Update README.md

README.md

@@ -92,7 +92,9 @@ model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
 tokenizer = AutoTokenizer.from_pretrained(model_path)
 
 question = "What is the average, minimum, and maximum age for all French musicians?"
-schema = ""stadium" "Stadium_ID" int , "Location" text , "Name" text , "Capacity" int , "Highest" int , "Lowest" int , "Average" int , foreign_key:  primary key: "Stadium_ID" [SEP] "singer" "Singer_ID" int , "Name" text , "Country" text , "Song_Name" text , "Song_release_year" text , "Age" int , "Is_male" bool , foreign_key:  primary key: "Singer_ID" [SEP] "concert" "concert_ID" int , "concert_Name" text , "Theme" text , "Year" text , foreign_key: "Stadium_ID" text from "stadium" "Stadium_ID" , primary key: "concert_ID" [SEP] "singer_in_concert"  foreign_key: "concert_ID" int from "concert" "concert_ID" , "Singer_ID" text from "singer" "Singer_ID" , primary key: "concert_ID" "Singer_ID""
+schema = """
+   "stadium" "Stadium_ID" int , "Location" text , "Name" text , "Capacity" int , "Highest" int , "Lowest" int , "Average" int , foreign_key:  primary key: "Stadium_ID" [SEP] "singer" "Singer_ID" int , "Name" text , "Country" text , "Song_Name" text , "Song_release_year" text , "Age" int , "Is_male" bool , foreign_key:  primary key: "Singer_ID" [SEP] "concert" "concert_ID" int , "concert_Name" text , "Theme" text , "Year" text , foreign_key: "Stadium_ID" text from "stadium" "Stadium_ID" , primary key: "concert_ID" [SEP] "singer_in_concert"  foreign_key: "concert_ID" int from "concert" "concert_ID" , "Singer_ID" text from "singer" "Singer_ID" , primary key: "concert_ID" "Singer_ID"
+"""
 
 input_text = " ".join(["Question: ",question, "Schema:", schema])
 
@@ -140,4 +142,6 @@ training_arguments = AdaptationArguments(output_dir="train_dir",
                                          num_train_epochs=10,
                                          evaluation_strategy="steps")
 
-```
+```
+The training is fairly easy to reproduce, but we do not wish to publish modified copies of the Spider datasets that it depends on. 
+If you'd like to investigate further in this direction, feel free to get in touch through a new PR, or via email to nikola.groverova(at)gaussalgo.com.