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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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+ 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:10053
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+ - loss:MultipleNegativesRankingLoss
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+ base_model: answerdotai/ModernBERT-base
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+ widget:
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+ - source_sentence: Fluorescence quenching of tryptophan residues
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+ sentences:
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+ - 'Fluorescence of buried tyrosine residues in proteins. '
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+ - 'A fluorescence quenching study of tryptophanyl residues of (Ca2+ + Mg2+)-ATPase
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+ from sarcoplasmic reticulum. '
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+ - 'Some hormonal influences on the acetylation of sulfanilamide in vivo. '
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+ - source_sentence: Human migration to the Americas
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+ sentences:
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+ - 'Homo sapiens in the Americas. Overview of the earliest human expansion in the
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+ New World. '
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+ - 'Profiles of College Drinkers Defined by Alcohol Behaviors at the Week Level:
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+ Replication Across Semesters and Prospective Associations With Hazardous Drinking
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+ and Dependence-Related Symptoms. '
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+ - 'Human migration. '
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+ - source_sentence: Human Mobility Prediction
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+ sentences:
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+ - 'Human mobility prediction from region functions with taxi trajectories. '
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+ - 'Understanding Human Mobility from Twitter. '
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+ - 'Ovarian cancer gene therapy using HPV-16 pseudovirion carrying the HSV-tk gene. '
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+ - source_sentence: Nevirapine Resistance
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+ sentences:
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+ - 'Nevirapine toxicity. '
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+ - 'Recognizing rhenium. '
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+ - 'Update on nevirapine: quest for a niche. '
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+ - source_sentence: EHL tendon reconstruction
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+ sentences:
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+ - 'A Combined Surgical Approach for Extensor Hallucis Longus Reconstruction: Two
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+ Case Reports. '
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+ - 'Flexor tendon reconstruction. '
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+ - 'Noble gases and neuroprotection: summary of current evidence. '
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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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+ - cosine_accuracy
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+ model-index:
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+ - name: SentenceTransformer based on answerdotai/ModernBERT-base
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+ results:
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+ - task:
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+ type: triplet
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+ name: Triplet
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+ dataset:
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+ name: triplet dev
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+ type: triplet-dev
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+ metrics:
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+ - type: cosine_accuracy
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+ value: 0.887
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+ name: Cosine Accuracy
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+ ---
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+
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+ # SentenceTransformer based on answerdotai/ModernBERT-base
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the json dataset. 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:** [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) <!-- at revision 1e8d43065c90b6370237c1474ba1445048b02898 -->
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+ - **Maximum Sequence Length:** 512 tokens
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+ - **Output Dimensionality:** 768 dimensions
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+ - **Similarity Function:** Cosine Similarity
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+ - **Training Dataset:**
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+ - json
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+ <!-- - **Language:** Unknown -->
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
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+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
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+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
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+
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+ ### Full Model Architecture
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+
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+ ```
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+ SentenceTransformer(
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+ (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: 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("sentence_transformers_model_id")
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+ # Run inference
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+ sentences = [
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+ 'EHL tendon reconstruction',
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+ 'A Combined Surgical Approach for Extensor Hallucis Longus Reconstruction: Two Case Reports. ',
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+ 'Flexor tendon reconstruction. ',
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+ ]
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+ embeddings = model.encode(sentences)
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+ print(embeddings.shape)
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+ # [3, 768]
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+
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+ # Get the similarity scores for the embeddings
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+ similarities = model.similarity(embeddings, embeddings)
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+ print(similarities.shape)
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+ # [3, 3]
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+ ```
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+
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+ <!--
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+ ### Direct Usage (Transformers)
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+
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+ <details><summary>Click to see the direct usage in Transformers</summary>
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+
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+ </details>
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+ -->
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+
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+ <!--
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+ ### Downstream Usage (Sentence Transformers)
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+
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+ You can finetune this model on your own dataset.
