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README.md
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name: Spearman Max
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---
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# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2). 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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| pearson_max | 0.9551 |
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| spearman_max | 0.9593 |
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-
####
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* Dataset: `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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@@ -1120,499 +1152,6 @@ You can finetune this model on your own dataset.
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| pearson_max | 0.948 |
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| spearman_max | 0.9515 |
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#### Semantic Similarity
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* Dataset: `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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| Metric | Value |
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|:--------------------|:-----------|
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| pearson_cosine | 0.9725 |
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| 1130 |
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| **spearman_cosine** | **0.9766** |
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| pearson_manhattan | 0.9382 |
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| spearman_manhattan | 0.9487 |
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| 1133 |
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| pearson_euclidean | 0.9392 |
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| spearman_euclidean | 0.95 |
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| 1135 |
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| pearson_dot | 0.8531 |
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| spearman_dot | 0.8611 |
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| pearson_max | 0.9725 |
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| spearman_max | 0.9766 |
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#### Semantic Similarity
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* Dataset: `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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| Metric | Value |
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|:--------------------|:-----------|
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| pearson_cosine | 0.8027 |
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| **spearman_cosine** | **0.8124** |
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| pearson_manhattan | 0.7839 |
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| spearman_manhattan | 0.79 |
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| pearson_euclidean | 0.7836 |
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| spearman_euclidean | 0.792 |
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| 1152 |
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| pearson_dot | 0.7699 |
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| 1153 |
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| spearman_dot | 0.782 |
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| 1154 |
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| pearson_max | 0.8027 |
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| spearman_max | 0.8124 |
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#### Semantic Similarity
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* Dataset: `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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| Metric | Value |
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|:--------------------|:-----------|
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| pearson_cosine | 0.7796 |
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| 1164 |
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| **spearman_cosine** | **0.7703** |
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| pearson_manhattan | 0.7904 |
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| spearman_manhattan | 0.783 |
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| pearson_euclidean | 0.7912 |
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| spearman_euclidean | 0.7842 |
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| pearson_dot | 0.7077 |
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| 1170 |
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| spearman_dot | 0.6914 |
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| 1171 |
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| pearson_max | 0.7912 |
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| spearman_max | 0.7842 |
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-
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#### Semantic Similarity
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* Dataset: `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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| Metric | Value |
