diff --git a/.github/workflows/a b/.github/workflows/a
new file mode 100644
index 000000000..e69de29bb
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index b4aad9943..a3e0fc3ee 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -4,7 +4,7 @@ on:
# push:
# branches: [ main, master ]
pull_request:
- branches: [ main, master ]
+ branches: [ main, master,develop ]
jobs:
test:
@@ -25,17 +25,24 @@ jobs:
- name: Lint with flake8
run: |
- flake8 day5/演習3 --count --select=E9,F63,F7,F82 --show-source --statistics
- flake8 day5/演習3 --count --exit-zero --max-complexity=10 --max-line-length=88 --statistics
+ flake8 day5/演習2 --count --select=E9,F63,F7,F82 --show-source --statistics
+ flake8 day5/演習2 --count --exit-zero --max-complexity=10 --max-line-length=88 --statistics
- name: Format check with black
run: |
- black --check day5/演習3
+ black --check day5/演習2
- - name: Run data tests
+ # - name: Run data tests
+ # run: |
+ # pytest day5/演習2/tests/test_data.py -v
+
+ # - name: Run model tests
+ # run: |
+ # pytest day5/演習2/tests/test_model.py -v
+ - name: Run main.py
run: |
- pytest day5/演習3/tests/test_data.py -v
+ python day5/演習2/main.py -v
- - name: Run model tests
+ - name: Run pytest on main.py
run: |
- pytest day5/演習3/tests/test_model.py -v
+ pytest day5/演習2/main.py -v
\ No newline at end of file
diff --git a/day1/01_streamlit_UI/app.py b/day1/01_streamlit_UI/app.py
index dcfbe6fec..0489da909 100644
--- a/day1/01_streamlit_UI/app.py
+++ b/day1/01_streamlit_UI/app.py
@@ -15,48 +15,48 @@
# ============================================
# タイトルと説明
# ============================================
-st.title("Streamlit 初心者向けデモ")
-st.markdown("### コメントを解除しながらStreamlitの機能を学びましょう")
-st.markdown("このデモコードでは、コメントアウトされた部分を順番に解除しながらUIの変化を確認できます。")
+st.title("個人情報アンケート")
+st.markdown("### 個人情報を打ち込んで、整理しましょう")
+st.markdown("このサイトでは、個人情報を入力していくことで、他の個人情報を入力する場面での入力をスムーズにします。")
# ============================================
# サイドバー
# ============================================
-st.sidebar.header("デモのガイド")
-st.sidebar.info("コードのコメントを解除して、Streamlitのさまざまな機能を確認しましょう。")
+st.sidebar.header("サイトのガイド")
+st.sidebar.info("個人情報を入力していくことで、他の個人情報を入力する場面での入力をスムーズにしましょう。")
# ============================================
# 基本的なUI要素
# ============================================
-st.header("基本的なUI要素")
+st.header("個人情報入力欄")
# テキスト入力
-st.subheader("テキスト入力")
+st.subheader("個人情報は大切にしてください")
name = st.text_input("あなたの名前", "ゲスト")
st.write(f"こんにちは、{name}さん!")
# ボタン
-# st.subheader("ボタン")
-# if st.button("クリックしてください"):
-# st.success("ボタンがクリックされました!")
+st.subheader("個人情報を入力しちゃうんですか?")
+if st.button("はい"):
+ st.success("変わってますね!")
# チェックボックス
-# st.subheader("チェックボックス")
-# if st.checkbox("チェックを入れると追加コンテンツが表示されます"):
-# st.info("これは隠れたコンテンツです!")
+st.subheader("個人情報を入力することに躊躇いがある")
+if st.checkbox("当てはまる場合はチェックを入れてください"):
+ st.info("なぜこんなサイトを訪れたんですか?")
# スライダー
-# st.subheader("スライダー")
-# age = st.slider("年齢", 0, 100, 25)
-# st.write(f"あなたの年齢: {age}")
+st.subheader("自身の年齢にスライダーを合わせてください")
+age = st.slider("年齢", 0, 100, 25)
+st.write(f"あなたの年齢は{age}歳です!覚えておいてくださいね!")
# セレクトボックス
-# st.subheader("セレクトボックス")
-# option = st.selectbox(
-# "好きなプログラミング言語は?",
-# ["Python", "JavaScript", "Java", "C++", "Go", "Rust"]
-# )
-# st.write(f"あなたは{option}を選びました")
+st.subheader("性別")
+option = st.selectbox(
+ "あなたの性別は?",
+ [ "男", "女", "その他"]
+ )
+st.write(f"あなたは{option}です。わかりましたか?")
