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April 26, 2025 11:47
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Shuu12121/CodeSearch-ModernBERT-Crow-Plus
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| "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.3/56.3 MB\u001b[0m \u001b[31m13.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", | |
| "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m127.9/127.9 MB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", | |
| "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.5/207.5 MB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", | |
| "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m102.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", | |
| "\u001b[?25h" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "!pip install -U -q sentence-transformers" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# cosine類似度計算" | |
| ], | |
| "metadata": { | |
| "id": "o-e8ZA4WpN9Q" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from sentence_transformers import SentenceTransformer, util\n", | |
| "\n", | |
| "# モデルのロード\n", | |
| "model_id = \"Shuu12121/CodeSearch-ModernBERT-Crow-Plus\"\n", | |
| "model = SentenceTransformer(model_id)\n", | |
| "\n", | |
| "# コードスニペットの定義\n", | |
| "code_snippet_1 = \"\"\"\n", | |
| "def add(a, b):\n", | |
| " return a + b\n", | |
| "\"\"\"\n", | |
| "\n", | |
| "code_snippet_2 = \"\"\"\n", | |
| "def sum_numbers(x, y):\n", | |
| " return x + y\n", | |
| "\"\"\"\n", | |
| "\n", | |
| "# コードスニペットをベクトルにエンコード\n", | |
| "embedding_1 = model.encode(code_snippet_1, convert_to_tensor=True)\n", | |
| "embedding_2 = model.encode(code_snippet_2, convert_to_tensor=True)\n", | |
| "\n", | |
| "# コサイン類似度の計算\n", | |
| "cosine_similarity = util.pytorch_cos_sim(embedding_1, embedding_2)\n", | |
| "\n", | |
| "print(f\"Cosine Similarity: {cosine_similarity.item():.4f}\")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 470, | |
| "referenced_widgets": [ | |
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| } | |
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| "metadata": {} | |
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| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "W0425 12:58:53.384000 223 torch/_inductor/utils.py:1137] [1/0] Not enough SMs to use max_autotune_gemm mode\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Cosine Similarity: 0.6369\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# コードスニペットの定義\n", | |
| "code_snippet_1 = \"\"\"\n", | |
| "func (v *View) Render(m ...string) trees.Markup {\n", | |
| "if len(m) <= 0 {\n", | |
| "m = []string{\".\"}\n", | |
| "}\n", | |
| "\"\"\"\n", | |
| "\n", | |
| "code_snippet_2 = \"\"\"\n", | |
| "def sum_numbers(x, y):\n", | |
| " return x + y\n", | |
| "\"\"\"\n", | |
| "\n", | |
| "# コードスニペットをベクトルにエンコード\n", | |
| "embedding_1 = model.encode(code_snippet_1, convert_to_tensor=True)\n", | |
| "embedding_2 = model.encode(code_snippet_2, convert_to_tensor=True)\n", | |
| "\n", | |
| "# コサイン類似度の計算\n", | |
| "cosine_similarity = util.pytorch_cos_sim(embedding_1, embedding_2)\n", | |
| "\n", | |
| "print(f\"Cosine Similarity: {cosine_similarity.item():.4f}\")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "rw8ULhbnaHpO", | |
| "outputId": "678ba336-7887-4636-d835-23f8c42a943f" | |
| }, | |
| "execution_count": 3, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Cosine Similarity: -0.0671\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# 類似コードのテスト２" | |
| ], | |
| "metadata": { | |
| "id": "u_PqMlVZl4y4" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "dataset = [\n", | |
| " {\n", | |
| " \"code\": \"\"\"\n", | |
| "def add(a, b):\n", | |
| " return a + b\n", | |
| "\"\"\",\n", | |
| " \"comment\": \"2つの数値を加算して結果を返す関数です。\"\n", | |
| " },\n", | |
| " {\n", | |
| " \"code\": \"\"\"\n", | |
| "def factorial(n):\n", | |
| " if n == 0:\n", | |
| " return 1\n", | |
| " else:\n", | |
| " return n * factorial(n - 1)\n", | |
| "\"\"\",\n", | |
