diff --git a/Playgrounds/nanosocrates-train-toy.ipynb b/Playgrounds/nanosocrates-train-toy.ipynb
index 44593d0..53dcaa7 100644
--- a/Playgrounds/nanosocrates-train-toy.ipynb
+++ b/Playgrounds/nanosocrates-train-toy.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "adbd9598",
    "metadata": {},
    "outputs": [
@@ -8357,7 +8357,7 @@
     "        ):\n",
     "            decoded_sentence = TOKENANO.decode(encoded_sentence)\n",
     "            decoded_target = TOKENANO.decode(expected_sentence)\n",
-    "            print(f\"\\tACTUAL:\\n\\t{decoded_sentence}\\n\\tEXPECTED:\\n\\t{decoded_target}\\n\")\n",
+    "            print(f\"\\tACTUAL:\\n\\t\\t{decoded_sentence}\\n\\tEXPECTED:\\n\\t\\t{decoded_target}\\n\")\n",
     "\n",
     "\n",
     "\n",
diff --git a/Playgrounds/prova.py b/Playgrounds/prova.py
index 39ee7f3..f05f239 100644
--- a/Playgrounds/prova.py
+++ b/Playgrounds/prova.py
@@ -27,9 +27,9 @@ TOKENANO = BPE.TokeNanoCore(VOCABULARY, SPECIAL_VOC)
 TOKEN_SPACE_SIZE = TOKENANO.vocabulary_size + 1
 EMBEDDED_SIZE = 256
 FEED_FORWARD_MULTIPLIER =  4
-ATTENTION_HEADS = 4
+ATTENTION_HEADS = 8
 SENTENCE_LENGTH = 256
-NUMBER_OF_BLOCKS = 2
+NUMBER_OF_BLOCKS = 4
 MAX_EPOCHS = int(1e3)
 
 
@@ -72,6 +72,25 @@ for index, row in TOY_DATASET.iterrows():
     TOY_BATCH_TARGET_LIST.append(output_tokens)
     TOY_BATCH_DECODER_DEFAULT.append(decoder_default_tokens)
 
+    output_tokens = TOKENANO.encode(RDFs)
+    input_tokens = TOKENANO.encode(Abstract)[1:]
+    decoder_default_tokens = TOKENANO.encode("<SOS>")
+
+    input_tokens, padding = Transformer.normalize_sequence(
+        input_tokens, SENTENCE_LENGTH, PAD_TOKEN, END_TOKEN
+    )
+    output_tokens, _ = Transformer.normalize_sequence(
+        output_tokens, SENTENCE_LENGTH, PAD_TOKEN, END_TOKEN
+    )
+    decoder_default_tokens, _ = Transformer.normalize_sequence(
+        decoder_default_tokens, SENTENCE_LENGTH, PAD_TOKEN, END_TOKEN, False
+    )
+
+    TOY_BATCH_INPUT_LIST.append(input_tokens)
+    TOY_BATCH_PADDING_LIST.append(padding)
+    TOY_BATCH_TARGET_LIST.append(output_tokens)
+    TOY_BATCH_DECODER_DEFAULT.append(decoder_default_tokens)
+
 # Training loop
 LOSS_HISTORY = []
 NANOSOCRATES = Transformer.TrainingModel(
@@ -91,15 +110,14 @@ while current_epoch < MAX_EPOCHS:
 
     optimizer.zero_grad()
 
-    encoder_list = torch.tensor([TOY_BATCH_INPUT_LIST[0]])
-    decoder_list = torch.tensor([TOY_BATCH_DECODER_DEFAULT[0]])
-    src_padding = torch.tensor([TOY_BATCH_PADDING_LIST[0]], dtype=torch.bool)
+    encoder_list = torch.tensor(TOY_BATCH_INPUT_LIST[:])
+    decoder_list = torch.tensor(TOY_BATCH_DECODER_DEFAULT[:])
+    src_padding = torch.tensor(TOY_BATCH_PADDING_LIST[:], dtype=torch.bool)
 
     # Transform target into logits
-    target_logits = torch.tensor([TOY_BATCH_TARGET_LIST[0]])
+    target_logits = torch.tensor(TOY_BATCH_TARGET_LIST[:])
 
     last_loss = 0
-    loss_list = []
     last_prediction: torch.Tensor
 
     for i in range(0, SENTENCE_LENGTH):
@@ -116,11 +134,10 @@ while current_epoch < MAX_EPOCHS:
         logits = logits[:,:i,:]
         logits = logits.permute(0, 2, 1)
 
-        loss : torch.Tensor = cross_entropy(logits, target_logits[:, :i])
+        loss : torch.Tensor = cross_entropy(logits, target_logits[:, 0:i])
         # loss : torch.Tensor = cross_entropy(logits, target_logits)
 
         last_loss = loss
-        loss_list.append(loss.item())
         loss.backward()
         optimizer.step()
         scheduler.step()
@@ -136,10 +153,7 @@ while current_epoch < MAX_EPOCHS:
     current_epoch += 1
 
     if current_epoch % 1 == 0:
-        loss_list = loss_list[1:]
         print(f"EPOCH {current_epoch}\n\tLoss: {last_loss}")
-        print(f"ALL LOSS HISTORY:{loss_list}")
-        print(f"Max loss:{max(loss_list)}, Min loss: {min(loss_list)}")
 
         for encoded_sentence, expected_sentence in zip(
             Transformer.tensor2token(last_prediction[:,:], END_TOKEN), # type: ignore
@@ -147,7 +161,7 @@ while current_epoch < MAX_EPOCHS:
         ):
             decoded_sentence = TOKENANO.decode(encoded_sentence)
             decoded_target = TOKENANO.decode(expected_sentence)
-            print(f"ACTUAL:\n\t\t{decoded_sentence}\nEXPECTED:\n\t\t{decoded_target}")
+            print(f"\tACTUAL:\n\t\t{decoded_sentence}\n\tEXPECTED:\n\t\t{decoded_target}\n")