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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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<!--- Describe your model here -->
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## Usage (Sentence-Transformers)
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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Some post-training logs:
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```
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2024-04-09 15:33:55 - EmbeddingSimilarityEvaluator: Evaluating the model on sts-dev dataset after epoch 0:
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2024-04-09 15:33:56 - Cosine-Similarity : Pearson: 0.8567 Spearman: 0.8662
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2024-04-09 15:33:56 - Manhattan-Distance: Pearson: 0.8540 Spearman: 0.8534
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2024-04-09 15:33:56 - Euclidean-Distance: Pearson: 0.8572 Spearman: 0.8571
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2024-04-09 15:33:56 - Dot-Product-Similarity: Pearson: 0.6507 Spearman: 0.6496
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```
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```
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2024-04-09 15:33:56 - EmbeddingSimilarityEvaluator: Evaluating the model on sts-test dataset:
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2024-04-09 15:33:56 - Cosine-Similarity : Pearson: 0.8294 Spearman: 0.8426
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2024-04-09 15:33:56 - Manhattan-Distance: Pearson: 0.8274 Spearman: 0.8225
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2024-04-09 15:33:56 - Euclidean-Distance: Pearson: 0.8303 Spearman: 0.8255
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2024-04-09 15:33:56 - Dot-Product-Similarity: Pearson: 0.5832 Spearman: 0.5722
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```
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The `Cosine-Similarity`, `Spearman` scores are most important.
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<!--- Describe your model here -->
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## Usage (Sentence-Transformers)
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