Saving weights and logs of epoch 60

config.json

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f07f648665ff8cbcecaeb7fba7e51c4f6244b86d7c83085bfca11925b8cbb79f
-size 699
+oid sha256:f84d74dd7be6033a337c5857b9bbc37993375140addaf83691e24e181e9d4b5c
+size 613

flax_model.msgpack

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:9ec75c0b5de5daae130fea6477aa7a279e3b0f7bc4c0441ce45e526b49e60b4d
+oid sha256:1672bb6f04da617591d445601d1fa273deedb722149ff324265c8f1ee4ace542
 size 1200715307

run_translation_t5_flax.py

@@ -430,8 +430,15 @@ def main():
 
     # Setting padding="max_length" as we need fixed length inputs for jitted functions
     def preprocess_function(examples):
-        inputs = examples[text_column]
-        targets = examples[summary_column]
+        #Make it two way
+        #inputs = examples[text_column]
+        inputs = examples[text_column][0:int(len(examples)/2)]
+        inputs.extend(examples[summary_column][int(len(examples)/2):])
+
+        #targets = examples[summary_column]
+        targets = examples[summary_column][0:int(len(examples)/2)]
+        targets.extend(examples[text_column][int(len(examples)/2):])
+
         inputs = [prefix + inp for inp in inputs]
         model_inputs = tokenizer(
             inputs, max_length=data_args.max_source_length, padding="max_length", truncation=True, return_tensors="np"