<jupyter_start><jupyter_text>Human evaluation results **Add human evaluation results manually**<jupyter_code>## Note... After the correction, the 12th model, will became actually the 6th model
naturalness = [
    
               ### 0-9 eval samples
            
               [[1,2,3,4,5,7,8,9,10,11,12]],
               [[1,2,3,4,5,7,8,9,10,11,12]],
               [[1,2,3,4,5,7,8,9,10,11,12]],
               [[1,2,3,4,5,7,8,9,11,12],[10]],
               [[9],[3,11,12],[4,5],[1,2,7,8],[10]],

               [[1,2,3,4,5,7,8,9,10,11,12]],    
               [[3,7],[1,8,10,11,12],[2,9],[4,5]],
               [[1,5,7,8,11,12],[4],[3],[9],[2],[10]],
               [[11],[3,4,5,7,8,10,12],[2],[1]],
               [[2,11],[1,3,4,5,7,8,9,10,12]],
    
               ### 10-19 eval samples
    
               [[1,2,3,4,5,7,8,9,10,11,12]],
               [[1,7,8,9,10,11,12],[2,3,4,5]],
               [[1,2,3,4,5,7,8,9,10,11,12]],
               [[1,2,3,4,5,7,8,9,10,11,12]],
               [[1,3,4,5,7,8,9,10,11,12],[2]],
    
               [[2,3,4,5,7,8,9,10,11,12],[1]],
               [[1,7,8,9,11,12],[2,3,4,5],[10]],
               [[1,2,3,4,5,7,8,9,11,12],[10]],
               [[1,11],[2,3,5,10,12],[4,8,9,],[7]],
               [[1,2,3,4,5,7,8,9,10,11,12]],                        
    
               ### 20-29 eval samples
    
               [[1,2,3,4,5,7,8,9,11,12],[10]],
               [[1,2,3,4,5,7,11,12],[10],[8,9]],
               [[2,3,4,5,7,8,9,11,12],[1],[10]],
               [[2,3,4,5,8,9,11],[1,7,12],[10]],
               [[11],[1,2,3,4,5,7,8,9,10],[12]],  
    
               [[1,3,4,5,8,10,11],[2,7,9,12]],
               [[11],[8,12],[1,7,10],[2,3,4,5,9]],
               [[2,3,4,8,9,10,12],[1,5,11],[7]],
               [[1,2,3,4,5,7,8,11,12],[9,10]],
               [[11],[1,2,3,4,5,7,8,9,10,12]],
               
               ### 30-39 eval samples
    
               [[1,2,3,4,5,7,8,9,10,11,12]],
               [[2,3,4,5,7,8,9,10,11],[1,12]],
               [[1,2,3,4,5,7,8,10,11,12],[9]],
               [[11],[1,2,3,4,5,7,8,9,10,12]],
               [[1,2,3,4,5,11],[7,8,12],[9],[10]],  
    
               [[1,2,3,4,5,8,9,10,11,12],[7]],
               [[11],[2,3,4,5,7,8,9,10],[1],[12]],
               [[1,2,3,4,5,7,8,9,12],[11],[10]],
               [[1,2,3,4,5,7,8,9,10,11,12]],
               [[2,3,9,10,11,12],[4,5],[1,7,8]],
    
               ### 40-49 eval samples
    
               [[1,2,3,4,5,7,9,10,11,12],[8]],
               [[1,2,3,4,5,7,8,9,11,12],[10]],
               [[2,3,4,5,7,8,11,12],[1,9,10]],
               [[2,3,4,5,7,8,9,11,12],[1,10]],
               [[1,2,3,4,5,7,8,9,10,11,12]],
    
               [[1,2,3,4,5,7,8,9,11,12],[10]],
               [[1,2,3,4,5,7,9,10,11,12],[8]],
               [[1,3,4,5,7,8,10,11,12],[2,9]],
               [[1,2,10,11,12],[7],[3,4,5,8,9]],
               [[1,2,3,4,5,7,8,9,12],[11],[10]],
    
               ### 50-50 eval samples
    
               [[1,3,4,5,7,8,9,10,11,12],[2]],
               [[1,2,3,5,7,8,9,10,11,12]],
               [[1,2,3,4,5,8,9,10,12],[7,11]],
               [[1,2,3,4,5,7,8,9,11,12],[10]],
               [[1,2,3,4,5,7,8,9,10,11,12]],
                
