diff --git a/README.md b/README.md
index 6ffefb10..2a04766e 100644
--- a/README.md
+++ b/README.md
@@ -4,9 +4,9 @@ PARAM Benchmarks is a repository of communication and compute micro-benchmarks a
 
 PARAM complements two broad categories of commonly used benchmarks:
 1. C++ based stand-alone compute and communication benchmarks using cuDNN, MKL, NCCL, MPI libraries - e.g., NCCL tests (https://github.com/NVIDIA/nccl-tests), OSU MPI benchmarks (https://mvapich.cse.ohio-state.edu/benchmarks/), and DeepBench (https://github.com/baidu-research/DeepBench).
-2. Application benchmarks such as Deep Learning Recommendation Model (DLRM) and the broader MLPerf benchmarks. Its worth noting that while MLPerf is the de-facto industry standard for benchmarking ML applications we hope to compliment this effort with broader workloads that are of more interest to Facebook with more in-depth analysis of each within this branch of Application benchmarks.
+2. Application benchmarks such as Deep Learning Recommendation Model (DLRM) and the broader MLPerf benchmarks. It's worth noting that while MLPerf is the de-facto industry standard for benchmarking ML applications we hope to compliment this effort with broader workloads that are of more interest to Facebook with more in-depth analysis of each within this branch of Application benchmarks.
 
-Our initial release of PARAM benchmarks focuses on AI training and comprises of:
+Our initial release of PARAM benchmarks focuses on AI training and comprises:
 1. Communication: PyTorch based collective benchmarks across arbitrary message sizes, effectiveness of compute-communication overlap, and DLRM communication patterns in fwd/bwd pass
 2. Compute: PyTorch based GEMM, embedding lookup, and linear layer
 3. DLRM: tracks the `ext_dist` branch of DRLM benchmark use Facebook's DLRM benchmark (https://github.com/facebookresearch/dlrm). In short, PARAM fully relies on DLRM benchmark for end-to-end workload evaluation; with additional extensions as required for scale-out AI training platforms.