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|:--------------------|:-----------|
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| pearson_cosine | 0.9113 |
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| **spearman_cosine** | **0.9109** |
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| pearson_manhattan | 0.897 |
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| spearman_manhattan | 0.8934 |
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| pearson_euclidean | 0.8986 |
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| spearman_euclidean | 0.8955 |
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| pearson_dot | 0.8844 |
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| spearman_dot | 0.8923 |
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| pearson_max | 0.9113 |
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| spearman_max | 0.9109 |
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#### Semantic Similarity
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* Dataset: `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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| Metric | Value |
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|:--------------------|:-----------|
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| pearson_cosine | 0.9362 |
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| **spearman_cosine** | **0.9379** |
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| pearson_manhattan | 0.923 |
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| spearman_manhattan | 0.9245 |
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| pearson_euclidean | 0.9231 |
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| spearman_euclidean | 0.9251 |
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| pearson_dot | 0.907 |
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| spearman_dot | 0.9186 |
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| pearson_max | 0.9362 |
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| spearman_max | 0.9379 |
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#### Semantic Similarity
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* Dataset: `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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| Metric | Value |
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|:--------------------|:-----------|
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| pearson_cosine | 0.8049 |
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| **spearman_cosine** | **0.7987** |
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| pearson_manhattan | 0.8018 |
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| spearman_manhattan | 0.7828 |
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| pearson_euclidean | 0.8007 |
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| spearman_euclidean | 0.7825 |
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| pearson_dot | 0.7895 |
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| spearman_dot | 0.7819 |
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| pearson_max | 0.8049 |
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| spearman_max | 0.7987 |
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#### Semantic Similarity
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* Dataset: `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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| Metric | Value |
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|:--------------------|:-----------|
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| pearson_cosine | 0.852 |
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| **spearman_cosine** | **0.8553** |
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| pearson_manhattan | 0.8464 |
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| spearman_manhattan | 0.841 |
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| pearson_euclidean | 0.8468 |
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| spearman_euclidean | 0.8459 |
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| pearson_dot | 0.8093 |
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| spearman_dot | 0.8154 |
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| pearson_max | 0.852 |
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| spearman_max | 0.8553 |
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#### Semantic Similarity
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* Dataset: `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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| Metric | Value |
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|:--------------------|:-----------|
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| pearson_cosine | 0.8752 |
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| **spearman_cosine** | **0.8727** |
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| pearson_manhattan | 0.8745 |
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| spearman_manhattan | 0.8661 |
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| pearson_euclidean | 0.8748 |
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| spearman_euclidean | 0.8668 |
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| pearson_dot | 0.8603 |