| " \"comment\": \"再帰的にnの階乗を計算する関数です。\"\n", | |
| " },\n", | |
| " {\n", | |
| " \"code\": \"\"\"\n", | |
| "def is_even(n):\n", | |
| " return n % 2 == 0\n", | |
| "\"\"\",\n", | |
| " \"comment\": \"与えられた数が偶数かどうかを判定する関数です。\"\n", | |
| " },\n", | |
| " {\n", | |
| " \"code\": \"\"\"\n", | |
| "def fibonacci(n):\n", | |
| " if n <= 1:\n", | |
| " return n\n", | |
| " else:\n", | |
| " return fibonacci(n-1) + fibonacci(n-2)\n", | |
| "\"\"\",\n", | |
| " \"comment\": \"フィボナッチ数列のn番目の項を返す関数です。\"\n", | |
| " },\n", | |
| " {\n", | |
| " \"code\": \"\"\"\n", | |
| "def reverse_string(s):\n", | |
| " return s[::-1]\n", | |
| "\"\"\",\n", | |
| " \"comment\": \"文字列を反転させる関数です。\"\n", | |
| " },\n", | |
| " {\n", | |
| " \"code\": \"\"\"\n", | |
| "def find_max(numbers):\n", | |
| " max_number = numbers[0]\n", | |
| " for number in numbers:\n", | |
| " if number > max_number:\n", | |
| " max_number = number\n", | |
| " return max_number\n", | |
| "\"\"\",\n", | |
| " \"comment\": \"リスト内の最大値を見つける関数です。\"\n", | |
| " },\n", | |
| " {\n", | |
| " \"code\": \"\"\"\n", | |
| "def read_file(filename):\n", | |
| " with open(filename, 'r') as f:\n", | |
| " contents = f.read()\n", | |
| " return contents\n", | |
| "\"\"\",\n", | |
| " \"comment\": \"ファイルを読み込み、内容を返す関数です。\"\n", | |
| " },\n", | |
| " {\n", | |
| " \"code\": \"\"\"\n", | |
| "def write_file(filename, contents):\n", | |
| " with open(filename, 'w') as f:\n", | |
| " f.write(contents)\n", | |
| "\"\"\",\n", | |
| " \"comment\": \"ファイルに内容を書き込む関数です。\"\n", | |
| " },\n", | |
| " {\n", | |
| " \"code\": \"\"\"\n", | |
| "def calculate_average(numbers):\n", | |
| " total = sum(numbers)\n", | |
| " count = len(numbers)\n", | |
| " return total / count\n", | |
| "\"\"\",\n", | |
| " \"comment\": \"数値のリストの平均値を計算する関数です。\"\n", | |
| " },\n", | |
| " {\n", | |
| " \"code\": \"\"\"\n", | |
| "def is_prime(n):\n", | |
| " if n <= 1:\n", | |
| " return False\n", | |
| " for i in range(2, int(n**0.5) + 1):\n", | |
| " if n % i == 0:\n", | |
| " return False\n", | |
| " return True\n", | |
| "\"\"\",\n", | |
| " \"comment\": \"与えられた数が素数かどうかを判定する関数です。\"\n", | |
| " }\n", | |
| "]" | |
| ], | |
| "metadata": { | |
| "id": "UMcRf49HD0A2" | |
| }, | |
| "execution_count": 6, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from sentence_transformers import SentenceTransformer, util\n", | |
| "\n", | |
| "# モデルのロード\n", | |
| "model_id = \"Shuu12121/CodeSearch-ModernBERT-Crow-Plus\"\n", | |
| "model = SentenceTransformer(model_id)\n", | |
| "\n", | |
| "# データセット中のコードの埋め込みを事前計算\n", | |
| "dataset_codes = [item[\"code\"] for item in dataset]\n", | |
| "dataset_comments = [item[\"comment\"] for item in dataset]\n", | |
| "dataset_embeddings = model.encode(dataset_codes, convert_to_tensor=True)\n", | |
| "\n", | |
| "def generate_comment_for_code(input_code, model, dataset_embeddings, dataset_comments):\n", | |
| " # 入力コードの埋め込みを取得\n", | |
| " input_embedding = model.encode(input_code, convert_to_tensor=True)\n", | |
| " # 各データセットコードとのコサイン類似度を計算\n", | |
| " cosine_scores = util.pytorch_cos_sim(input_embedding, dataset_embeddings)\n", | |
| " # 類似度が最も高いコードのインデックスを取得\n", | |
| " best_idx = cosine_scores.argmax()\n", | |
| " return dataset_comments[best_idx], cosine_scores[0][best_idx].item()\n", | |
| "\n", | |
| "# テスト用の入力コードスニペット\n", | |
| "input_code = \"\"\"\n", | |
| "def sum_numbers(x, y):\n", | |
| " return x + y\n", | |
| "\"\"\"\n", | |
| "\n", | |
| "# コメント生成（類似コードに基づく要約）\n", | |
| "generated_comment, score = generate_comment_for_code(input_code, model, dataset_embeddings, dataset_comments)\n", | |
| "print(\"生成されたコメント:\", generated_comment)\n", | |
| "print(\"類似度スコア:\", score)\n" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "tzmVSpkDly4H", | |
| "outputId": "47c99cef-73fd-4138-a962-31638f750c76" | |
| }, | |
| "execution_count": 7, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "生成されたコメント: 2つの数値を加算して結果を返す関数です。\n", | |
| "類似度スコア: 0.636911153793335\n" | |
| ] | |
| } | |
| ] | |
| } | |
| ] | |
| } |
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