Update README.md

README.md

@@ -78,25 +78,31 @@ Where the arguements are:
 
 `Adila` has three steps:
 
-1. Labeling: Based on the distribution of experts on teams, which is power law (long tail) as shown in the figure, we label those in the `tail` as `nonpopular` and those in the `head` as popular.
-   To find the cutoff between `head` and `tail`, we calculate the average number of teams per expert over the whole dataset. As seen in table, this number is `62.45` and the popular/nonpopular ratio is `0.426/0.574`.  The result is a Boolean value in `{popular: True, nonpopular: False}` for each expert and is save in `{output}/popularity.csv` like [`./output/toy.dblp.v12.json/bnn/t31.s11.m13.l[100].lr0.1.b4096.e20.s1/rerank/popularity.csv`](./output/toy.dblp.v12.json/bnn/t31.s11.m13.l[100].lr0.1.b4096.e20.s1/rerank/popularity.csv) 
+### 3.1. Labeling
+
+Based on the distribution of experts on teams, which is power law (long tail) as shown in the figure, we label those in the `tail` as `nonpopular` and those in the `head` as popular. To find the cutoff between `head` and `tail`, we calculate the average number of teams per expert over the whole dataset. As seen in table, this number is `62.45` and the popular/nonpopular ratio is `0.426/0.574`.  The result is a Boolean value in `{popular: True, nonpopular: False}` for each expert and is save in `{output}/popularity.csv` like [`./output/toy.dblp.v12.json/bnn/t31.s11.m13.l[100].lr0.1.b4096.e20.s1/rerank/popularity.csv`](./output/toy.dblp.v12.json/bnn/t31.s11.m13.l[100].lr0.1.b4096.e20.s1/rerank/popularity.csv) 
 
-    `Future:` We will consider equal area under the curve for the cutoff.
+`Future:` We will consider equal area under the curve for the cutoff.
 
-2. Reranking: We apply rerankers from [`deterministic greedy re-ranking methods [Geyik et al. KDD'19]`](https://dl.acm.org/doi/10.1145/3292500.3330691), including `{'det_greedy', 'det_cons', 'det_relaxed'}` to mitigate `populairty bias`. The reranker needs a cutoff `k_max` which is set to `10` by default. 
-   The result of predictions after reranking is saved in `{output}/rerank/{fpred}.rerank.{reranker}.{k_max}` like ***.
+### 3.2. Reranking 
+
+We apply rerankers from [`deterministic greedy re-ranking methods [Geyik et al. KDD'19]`](https://dl.acm.org/doi/10.1145/3292500.3330691), including `{'det_greedy', 'det_cons', 'det_relaxed'}` to mitigate `populairty bias`. The reranker needs a cutoff `k_max` which is set to `10` by default. 
 
-3. Evaluations: We evaluate `fairness` and `utility` metrics `before` and `after` applying rerankers on team predictions to answer two research questions (RQs):
+The result of predictions after reranking is saved in `{output}/rerank/{fpred}.rerank.{reranker}.{k_max}` like ***.
+
+### 3.3. Evaluations 
+
+We evaluate `fairness` and `utility` metrics `before` and `after` applying rerankers on team predictions to answer two research questions (RQs):
 
-    **`RQ1:`** Do state-of-the-art neural team formation models produce fair teams of experts in terms of popularity bias? To this end, we measure the fairness scores of predicted teams `before` applying rerankers. 
+**`RQ1:`** Do state-of-the-art neural team formation models produce fair teams of experts in terms of popularity bias? To this end, we measure the fairness scores of predicted teams `before` applying rerankers. 
 
-    **`RQ2:`** Do state-of-the-art deterministic greedy re-ranking algorithms improve the fairness of neural team formation models while maintaining their accuracy? To this end, we measure the `fairness` and `utility` metrics `before` and `after` applying rerankers.
+**`RQ2:`** Do state-of-the-art deterministic greedy re-ranking algorithms improve the fairness of neural team formation models while maintaining their accuracy? To this end, we measure the `fairness` and `utility` metrics `before` and `after` applying rerankers.
 
-    The result of `fairness` metrics `before` and `after` will be stored in `{output}.{algorithm}.{k_max}.{faireval}.csv` like ***.
+The result of `fairness` metrics `before` and `after` will be stored in `{output}.{algorithm}.{k_max}.{faireval}.csv` like ***.
 
-    The result of `utility` metrics `before` and `after` will be stored in `{output}.{algorithm}.{k_max}.{utileval}.csv` like ***.
+The result of `utility` metrics `before` and `after` will be stored in `{output}.{algorithm}.{k_max}.{utileval}.csv` like ***.
 
-    `Future:` We will consider other fairness metrics.
+`Future:` We will consider other fairness metrics.
 
 ## 4. Result
 ***