# ============================================
# レイアウト
@@ -82,10 +82,10 @@
# st.write("これは第2タブの内容です")
# エクスパンダー
-# st.subheader("エクスパンダー")
-# with st.expander("詳細を表示"):
-# st.write("これはエクスパンダー内の隠れたコンテンツです")
-# st.code("print('Hello, Streamlit!')")
+st.subheader("その他")
+with st.expander("詳細を表示"):
+ st.write("もう入力するのは終わりです、、、")
+ st.code("print('お疲れ様でした')")
# ============================================
# データ表示
@@ -93,11 +93,11 @@
# st.header("データの表示")
# サンプルデータフレームを作成
-# df = pd.DataFrame({
-# '名前': ['田中', '鈴木', '佐藤', '高橋', '伊藤'],
-# '年齢': [25, 30, 22, 28, 33],
-# '都市': ['東京', '大阪', '福岡', '札幌', '名古屋']
-# })
+df = pd.DataFrame({
+ '名前': ['田中', '鈴木', '佐藤', '高橋', '伊藤'],
+ '年齢': [25, 30, 22, 28, 33],
+ '都市': ['東京', '大阪', '福岡', '札幌', '名古屋']
+ })
# データフレーム表示
# st.subheader("データフレーム")
@@ -183,21 +183,21 @@
# ============================================
# デモの使用方法
# ============================================
-st.divider()
-st.subheader("このデモの使い方")
-st.markdown("""
-1. コードエディタでコメントアウトされた部分を見つけます(#で始まる行)
-2. 確認したい機能のコメントを解除します(先頭の#を削除)
-3. 変更を保存して、ブラウザで結果を確認します
-4. 様々な組み合わせを試して、UIがどのように変化するか確認しましょう
-""")
+# st.divider()
+# st.subheader("このデモの使い方")
+# st.markdown("""
+# 1. コードエディタでコメントアウトされた部分を見つけます(#で始まる行)
+# 2. 確認したい機能のコメントを解除します(先頭の#を削除)
+# 3. 変更を保存して、ブラウザで結果を確認します
+#4. 様々な組み合わせを試して、UIがどのように変化するか確認しましょう
+# """)
-st.code("""
+# st.code("""
# コメントアウトされた例:
# if st.button("クリックしてください"):
# st.success("ボタンがクリックされました!")
# コメントを解除した例:
-if st.button("クリックしてください"):
- st.success("ボタンがクリックされました!")
-""")
\ No newline at end of file
+# if st.button("クリックしてください"):
+# st.success("ボタンがクリックされました!")
+# """)
diff --git a/day1/day1_practice.ipynb b/day1/day1_practice.ipynb
index a1dec3a9e..a22d326f7 100644
--- a/day1/day1_practice.ipynb
+++ b/day1/day1_practice.ipynb
@@ -115,11 +115,38 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "bvEowFfg5lrq"
- },
- "outputs": [],
+ "execution_count": 4,
+ "metadata": {
+ "id": "bvEowFfg5lrq",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "fb6acecb-d475-4b34-e90c-7f673140fe6a"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Collecting python-dotenv\n",
+ " Downloading python_dotenv-1.1.0-py3-none-any.whl.metadata (24 kB)\n",
+ "Downloading python_dotenv-1.1.0-py3-none-any.whl (20 kB)\n",
+ "Installing collected packages: python-dotenv\n",
+ "Successfully installed python-dotenv-1.1.0\n",
+ "/content/lecture-ai-engineering/day1\n"
+ ]
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 4
+ }
+ ],
"source": [
"!pip install python-dotenv\n",
"from dotenv import load_dotenv, find_dotenv\n",
@@ -141,11 +168,23 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "S28XgOm0ELSM"
- },
- "outputs": [],
+ "execution_count": 5,
+ "metadata": {
+ "id": "S28XgOm0ELSM",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "c8086931-df0a-4d1b-b471-7806a0dc0268"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "/content/lecture-ai-engineering/day1/01_streamlit_UI\n"
+ ]
+ }
+ ],
"source": [
"%cd /content/lecture-ai-engineering/day1/01_streamlit_UI"
]
@@ -161,7 +200,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 6,
"metadata": {
"id": "nBe41LFiELSN"
},
@@ -182,11 +221,23 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "aYw1q0iXELSN"
