diff --git a/README.md b/README.md
index 92cf0ef868d6d0f206971df1bb24b715cf9b4d1b..ea2879f1a3878d45add9c6e5564c81a7e91df7b7 100644
--- a/README.md
+++ b/README.md
@@ -26,7 +26,7 @@ To use the three models at inference time, the script to run is quite similar to
 Some arguments are common to three models, such as:
 * **--mode** which is equal to 'inference' at inference time
 * **--ckpt_name** to load an already fine-tuned model
-* **--decoding** which can take as values 'greedy', 'beam', 'top_k' or nucleus representing respectively response generation strategies such as the greedy approach, beam search, top_k or nucleus sampling 
+* **--decoding** which can take as values 'greedy', 'beam', 'top_k' or 'nucleus' representing respectively response generation strategies such as the greedy approach, beam search, top_k or nucleus sampling 
 
 For the GPT-2 model, we can run the following script which includes the **--max_time** argument that represents the number of successive messages considered as inputs to generate a reply.
 ```
@@ -43,7 +43,7 @@ python3 models/conditional_gpt2_model.py \
     --mode='inference' \
     --ckpt_name=best_ckpt \
     --decoding=top_k \
-    --age='over 18'
+    --age='over 18' \
     --gender='male' \
     --topic='anxiety' 
 ```
@@ -51,7 +51,7 @@ python3 models/conditional_gpt2_model.py \
 For StyleGPT2, the model can generate replies based on the chosen style token thanks to the **--style_label** argument.
 ```
 python3 models/style_tokens_model/run_styleGPT2.py \
-    --mode='inference'
+    --mode='inference' \
     --ckpt_name=best_ckpt \
     --decoding=greedy \
     --style_label=1
@@ -66,11 +66,11 @@ The directory 'pre-processing' includes all the pre-processing techniques perfor
 
 ## Clustering
 The directory 'clustering' is dedicated to the generation of conversation embeddings and cluster analysis on them:
-* clustering.ipynb is used to perform cluster analysis on features created on simple models such as TF-IDF but also to prepare the data to be used to generate embeddings using transformer methods
+* clustering.ipynb is used to perform cluster analysis on features created with simple models such as TF-IDF but also to prepare the data to be used to generate embeddings using transformer methods
 * gpt2_features.py allows the generation of GPT-2 embeddings for each conversation
 * gpt2_emb_clusters.py aims to perform cluster analysis on these GPT-2 embeddings to extract some style tokens.
 
 ## Helpers 
-The directory 'helpers' contains useful methods for training models such as providing the pre-processed dataset with custom_data.py and extracting information from surveys such conversation topics, the age and the gender of the texter.
+The directory 'helpers' contains useful methods for training models such as providing the pre-processed dataset with custom_data.py and extracting information from surveys such as conversation topics, the age and the gender of the texter.