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+
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+ <details><summary>Click to expand</summary>
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+
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+ </details>
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+ -->
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+
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+ <!--
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+ ### Out-of-Scope Use
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+
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+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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+ -->
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+
148
+ ## Evaluation
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+
150
+ ### Metrics
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+
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+ #### Triplet
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+
154
+ * Dataset: `triplet-dev`
155
+ * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
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+
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+ | Metric | Value |
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+ |:--------------------|:----------|
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+ | **cosine_accuracy** | **0.887** |
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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 Dataset
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+
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+ #### json
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+
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+ * Dataset: json
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+ * Size: 10,053 training samples
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+ * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | anchor | positive | negative |
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+ |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
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+ | type | string | string | string |
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+ | details | <ul><li>min: 4 tokens</li><li>mean: 8.86 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 21.84 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 13.65 tokens</li><li>max: 50 tokens</li></ul> |
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+ * Samples:
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+ | anchor | positive | negative |
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+ |:-------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------|
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+ | <code>COM-induced secretome changes in U937 monocytes</code> | <code>Characterization of calcium oxalate crystal-induced changes in the secretome of U937 human monocytes. </code> | <code>Monocytes. </code> |
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+ | <code>Metamaterials</code> | <code>Sound attenuation optimization using metaporous materials tuned on exceptional points. </code> | <code>Metamaterials: A cat's eye for all directions. </code> |
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+ | <code>Pediatric Parasitology</code> | <code>Parasitic infections among school age children 6 to 11-years-of-age in the Eastern province. </code> | <code>[DIALOGUE ON PEDIATRIC PARASITOLOGY]. </code> |
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+ * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
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+ ```json
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+ {
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+ "scale": 20.0,
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+ "similarity_fct": "cos_sim"
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+ }
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+ ```
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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`: 64
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+ - `per_device_eval_batch_size`: 64
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+ - `learning_rate`: 0.0002
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+ - `num_train_epochs`: 2
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+ - `lr_scheduler_type`: cosine_with_restarts
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+ - `warmup_ratio`: 0.1
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+ - `bf16`: True
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+ - `batch_sampler`: no_duplicates
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+
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+ #### All Hyperparameters
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+ <details><summary>Click to expand</summary>
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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`: 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`: 0.0002
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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`: 2
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+ - `max_steps`: -1
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+ - `lr_scheduler_type`: cosine_with_restarts
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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`: 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`: False
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+ - `dataloader_num_workers`: 0
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+ - `dataloader_prefetch_factor`: None
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+ - `past_index`: -1
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+ - `disable_tqdm`: False
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+ - `remove_unused_columns`: True
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+ - `label_names`: None
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+ - `load_best_model_at_end`: False
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+ - `ignore_data_skip`: False
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+ - `fsdp`: []
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+ - `fsdp_min_num_params`: 0
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+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
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+ - `fsdp_transformer_layer_cls_to_wrap`: None
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+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
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+ - `deepspeed`: None
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+ - `label_smoothing_factor`: 0.0