               [[2],[1,3,4,7,8,9,10,11,12],[5]],
               [[1,2,3,4,5,7,8,9,10,11,12]],
               [[1,2,3,4,5,8,9,10,11,12],[7]],
               [[8,9,10],[7,12],[1,2,3,4,5,11]],
               [[1,2,3,4,5,7,9,10,11],[8,12]],
    
               ### 60-69 eval samples
    
               [[1,2,7,8,9,10,11,12],[3,4,5]],
               [[11],[1,2,3,4,5,7,8,9,10,12]],
               [[8,11],[2,3,4,5,7,8,9,10,12],[1],[3]],
               [[1,2,3,4,5,7,8,9,10,12]],
               [[1,2,3,4,5,7,8,9,10,12]],
    
               [[1,5,7,8,9,11,12],[2],[3,4,10]],
               [[1,2,3,7,8,10,12],[9],[4,5]],
               [[1,7,8,11,12],[2,3,4,5],[9,10]],
               [[11],[2,3,4,5,7,8,12],[9,10],[1]],
               # 69 (duplicated)
   
               ### 70-79 eval samples
    
               [[9],[2,3,4,5,7,8,11,12],[1,10]],
               [[2],[1,3,4,5,7,8,9,10,11],[12]],
               [[2],[1,3,4,5,7,8,9,10,11],[12]],
               [[1,2,3,4,5,7,8,9,10,11,12]],
               [[1,2,3,4,5,7,8,9,10,11,12]],
    
               [[1,2,3,4,5,7,8,9,10,11,12]],
               [[1,3,4,5,7,8,9,10,11,12],[2]],
               [[1,9,10,11,12],[3,4,5,7,8],[2]],
               [[11],[12],[1,2,3,4,5,8,9,10],[7]],
               [[1,2,3,4,5,7,8,9,10,11,12]],

               ### 80 eval sample 
    
               [[1,2,3,4,5,7,8,9,10,11,12]],
]
realiability = [
    
              ### 0-9 eval samples
    
              [[7,8,9,10],[3,4,5,12],[1,2]],
              [[1,2,3,4,5,7,8,9,10,12]],
              [[3,4,5,7,8,12],[9,10],[2],[1]],
              [[1,2,3,4,5,7,8,9,12],[10],[9]],
              [[7,10],[4,5],[1,8],[9],[2,3,12]],
              
              [[7],[5],[1,8,10,12],[9],[3,4],[2]],
              [[1,7,8],[12],[10],[2,3],[9],[4,5]],
              [[7],[4,5,8,12],[1,2,3],[9],[10]],
              [[2,3,4,5,7,8,9,12],[10],[1]],
              [[7],[1,2,4,5,8,10,12],[3],[9]],
              
              ### 10-19 eval samples             
              
              [[2,7,12],[3,4,5,8,10],[9],[1]],
              [[1,2,3,4,5,7,8,9,10,12]],
              [[1,2,3,4,5,7,8,10,12],[9]],
              [[1,2,3,4,5,7,8,9,12],[10]],
              [[7],[3,4,5],[1],[9],[8,10,12],[2]],
              
              [[3,4,5],[1],[7,8,9],[2],[10,12]],
              [[1,7,9],[2,3,4,5,8,12],[10]],
              [[7,8],[9],[2,3,4,5,10],[1,12]],
              [[12],[8],[2,5],[3,10],[7],[4,9],[1]],
              [[1,2,3,4,5,7,8,9,10,12]],

              ### 20-29 eval samples
              
              [[1,2,7,8,12],[3,4,5],[9],[10]],
              [[4,5,8],[10],[2],[7,9,12],[3],[1]],
              [[1,2,3,4,5,7,8,10,12],[9]],
              [[1,7,8,12],[2,3,4,5],[9,10]],
              [[2,3,4,5,9,10],[12],[1,7,8]],
            
              [[1,2,3,4,5,7,9,10,12],[8]],
              [[1],[8,10,12],[7],[2,3,4,5,9]],
              [[1,3,4,5,7,8,10,12],[2],[9]],
              [[1,2,3,4,5,7],[8,12],[9,10]],
              [[2,3,4,5],[1,7,8,10,12],[9]],
    
              ### 30-39 eval samples
    
              [[12],[7,9],[8],[1,3,4,5,10],[2]],
              [[2,3,4,5,7,8,10,12],[9],[1]],
              [[7,8,10],[1,2,3,4,5],[12],[9]],
              [[1,2,3,4,5,7,8,9,12],[10]],
              [[4,5],[2,3],[1],[12],[9],[10],[7,8]],
    