- },
- "outputs": [],
+ "execution_count": 7,
+ "metadata": {
+ "id": "aYw1q0iXELSN",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "40578aa1-2a46-46ef-c376-c986614e0f79"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Authtoken saved to configuration file: /root/.config/ngrok/ngrok.yml\n"
+ ]
+ }
+ ],
"source": [
"!ngrok authtoken $$NGROK_TOKEN"
]
@@ -204,9 +255,68 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "id": "f-E7ucR6ELSN"
- },
- "outputs": [],
+ "id": "f-E7ucR6ELSN",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "c85d6711-55e6-4787-abe6-aa66510bcb93"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "公開URL: https://a54b-35-233-225-230.ngrok-free.app\n",
+ "\n",
+ "Collecting usage statistics. To deactivate, set browser.gatherUsageStats to false.\n",
+ "\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[34m\u001b[1m You can now view your Streamlit app in your browser.\u001b[0m\n",
+ "\u001b[0m\n",
+ "\u001b[34m Local URL: \u001b[0m\u001b[1mhttp://localhost:8501\u001b[0m\n",
+ "\u001b[34m Network URL: \u001b[0m\u001b[1mhttp://172.28.0.12:8501\u001b[0m\n",
+ "\u001b[34m External URL: \u001b[0m\u001b[1mhttp://35.233.225.230:8501\u001b[0m\n",
+ "\u001b[0m\n",
+ "2025-04-25 04:08:15.825 Script compilation error\n",
+ "Traceback (most recent call last):\n",
+ " File \"/usr/local/lib/python3.11/dist-packages/streamlit/runtime/scriptrunner/script_runner.py\", line 549, in _run_script\n",
+ " code = self._script_cache.get_bytecode(script_path)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/local/lib/python3.11/dist-packages/streamlit/runtime/scriptrunner/script_cache.py\", line 72, in get_bytecode\n",
+ " filebody = magic.add_magic(filebody, script_path)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/local/lib/python3.11/dist-packages/streamlit/runtime/scriptrunner/magic.py\", line 46, in add_magic\n",
+ " tree = ast.parse(code, script_path, \"exec\")\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib/python3.11/ast.py\", line 50, in parse\n",
+ " return compile(source, filename, mode, flags,\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/content/lecture-ai-engineering/day1/01_streamlit_UI/app.py\", line 39\n",
+ " st.subheader(\"ボタン\")\n",
+ "IndentationError: unexpected indent\n",
+ "2025-04-25 04:08:15.829 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
+ "2025-04-25 04:08:23.075 Script compilation error\n",
+ "Traceback (most recent call last):\n",
+ " File \"/usr/local/lib/python3.11/dist-packages/streamlit/runtime/scriptrunner/script_runner.py\", line 549, in _run_script\n",
+ " code = self._script_cache.get_bytecode(script_path)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/local/lib/python3.11/dist-packages/streamlit/runtime/scriptrunner/script_cache.py\", line 72, in get_bytecode\n",
+ " filebody = magic.add_magic(filebody, script_path)\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/local/lib/python3.11/dist-packages/streamlit/runtime/scriptrunner/magic.py\", line 46, in add_magic\n",
+ " tree = ast.parse(code, script_path, \"exec\")\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/usr/lib/python3.11/ast.py\", line 50, in parse\n",
