Update README.md

README.md

@@ -55,13 +55,13 @@ Learned Neural / Dense retrievers (DPR, Sentence transformers*, BGE* models) wit
 
 **3. The big idea:**
 
-Getting pros of both searches made sense and that gave rise to interest in learning sparse representations for queries and documents with some interpretability. The sparse representations also double as implicit or explicit (latent, contextualized) expansion mechanisms for both query and documents. If you are new to query expansion learn more here from the master himself Daniel Tunkelang (link below).
+Getting pros of both searches made sense and that gave rise to interest in learning sparse representations for queries and documents with some interpretability. The sparse representations also double as implicit or explicit (latent, contextualized) expansion mechanisms for both query and documents. If you are new to query expansion learn more here from the master himself Daniel Tunkelang.
 
 **4. What a Sparse model learns ?**
 
 The model learns to project it's learned dense representations over a MLM head to give a vocabulary distribution. Which is just to say the model can do automatic token expansion. (Image courtesy of pinecone)
 
-<img src="./expansion.png" width=650 height=500/>
+<img src="./expansion.png" width=600 height=550/>
   
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@@ -76,7 +76,7 @@ SPLADE models are a fine balance between retrieval effectiveness (quality) and r
 4. Achieves a modest yet competitive effectiveness **MRR@10 37.22** in ID data (& OOD) and a retrieval latency of - **47.27ms**. (multi-threaded) all On **Consumer grade-GPUs** with **only 5 negatives per query**.
 4. For Industry setting: Effectiveness on custom domains needs more than just **Trading FLOPS for tiny gains** and The Premise "SPLADE++ are not well suited to mono-cpu retrieval" does not hold.
 
-<img src="./ID.png" width=650 height=500/>
+<img src="./ID.png" width=750 height=650/>
 
 *Note: The paper refers to the best performing models as SPLADE++, hence for consistency we are reusing the same.*