              [[12],[7,8],[10],[1],[2,3],[4,5],[9]],
              [[2,3,5,7,10,12],[9],[4,8],[1]],
              [[1,2,3,4,5,7,8,9,11,12],[10]],
              [[1,2,3,4,5,8,9,12],[7],[10]],
              [[1],[8,10],[12],[7],[2,3,4,5,9]],
    
              ### 40-49 eval samples
            
              [[2,3,4,5,7,8,9,10],[1]],
              [[7,12],[1,2,3,4,5,7,8,9,10]],
              [[1,2,3,4,5,7,8,9,10,12]],
              [[3,4,5,7,8,10],[9,12],[2],[1]],
              [[1,2,3,4,5,7,8,12],[9,10]],

              [[12],[9],[7],[1],[2,3,4,5,8],[10]],
              [[1,2,3,4,5,7,9,10,12],[8]],
              [[1,7,8,12],[10],[3,4,5],[2],[9]],
              [[7],[1,3,4,5,8,12],[9],[10],[2]],
              [[3,4,5,7,8,9,10,12],[1,2]],
    
              ### 50-59 eval samples
    
              [[4,5,9],[3,7,8,10],[1],[2]],
              [[3,4,5,7,8,9,10],[1,2,8,12]],
              [[1,9,10],[2,3,4,5,7,8,12]],
              [[2,3,4,5,7,12],[8,10],[1],[9]],
              [[1,2,3,4,5,7,8,10],[9],[12]],
               
              [[7],[2,4,8,9],[1,3,10,12],[5]],
              [[9,10],[1,2,3,4,5,7,8,12]],
              [[1,2,3,4,5,7,8,12],[9,10]],
              [[1],[2,3,4,5,7,10],[8,9,12]],
              [[7,8],[1,2,3,4,5,9,10,12]],
    
              ### 60-69 eval samples
                
              [[1],[2,7,9],[8,10],[12],[3,4],[5]],
              [[3,4,5,7,8,12],[2,10],[1,9]],
              [[7,11,12],[1,3,4,5,10],[2,9],[8]],
              [[1,2,3,4,5,7,8,9,12],[10]],
              [[2],[1,3,4,5,7,8,9,10,12]],
    
              [[5,12],[8],[1,7],[3,4,9,10],[2]],
              [[4,5,8,10],[2,3,7,9,12],[1]],
              [[2,3,4,5,7,8,12],[1,9],[10]],
              [[7],[2,3,4,5,8,12],[10],[9],[1]],
              # 69 (duplicated)
    
              ### 70-79 eval samples 
    
              [[12],[8,9,10],[1,2,3,4,5,7]],
              [[1,5,7,8,12],[9,10],[2,3,4]],
              [[2,3,4,5,7,8,9,10],[1,12]],
              [[1,3,4,5,9,10],[2,7,8,12]],
              [[8,12],[1,2,3,4,5,7,9,10]],
    
              [[7,8],[2,3,4,5,12],[1,10],[9]],
              [[1,4,5,10,12],[2,3,8,9],[7]],
              [[4,7,9,10,12],[1,3,8],[2,5]],
              [[8],[4,5,12],[1,2,3],[7,9],[10]],
              [[1,2,3,4,5,7,8,9,10,12]],
            
              ### 80 eval samples
    
              [[2,3,4,5,7,8,9,12],[10],[1]]
]
informativeness = [
    
                  ### 0-9 eval samples
    
                  [[5,12],[2,3,4,7,8,9,10],[1]],
                  [[1,2,3,4,5,7,8,9,10,12]],
                  [[2,3,4,5,7,8,12],[1,9,10]],
                  [[1,2,3,4,5,7,8,9,10,12]],
                  [[2,3,4,5,7,8,9,12],[1,10]],
    
                  [[5,7],[1,3,4,8,10,12],[2,9]],
                  [[7],[8,12],[1,2],[3,9],[10],[4,5]],
                  [[2,3,4,5],[1,8],[7,9,12],[10]],
                  [[2,3,4,5,7],[1,12],[8,9],[10]],
                  [[2,3,4,5,7],[1,8,12],[10],[9]],
    
                  ### 10-19 eval samples
    
                  [[7],[2],[3,12],[4,5,8,9],[1,10]],
                  [[2,3,4,5,7,8,12],[9],[1],[10]],
                  [[2,7],[3,4,5,8],[9,12],[1,10]],
                  [[2,3,4,5,7,9,12],[1,8,10]],
                  [[7],[3,4,5],[2,9],[8,10,12],[1]],
                   
