diff --git a/Evaluation/rouge_metric/__init__.py b/Evaluation/rouge_metric/__init__.py
new file mode 100644
index 0000000..43a773e
--- /dev/null
+++ b/Evaluation/rouge_metric/__init__.py
@@ -0,0 +1 @@
+__author__ = 'vrama91'
diff --git a/Evaluation/rouge_metric/rouge.py b/Evaluation/rouge_metric/rouge.py
new file mode 100644
index 0000000..3a10f5a
--- /dev/null
+++ b/Evaluation/rouge_metric/rouge.py
@@ -0,0 +1,105 @@
+#!/usr/bin/env python
+# 
+# File Name : rouge.py
+#
+# Description : Computes ROUGE-L metric as described by Lin and Hovey (2004)
+#
+# Creation Date : 2015-01-07 06:03
+# Author : Ramakrishna Vedantam <vrama91@vt.edu>
+
+import numpy as np
+import pdb
+
+def my_lcs(string, sub):
+    """
+    Calculates longest common subsequence for a pair of tokenized strings
+    :param string : list of str : tokens from a string split using whitespace
+    :param sub : list of str : shorter string, also split using whitespace
+    :returns: length (list of int): length of the longest common subsequence between the two strings
+
+    Note: my_lcs only gives length of the longest common subsequence, not the actual LCS
+    """
+    if(len(string)< len(sub)):
+        sub, string = string, sub
+
+    lengths = [[0 for i in range(0,len(sub)+1)] for j in range(0,len(string)+1)]
+
+    for j in range(1,len(sub)+1):
+        for i in range(1,len(string)+1):
+            if(string[i-1] == sub[j-1]):
+                lengths[i][j] = lengths[i-1][j-1] + 1
+            else:
+                lengths[i][j] = max(lengths[i-1][j] , lengths[i][j-1])
+
+    return lengths[len(string)][len(sub)]
+
+class Rouge():
+    '''
+    Class for computing ROUGE-L score for a set of candidate sentences for the MS COCO test set
+
+    '''
+    def __init__(self):
+        # vrama91: updated the value below based on discussion with Hovey
+        self.beta = 1.2
+
+    def calc_score(self, candidate, refs):
+        """
+        Compute ROUGE-L score given one candidate and references for an image
+        :param candidate: str : candidate sentence to be evaluated
+        :param refs: list of str : COCO reference sentences for the particular image to be evaluated
+        :returns score: int (ROUGE-L score for the candidate evaluated against references)
+        """
+        assert(len(candidate)==1)	
+        assert(len(refs)>0)         
+        prec = []
+        rec = []
+
+        # split into tokens
+        token_c = candidate[0].split(" ")
+    	
+        for reference in refs:
+            # split into tokens
+            token_r = reference.split(" ")
+            # compute the longest common subsequence
+            lcs = my_lcs(token_r, token_c)
+            prec.append(lcs/float(len(token_c)))
+            rec.append(lcs/float(len(token_r)))
+
+        prec_max = max(prec)
+        rec_max = max(rec)
+
+        if(prec_max!=0 and rec_max !=0):
+            score = ((1 + self.beta**2)*prec_max*rec_max)/float(rec_max + self.beta**2*prec_max)
+        else:
+            score = 0.0
+        return score
+
+    def compute_score(self, gts, res):
+        """
+        Computes Rouge-L score given a set of reference and candidate sentences for the dataset
+        Invoked by evaluate_captions.py 
+        :param hypo_for_image: dict : candidate / test sentences with "image name" key and "tokenized sentences" as values 
+        :param ref_for_image: dict : reference MS-COCO sentences with "image name" key and "tokenized sentences" as values
+        :returns: average_score: float (mean ROUGE-L score computed by averaging scores for all the images)
+        """
+        assert(gts.keys() == res.keys())
+        imgIds = gts.keys()
+
+        score = []
+        for id in imgIds:
+            hypo = res[id]
+            ref  = gts[id]
+
+            score.append(self.calc_score(hypo, ref))
+
+            # Sanity check.
+            assert(type(hypo) is list)
+            assert(len(hypo) == 1)
+            assert(type(ref) is list)
+            assert(len(ref) > 0)
+
+        average_score = np.mean(np.array(score))
+        return average_score, np.array(score)
+
+    def method(self):
+        return "Rouge"
diff --git a/Leaderboard Results/GUMNLGEN09042019.txt b/Leaderboard Results/GUMNLGEN09042019.txt
new file mode 100644
index 0000000..75fb16c
--- /dev/null
+++ b/Leaderboard Results/GUMNLGEN09042019.txt	
@@ -0,0 +1,22 @@
+QnA Metrics
+{'testlen': 595574, 'reflen': 965609, 'guess': [595574, 539985, 484398, 428846], 'correct': [262038, 105860, 62640, 41489]}
+ratio: 0.6167858833130173
+############################
+F1: 0.7095818893165093
+bleu_1: 0.23637297986544653
+bleu_2: 0.15778243503336944
+bleu_3: 0.12003613413452242
+bleu_4: 0.09737202953134436
+rouge_l: 0.30710908155907457
+############################
+NLGEN Metrics
+{'testlen': 179238, 'reflen': 185678, 'guess': [179238, 162761, 146284, 129817], 'correct': [81382, 44263, 28435, 18526]}
+ratio: 0.9653163002617383
+############################
+F1: 1.0
+bleu_1: 0.43802018103794066
+bleu_2: 0.3389926123404134
+bleu_3: 0.278277167790246
+bleu_4: 0.23338324092925958
+rouge_l: 0.37453516033239237
+############################
diff --git a/Leaderboard Results/ranking+nlg08122019.txt b/Leaderboard Results/ranking+nlg08122019.txt
new file mode 100644
index 0000000..acc5a9e
--- /dev/null
+++ b/Leaderboard Results/ranking+nlg08122019.txt	
@@ -0,0 +1,22 @@
+QnA Metrics
+{'testlen': 775580, 'reflen': 966391, 'guess': [775580, 719991, 664402, 608813], 'correct': [407563, 250275, 193605, 159369]}
+ratio: 0.802553003908355
+############################
+F1: 0.7095818893165093
+bleu_1: 0.4108860984489985
+bleu_2: 0.3341816312622258
+bleu_3: 0.29412686598173166
+bleu_4: 0.26863916497393286
+rouge_l: 0.4339947235728949
+############################
+NLGEN Metrics
+{'testlen': 229910, 'reflen': 188685, 'guess': [229910, 213433, 196956, 180479], 'correct': [106372, 69349, 51811, 40179]}
+ratio: 1.2184858361819901
+############################
+F1: 1.0
+bleu_1: 0.4626680005219414
+bleu_2: 0.3877252179823093
+bleu_3: 0.3406959353239066
+bleu_4: 0.30631504351834743
+rouge_l: 0.43941356310975876
+############################