Update app.py

app.py

@@ -23,6 +23,7 @@ def get_datasets():
 
 all_datasets = get_datasets()
 
+'''
 def get_split(dataset_name):
 	if dataset_name == "Communication Networks: unseen questions":
 		split = load_dataset("Short-Answer-Feedback/saf_communication_networks_english", split="test_unseen_questions")
@@ -37,6 +38,7 @@ def get_split(dataset_name):
 	if dataset_name	== "Legal Domain: unseen answers":
 		split = load_dataset("Short-Answer-Feedback/saf_legal_domain_german", split="test_unseen_answers")
 	return split
+ '''
 
 def get_model(datasetname):
 	if datasetname == "Communication Networks: unseen questions" or datasetname == "Communication Networks: unseen answers":
@@ -47,6 +49,7 @@ def get_model(datasetname):
 		model = "Short-Answer-Feedback/mbart-finetuned-saf-legal-domain"
 	return model
 
+'''
 def get_tokenizer(datasetname):
 	if datasetname == "Communication Networks: unseen questions" or datasetname == "Communication Networks: unseen answers":
 		tokenizer = "Short-Answer-Feedback/bart-finetuned-saf-communication-networks"
@@ -234,6 +237,102 @@ def load_data():
         
         df = pd.concat([df, new_row])
     return df
+'''
+
+def get_rows(datasetname):
+	if datasetname == "Communication Networks: unseen questions":
+		row = pd.DataFrame(
+			{
+				'Model': get_model(datasetname),
+				'Dataset': datasetname,
+				'SacreBLEU': [2.4],
+				'ROUGE-2': [20.1],
+				'METEOR': [28.5],
+				'BERTScore': [36.6],
+				'Accuracy': [51.6],
+				'Weighted F1': [41.0],
+				'Macro F1': [27.9],
+			}
+		)
+
+	if datasetname == "Communication Networks: unseen answers":
+		row = pd.DataFrame(
+			{
+				'Model': get_model(datasetname),
+				'Dataset': datasetname,
+				'SacreBLEU': [36.0],
+				'ROUGE-2': [49.1],
+				'METEOR': [60.8],
+				'BERTScore': [69.5],
+				'Accuracy': [76.0],
+				'Weighted F1': [73.0],
+				'Macro F1: [53.4]'
+			}
+		)
+	if datasetname == "Micro Job: unseen questions":
+		row = pd.DataFrame(
+			{
+				'Model': get_model(datasetname),
+				'Dataset': datasetname,
+				'SacreBLEU': [0.3],
+				'ROUGE-2': [0.5],
+				'METEOR': [33.8],
+				'BERTScore': [31.3],
+				'Accuracy': [48.7],
+				'Weighted F1': [46.5],
+				'Macro F1': [40.6],
+			}
+		)
+	if datasetname == "Micro Job: unseen answers":
+		row = pd.DataFrame(
+			{
+				'Model': get_model(datasetname),
+				'Dataset': datasetname,
+				'SacreBLEU': [39.5],
+				'ROUGE-2': [29.8],
+				'METEOR': [63.3],
+				'BERTScore': [63.1],
+				'Accuracy': [80.1],
+				'Weighted F1': [80.3],
+				'Macro F1': [80.7],
+			}
+		)
+	if datasetname	== "Legal Domain: unseen questions":
+		row = pd.DataFrame(
+			{
+				'Model': get_model(datasetname),
+				'Dataset': datasetname,
+				'SacreBLEU': [3.2],
+				'ROUGE-2': [5.0],
+				'METEOR': [20.0],
+				'BERTScore': [14.8],
+				'Accuracy': [60.7],
+				'Weighted F1': [55.3],
+				'Macro F1': [55.4],
+			}
+		)
+	if datasetname	== "Legal Domain: unseen answers":
+		row = pd.DataFrame(
+			{
+				'Model': get_model(datasetname),
+				'Dataset': datasetname,
+				'SacreBLEU': [42.8],
+				'ROUGE-2': [43.7],
+				'METEOR': [58.2],
+				'BERTScore': [57.5],
+				'Accuracy': [81.0],
+				'Weighted F1': [80.1],
+				'Macro F1': [74.6],
+			}
+		)
+	return row
+
+def load_data():
+    df = pd.DataFrame(columns=['Model', 'Dataset', 'SacreBLEU', 'ROUGE-2', 'METEOR', 'BERTScore', 'Accuracy', 'Weighted F1', 'Macro F1'])
+    for ds in all_datasets:
+        new_row = get_rows(ds)
+        df = pd.concat([df, new_row])
+    return df
 
 dataframe = load_data()