                  [[1,2,3,4,5,7,8],[9,10,12]],
                  [[9],[4,5,7],[2,8],[3,1,12],[10]],
                  [[2,3,4,5,7],[8,12],[9],[1,10]],
                  [[8],[4,7,12],[1,2,5,10],[3,9]],
                  [[7],[1,8,9,12],[2,3,4,5,10]],
                  
                  ### 20-29 eval samples
                   
                  [[2,7],[3,4,5,8,12],[1,9,10]],
                  [[2,3,4,5,7,8,9,12],[1,10]],
                  [[2,3,4,5,7,9],[1,8,10,12]],
                  [[7],[12,8],[4,5,9],[1,2,3],[10]],
                  [[7,9],[1,2,3,4,5,8,10],[12]],
                   
                  [[1,2,3,4,5,7,9,12],[8],[10]],
                  [[12],[7,8,10],[1],[2,3,4,5,9]],
                  [[7,8],[2,3,4,12],[1,5,10,12],[9]],
                  [[2,3,4,5,7],[12],[8,9],[1,10]],
                  [[2,3,4,5],[7,8,9,12],[1,10]],
                        
                  ### 30-39 eval samples
    
                  [[3,4,5,7,8,9,12],[1,2,10]],
                  [[1,3,8],[2,4,5,7],[12],[9],[10]],
                  [[1,2,3,4,5,7,8,10],[9,12]],
                  [[2,3,4,5,7],[1,8,12],[9],[10]],
                  [[2,3,4,5,9],[1,7,8,12],[10]],
                   
                  [[7],[2],[3,4,5],[8,9,12],[1],[10]],
                  [[5,8],[4],[1,2,3,7,9,12],[10]],
                  [[1,2,3,4,5,7,8,9,10,12]],
                  [[3,4,5,8,9,10],[1,2,7,12]],
                  [[8],[4,5,7],[2,9,12],[3],[1,10]], 
    
                  ### 40-49 eval samples
            
                  [[2,3],[8,12],[1,4,5,7],[9,10]],
                  [[1,2,3,4,5,7,8,9,10,12]],
                  [[2,3,4,5,7,8],[9,10,12],[1]],
                  [[1],[2,3,4,5,7,9],[8,10,12]],
                  [[1,3,4,5,7,8,12],[2],[9],[10]],
                  
                  [[1,7,12],[2,3,4,5,8,9,10]],
                  [[3,4,5,7,8,12],[1,2,9],[10]],
                  [[2,3,4,5,7],[1],[8,12],[9],[10]],
                  [[4,5,12],[1,3,9],[8],[2,7],[10]],
                  [[3,4,5,8,9],[2,7],[10],[1,12]],

                  ### 50-59 eval samples
                   
                  [[8],[4,5,7,9,10,12],[1,3],[2]],
                  [[7,9,10],[3,4,5,8],[1,2,12]],
                  [[7],[2,3,4,5,8,9,10],[1,12]],
                  [[2,3,4,5,7,8,12],[1,9],[10]],
                  [[2,3,4,5,7,8],[1,9,10],[12]],
                  
                  [[2,7,9,10],[3,4,8,12],[1,5]],
                  [[4,5,8,12],[2,3,9,10],[1,7]],
                  [[5,8],[3,4,7,12],[1,2,9],[10]],
                  [[1,2,3,4,5,7,8,9,12],[10]],
                  [[7],[4,5],[2,3,8],[9,12],[1,10]],

                  ### 60-69 eval samples
    
                  [[7,8],[1,2,4,5,9,10],[3,12]],
                  [[1,2,3,4,5,7,11],[8,12],[9,10]],
                  [[2,3,8],[1,4,5,7,9,11],[12],[10]],
                  [[3,4,5,7],[2],[8,12],[1,9,10]],
                  [[4,5,7],[1,2,3,8,9,12],[10]],
                
                  [[5,7,8,12],[1],[2],[9],[3,4],[10]],
                  [[4,5,8],[1,2,3,7,9,10,12]],
                  [[2,3,4,5,7,8,9,12],[10],[1]],
                  [[2,3,4,5,7,8,9,10,12],[1]],    
                  # 69 (duplicated)
    
                  ### 70-79 eval samples
    
                  [[9],[1,2,3,4,5,7,8,12],[10]],
                  [[5,8],[7,9,12],[3,4,10],[2],[1]],
                  [[7],[3,4,5],[2,9,12],[1,8],[10]],
                  [[8],[2,3,4,5,9,12],[1,7],[10]],
                  [[8,12],[1,2,3,4,5,7,9,10]],
    