+ " return compile(source, filename, mode, flags,\n",
+ " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+ " File \"/content/lecture-ai-engineering/day1/01_streamlit_UI/app.py\", line 39\n",
+ " st.subheader(\"ボタン\")\n",
+ "IndentationError: unexpected indent\n",
+ "2025-04-25 04:08:23.077 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
+ "2025-04-25 04:08:56.234 Received event for non-watched file: /content/lecture-ai-engineering/day1/01_streamlit_UI/app.py\n"
+ ]
+ }
+ ],
"source": [
"from pyngrok import ngrok\n",
"\n",
@@ -215,6 +325,18 @@
"!streamlit run app.py"
]
},
+ {
+ "cell_type": "code",
+ "source": [
+ "from google.colab import drive\n",
+ "drive.mount('/content/drive')"
+ ],
+ "metadata": {
+ "id": "OSCJrRYSTrgo"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
{
"cell_type": "markdown",
"metadata": {
@@ -391,7 +513,9 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "-N8qKmC6HrJ7"
+ },
"source": [
"アプリケーションの機能としては、チャット機能や履歴閲覧があります。\n",
"\n",
@@ -567,4 +691,4 @@
},
"nbformat": 4,
"nbformat_minor": 0
-}
+}
\ No newline at end of file
diff --git "a/day5/\346\274\224\347\277\2221/models/titanic_model.pkl" "b/day5/\346\274\224\347\277\2221/models/titanic_model.pkl"
index 6fec87e47..7288555cb 100644
Binary files "a/day5/\346\274\224\347\277\2221/models/titanic_model.pkl" and "b/day5/\346\274\224\347\277\2221/models/titanic_model.pkl" differ
diff --git "a/day5/\346\274\224\347\277\2222/black_check.py" "b/day5/\346\274\224\347\277\2222/black_check.py"
deleted file mode 100644
index 3158f952d..000000000
--- "a/day5/\346\274\224\347\277\2222/black_check.py"
+++ /dev/null
@@ -1,7 +0,0 @@
-
-def say_hello(name):print("Hello,"+name+"!") # greet
-def say_hello(name):print("Hello," + name +"!") # greet
-def add( a,b):return a+b
-def add( a , b ):return a+b
-def add(a, b):
- return a+b
\ No newline at end of file
diff --git "a/day5/\346\274\224\347\277\2222/main.py" "b/day5/\346\274\224\347\277\2222/main.py"
index 776b70e75..79a4fd590 100644
--- "a/day5/\346\274\224\347\277\2222/main.py"
+++ "b/day5/\346\274\224\347\277\2222/main.py"
@@ -11,6 +11,7 @@
import time
import great_expectations as gx
+
class DataLoader:
"""データロードを行うクラス"""
@@ -18,12 +19,30 @@ class DataLoader:
def load_titanic_data(path=None):
"""Titanicデータセットを読み込む"""
if path:
- return pd.read_csv(path)
+ # 引数にパスが指定された場合はそれを優先
+ if os.path.exists(path):
+ return pd.read_csv(path)
+ else:
+ print(f"Error: Specified path '{path}' does not exist.")
+ return None
else:
- # ローカルのファイル
- local_path = "data/Titanic.csv"
- if os.path.exists(local_path):
- return pd.read_csv(local_path)
+ # 引数が指定されない場合、現在のスクリプトからの相対パスを試す
+ # スクリプトがあるディレクトリの絶対パスを取得
+ script_dir = os.path.dirname(__file__)
+ # 'data'ディレクトリがスクリプトの隣にあると仮定
+ relative_path = os.path.join(script_dir, "data", "Titanic.csv")
+
+ # または、より上位の 'lecture-ai-engineering' ディレクトリ直下に 'data' がある場合
+ # project_root = os.path.abspath(os.path.join(script_dir, '..', '..')) # day5/演習2 から2つ上に上がる
+ # relative_path = os.path.join(project_root, "data", "Titanic.csv")
+
+ if os.path.exists(relative_path):
+ print(f"Loading data from: {relative_path}") # デバッグ用
+ return pd.read_csv(relative_path)
+ else:
+ print(f"Error: Data file not found at local path '{relative_path}'.")
