Update README.md

README.md

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 **MODEL DETAILS:** 3rd iteration, K=1000, HuBERT base architecture (95M parameters), 147 languages.
 
+# mHuBERT-147 models
+
+mHuBERT-147 are compact and competitive multilingual HuBERT models trained on 90K hours of open-license data in 147 languages. 
+Different from *traditional* HuBERTs, mHuBERT-147 models are trained using faiss IVF discrete speech units. 
+Training employs a two-level language, data source up-sampling during training. See more information in [our paper](https://arxiv.org/pdf/2406.06371).
+
+
 # Table of Contents:
 
 1. [Summary](https://huggingface.co/utter-project/mHuBERT-147#mhubert-147-models)
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 5. [Intermediate Checkpoints](https://huggingface.co/utter-project/mHuBERT-147#intermediate-checkpoints)
 6. [Citing and Funding Information](https://huggingface.co/utter-project/mHuBERT-147#citing-and-funding-information)
    
-# mHuBERT-147 models
-
-mHuBERT-147 are compact and competitive multilingual HuBERT models trained on 90K hours of open-license data in 147 languages. 
-Different from *traditional* HuBERTs, mHuBERT-147 models are trained using faiss IVF discrete speech units. 
-Training employs a two-level language, data source up-sampling during training. See more information in [our paper](https://arxiv.org/pdf/2406.06371).
 
 **This repository contains:**
 * Fairseq checkpoint (original);