                  [[7,8],[1,2,3,4,5,12],[9,10]],
                  [[2,4,5],[1,3,12,9],[7,8,10]],
                  [[3,4,7,9,10,12],[1,8],[2,5]],
                  [[4],[5,12],[1,2,3,7,8],[9],[10]],
                  [[1,7,8,9,12],[2,3,4,5,10]],
                
                  ### 80 eval sample
    
                  [[7,8],[1,2,3,4,5,9,12],[10]],   
]<jupyter_output><empty_output><jupyter_text>**Create human evaluation scoring**<jupyter_code>rel_rank = [0,0,0,0,0,0,0,0,0,0,0,0,0]
inf_rank = [0,0,0,0,0,0,0,0,0,0,0,0,0]
nat_rank = [0,0,0,0,0,0,0,0,0,0,0,0,0]
for groups in realiability:
    for n_group, group in enumerate(groups):
        for n_model, model in enumerate(group):
            
            for i in range(len(group)-(n_model+1)):
                model_2 = group[n_model+i+1]
                
                if (model==11 or model_2==11):
                    continue
                else:
                    rel_rank[model] += 0.05
                    rel_rank[model_2] += 0.05

            
            for i in range(len(groups)-(n_group+1)):
                for model_2 in groups[n_group+i+1]:
                    if (model==11 or model_2==11):
                        continue
                    else:
                        rel_rank[model] += 0.1
                        
####

rel_rank[6] = rel_rank[12]
rel_rank = rel_rank[1:11]<jupyter_output><empty_output><jupyter_text>[137, 105, 120, 135, 145, 156, 232, 169, 118, 132]<jupyter_code>for groups in naturalness:
    for n_group, group in enumerate(groups):
        for n_model, model in enumerate(group):
            
            for i in range(len(group)-(n_model+1)):
                model_2 = group[n_model+i+1]
                
                nat_rank[model] += 0.05
                nat_rank[model_2] += 0.05
            
            for i in range(len(groups)-(n_group+1)):
                for model_2 in groups[n_group+i+1]:
                    nat_rank[model] += 0.1

####

nat_rank[6] = nat_rank[12]
nat_ref = nat_rank[11]
nat_rank = nat_rank[1:11]<jupyter_output><empty_output><jupyter_text>[82, 89, 90, 75, 75, 97, 82, 86, 76, 67]<jupyter_code>for groups in informativeness:
    for n_group, group in enumerate(groups):
        for n_model, model in enumerate(group):
            
            for i in range(len(group)-(n_model+1)):
                model_2 = group[n_model+i+1]
                
                if (model==11 or model_2==11):
                    continue
                else:
                    inf_rank[model] += 0.05
                    inf_rank[model_2] += 0.05
                    
                    
            for i in range(len(groups)-(n_group+1)):
                for model_2 in groups[n_group+i+1]:
                    if (model==11 or model_2==11):
                        continue
                    else:
                        inf_rank[model] += 0.1

####
                        
inf_rank[6] = inf_rank[12]
inf_rank = inf_rank[1:11]<jupyter_output><empty_output><jupyter_text>[87, 178, 183, 202, 210, 158, 258, 196, 123, 47] **Create eval dataframe**<jupyter_code>df_eval_human = pd.DataFrame()
df_eval_human['models'] = ['t5-base vanilla b=0', 
                           't5-base strict b=0',
                           't5-base strict b=2',
                           't5-base strict b=5',
                           't5-base strict b=10',
                           't5-base relaxed b=10',
                           't5-small strict b=10',
                           't5-small relaxed b=10',
                           'mt5-small strict b=10',
                           'mt5-small relaxed b=10']
df_eval_human['naturalness'] = nat_rank
df_eval_human['reliability'] = rel_rank
df_eval_human['informativeness'] = inf_rank
df_eval_human['overall quality'] = round((df_eval_human['naturalness'] + df_eval_human['reliability'] + df_eval_human['informativeness']),2)
nat_ref 
df_eval_human = df_eval_human.sort_values('overall quality',ascending=False)
df_eval_human = df_eval_human.set_index('models')
df_eval_human<jupyter_output><empty_output><jupyter_text>___<jupyter_code>print(df_eval_human.to_latex())<jupyter_output>\begin{tabular}{lrrrr}
\toprule
{} &  naturalness &  reliability &  informativeness &  overall quality \\
models                 &              &              &                  &                  \\
\midrule
t5-small strict b=10   &        39.15 &        46.05 &            48.45 &           133.65 \\
t5-small relaxed b=10  &        41.95 &        41.00 &            41.90 &           124.85 \\
t5-base strict b=10    &        39.00 &        40.00 &            45.20 &           124.20 \\
t5-base strict b=5     &        38.20 &        38.60 &            44.55 &           121.35 \\
t5-base strict b=2     &        41.05 &        35.35 &            40.55 &           116.95 \\
t5-base relaxed b=10   &        41.75 &        39.15 &            35.05 &           115.95 \\
t5-base strict b=0     &        39.55 &        31.10 &            38.15 &           108.80 \\
mt5-small strict b=10  &        38.20 &        27.75 &            29.70 &            95.65 \\
t5-base vanilla b=0    &        38.20 &[...]<jupyter_text>Automatic evaluation results<jupyter_code>file_names = ['references',
              't5-base_vanilla_b0', 
              't5-base_strict_b0',
              't5-base_strict_b2',
              't5-base_strict_b5',
              't5-base_strict_b10',
              't5-base_relaxed_b10',
              't5-small_strict_b10',
              't5-small_relaxed_b10',
              'mt5-small_strict_b10',
              'mt5-small_relaxed_b10']

