Update README.md

README.md

@@ -37,20 +37,25 @@ pipeline_tag: sentence-similarity
 </center>
 
 
-  - [License and Terms:](#license-and-terms)
-  - [Detailed comparison & Our Contribution:](#detailed-comparison--our-contribution)
-  - [ONNX & GGUF Variants:](#detailed-comparison--our-contribution)
-  - [Usage:](#usage)
+- [License and Terms:](#license-and-terms)
+- [Detailed comparison & Our Contribution:](#detailed-comparison--our-contribution)
+- [ONNX & GGUF Variants:](#detailed-comparison--our-contribution)
+- [Usage:](#usage)
     - [With Sentence Transformers:](#with-sentence-transformers)
     - [With Huggingface Transformers:](#with-huggingface-transformers)
+- [FAQs](#faqs)
+    - [How can we run these models with out heavy torch dependency?](#how-can-we-run-these-models-with-out-heavy-torch-dependency)
     - [How do I optimise vector index cost?](#how-do-i-optimise-vector-index-cost)
     - [How do I offer hybrid search to address Vocabulary Mismatch Problem?](#how-do-i-offer)
+    - [Why not run MTEB?](#why-not-run-mteb)
+- [Roadmap](#roadmap)
 - [Notes on Reproducing:](#notes-on-reproducing)
 - [Reference:](#reference)
 - [Note on model bias](#note-on-model-bias)
 
 
-## License and Terms:
+
+# License and Terms:
 
 <center>
   <img src="./terms.png" width=200%/>
@@ -81,7 +86,7 @@ Full set of evaluation numbers for our model
 
 <br/>
 
-## Usage:
+# Usage:
 
 #### With Sentence Transformers:
 
@@ -136,6 +141,11 @@ for query, query_embedding in zip(queries, query_embeddings):
 #### With Huggingface Transformers:
 - T.B.A
 
+# FAQS
+
+#### How can we run these models with out heavy torch dependency?
+- You can use ONNX flavours of these models via [FlashRetrieve](https://github.com/PrithivirajDamodaran/FlashRetrieve) library.
+
 #### How do I optimise vector index cost ? 
 [Use Binary and Scalar Quantisation](https://huggingface.co/blog/embedding-quantization)
 
@@ -149,6 +159,22 @@ The below numbers are with mDPR model, but miniMiracle_te_v1 should give a even
 
 *Note: MIRACL paper shows a different (higher) value for BM25 Telugu, So we are taking that value from BGE-M3 paper, rest all are form the MIRACL paper.*
 
+#### Why not run MTEB?
+MTEB is a general purpose embedding evaluation bechmark covering wide range of tasks available currently only for English, Chinese, French and few other languages but not Indic languages. Besides like BGE-M3, miniMiracle models are predominantly tuned for retireval tasks aimed at search & IR based usecases.
+At the moment MIRACL is the gold standard for a subset of Indic languages.
+
+
+
+# Roadmap
+We will add miniMiracle series of models for all popular languages as we see fit or based on community requests in phases. Some of the languages we have in our list are
+
+- Spanish
+- Tamil
+- Arabic
+- German
+- English ?
+
+
 # Notes on reproducing:
 
 We welcome anyone to reproduce our results. Here are some tips and observations:
@@ -166,7 +192,7 @@ Here are our numbers for the full hindi run on BGE-M3
 {'MRR@10': 0.60893, 'MRR@100': 0.615, 'MRR@1000': 0.6151}
 ```
 
-Fair warning BGE-M3 is $ expensive to evaluate, probably that's why it's not part of any of the MTEB benchmarks.
+Fair warning BGE-M3 is $ expensive to evaluate, probably that's why it's not part of any of the retrieval slice of MTEB benchmarks.
 
 # Reference:
 - [All Cohere numbers are copied form here](https://huggingface.co/datasets/Cohere/miracl-en-queries-22-12)