20800 | 0.0002 | 0.0541 | 0.8106 | - |
588
+ | 7.5615 | 20900 | 0.0002 | 0.0538 | 0.8095 | - |
589
+ | 7.5977 | 21000 | 0.0003 | 0.0523 | 0.8136 | - |
590
+ | 7.6339 | 21100 | 0.0002 | 0.0544 | 0.8108 | - |
591
+ | 7.6700 | 21200 | 0.0002 | 0.0525 | 0.8090 | - |
592
+ | 7.7062 | 21300 | 0.0002 | 0.0528 | 0.8108 | - |
593
+ | 7.7424 | 21400 | 0.0002 | 0.0531 | 0.8115 | - |
594
+ | 7.7786 | 21500 | 0.0002 | 0.0541 | 0.8107 | - |
595
+ | 7.8148 | 21600 | 0.0001 | 0.0525 | 0.8117 | - |
596
+ | 7.8509 | 21700 | 0.0002 | 0.0534 | 0.8115 | - |
597
+ | 7.8871 | 21800 | 0.0002 | 0.0541 | 0.8105 | - |
598
+ | 7.9233 | 21900 | 0.0002 | 0.0538 | 0.8094 | - |
599
+ | 7.9595 | 22000 | 0.0002 | 0.0530 | 0.8106 | - |
600
+ | 7.9957 | 22100 | 0.0002 | 0.0527 | 0.8104 | - |
601
+ | 8.0318 | 22200 | 0.0001 | 0.0534 | 0.8098 | - |
602
+ | 8.0680 | 22300 | 0.0002 | 0.0537 | 0.8090 | - |
603
+ | 8.1042 | 22400 | 0.0001 | 0.0533 | 0.8103 | - |
604
+ | 8.1404 | 22500 | 0.0002 | 0.0528 | 0.8099 | - |
605
+ | 8.1766 | 22600 | 0.0001 | 0.0531 | 0.8106 | - |
606
+ | 8.2127 | 22700 | 0.0001 | 0.0534 | 0.8116 | - |
607
+ | 8.2489 | 22800 | 0.0001 | 0.0538 | 0.8102 | - |
608
+ | 8.2851 | 22900 | 0.0001 | 0.0530 | 0.8108 | - |
609
+ | 8.3213 | 23000 | 0.0002 | 0.0529 | 0.8112 | - |
610
+ | 8.3575 | 23100 | 0.0001 | 0.0533 | 0.8099 | - |
611
+ | 8.3936 | 23200 | 0.0001 | 0.0534 | 0.8107 | - |
612
+ | 8.4298 | 23300 | 0.0002 | 0.0535 | 0.8110 | - |
613
+ | 8.4660 | 23400 | 0.0001 | 0.0543 | 0.8108 | - |
614
+ | 8.5022 | 23500 | 0.0001 | 0.0530 | 0.8119 | - |
615
+ | 8.5384 | 23600 | 0.0001 | 0.0530 | 0.8132 | - |
616
+ | 8.5745 | 23700 | 0.0001 | 0.0531 | 0.8128 | - |
617
+ | 8.6107 | 23800 | 0.0002 | 0.0532 | 0.8119 | - |
618
+ | 8.6469 | 23900 | 0.0002 | 0.0531 | 0.8120 | - |
619
+ | 8.6831 | 24000 | 0.0001 | 0.0531 | 0.8121 | - |
620
+ | 8.7192 | 24100 | 0.0001 | 0.0525 | 0.8134 | - |
621
+ | 8.7554 | 24200 | 0.0002 | 0.0524 | 0.8133 | - |
622
+ | 8.7916 | 24300 | 0.0001 | 0.0535 | 0.8141 | - |
623
+ | 8.8278 | 24400 | 0.0002 | 0.0529 | 0.8118 | - |
624
+ | 8.8640 | 24500 | 0.0001 | 0.0529 | 0.8115 | - |
625
+ | 8.9001 | 24600 | 0.0001 | 0.0528 | 0.8127 | - |
626
+ | 8.9363 | 24700 | 0.0002 | 0.0527 | 0.8111 | - |
627
+ | 8.9725 | 24800 | 0.0001 | 0.0536 | 0.8114 | - |
628
+ | 9.0087 | 24900 | 0.0001 | 0.0531 | 0.8124 | - |
629
+ | 9.0449 | 25000 | 0.0001 | 0.0532 | 0.8123 | - |
630
+ | 9.0810 | 25100 | 0.0001 | 0.0534 | 0.8130 | - |
631
+ | 9.1172 | 25200 | 0.0001 | 0.0533 | 0.8121 | - |
632
+ | 9.1534 | 25300 | 0.0002 | 0.0534 | 0.8119 | - |
633
+ | 9.1896 | 25400 | 0.0001 | 0.0532 | 0.8118 | - |
634
+ | 9.2258 | 25500 | 0.0001 | 0.0532 | 0.8112 | - |