models = ['t5-base vanilla b=0', 
          't5-base strict b=0',
          't5-base strict b=2',
          't5-base strict b=5',
          't5-base strict b=10',
          't5-base relaxed b=10',
          't5-small strict b=10',
          't5-small relaxed b=10',
          'mt5-small strict b=10',
          'mt5-small relaxed b=10']
def add_automatic_metrics(ind):

    path = '/home/hlaboa-server/jupyter/TFM/nlg_wikimusica/Evaluation/'
    metrics_dict = compute_metrics(hypothesis=path+file_names[ind+1]+'.txt',
                                   references=[path+file_names[0]+'.txt'])
    
    return metrics_dict


########

automatic_metrics = list()
for ind,file in enumerate(file_names):
    if ind==10:
        continue
    automatic_metrics.append(add_automatic_metrics(ind))
    
########

df_eval_auto = pd.DataFrame(automatic_metrics)
df_eval_auto = df_eval_auto.filter(list(df_eval_auto.columns[:7]))
df_eval_auto['models'] = models
df_eval_auto = df_eval_auto.set_index('models')
df_eval_auto.sort_values('Bleu_4',ascending=False)
print(df_eval_auto.to_latex())<jupyter_output>\begin{tabular}{lrrrrrrr}
\toprule
{} &    Bleu\_1 &    Bleu\_2 &    Bleu\_3 &    Bleu\_4 &    METEOR &   ROUGE\_L &     CIDEr \\
models                 &           &           &           &           &           &           &           \\
\midrule
t5-base vanilla b=0    &  0.465694 &  0.387567 &  0.327278 &  0.278649 &  0.304988 &  0.554218 &  2.075255 \\
t5-base strict b=0     &  0.492366 &  0.410224 &  0.345068 &  0.292679 &  0.314252 &  0.557879 &  2.276591 \\
t5-base strict b=2     &  0.491959 &  0.412486 &  0.349418 &  0.298602 &  0.321605 &  0.564761 &  2.375602 \\
t5-base strict b=5     &  0.500554 &  0.423250 &  0.362857 &  0.313421 &  0.326391 &  0.581079 &  2.510812 \\
t5-base strict b=10    &  0.504787 &  0.427901 &  0.367294 &  0.318154 &  0.330541 &  0.585316 &  2.582301 \\
t5-base relaxed b=10   &  0.497789 &  0.424034 &  0.363422 &  0.313644 &  0.329741 &  0.580011 &  2.459591 \\
t5-small strict b=10   &  0.522158 &  0.435555 &  0.367728 &  0.312279 &  0.330866 &  0.579[...]
{'fraction_non_alphanumeric': 0.2641771062446208, 'fraction_numerical': 0.18891651525294062, 'mean_word_length': 1.5662576687116565, 'pattern_counts': {'":': 0, '<': 27, '<?xml version=': 0, '>': 27, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 1, 'word_list_counts': {'cursed_substrings.json': 12, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '7', 'dir': 'jupyter-structured-clean-dedup', 'id': '441504', 'license': 'MIT', 'n_tokens_mistral': 10401, 'n_tokens_neox': 8700, 'n_words': 799, 'path': 'Scripts/7_evaluation_II.ipynb', 'repo_name': 'Hlaboa/NLG-WikiMusica', 'size': '20914'}