Update README.md

README.md

@@ -86,36 +86,6 @@ model-index:
       name: sts test
       type: sts-test
     metrics:
-    - type: pearson_cosine
-      value: 0.4121931859939639
-      name: Pearson Cosine
-    - type: spearman_cosine
-      value: 0.4188435395565816
-      name: Spearman Cosine
-    - type: pearson_manhattan
-      value: 0.43722674169112186
-      name: Pearson Manhattan
-    - type: spearman_manhattan
-      value: 0.4419489193187135
-      name: Spearman Manhattan
-    - type: pearson_euclidean
-      value: 0.4165228130620452
-      name: Pearson Euclidean
-    - type: spearman_euclidean
-      value: 0.42369527784158983
-      name: Spearman Euclidean
-    - type: pearson_dot
-      value: 0.13511926964573803
-      name: Pearson Dot
-    - type: spearman_dot
-      value: 0.13030376975519165
-      name: Spearman Dot
-    - type: pearson_max
-      value: 0.43722674169112186
-      name: Pearson Max
-    - type: spearman_max
-      value: 0.4419489193187135
-      name: Spearman Max
     - type: pearson_cosine
       value: 0.7746195773286169
       name: Pearson Cosine
@@ -176,42 +146,13 @@ model-index:
     - type: spearman_max
       value: 0.7193195268794856
       name: Spearman Max
-    - type: pearson_cosine
-      value: 0.7408543477349779
-      name: Pearson Cosine
-    - type: spearman_cosine
-      value: 0.7193195268794856
-      name: Spearman Cosine
-    - type: pearson_manhattan
-      value: 0.7347205138738226
-      name: Pearson Manhattan
-    - type: spearman_manhattan
-      value: 0.716277121285963
-      name: Spearman Manhattan
-    - type: pearson_euclidean
-      value: 0.7317357204840789
-      name: Pearson Euclidean
-    - type: spearman_euclidean
-      value: 0.7133569462956698
-      name: Spearman Euclidean
-    - type: pearson_dot
-      value: 0.5412116736741877
-      name: Pearson Dot
-    - type: spearman_dot
-      value: 0.5324862690078268
-      name: Spearman Dot
-    - type: pearson_max
-      value: 0.7408543477349779
-      name: Pearson Max
-    - type: spearman_max
-      value: 0.7193195268794856
-      name: Spearman Max
 ---
 
 # SentenceTransformer based on microsoft/deberta-v3-small
 
 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the [nli-pairs](https://huggingface.co/datasets/sentence-transformers/all-nli), [sts-label](https://huggingface.co/datasets/sentence-transformers/stsb), [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc), [qnli-contrastive](https://huggingface.co/datasets/nyu-mll/glue), [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail), [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail), [xsum-pairs](https://huggingface.co/datasets/sentence-transformers/xsum), [compression-pairs](https://huggingface.co/datasets/sentence-transformers/sentence-compression), [sciq_pairs](https://huggingface.co/datasets/allenai/sciq), [qasc_pairs](https://huggingface.co/datasets/allenai/qasc), [openbookqa_pairs](https://huggingface.co/datasets/allenai/openbookqa), [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3), [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions), [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa), [quora_pairs](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) and [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) datasets. 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.
 
+
 ## Model Details
 
 ### Model Description
@@ -315,22 +256,7 @@ You can finetune this model on your own dataset.
 
 ### Metrics
 
-#### Semantic Similarity
-* Dataset: `sts-test`
-* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
 
-| Metric              | Value      |
-|:--------------------|:-----------|
-| pearson_cosine      | 0.4122     |
-| **spearman_cosine** | **0.4188** |
-| pearson_manhattan   | 0.4372     |
-| spearman_manhattan  | 0.4419     |
-| pearson_euclidean   | 0.4165     |
-| spearman_euclidean  | 0.4237     |
-| pearson_dot         | 0.1351     |
-| spearman_dot        | 0.1303     |
-| pearson_max         | 0.4372     |
-| spearman_max        | 0.4419     |
 
 #### Semantic Similarity
 * Dataset: `sts-test`
@@ -349,39 +275,7 @@ You can finetune this model on your own dataset.
 | pearson_max         | 0.7746    |
 | spearman_max        | 0.769     |
 
-#### Semantic Similarity
-* Dataset: `sts-test`
-* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
-
-| Metric              | Value      |
-|:--------------------|:-----------|
-| pearson_cosine      | 0.7409     |
-| **spearman_cosine** | **0.7193** |
-| pearson_manhattan   | 0.7347     |
-| spearman_manhattan  | 0.7163     |
-| pearson_euclidean   | 0.7317     |
-| spearman_euclidean  | 0.7134     |
-| pearson_dot         | 0.5412     |
-| spearman_dot        | 0.5325     |
-| pearson_max         | 0.7409     |
-| spearman_max        | 0.7193     |
-
-#### Semantic Similarity
-* Dataset: `sts-test`
-* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
 
-| Metric              | Value      |
-|:--------------------|:-----------|
-| pearson_cosine      | 0.7409     |
-| **spearman_cosine** | **0.7193** |
-| pearson_manhattan   | 0.7347     |
-| spearman_manhattan  | 0.7163     |
-| pearson_euclidean   | 0.7317     |
-| spearman_euclidean  | 0.7134     |
-| pearson_dot         | 0.5412     |
-| spearman_dot        | 0.5325     |
-| pearson_max         | 0.7409     |
-| spearman_max        | 0.7193     |
 
 <!--
 ## Bias, Risks and Limitations
@@ -1227,16 +1121,6 @@ You can finetune this model on your own dataset.
 }
 ```
 
-#### CoSENTLoss
-```bibtex
-@online{kexuefm-8847,
-    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
-    author={Su Jianlin},
-    year={2022},
-    month={Jan},
-    url={https://kexue.fm/archives/8847},
-}
-```
 
 #### GISTEmbedLoss
 ```bibtex