635
+ | 9.2619 | 25600 | 0.0001 | 0.0532 | 0.8121 | - |
636
+ | 9.2981 | 25700 | 0.0002 | 0.0537 | 0.8120 | - |
637
+ | 9.3343 | 25800 | 0.0001 | 0.0535 | 0.8127 | - |
638
+ | 9.3705 | 25900 | 0.0001 | 0.0529 | 0.8133 | - |
639
+ | 9.4067 | 26000 | 0.0001 | 0.0529 | 0.8138 | - |
640
+ | 9.4428 | 26100 | 0.0001 | 0.0534 | 0.8131 | - |
641
+ | 9.4790 | 26200 | 0.0001 | 0.0529 | 0.8137 | - |
642
+ | 9.5152 | 26300 | 0.0002 | 0.0529 | 0.8135 | - |
643
+ | 9.5514 | 26400 | 0.0001 | 0.0528 | 0.8129 | - |
644
+ | 9.5876 | 26500 | 0.0001 | 0.0530 | 0.8124 | - |
645
+ | 9.6237 | 26600 | 0.0001 | 0.0529 | 0.8132 | - |
646
+ | 9.6599 | 26700 | 0.0001 | 0.0530 | 0.8128 | - |
647
+ | 9.6961 | 26800 | 0.0001 | 0.0530 | 0.8132 | - |
648
+ | 9.7323 | 26900 | 0.0001 | 0.0529 | 0.8129 | - |
649
+ | 9.7685 | 27000 | 0.0002 | 0.0528 | 0.8131 | - |
650
+ | 9.8046 | 27100 | 0.0001 | 0.0529 | 0.8131 | - |
651
+ | 9.8408 | 27200 | 0.0002 | 0.0531 | 0.8128 | - |
652
+ | 9.8770 | 27300 | 0.0001 | 0.0532 | 0.8130 | - |
653
+ | 9.9132 | 27400 | 0.0001 | 0.0531 | 0.8129 | - |
654
+ | 9.9493 | 27500 | 0.0001 | 0.0531 | 0.8129 | - |
655
+ | 9.9855 | 27600 | 0.0001 | 0.0531 | 0.8130 | - |
656
+ | -1 | -1 | - | - | - | 0.7644 |
657
+
658
+ </details>
659
+
660
+ ### Framework Versions
661
+ - Python: 3.10.16
662
+ - Sentence Transformers: 3.4.1
663
+ - Transformers: 4.49.0
664
+ - PyTorch: 2.5.1+cu124
665
+ - Accelerate: 0.34.2
666
+ - Datasets: 2.19.2
667
+ - Tokenizers: 0.21.0
668
+
669
+ ## Citation
670
+
671
+ ### BibTeX
672
+
673
+ #### Sentence Transformers
674
+ ```bibtex
675
+ @inproceedings{reimers-2019-sentence-bert,
676
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
677
+ author = "Reimers, Nils and Gurevych, Iryna",
678
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
679
+ month = "11",
680
+ year = "2019",
681
+ publisher = "Association for Computational Linguistics",
682
+ url = "https://arxiv.org/abs/1908.10084",
683
+ }
684
+ ```
685
+
686
+ <!--
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+ ## Glossary
688
+
689
+ *Clearly define terms in order to be accessible across audiences.*
690
+ -->
691
+
692
+ <!--
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+ ## Model Card Authors
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+
695
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
696
+ -->
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+
698
+ <!--
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+ ## Model Card Contact
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+
701
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
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