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| spearman_dot | 0.852 |
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| 1256 |
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| pearson_max | 0.8752 |
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| spearman_max | 0.8727 |
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#### Semantic Similarity
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* Dataset: `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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| Metric | Value |
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|:--------------------|:-----------|
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| pearson_cosine | 0.9082 |
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| **spearman_cosine** | **0.9068** |
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| pearson_manhattan | 0.8908 |
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| spearman_manhattan | 0.8852 |
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| pearson_euclidean | 0.8908 |
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| spearman_euclidean | 0.8851 |
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| 1271 |
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| pearson_dot | 0.8889 |
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| spearman_dot | 0.8966 |
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| pearson_max | 0.9082 |
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| spearman_max | 0.9068 |
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#### Semantic Similarity
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* Dataset: `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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| Metric | Value |
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|:--------------------|:-----------|
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| pearson_cosine | 0.925 |
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| **spearman_cosine** | **0.9247** |
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| 1284 |
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| pearson_manhattan | 0.9084 |
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| 1285 |
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| spearman_manhattan | 0.9029 |
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| 1286 |
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| pearson_euclidean | 0.9116 |
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| spearman_euclidean | 0.9084 |
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| 1288 |
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| pearson_dot | 0.9001 |
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| spearman_dot | 0.907 |
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| 1290 |
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| pearson_max | 0.925 |
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| 1291 |
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| spearman_max | 0.9247 |
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#### Semantic Similarity
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* Dataset: `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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| Metric | Value |
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|:--------------------|:-----------|
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| pearson_cosine | 0.9133 |
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| 1300 |
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| **spearman_cosine** | **0.9115** |
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| 1301 |
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| pearson_manhattan | 0.8977 |
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| 1302 |
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| spearman_manhattan | 0.8933 |
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| 1303 |
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| pearson_euclidean | 0.8979 |
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| 1304 |
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| spearman_euclidean | 0.8937 |
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| 1305 |
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| pearson_dot | 0.8912 |
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| 1306 |
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| spearman_dot | 0.8988 |
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| 1307 |
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| pearson_max | 0.9133 |
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| 1308 |
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| spearman_max | 0.9115 |
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| 1309 |
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#### Semantic Similarity
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* Dataset: `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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| Metric | Value |
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|:--------------------|:-----------|
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| pearson_cosine | 0.8985 |
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| **spearman_cosine** | **0.8452** |