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+ - `optim`: adamw_torch
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+ - `optim_args`: None
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+ - `adafactor`: False
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+ - `group_by_length`: False
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+ - `length_column_name`: length
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+ - `ddp_find_unused_parameters`: None
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+ - `ddp_bucket_cap_mb`: None
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+ - `ddp_broadcast_buffers`: False
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+ - `dataloader_pin_memory`: True
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+ - `dataloader_persistent_workers`: False
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+ - `skip_memory_metrics`: True
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+ - `use_legacy_prediction_loop`: False
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+ - `push_to_hub`: False
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+ - `resume_from_checkpoint`: None
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+ - `hub_model_id`: None
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+ - `hub_strategy`: every_save
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+ - `hub_private_repo`: None
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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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+ - `include_for_metrics`: []
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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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+ - `average_tokens_across_devices`: False
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+ - `prompts`: None
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+ - `batch_sampler`: no_duplicates
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+ - `multi_dataset_batch_sampler`: proportional
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+
333
+ </details>
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+
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+ ### Training Logs
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+ <details><summary>Click to expand</summary>
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+
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+ | Epoch | Step | Training Loss | triplet-dev_cosine_accuracy |
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+ |:------:|:----:|:-------------:|:---------------------------:|
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+ | 0 | 0 | - | 0.457 |
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+ | 0.0189 | 1 | 5.2934 | - |
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+ | 0.0377 | 2 | 5.2413 | - |
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+ | 0.0566 | 3 | 4.9969 | - |
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+ | 0.0755 | 4 | 4.5579 | - |
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+ | 0.0943 | 5 | 3.9145 | - |
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+ | 0.1132 | 6 | 3.3775 | - |
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+ | 0.1321 | 7 | 2.8787 | - |
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+ | 0.1509 | 8 | 3.0147 | - |
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+ | 0.1698 | 9 | 2.7166 | - |
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+ | 0.1887 | 10 | 2.7875 | - |
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+ | 0.2075 | 11 | 2.3848 | - |
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+ | 0.2264 | 12 | 2.1921 | - |
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+ | 0.2453 | 13 | 1.7009 | - |
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+ | 0.2642 | 14 | 1.7649 | - |
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+ | 0.2830 | 15 | 1.7948 | - |
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+ | 0.3019 | 16 | 1.5384 | - |
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+ | 0.3208 | 17 | 1.6039 | - |
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+ | 0.3396 | 18 | 1.3364 | - |
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+ | 0.3585 | 19 | 1.3852 | - |
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+ | 0.3774 | 20 | 1.2427 | - |
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+ | 0.3962 | 21 | 1.3216 | - |
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+ | 0.4151 | 22 | 1.4202 | - |
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+ | 0.4340 | 23 | 1.2754 | - |
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+ | 0.4528 | 24 | 1.281 | - |
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+ | 0.4717 | 25 | 1.1709 | 0.815 |
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+ | 0.4906 | 26 | 1.2363 | - |
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+ | 0.5094 | 27 | 1.2169 | - |
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+ | 0.5283 | 28 | 1.1495 | - |
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+ | 0.5472 | 29 | 1.0066 | - |
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+ | 0.5660 | 30 | 1.0478 | - |
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+ | 0.5849 | 31 | 1.1511 | - |
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+ | 0.6038 | 32 | 0.9992 | - |
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+ | 0.6226 | 33 | 1.095 | - |
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+ | 0.6415 | 34 | 1.1699 | - |
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+ | 0.6604 | 35 | 0.9866 | - |
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+ | 0.6792 | 36 | 1.1303 | - |
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+ | 0.6981 | 37 | 1.1126 | - |
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+ | 0.7170 | 38 | 0.889 | - |
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+ | 0.7358 | 39 | 1.0355 | - |
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+ | 0.7547 | 40 | 1.0129 | - |
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+ | 0.7736 | 41 | 1.118 | - |
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+ | 0.7925 | 42 | 0.8494 | - |
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+ | 0.8113 | 43 | 1.0829 | - |
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+ | 0.8302 | 44 | 0.8751 | - |
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+ | 0.8491 | 45 | 0.8115 | - |
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+ | 0.8679 | 46 | 0.8579 | - |
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+ | 0.8868 | 47 | 1.1111 | - |
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+ | 0.9057 | 48 | 0.9032 | - |
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+ | 0.9245 | 49 | 1.0394 | - |
