Small README updates

Author: RouxJohannes

README.md

@@ -202,6 +202,7 @@ Please note we are not currently accepting pull requests or issues on this repos
 The [utils/](utils) folder contains utilities libraries and scripts that are used across the other code examples. This includes:
 
 * [utils/examples_tests](utils/examples_tests) - Common Python helper functions for the repository's unit tests
+* [utils/benchmarks](utils/benchmarks) - Common Python helper functions for running benchmarks on the IPU in different frameworks
 
 <br>
 

gnn/cluster_gcn/tensorflow2/README.md

@@ -1,11 +1,15 @@
 # Cluster GCN model
 
+Run our Cluster GCN training on arXiv dataset on Paperspace.
+<br>
+[![Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://console.paperspace.com/github/gradient-ai/Graphcore-Tensorflow2?machine=Free-IPU-POD16&container=graphcore%2Ftensorflow-jupyter%3A2-amd-2.6.0-ubuntu-20.04-20220804&file=%2Fget-started%2Frun_cluster_gcn_notebook.ipynb)
+
 ## Table of contents
 
 1. [Introduction](#intro)
 2. [Prepare environment](#environment)
 3. [Datasets](#datasets)
-    1. [PPI](#ppi)
+    1. [PPI](#ppi) 
     2. [Reddit](#reddit)
     3. [arXiv](#arxiv)
     4. [Products](#products)

gnn/tgn/tensorflow1/README.md

@@ -1,7 +1,9 @@
 # TGN: Temporal Graph Networks
 
-An implementation of the [TGN](https://arxiv.org/abs/2006.10637) in TensorFlow 1 for IPU.
-
+An implementation of [TGN](https://arxiv.org/abs/2006.10637) in TensorFlow 1 for IPU. <br><br>
+Run our Temporal Graph Networks on JODIE Wikipedia dataset on Paperspace.
+<br>
+[![Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://console.paperspace.com/github/gradient-ai/Graphcore-Tensorflow1?machine=Free-IPU-POD16&container=graphcore%2Ftensorflow-jupyter%3A1-amd-2.6.0-ubuntu-18.04-20220804&file=%2Fget-started%2FTrainingTGN.ipynb)
 
 ## About
 

nlp/bert/pytorch/README.md

@@ -5,6 +5,10 @@ This directory contains an implementation of BERT models in PyTorch for the IPU,
 1. BERT for pre-training - `run_pretraining.py`
 2. BERT for SQuAD - `run_squad.py`
 
+Run our BERT-L Fine-tuning on SQuAD dataset on Paperspace.
+<br>
+[![Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://console.paperspace.com/github/gradient-ai/Graphcore-PyTorch?machine=Free-IPU-POD16&container=graphcore%2Fpytorch-jupyter%3A2.6.0-ubuntu-20.04-20220804&file=%2Fget-started%2FFine-tuning-BERT.ipynb)
+
 ## Environment setup
 
 First, install the Poplar SDK following the instructions in the Getting Started guide for your IPU system. Make sure to source the `enable.sh` scripts for Poplar and PopART.