+ # ここでエラーを発生させるか、Noneを返すかを決める
+ return None # Noneを返すと、その後の処理でNoneTypeエラーが発生する可能性がある
@staticmethod
def preprocess_titanic_data(data):
@@ -255,7 +274,7 @@ def test_model_performance():
# データバリデーション
success, results = DataValidator.validate_titanic_data(X)
- print(f"データ検証結果: {'成功' if success else '失敗'}")
+ print(f"データ検証結果: {'成功✅' if success else '失敗❌'}")
for result in results:
# "success": falseの場合はエラーメッセージを表示
if not result["success"]:
@@ -284,4 +303,4 @@ def test_model_performance():
# ベースラインとの比較
baseline_ok = ModelTester.compare_with_baseline(metrics)
- print(f"ベースライン比較: {'合格' if baseline_ok else '不合格'}")
+ print(f"ベースライン比較: {'合格✅' if baseline_ok else '不合格❌'}")
diff --git "a/day5/\346\274\224\347\277\2223/tests/test_model.py" "b/day5/\346\274\224\347\277\2223/tests/test_model.py"
index e11a19a5c..e545171d6 100644
--- "a/day5/\346\274\224\347\277\2223/tests/test_model.py"
+++ "b/day5/\346\274\224\347\277\2223/tests/test_model.py"
@@ -171,3 +171,27 @@ def test_model_reproducibility(sample_data, preprocessor):
assert np.array_equal(
predictions1, predictions2
), "モデルの予測結果に再現性がありません"
+
+
+def test_model_performance_against_baseline(train_model):
+ """現在のモデルの精度がベースライン以上であることを検証"""
+ model, X_test, y_test = train_model
+
+ # 予測と精度計算
+ y_pred = model.predict(X_test)
+ current_accuracy = accuracy_score(y_test, y_pred)
+
+ print(f"現在のモデルの精度({current_accuracy:.4f})")
+
+ # 環境変数からベースライン精度を取得
+ baseline_accuracy_str = os.environ.get("BASELINE_ACCURACY")
+ if baseline_accuracy_str is None:
+ pytest.fail("環境変数 BASELINE_ACCURACY が設定されていません")
+ try:
+ baseline_accuracy = float(baseline_accuracy_str)
+ except ValueError:
+ pytest.fail("環境変数 BASELINE_ACCURACY の値が float に変換できません")
+
+ assert (
+ current_accuracy >= baseline_accuracy
+ ), f"現在のモデルの精度({current_accuracy:.4f})がベースライン({baseline_accuracy:.4f})を下回っています"
diff --git "a/\347\254\2543\345\233\236\350\252\262\351\241\214.ipynb" "b/\347\254\2543\345\233\236\350\252\262\351\241\214.ipynb"
new file mode 100644
index 000000000..a3875d6aa
--- /dev/null
+++ "b/\347\254\2543\345\233\236\350\252\262\351\241\214.ipynb"
@@ -0,0 +1,10987 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "e4EN4GmtStsN"
+ },
+ "source": [
+ "# 演習の方針\n",
+ "\n",
+ "1. **ベースラインモデル評価** \n",
+ " 素のモデルで回答を生成し、講義内容との整合性の低さを観察する。これにより、特別な学習なしでのモデルの限界を確認する。\n",
+ "\n",
+ "2. **作成したテキストの活用** \n",
+ " リサーチを行い作成したテキストをモデルに学習させ、RAGを導入する。\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "PPI1pj4mFavt"
+ },
+ "source": [
+ "## 扱う質問\n",
+ "\n",
+ "「NintetndoSwich2」に関する質問を取り扱う\n",
+ "\n",
+ "## 扱うモデル\n",
+ "\n",
+ "「google/gemma-2-2b-jpn-it」を使用する。このモデルは、リリース時期の関係上、「Switch2」が発表される前に訓練されており、このトピックに関する知識を持たないと想定される\n",
+ "- この特性を活かし、純粋なベースライン評価から各手法の効果を観察する"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "bla6WHyQStsO"
+ },
+ "source": [
+ "### 演習環境の準備"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "id": "vM50WAI7GXwC",
+ "outputId": "233491c5-6fa7-4d7f-f96b-e9189d67a355",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "collapsed": true
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
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+ " Attempting uninstall: nvidia-nvjitlink-cu12\n",
+ " Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n",
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+ " Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n",
+ " Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n",
+ " Attempting uninstall: nvidia-cuda-cupti-cu12\n",
+ " Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n",
+ " Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n",
+ " Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n",
+ " Attempting uninstall: nvidia-cublas-cu12\n",
+ " Found existing installation: nvidia-cublas-cu12 12.5.3.2\n",
