data set complete

README.md

@@ -1,5 +1,8 @@
 
 # Data source
----
-license: mit
----
+Downloaded via Andrej Karpathy's nanogpt repo from this [link](https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt)
+
+# Data Format
+- The entire dataset is split into train (90%) and test (10%).
+- All rows are at most 1024 tokens, using the Llama 2 tokenizer.
+- All rows are split cleanly so that sentences are whole and unbroken.

create_dataset.py

@@ -0,0 +1,60 @@
+import csv
+from transformers import AutoTokenizer
+
+# Initialize the tokenizer
+tokenizer = AutoTokenizer.from_pretrained("TheBloke/Yarn-Llama-2-7B-128K-GPTQ", use_fast=True)
+
+# Read the input data
+with open('input.txt', 'r') as f:
+    data = f.readlines()
+
+# Initialize variables
+train_data = []
+test_data = []
+current_row = ""
+current_token_count = 0
+carry_over = ""
+
+# Iterate over each line and add to train or test data
+for i, line in enumerate(data):
+    line_to_add = carry_over + line.strip()
+    carry_over = ""
+    
+    # Tokenize the line to count tokens
+    tokens = tokenizer(line_to_add)['input_ids']
+    num_tokens = len(tokens)
+
+    # Check if adding the line would exceed the token limit
+    if current_token_count + num_tokens > 1024:
+        # Find the last period followed by a space in the current row
+        last_period_idx = current_row.rfind('. ')
+        
+        if last_period_idx != -1:
+            # Carry over the content after the last period
+            carry_over = current_row[last_period_idx+2:].strip() + "\n"
+            current_row = current_row[:last_period_idx+1]
+        
+        if i < len(data) * 0.9:
+            train_data.append(current_row.strip())
+        else:
+            test_data.append(current_row.strip())
+        
+        current_row = carry_over
+        current_token_count = len(tokenizer(current_row.strip())['input_ids'])
+
+    # Add the line to the current row
+    current_row += (line_to_add + "\n") if current_row else (line_to_add + "\n")
+    current_token_count += num_tokens
+
+# Save as train.csv and test.csv
+with open('train.csv', 'w', newline='') as f:
+    writer = csv.writer(f)
+    writer.writerow(['Text'])
+    for row in train_data:
+        writer.writerow([row])
+
+with open('test.csv', 'w', newline='') as f:
+    writer = csv.writer(f)
+    writer.writerow(['Text'])
+    for row in test_data:
+        writer.writerow([row])