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+ | 0.9434 | 50 | 0.9691 | 0.862 |
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+ | 0.9623 | 51 | 1.023 | - |
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+ | 0.9811 | 52 | 0.9465 | - |
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+ | 1.0 | 53 | 0.6713 | - |
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+ | 1.0189 | 54 | 0.9773 | - |
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+ | 1.0377 | 55 | 0.8693 | - |
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+ | 1.0566 | 56 | 0.7187 | - |
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+ | 1.0755 | 57 | 0.805 | - |
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+ | 1.0943 | 58 | 0.728 | - |
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+ | 1.1132 | 59 | 1.0967 | - |
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+ | 1.1321 | 60 | 0.7036 | - |
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+ | 1.1509 | 61 | 0.8213 | - |
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+ | 1.1698 | 62 | 0.57 | - |
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+ | 1.1887 | 63 | 0.7006 | - |
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+ | 1.2075 | 64 | 0.5091 | - |
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+ | 1.2264 | 65 | 0.5758 | - |
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+ | 1.2453 | 66 | 0.4484 | - |
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+ | 1.2642 | 67 | 0.397 | - |
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+ | 1.2830 | 68 | 0.6172 | - |
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+ | 1.3019 | 69 | 0.513 | - |
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+ | 1.3208 | 70 | 0.4447 | - |
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+ | 1.3396 | 71 | 0.3205 | - |
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+ | 1.3585 | 72 | 0.5881 | - |
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+ | 1.3774 | 73 | 0.2543 | - |
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+ | 1.3962 | 74 | 0.3648 | - |
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+ | 1.4151 | 75 | 0.4849 | 0.876 |
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+ | 1.4340 | 76 | 0.3455 | - |
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+ | 1.4528 | 77 | 0.3424 | - |
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+ | 1.4717 | 78 | 0.224 | - |
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+ | 1.4906 | 79 | 0.18 | - |
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+ | 1.5094 | 80 | 0.2255 | - |
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+ | 1.5283 | 81 | 0.3024 | - |
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+ | 1.5472 | 82 | 0.1835 | - |
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+ | 1.5660 | 83 | 0.1946 | - |
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+ | 1.5849 | 84 | 0.1958 | - |
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+ | 1.6038 | 85 | 0.1568 | - |
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+ | 1.6226 | 86 | 0.1626 | - |
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+ | 1.6415 | 87 | 0.1774 | - |
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+ | 1.6604 | 88 | 0.1934 | - |
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+ | 1.6792 | 89 | 0.2426 | - |
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+ | 1.6981 | 90 | 0.2958 | - |
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+ | 1.7170 | 91 | 0.1606 | - |
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+ | 1.7358 | 92 | 0.2281 | - |
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+ | 1.7547 | 93 | 0.1786 | - |
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+ | 1.7736 | 94 | 0.2241 | - |
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+ | 1.7925 | 95 | 0.1909 | - |
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+ | 1.8113 | 96 | 0.236 | - |
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+ | 1.8302 | 97 | 0.1332 | - |
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+ | 1.8491 | 98 | 0.1247 | - |
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+ | 1.8679 | 99 | 0.156 | - |
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+ | 1.8868 | 100 | 0.2152 | 0.889 |
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+ | 1.9057 | 101 | 0.1549 | - |
442
+ | 1.9245 | 102 | 0.2226 | - |
443
+ | 1.9434 | 103 | 0.21 | - |
444
+ | 1.9623 | 104 | 0.2139 | - |
445
+ | 1.9811 | 105 | 0.1864 | - |
446
+ | 2.0 | 106 | 0.0719 | 0.887 |
447
+
448
+ </details>
449
+
450
+ ### Framework Versions
451
+ - Python: 3.12.3
452
+ - Sentence Transformers: 3.3.1
453
+ - Transformers: 4.48.0.dev0
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+ - PyTorch: 2.5.1
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+ - Accelerate: 1.2.1
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+ - Datasets: 3.2.0
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+ - Tokenizers: 0.21.0
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+
459
+ ## Citation
460
+
461
+ ### BibTeX
462
+
463
+ #### Sentence Transformers
464
+ ```bibtex
465
+ @inproceedings{reimers-2019-sentence-bert,
466
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
467
+ author = "Reimers, Nils and Gurevych, Iryna",
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+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
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+ month = "11",
470
+ year = "2019",
471
+ publisher = "Association for Computational Linguistics",
472
+ url = "https://arxiv.org/abs/1908.10084",
473
+ }
474
+ ```
475
+
476
+ #### MultipleNegativesRankingLoss
477
+ ```bibtex
478
+ @misc{henderson2017efficient,
479
+ title={Efficient Natural Language Response Suggestion for Smart Reply},
480
+ author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
481
+ year={2017},
482
+ eprint={1705.00652},
483
+ archivePrefix={arXiv},
484
+ primaryClass={cs.CL}
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+ }
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+ ```
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+
488
+ <!--
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+ ## Glossary
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+
491
+ *Clearly define terms in order to be accessible across audiences.*
492
+ -->
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+
494
+ <!--
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+ ## Model Card Authors
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+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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+ -->
499
+
500
+ <!--
501
+ ## Model Card Contact
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+
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+ *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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