+ " Uninstalling nvidia-cublas-cu12-12.5.3.2:\n",
+ " Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n",
+ " Attempting uninstall: nvidia-cusparse-cu12\n",
+ " Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n",
+ " Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n",
+ " Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n",
+ " Attempting uninstall: nvidia-cudnn-cu12\n",
+ " Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n",
+ " Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n",
+ " Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n",
+ " Attempting uninstall: nvidia-cusolver-cu12\n",
+ " Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n",
+ " Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n",
+ " Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n",
+ "Successfully installed bitsandbytes-0.45.5 nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install --upgrade transformers\n",
+ "!pip install google-colab-selenium\n",
+ "!pip install bitsandbytes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "id": "V2PStE0uqM03",
+ "outputId": "4d13378a-89af-4e6e-d18a-4a85885c1664",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Cloning into 'lecture-ai-engineering'...\n",
+ "remote: Enumerating objects: 81, done.\u001b[K\n",
+ "remote: Total 81 (delta 0), reused 0 (delta 0), pack-reused 81 (from 2)\u001b[K\n",
+ "Receiving objects: 100% (81/81), 1.34 MiB | 2.45 MiB/s, done.\n",
+ "Resolving deltas: 100% (12/12), done.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 演習用のコンテンツを取得\n",
+ "!git clone https://github.com/matsuolab/lecture-ai-engineering.git"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "id": "zXo_kFASXlvp",
+ "outputId": "2ebf93f7-0d85-477f-9711-f9be816cd3d2",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 17,
+ "referenced_widgets": [
+ "4d4745f884854cbd909c4b044cb59947",
+ "e3c87e236f20452285575db1a7748d5e",
+ "4ea10de1d02f4e8d9052a297fa721081",
+ "509fd6d2ea734d0c80d12d2beae9fc3e",
+ "3c34a5efd89b4d99b339e92504701eea",
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+ "4eab33d690164aa7896cb014eaebcf33",
+ "bb723bf7f4684177a4fbfdf0bcc82f67",
+ "6b47e292a17a40e9a99914eed78775c4",
+ "91ee2f82ce4d44dcbb4f109f03aec488",
+ "dddea71838b240b7bce8f55c4c4d77c6",
+ "a2652636b85841b8af913200365ce9a9",
+ "b53048743c0747dd98930174888fd4f7",
+ "466afcee18d447718d9ded2f95fc6c90",
+ "ea5eef67110c4f7e93c34dd25cbdefe4",
+ "ee80a16b833b4afaa9da5937fc4ec2a7"
+ ]
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "VBox(children=(HTML(value='
\")\n",
+ " ]\n",
+ "\n",
+ " outputs = model.generate(\n",
+ " input_ids,\n",
+ " max_new_tokens=256,\n",
+ " eos_token_id=terminators,\n",
+ " do_sample=False,\n",
+ " # temperature=0.6, # If do_sample=True\n",
+ " # top_p=0.9, # If do_sample=True\n",
+ " )\n",
+ "\n",
+ " response = outputs[0][input_ids.shape[-1]:]\n",
+ " return tokenizer.decode(response, skip_special_tokens=True)"
+ ],
+ "metadata": {
+ "id": "huDNYbXW3lOm"
+ },
+ "execution_count": 8,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "id": "NBUZ3o6dhMlC",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "8d954419-91b1-407b-b062-73237193d0e8"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "質問: Nintendo Switch2の国内専用本体価格はいくらですか\n",
+ "回答: 残念ながら、Nintendo Switch 2の国内専用本体価格はまだ発表されていません。\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "質問: Nintendo Switchの本体価格はいくらですか\n",
+ "回答: Nintendo Switch本体の価格は、**約19,800円~21,800円**です。 \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "質問: Nintendo Switch2の発売日はいつですか\n",
+ "回答: 残念ながら、Nintendo Switch 2の発売日は現在発表されていません。 \n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "質問: Nintendo SwitchとNintendo Switch2の違いを教えてください\n",
+ "回答: ## Nintendo SwitchとNintendo Switch2の違い\n",