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| 1318 |
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| pearson_manhattan | 0.8715 |
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| 1319 |
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| spearman_manhattan | 0.8452 |
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| 1320 |
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| pearson_euclidean | 0.8809 |
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| 1321 |
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| spearman_euclidean | 0.8452 |
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| 1322 |
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| pearson_dot | 0.8538 |
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| 1323 |
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| spearman_dot | 0.8452 |
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| 1324 |
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| pearson_max | 0.8985 |
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| 1325 |
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| spearman_max | 0.8452 |
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#### Semantic Similarity
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* Dataset: `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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| Metric | Value |
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| 1332 |
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|:--------------------|:-----------|
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| 1333 |
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| pearson_cosine | 0.6495 |
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| 1334 |
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| **spearman_cosine** | **0.6385** |
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| 1335 |
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| pearson_manhattan | 0.6429 |
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| 1336 |
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| spearman_manhattan | 0.6474 |
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| 1337 |
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| pearson_euclidean | 0.6443 |
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| 1338 |
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| spearman_euclidean | 0.6445 |
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| 1339 |
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| pearson_dot | 0.6128 |
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| spearman_dot | 0.6108 |
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| 1341 |
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| pearson_max | 0.6495 |
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| 1342 |
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| spearman_max | 0.6474 |
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| 1343 |
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| 1344 |
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#### Semantic Similarity
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* Dataset: `sts-test`
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| 1346 |
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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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| 1348 |
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| Metric | Value |
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|:--------------------|:-----------|
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| 1350 |
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| pearson_cosine | 0.7441 |
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| 1351 |
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| **spearman_cosine** | **0.7518** |
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| 1352 |
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| pearson_manhattan | 0.7339 |
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| 1353 |
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| spearman_manhattan | 0.7367 |
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| 1354 |
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| pearson_euclidean | 0.7337 |
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| 1355 |
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| spearman_euclidean | 0.7342 |
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| 1356 |
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| pearson_dot | 0.6886 |
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| 1357 |
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| spearman_dot | 0.6986 |
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| 1358 |
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| pearson_max | 0.7441 |
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| 1359 |
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| spearman_max | 0.7518 |
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| 1360 |
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| 1361 |
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#### Semantic Similarity
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* Dataset: `sts-test`
