Update README.md

README.md

@@ -1,115 +1,115 @@
----
-base_model: FacebookAI/xlm-roberta-large
-library_name: sentence-transformers
-metrics:
-- pearson_cosine
-- spearman_cosine
-- pearson_manhattan
-- spearman_manhattan
-- pearson_euclidean
-- spearman_euclidean
-- pearson_dot
-- spearman_dot
-- pearson_max
-- spearman_max
-pipeline_tag: sentence-similarity
-tags:
-- sentence-transformers
-- sentence-similarity
-- feature-extraction
-- mteb
-- bilingual
-model-index:
-- name: omarelshehy/Arabic-English-Matryoshka-STS
-  results:
-  - dataset:
-      config: en-ar
-      name: MTEB STS17 (en-ar)
-      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
-      split: test
-      type: mteb/sts17-crosslingual-sts
-    metrics:
-    - type: cosine_pearson
-      value: 79.79480510851795
-    - type: cosine_spearman
-      value: 79.67609346073252
-    - type: euclidean_pearson
-      value: 81.64087935350051
-    - type: euclidean_spearman
-      value: 80.52588414802709
-    - type: main_score
-      value: 79.67609346073252
-    - type: manhattan_pearson
-      value: 81.57042957417305
-    - type: manhattan_spearman
-      value: 80.44331526051143
-    - type: pearson
-      value: 79.79480418294698
-    - type: spearman
-      value: 79.67609346073252
-    task:
-      type: STS
-  - dataset:
-      config: ar-ar
-      name: MTEB STS17 (ar-ar)
-      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
-      split: test
-      type: mteb/sts17-crosslingual-sts
-    metrics:
-    - type: cosine_pearson
-      value: 82.22889478671283
-    - type: cosine_spearman
-      value: 83.0533648934447
-    - type: euclidean_pearson
-      value: 81.15891941165452
-    - type: euclidean_spearman
-      value: 82.14034597386936
-    - type: main_score
-      value: 83.0533648934447
-    - type: manhattan_pearson
-      value: 81.17463976232014
-    - type: manhattan_spearman
-      value: 82.09804987736345
-    - type: pearson
-      value: 82.22889389569819
-    - type: spearman
-      value: 83.0529662284269
-    task:
-      type: STS
-  - dataset:
-      config: en-en
-      name: MTEB STS17 (en-en)
-      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
-      split: test
-      type: mteb/sts17-crosslingual-sts
-    metrics:
-    - type: cosine_pearson
-      value: 87.17053120821998
-    - type: cosine_spearman
-      value: 87.05959159411456
-    - type: euclidean_pearson
-      value: 87.63706739480517
-    - type: euclidean_spearman
-      value: 87.7675347222274
-    - type: main_score
-      value: 87.05959159411456
-    - type: manhattan_pearson
-      value: 87.7006832512623
-    - type: manhattan_spearman
-      value: 87.80128473941168
-    - type: pearson
-      value: 87.17053012311975
-    - type: spearman
-      value: 87.05959159411456
-    task:
-      type: STS
-Language:
-- ar
-- en
-language:
-- ar
-- en
----
+---
+base_model: FacebookAI/xlm-roberta-large
+library_name: sentence-transformers
+metrics:
+- pearson_cosine
+- spearman_cosine
+- pearson_manhattan
+- spearman_manhattan
+- pearson_euclidean
+- spearman_euclidean
+- pearson_dot
+- spearman_dot
+- pearson_max
+- spearman_max
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- mteb
+- bilingual
+model-index:
+- name: omarelshehy/Arabic-English-Matryoshka-STS
+  results:
+  - dataset:
+      config: en-ar
+      name: MTEB STS17 (en-ar)
+      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
+      split: test
+      type: mteb/sts17-crosslingual-sts
+    metrics:
+    - type: cosine_pearson
+      value: 79.79480510851795
+    - type: cosine_spearman
+      value: 79.67609346073252
+    - type: euclidean_pearson
+      value: 81.64087935350051
+    - type: euclidean_spearman
+      value: 80.52588414802709
+    - type: main_score
+      value: 79.67609346073252
+    - type: manhattan_pearson
+      value: 81.57042957417305
+    - type: manhattan_spearman
+      value: 80.44331526051143
+    - type: pearson
+      value: 79.79480418294698
+    - type: spearman
+      value: 79.67609346073252
+    task:
+      type: STS
+  - dataset:
+      config: ar-ar
+      name: MTEB STS17 (ar-ar)
+      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
+      split: test
+      type: mteb/sts17-crosslingual-sts
+    metrics:
+    - type: cosine_pearson
+      value: 82.22889478671283
+    - type: cosine_spearman
+      value: 83.0533648934447
+    - type: euclidean_pearson
+      value: 81.15891941165452
+    - type: euclidean_spearman
+      value: 82.14034597386936
+    - type: main_score
+      value: 83.0533648934447
+    - type: manhattan_pearson
+      value: 81.17463976232014
+    - type: manhattan_spearman
+      value: 82.09804987736345
+    - type: pearson
+      value: 82.22889389569819
+    - type: spearman
+      value: 83.0529662284269
+    task:
+      type: STS
+  - dataset:
+      config: en-en
+      name: MTEB STS17 (en-en)
+      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
+      split: test
+      type: mteb/sts17-crosslingual-sts
+    metrics:
+    - type: cosine_pearson
+      value: 87.17053120821998
+    - type: cosine_spearman
+      value: 87.05959159411456
+    - type: euclidean_pearson
+      value: 87.63706739480517
+    - type: euclidean_spearman
+      value: 87.7675347222274
+    - type: main_score
+      value: 87.05959159411456
+    - type: manhattan_pearson
+      value: 87.7006832512623
+    - type: manhattan_spearman
+      value: 87.80128473941168
+    - type: pearson
+      value: 87.17053012311975
+    - type: spearman
+      value: 87.05959159411456
+    task:
+      type: STS
+Language:
+- ar
+- en
+language:
+- ar
+- en
+---
 
 # SentenceTransformer based on FacebookAI/xlm-roberta-large
 
@@ -148,7 +148,7 @@ Then you can load this model and run inference.
 from sentence_transformers import SentenceTransformer
 
 # Download from the 🤗 Hub
-model = SentenceTransformer("omarelshehy/Arabic-English-Matryoshka-STS")
+model = SentenceTransformer("omarelshehy/arabic-english-sts-matryoshka")
 # Run inference
 sentences = [
     'حب سعيد الواضح للأدب والموسيقى الغربية يتصادم باستمرار مع غضبه الصالح لما فعله الغرب للبقية.',