+ "\n",
+ "残念ながら、Nintendo Switch2の具体的な情報はまだ公開されていません。 \n",
+ "\n",
+ "しかし、いくつかの噂や推測に基づいて、Switch2がどのような進化をするのかを想像してみましょう。\n",
+ "\n",
+ "**Switch2の期待される特徴:**\n",
+ "\n",
+ "* **より高性能なハードウェア:** Switch2は、Switchの性能を大幅に向上させることが期待されます。CPUやGPUの性能向上、より高速な通信、より高画質のゲームプレイを実現する可能性があります。\n",
+ "* **VR対応:** VR技術の進化により、Switch2ではVR対応が期待されます。\n",
+ "* **より高画質のゲーム:** Switch2は、より高画質のゲームをプレイできる可能性があります。\n",
+ "* **より進化した操作性:** Switch2では、より革新的な操作性を実現する可能性があります。例えば、より自然な手動操作や、より複雑な操作に対応する技術が導入されるかもしれません。\n",
+ "* **新しいゲーム体験:** Switch2では、従来のゲーム体験に加え、新しいゲーム体験を提供する可能性があります。例えば、よりリアルな世界観や、より没入感のあるゲーム体験を提供する技術が導入されるかもしれません。\n",
+ "* **\n",
+ "\n",
+ "質問: Nintendo Switchの発売日はいつですか\n",
+ "回答: Nintendo Switchの発売日は **2017年3月3日** です。 😊\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "questions = [\n",
+ " \"Nintendo Switch2の国内専用本体価格はいくらですか\",\n",
+ " \"Nintendo Switchの本体価格はいくらですか\",\n",
+ " \"Nintendo Switch2の発売日はいつですか\",\n",
+ " \"Nintendo SwitchとNintendo Switch2の違いを教えてください\",\n",
+ " \"Nintendo Switchの発売日はいつですか\"\n",
+ "]\n",
+ "\n",
+ "for question in questions:\n",
+ " response = generate_output(question)\n",
+ " print(f\"質問: {question}\")\n",
+ " print(f\"回答: {response}\\n\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ZSCNnRf9pJif"
+ },
+ "source": [
+ "## 結果 (ベースモデル)\n",
+ "\n",
+ "「google/gemma-2-2b-jpn-it」は「Nintendo Switch2」についての情報を知らない旨の回答をし、ハルシネーションは起こさなかった。モデルの作成時期の関係で、Switch2の情報を持たないと考えられる。\n",
+ "\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "k4R-hiKNGyJd"
+ },
+ "source": [
+ "# 2. 作成したテキストの活用"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## 2.1 作成したテキストをソースとして活用 (RAG導入)\n",
+ "\n",
+ "モデルの回答の事実性を向上させるためにRetrieval Augmented Generation (RAG)技術を導入する:\n",
+ "\n",
+ "* **知識ソース**: 自身でリサーチして作成したテキスト\n",
+ "* **目的**: モデルに「NintendoSwitch2」に関する正確な知識と文脈を提供し、事実に基づいた回答を促す\n",
+ "\n",
+ "**初期RAG実装(ベーシックアプローチ)**:\n",
+ "* **ドキュメント処理**:作成したテキストを使用\n",
+ "* **分割方法**: 「。」(句点)で区切られた文単位でテキストを分割\n",
+ "* **検索手法**: シンプルな類似度ベースの検索でクエリに関連する文を抽出\n",
+ "* **制約条件**: モデルの入力トークン制限に収まるよう関連文のみを選択"
+ ],
+ "metadata": {
+ "id": "qcZCmKegHyrl"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "id": "47GvcceyObAl",
+ "outputId": "4fdd1ee3-daf2-40de-e2cd-56d19d20e0e7",
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+ "height": 621,
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+ "A new version of the following files was downloaded from https://huggingface.co/infly/inf-retriever-v1-1.5b:\n",
+ "- tokenization_qwen.py\n",
+ ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
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+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "added_tokens.json: 0%| | 0.00/80.0 [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "217d95266a2d4936b26a875aee931ef5"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "special_tokens_map.json: 0%| | 0.00/370 [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "a53b40d243ea401a898522a00496b79b"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
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+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "ac5104ecc3774f719e076f56aa02191c"
+ }
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "from sentence_transformers import SentenceTransformer\n",
+ "\n",
+ "emb_model = SentenceTransformer(\"infly/inf-retriever-v1-1.5b\", trust_remote_code=True)\n",
+ "# In case you want to reduce the maximum length:\n",
+ "emb_model.max_seq_length = 4096"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "id": "kPwggQfUS5yl"
+ },
+ "outputs": [],
+ "source": [
+ "with open(\"/content/lecture-ai-engineering/day3/data/about_switch2.txt\", \"r\") as f:\n",
+ " raw_writedown = f.read()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "id": "kxzKF6L2THIw",
