Add commands192024.py

commands192024.py

@@ -0,0 +1,49 @@
+pip install happytransformer
+pip install datasets
+
+
+import csv
+from happytransformer import HappyTextToText
+from happytransformer import TTSettings
+from datasets import load_dataset
+happy_tt = HappyTextToText("T5", "t5-base")
+
+train_dataset = load_dataset("jfleg", split='validation[:]')
+eval_dataset = load_dataset("jfleg", split='test[:]')
+
+
+def generate_csv(csv_path, dataset):
+    with open(csv_path, 'w', newline='') as csvfile:
+        writter = csv.writer(csvfile)
+        writter.writerow(["input", "target"])
+        for case in dataset:
+     	    # Adding the task's prefix to input 
+            input_text = "grammar: " + case["sentence"]
+            for correction in case["corrections"]:
+                # a few of the cases contain blank strings. 
+                if input_text and correction:
+                    writter.writerow([input_text, correction])
+                    
+
+
+generate_csv("train.csv", train_dataset)
+generate_csv("eval.csv", eval_dataset)
+
+evalsettings = TTTrainArgs(max_input_length=4096, max_output_length=4096)
+before_result = happy_tt.eval("eval.csv", args=evalsettings)
+print("Before loss:", before_result.loss)
+
+from happytransformer import TTTrainArgs
+args = TTTrainArgs(batch_size=8, max_input_length = 10000, max_output_length = 10000)
+happy_tt.train("train.csv", args=args)
+
+after_loss = happy_tt.eval("eval.csv", args=evalsettings)
+print("After loss: ", after_loss.loss)
+
+beam_settings =  TTSettings(num_beams=5, min_length=1, max_length=20)
+example_1 = "grammar: This sentences, has bads grammar and spelling!"
+result_1 = happy_tt.generate_text(example_1, args=beam_settings)
+print(result_1.text)
+example_2 = "grammar: I am enjoys, writtings articles ons AI."
+result_2 = happy_tt.generate_text(example_2, args=beam_settings)
+print(result_2.text)