Update README.md

examples/factscore_eval/README.md

@@ -1,7 +1,9 @@
 # FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation
 
 This is a replication of the experiments from
-[FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://aclanthology.org/2023.emnlp-main.741) (Min et al., EMNLP 2023).
+[FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form
+Text Generation](https://aclanthology.org/2023.emnlp-main.741) (Min et al., EMNLP
+2023).
 
 ## Dependencies
 
@@ -19,16 +21,21 @@ pip install -r requirements.txt
 
 To access and use OpenAI's services (such as GPT models),
 you must obtain an API key from OpenAI.
-After acquiring your API key, store it in a txt file such as ```key.txt``` amd pass it when creating an instance of the ```FactScorer``` class.
+After acquiring your API key, store it in a txt file 
+such as `key.txt` amd pass it when creating an instance 
+of the `FactScorer` class.
 
 ### Data Preparation
 
-Before running the code to generate the CommunityLM responses, make sure you have created the following directory to store the data:
+Before running the code to generate the CommunityLM responses, 
+make sure you have created the following directory to store the data:
 
 ```bash
 mkdir -p factscore_data
 ```
-This will be the data directory for the FactScore analysis and all CSV files must be inside this folder.
+
+This will be the data directory for the FactScore analysis 
+and all CSV files must be inside this folder.
 
 ## Reference