diff --git a/Textcat_conversion.py b/Textcat_conversion.py
new file mode 100644
index 000000000..4ba61f7b1
--- /dev/null
+++ b/Textcat_conversion.py
@@ -0,0 +1,64 @@
+from pathlib import Path
+import plac
+import spacy
+from spacy.tokens import DocBin
+import srsly
+import sys
+
+
+@plac.annotations(
+    model=("Model name. Defaults to 'en'.", "option", "m", str),
+    input_file=("Input file (jsonl)", "positional", None, Path),
+    output_dir=("Output directory", "positional", None, Path),
+    n_texts=("Number of texts to convert", "option", "t", int),
+)
+def convert(model="en", input_file=None, output_dir=None, n_texts=0):
+    # Load model with tokenizer + sentencizer only
+    try:
+        nlp = spacy.load(model)
+    except OSError:
+        print(f"Model '{model}' not found. Please download the model using: python -m spacy download {model}")
+        sys.exit(1)
+        
+    nlp.select_pipes(disable=[pipe for pipe in nlp.pipe_names if pipe != "sentencizer"])
+    if "sentencizer" not in nlp.pipe_names:
+        nlp.add_pipe("sentencizer", first=True)
+    
+    # AI-driven feature: Efficient batch processing for large datasets
+    texts = []
+    cats = []
+    count = 0
+
+    if not input_file.exists():
+        print("Input file not found:", input_file)
+        sys.exit(1)
+    else:
+        with open(input_file) as fileh:
+            for line in fileh:
+                data = srsly.json_loads(line)
+                texts.append(data.get("text", ""))
+                cats.append(data.get("cats", {}))
+
+    if output_dir is not None:
+        output_dir = Path(output_dir)
+        output_dir.mkdir(parents=True, exist_ok=True)
+    else:
+        output_dir = Path(".")
+
+    # AI-driven feature: Optimize memory usage with DocBin
+    doc_bin = DocBin(store_user_data=True)
+
+    for i, doc in enumerate(nlp.pipe(texts, batch_size=100)):
+        doc.cats = cats[i]
+        doc_bin.add(doc)
+        if n_texts > 0 and count == n_texts:
+            break
+        count += 1
+
+    output_path = output_dir / input_file.with_suffix(".spacy")
+    doc_bin.to_disk(output_path)
+
+    print(f"Conversion complete. Output saved to {output_path}")
+
+if __name__ == "__main__":
+    plac.call(convert)