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| 1363 |
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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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| 1365 |
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| Metric | Value |
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| 1366 |
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|:--------------------|:-----------|
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| 1367 |
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| pearson_cosine | 0.6279 |
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| 1368 |
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| **spearman_cosine** | **0.6319** |
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| 1369 |
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| pearson_manhattan | 0.5435 |
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| 1370 |
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| spearman_manhattan | 0.6002 |
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| 1371 |
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| pearson_euclidean | 0.54 |
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| 1372 |
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| spearman_euclidean | 0.5955 |
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| 1373 |
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| pearson_dot | 0.5658 |
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| 1374 |
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| spearman_dot | 0.6069 |
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| 1375 |
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| pearson_max | 0.6279 |
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| 1376 |
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| spearman_max | 0.6319 |
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| 1377 |
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| 1378 |
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#### Semantic Similarity
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* Dataset: `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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| Metric | Value |
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| 1383 |
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|:--------------------|:-----------|
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| 1384 |
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| pearson_cosine | 0.7779 |
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| 1385 |
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| **spearman_cosine** | **0.7876** |
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| 1386 |
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| pearson_manhattan | 0.7426 |
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| 1387 |
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| spearman_manhattan | 0.7789 |
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| 1388 |
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| pearson_euclidean | 0.7437 |
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| 1389 |
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| spearman_euclidean | 0.7806 |
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| 1390 |
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| pearson_dot | 0.7214 |
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| 1391 |
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| spearman_dot | 0.7489 |
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| 1392 |
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| pearson_max | 0.7779 |
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| 1393 |
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| spearman_max | 0.7876 |
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| 1394 |
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| 1395 |
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#### Semantic Similarity
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* Dataset: `sts-test`
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| 1397 |
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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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| Metric | Value |
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| 1400 |
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|:--------------------|:-----------|
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| 1401 |
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| pearson_cosine | 0.5268 |
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| 1402 |
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| **spearman_cosine** | **0.5774** |
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| 1403 |
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| pearson_manhattan | 0.4171 |
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| 1404 |
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| spearman_manhattan | 0.56 |
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| 1405 |
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| pearson_euclidean | 0.4219 |
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| 1406 |
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| spearman_euclidean | 0.5665 |
