readme table test

README.md

@@ -46,6 +46,7 @@ Sums the distance of all representatives of the same class in the numerator, and
 ### MyLoss
 Testing my own loss as the RepMet's don't make sense to me yet... (never reach 0 as denom is always greater than
  numerator). My loss is defined as:
+
 ![eq mylossv1](https://latex.codecogs.com/gif.latex?L%28%5CTheta%20%29%20%3D%20%5Cfrac%7B1%7D%7BN%7D%20%5Csum_%7Bn%3D1%7D%5E%7BN%7D%20%5Cfrac%7B1%7D%7BJ%7D%20%5Csum_%7Bj%3D1%7D%5E%7BJ%7D%20%5Cleft%20%7C%20-%5Cfrac%7B1%7D%7B2%5Csigma%20%5E2%7D%20%28%5Cleft%20%5C%7C%20E_n%20-%20R_j%20%5Cright%20%5C%7C_2%20-%20%5Cunderset%7Bi%3AC%28R_i%29%3DC%28E_n%29%7D%7B%5Ctextup%7Bmax%7D%7D%28%5Cleft%20%5C%7C%20E_n%20-%20R_i%5Cright%20%5C%7C_2%29%20-%20%5Calpha%29%20%5Cright%20%7C_+)
 
 Takes max distance of embeddings with their same-class clusters/reps/modes plus the alpha margin, and subtracts this from
@@ -85,4 +86,10 @@ where ![sig2](https://latex.codecogs.com/gif.latex?%5Csigma%20%5E2) is set to `0
 **5. Unsupervised Accuracy:** 
 Run K-Means on set (ie. don't use trained clusters) and then greedily assign classes to clusters based on the class of samples that fall in that cluster.  
 
-*Test Error can be considered `1-a` (1 minus these accuracies)*
+*Test Error can be considered `1-a` (1 minus these accuracies)*
+
+## Results
+| First Header  | Second Header |
+| ------------- | ------------- |
+| Content Cell  | Content Cell  |
+| Content Cell  | Content Cell  |