From 7b8f054c80b0aecaadd8ae4fc6a6fe37cf1c749f Mon Sep 17 00:00:00 2001
From: pks <pks@pks.rocks>
Date: Thu, 20 Nov 2025 20:47:31 +0100
Subject: init

---
 format-data.py | 46 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 46 insertions(+)
 create mode 100644 format-data.py

(limited to 'format-data.py')

diff --git a/format-data.py b/format-data.py
new file mode 100644
index 0000000..60c13d7
--- /dev/null
+++ b/format-data.py
@@ -0,0 +1,46 @@
+import json
+
+from glob import glob
+
+from PIL import Image
+
+
+def format_example_for_gemma3_preferences(data, target_score, translation_score):
+    prompt = """<bos><start_of_turn>user
+You are a professional English-German translator and also a renowned photography critic.
+
+<start_of_image>
+Write a detailed caption for this image in a single sentence. Translate the caption into German. The output needs to be JSON, the keys being 'English' and 'German' for the respective captions. Only output the JSON, nothing else.<end_of_turn>"""
+    translation = f"""<start_of_turn>model
+{data['Translation']}<end_of_turn>"""
+    target = f"""<start_of_turn>model
+{data['German']}<end_of_turn>"""
+
+    if target_score == translation_score:
+        return None, None, None
+    elif target_score > translation_score:
+        return prompt, target, translation
+    else:
+        return prompt, translation, target
+
+
+def main():
+    with open("baseline/target.gemba-gpt4.1.scores", "r") as f:
+        scores_target = [int(line.strip()) for line in f.readlines()]
+    with open("baseline/translation.gemba-gpt4.1.scores", "r") as f:
+        scores_translation = [int(line.strip()) for line in f.readlines()]
+
+    for index, filename in enumerate(sorted(glob("baseline/*.jsonl"))):
+        with open(filename, "r") as f:
+            data = json.loads(f.read())
+        prompt, c, r = format_example_for_gemma3_preferences(data, scores_target[index], scores_translation[index])
+        print(f"{c=} {scores_target[index] > scores_translation[index]}")
+
+
+    from transformers import AutoTokenizer
+    model_id = "google/gemma-3-4b-it"
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
+        
+
+if __name__ == "__main__":
+    main()
-- 
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