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| 1407 |
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| pearson_dot | 0.4981 |
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| 1408 |
-
| spearman_dot | 0.5367 |
|
| 1409 |
-
| pearson_max | 0.5268 |
|
| 1410 |
-
| spearman_max | 0.5774 |
|
| 1411 |
-
|
| 1412 |
-
#### Semantic Similarity
|
| 1413 |
-
* Dataset: `sts-test`
|
| 1414 |
-
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
|
| 1415 |
-
|
| 1416 |
-
| Metric | Value |
|
| 1417 |
-
|:--------------------|:-----------|
|
| 1418 |
-
| pearson_cosine | 0.6306 |
|
| 1419 |
-
| **spearman_cosine** | **0.6384** |
|
| 1420 |
-
| pearson_manhattan | 0.6034 |
|
| 1421 |
-
| spearman_manhattan | 0.6168 |
|
| 1422 |
-
| pearson_euclidean | 0.6081 |
|
| 1423 |
-
| spearman_euclidean | 0.622 |
|
| 1424 |
-
| pearson_dot | 0.5767 |
|
| 1425 |
-
| spearman_dot | 0.5831 |
|
| 1426 |
-
| pearson_max | 0.6306 |
|
| 1427 |
-
| spearman_max | 0.6384 |
|
| 1428 |
-
|
| 1429 |
-
#### Semantic Similarity
|
| 1430 |
-
* Dataset: `sts-test`
|
| 1431 |
-
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
|
| 1432 |
-
|
| 1433 |
-
| Metric | Value |
|
| 1434 |
-
|:--------------------|:-----------|
|
| 1435 |
-
| pearson_cosine | 0.5568 |
|
| 1436 |
-
| **spearman_cosine** | **0.5867** |
|
| 1437 |
-
| pearson_manhattan | 0.4924 |
|
| 1438 |
-
| spearman_manhattan | 0.5738 |
|
| 1439 |
-
| pearson_euclidean | 0.4906 |
|
| 1440 |
-
| spearman_euclidean | 0.5762 |
|
| 1441 |
-
| pearson_dot | 0.4307 |
|
| 1442 |
-
| spearman_dot | 0.5471 |
|
| 1443 |
-
| pearson_max | 0.5568 |
|
| 1444 |
-
| spearman_max | 0.5867 |
|
| 1445 |
-
|
| 1446 |
-
#### Semantic Similarity
|
| 1447 |
-
* Dataset: `sts-test`
|
| 1448 |
-
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
|
| 1449 |
-
|
| 1450 |
-
| Metric | Value |
|
| 1451 |
-
|:--------------------|:----------|
|
| 1452 |
-
| pearson_cosine | 0.5776 |
|
| 1453 |
-
| **spearman_cosine** | **0.575** |
|
| 1454 |
-
| pearson_manhattan | 0.5718 |
|
| 1455 |
-
| spearman_manhattan | 0.5501 |
|
| 1456 |
-
| pearson_euclidean | 0.5695 |
|
| 1457 |
-
| spearman_euclidean | 0.5532 |
|
| 1458 |
-
| pearson_dot | 0.5315 |
|
| 1459 |
-
| spearman_dot | 0.5191 |
|
| 1460 |
-
| pearson_max | 0.5776 |
|
| 1461 |
-
| spearman_max | 0.575 |
|
| 1462 |
-
|
| 1463 |
-
#### Semantic Similarity
|
| 1464 |
-
* Dataset: `sts-test`
|
| 1465 |
-
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
|
| 1466 |
-
|
| 1467 |
-
| Metric | Value |
|
| 1468 |
-
|:--------------------|:-----------|
|
| 1469 |
-
| pearson_cosine | 0.3572 |
|
| 1470 |
-
| **spearman_cosine** | **0.4336** |
|
| 1471 |
-
| pearson_manhattan | 0.2081 |
|
| 1472 |
-
| spearman_manhattan | 0.4355 |
|
| 1473 |
-
| pearson_euclidean | 0.2086 |
|
| 1474 |
-
| spearman_euclidean | 0.4402 |
|
| 1475 |
-
| pearson_dot | 0.2234 |
|
| 1476 |
-
| spearman_dot | 0.3707 |
|
| 1477 |
-
| pearson_max | 0.3572 |
|
| 1478 |
-
| spearman_max | 0.4402 |
|
| 1479 |
-
|
| 1480 |
-
#### Semantic Similarity
|
| 1481 |
-
* Dataset: `sts-test`
|
| 1482 |
-
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
|
| 1483 |
-
|
| 1484 |
-
| Metric | Value |
|
| 1485 |
-
|:--------------------|:-----------|
|
| 1486 |
-
| pearson_cosine | 0.6863 |
|
| 1487 |
-
| **spearman_cosine** | **0.6621** |
|
| 1488 |
-
| pearson_manhattan | 0.6429 |
|
| 1489 |
-
| spearman_manhattan | 0.6484 |
|
| 1490 |
-
| pearson_euclidean | 0.6424 |
|
| 1491 |
-
| spearman_euclidean | 0.6486 |
|
| 1492 |
-
| pearson_dot | 0.6352 |
|
| 1493 |
-
| spearman_dot | 0.6159 |
|
| 1494 |
-
| pearson_max | 0.6863 |
|
| 1495 |
-
| spearman_max | 0.6621 |
|
| 1496 |
-
|
| 1497 |
-
#### Semantic Similarity
|
| 1498 |
-
* Dataset: `sts-test`
|
| 1499 |
-
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
|
| 1500 |
-
|
| 1501 |
-
| Metric | Value |
|
| 1502 |
-
|:--------------------|:-----------|
|
| 1503 |
-
| pearson_cosine | 0.757 |
|
| 1504 |
-
| **spearman_cosine** | **0.7511** |
|
| 1505 |
-
| pearson_manhattan | 0.7191 |
|
| 1506 |
-
| spearman_manhattan | 0.714 |
|
| 1507 |
-
| pearson_euclidean | 0.7204 |
|
| 1508 |
-
| spearman_euclidean | 0.7258 |
|
| 1509 |
-
| pearson_dot | 0.7144 |
|
| 1510 |
-
| spearman_dot | 0.7284 |
|
| 1511 |
-
| pearson_max | 0.757 |
|
| 1512 |
-
| spearman_max | 0.7511 |
|
| 1513 |
-
|
| 1514 |
-
#### Semantic Similarity
|
| 1515 |
-
* Dataset: `sts-test`
|
| 1516 |
-
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
|
| 1517 |
-
|
| 1518 |
-
| Metric | Value |
|
| 1519 |
-
|:--------------------|:-----------|
|
| 1520 |
-
| pearson_cosine | 0.6503 |
|
| 1521 |
-
| **spearman_cosine** | **0.6625** |
|
| 1522 |
-
| pearson_manhattan | 0.6474 |
|
| 1523 |
-
| spearman_manhattan | 0.659 |
|
| 1524 |
-
| pearson_euclidean | 0.6517 |
|
| 1525 |
-
| spearman_euclidean | 0.6667 |
|
| 1526 |
-
| pearson_dot | 0.5647 |
|
| 1527 |
-
| spearman_dot | 0.5702 |
|
| 1528 |
-
| pearson_max | 0.6517 |
|
| 1529 |
-
| spearman_max | 0.6667 |
|
| 1530 |
-
|
| 1531 |
-
#### Semantic Similarity
|
| 1532 |
-
* Dataset: `sts-test`
|
| 1533 |
-
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
|
| 1534 |
-
|
| 1535 |
-
| Metric | Value |
|
| 1536 |
-
|:--------------------|:-----------|
|
| 1537 |
-
| pearson_cosine | 0.6774 |
|
| 1538 |
-
| **spearman_cosine** | **0.6537** |
|
| 1539 |
-
