Update README.md

README.md

@@ -63,10 +63,10 @@ pipeline_tag: sentence-similarity
 
 # Request, Terms, Disclaimers
 
+[https://github.com/sponsors/PrithivirajDamodaran](https://github.com/sponsors/PrithivirajDamodaran)
 
 <center>
   <img src="./ar_terms.png" width=250%/>
-  <b><p>[https://github.com/sponsors/PrithivirajDamodaran](https://github.com/sponsors/PrithivirajDamodaran)</p><b>
 </center>
 
 
@@ -173,7 +173,7 @@ The below numbers are with mDPR model, but miniDense_arabic_v1 should give a eve
 
 | Language  | ISO | nDCG@10 BM25 | nDCG@10 mDPR | nDCG@10 Hybrid |
 |-----------|-----|--------------|--------------|----------------|
-| **Arabic**     | **ar**  | **0.395**        | **0.499**        | **0.67.3**          |
+| **Arabic**     | **ar**  | **0.395**        | **0.499**        | **0.673**          |
 
 *Note: MIRACL paper shows a different (higher) value for BM25 Arabic, So we are taking that value from BGE-M3 paper, rest all are form the MIRACL paper.*
 
@@ -184,7 +184,7 @@ So it makes sense to evaluate our models in retrieval slice of the MTEB benchmar
 ##### Long Document Retrieval 
 
 <center>
-<img src="./ar_metrics_4.png" width=100%/>
+<img src="./ar_metrics_4.png" width=80%/>
   <b><p>Table 3: Detailed Arabic retrieval performance on the MultiLongDoc dev set (measured by nDCG@10)</p></b>
 </center>
 
@@ -194,7 +194,7 @@ So it makes sense to evaluate our models in retrieval slice of the MTEB benchmar
 Almost all models below are monolingual arabic models based so they have no notion of any other languages.
 
 <center>
-<img src="./ar_metrics_5.png" width=100%/>
+<img src="./ar_metrics_5.png" width=80%/>
   <b><p>Table 4: Detailed Arabic retrieval performance on the 3 X-lingual test set (measured by nDCG@10)</p></b>
 </center>