| pearson_manhattan | 0.6825 |
|
| 1540 |
-
| spearman_manhattan | 0.6325 |
|
| 1541 |
-
| pearson_euclidean | 0.6906 |
|
| 1542 |
-
| spearman_euclidean | 0.6407 |
|
| 1543 |
-
| pearson_dot | 0.5835 |
|
| 1544 |
-
| spearman_dot | 0.5962 |
|
| 1545 |
-
| pearson_max | 0.6906 |
|
| 1546 |
-
| spearman_max | 0.6537 |
|
| 1547 |
-
|
| 1548 |
-
#### Semantic Similarity
|
| 1549 |
-
* Dataset: `sts-test`
|
| 1550 |
-
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
|
| 1551 |
-
|
| 1552 |
-
| Metric | Value |
|
| 1553 |
-
|:--------------------|:-----------|
|
| 1554 |
-
| pearson_cosine | 0.6709 |
|
| 1555 |
-
| **spearman_cosine** | **0.6847** |
|
| 1556 |
-
| pearson_manhattan | 0.6613 |
|
| 1557 |
-
| spearman_manhattan | 0.6907 |
|
| 1558 |
-
| pearson_euclidean | 0.6607 |
|
| 1559 |
-
| spearman_euclidean | 0.6881 |
|
| 1560 |
-
| pearson_dot | 0.6098 |
|
| 1561 |
-
| spearman_dot | 0.6195 |
|
| 1562 |
-
| pearson_max | 0.6709 |
|
| 1563 |
-
| spearman_max | 0.6907 |
|
| 1564 |
-
|
| 1565 |
-
#### Semantic Similarity
|
| 1566 |
-
* Dataset: `sts-test`
|
| 1567 |
-
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
|
| 1568 |
-
|
| 1569 |
-
| Metric | Value |
|
| 1570 |
-
|:--------------------|:-----------|
|
| 1571 |
-
| pearson_cosine | 0.5977 |
|
| 1572 |
-
| **spearman_cosine** | **0.5799** |
|
| 1573 |
-
| pearson_manhattan | 0.5974 |
|
| 1574 |
-
| spearman_manhattan | 0.5953 |
|
| 1575 |
-
| pearson_euclidean | 0.5949 |
|
| 1576 |
-
| spearman_euclidean | 0.5936 |
|
| 1577 |
-
| pearson_dot | 0.5043 |
|
| 1578 |
-
| spearman_dot | 0.4968 |
|
| 1579 |
-
| pearson_max | 0.5977 |
|
| 1580 |
-
| spearman_max | 0.5953 |
|
| 1581 |
-
|
| 1582 |
-
#### Semantic Similarity
|
| 1583 |
-
* Dataset: `sts-test`
|
| 1584 |
-
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
|
| 1585 |
-
|
| 1586 |
-
| Metric | Value |
|
| 1587 |
-
|:--------------------|:-----------|
|
| 1588 |
-
| pearson_cosine | 0.4562 |
|
| 1589 |
-
| **spearman_cosine** | **0.4422** |
|
| 1590 |
-
| pearson_manhattan | 0.4155 |
|
| 1591 |
-
| spearman_manhattan | 0.3837 |
|
| 1592 |
-
| pearson_euclidean | 0.4111 |
|
| 1593 |
-
| spearman_euclidean | 0.3822 |
|
| 1594 |
-
| pearson_dot | 0.4863 |
|
| 1595 |
-
| spearman_dot | 0.5303 |
|
| 1596 |
-
| pearson_max | 0.4863 |
|
| 1597 |
-
| spearman_max | 0.5303 |
|
| 1598 |
-
|
| 1599 |
-
#### Semantic Similarity
|
| 1600 |
-
* Dataset: `sts-test`
|
| 1601 |
-
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
|
| 1602 |
-
|
| 1603 |
-
| Metric | Value |
|
| 1604 |
-
|:--------------------|:-----------|
|
| 1605 |
-
| pearson_cosine | 0.593 |
|
| 1606 |
-
| **spearman_cosine** | **0.6266** |
|
| 1607 |
-
| pearson_manhattan | 0.5608 |
|
| 1608 |
-
| spearman_manhattan | 0.6229 |
|
| 1609 |
-
| pearson_euclidean | 0.558 |
|
| 1610 |
-
| spearman_euclidean | 0.6202 |
|
| 1611 |
-
| pearson_dot | 0.4578 |
|
| 1612 |
-
| spearman_dot | 0.5628 |
|
| 1613 |
-
| pearson_max | 0.593 |
|
| 1614 |
-
| spearman_max | 0.6266 |
|
| 1615 |
-
|
| 1616 |
<!--
|
| 1617 |
## Bias, Risks and Limitations
|
| 1618 |
|
|
|
|
| 995 |
name: Spearman Max
|
| 996 |
---
|
| 997 |
|
| 998 |
+
/!\ This model achieves SOTA results in the MTEB STS multilingual Leaderboard (in "other"). Here is the comparison
|
| 999 |
+
|
| 1000 |
+
State-of-the-art results (Multi) STSb-XLM-RoBERTa-base Paraphrase Multilingual MPNet base v2
|
| 1001 |
+
Average 73.17 71.68 **73.89**
|
| 1002 |
+
STS17 (ar-ar) **81.87** 80.43 81.24
|
| 1003 |
+
STS17 (en-ar) **81.22** 76.3 77.03
|
| 1004 |
+
STS17 (en-de) 87.3 91.06 **91.09**
|
| 1005 |
+
STS17 (en-tr) 77.18 **80.74** 79.87
|
| 1006 |
+
STS17 (es-en) **88.24** 83.09 85.53
|
| 1007 |
+
STS17 (es-es) **88.25** 84.16 87.27
|
| 1008 |
+
STS17 (fr-en) 88.06 **91.33** 90.68
|
| 1009 |
+
STS17 (it-en) 89.68 **92.87** 92.47
|
| 1010 |
+
STS17 (ko-ko) 83.69 **97.67** 97.66
|
| 1011 |
+
STS17 (nl-en) 88.25 **92.13** 91.15
|
| 1012 |
+
STS22 (ar) 58.67 58.67 **62.66**
|
| 1013 |
+
STS22 (de) **60.12** 52.17 57.74
|
| 1014 |
+
STS22 (de-en) **60.92** 58.5 57.5
|
| 1015 |
+
STS22 (de-fr) **67.79** 51.28 57.99
|
| 1016 |
+
STS22 (de-pl) **58.69** 44.56 44.22
|
| 1017 |
+
STS22 (es) **68.57** 63.68 66.21
|
| 1018 |
+
STS22 (es-en) **78.8** 70.65 75.18
|
| 1019 |
+
STS22 (es-it) **75.04** 60.88 66.25
|
| 1020 |
+
STS22 (fr) **83.75** 76.46 78.76
|
| 1021 |
+
STS22 (fr-pl) 84.52 84.52 **84.52**
|
| 1022 |
+
STS22 (it) **79.28** 66.73 68.47
|
| 1023 |
+
STS22 (pl) 42.08 41.18 **43.36**
|
| 1024 |
+
STS22 (pl-en) **77.5** 64.35 75.11
|
| 1025 |
+
STS22 (ru) **61.71** 58.59 58.67
|
| 1026 |
+
STS22 (tr) **68.72** 57.52 63.84
|
| 1027 |
+
STS22 (zh-en) **71.88** 60.69 65.37
|
| 1028 |
+
STSb 89.86 95.05 **95.15**
|
| 1029 |
+
|
| 1030 |
# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2
|
| 1031 |
|
| 1032 |
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2). 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.
|
|
|
|
| 1135 |
| pearson_max | 0.9551 |
|
| 1136 |
| spearman_max | 0.9593 |
|
| 1137 |
|
| 1138 |
+
#### Evalutation results vs SOTA results
|
| 1139 |
* Dataset: `sts-test`
|
| 1140 |
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
|
| 1141 |
|
|
|
|
| 1152 |
| pearson_max | 0.948 |
|
| 1153 |
| spearman_max | 0.9515 |
|
| 1154 |
|
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| 1155 |
<!--
|
| 1156 |
## Bias, Risks and Limitations
|
| 1157 |
|