+ "outputId": "a33bf4c5-cf51-4e23-d458-b1ae9fa75984",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ドキュメントサイズ: 80\n",
+ "ドキュメントの例: \n",
+ " 発売されるのは『ゼルダの伝説 ブレス オブ ザ ワイルド』、『ゼルダの伝説 ティアーズ オブ ザ キングダム』、『メトロイドプライム4 ビヨンド』、『星のカービィ ディスカバリー』、『ポケモンレジェンズZ-A』など\n"
+ ]
+ }
+ ],
+ "source": [
+ "# ドキュメントを用意する。\n",
+ "documents = [text.strip() for text in raw_writedown.split(\"。\")]\n",
+ "print(\"ドキュメントサイズ: \", len(documents))\n",
+ "print(\"ドキュメントの例: \\n\", documents[40])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "id": "nK4cYURzTHIx",
+ "outputId": "f6642f6f-f43f-4dc9-8359-82ae154e5a76",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "[[78.79585266113281, 77.43077087402344, 67.54423522949219, 71.13761901855469, 74.60729217529297, 68.56951141357422, 66.97605895996094, 67.78649139404297, 84.27302551269531, 69.49694061279297, 71.57652282714844, 67.57599639892578, 82.29217529296875, 76.49646759033203, 76.24617767333984, 76.72381591796875, 71.3587646484375, 75.36528778076172, 74.86466217041016, 66.39753723144531, 67.20764923095703, 66.77053833007812, 67.10160827636719, 66.38654327392578, 65.05815887451172, 67.47876739501953, 76.964599609375, 68.81197357177734, 76.7423324584961, 68.98033905029297, 76.5891342163086, 69.84087371826172, 73.93375396728516, 71.57652282714844, 67.57599639892578, 69.35090637207031, 66.83055877685547, 72.60383605957031, 67.0440444946289, 83.22439575195312, 78.46623992919922, 79.72152709960938, 76.00529479980469, 78.34591674804688, 78.34591674804688, 78.78157043457031, 69.39987182617188, 80.57124328613281, 74.74620056152344, 74.9588394165039, 80.2362060546875, 69.46419525146484, 70.63797760009766, 68.72293853759766, 73.43087005615234, 72.20044708251953, 79.8802719116211, 71.40036010742188, 71.41456604003906, 71.42335510253906, 72.32913208007812, 70.2642822265625, 67.92091369628906, 80.24922943115234, 70.58370971679688, 71.43704986572266, 66.62680053710938, 67.77366638183594, 82.29747772216797, 69.50328063964844, 71.24822998046875, 80.4920654296875, 77.33888244628906, 77.81475067138672, 69.83155059814453, 67.98486328125, 66.69408416748047, 69.1732177734375, 80.24356079101562, 58.23028564453125]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Retrievalの実行\n",
+ "question = \"LLMにおけるInference Time Scalingとは?\"\n",
+ "questions = [\n",
+ " \"Nintendo Switch2の国内専用本体価格はいくらですか\",\n",
+ " \"Nintendo Switchの本体価格はいくらですか\",\n",
+ " \"Nintendo Switch2の発売日はいつですか\",\n",
+ " \"Nintendo SwitchとNintendo Switch2の違いを教えてください\",\n",
+ " \"Nintendo Switchの発売日はいつですか\"\n",
+ "]\n",
+ "\n",
+ "for question in questions:\n",
+ " query_embeddings = emb_model.encode([question], prompt_name=\"query\")\n",
+ " document_embeddings = emb_model.encode(documents)\n",
+ "\n",
+ "# 各ドキュメントの類似度スコア\n",
+ "scores = (query_embeddings @ document_embeddings.T) * 100\n",
+ "print(scores.tolist())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "id": "b_v8gx_tTHIx",
+ "outputId": "d9b6afc2-6015-4997-d226-49c3ceeb9301",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "取得したドキュメント1: (Score: 84.27302551269531)\n",
+ "Switch2の発売日は2025年6月5日\n",
+ " Switch2に内蔵されているのは、リチウムイオンバッテリー(電池容量 5220mAh) \n",
+ "\n",
+ "\n",
+ "取得したドキュメント2: (Score: 83.22439575195312)\n",
+ "Nintendo Switchのソフトを高性能なNintendo Switch 2で遊べる、“Nintendo Switch 2エディション”が複数のタイトルで展開される \n",
+ "\n",
+ "\n",
+ "取得したドキュメント3: (Score: 82.29747772216797)\n",
+ "本体と同日にNintendo Switch 2カメラが発売 \n",
+ "\n",
+ "\n",
+ "取得したドキュメント4: (Score: 82.29217529296875)\n",
+ "switch2は、日本語・国内専用本体は49980円[税込] \n",
+ "\n",
+ "\n",
+ "取得したドキュメント5: (Score: 80.57124328613281)\n",
+ "Nintendo Switch 2では、初代Nintendo Switch向けタイトルもプレイ可能 \n",
+ "\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "topk = 5\n",
+ "for i, index in enumerate(scores.argsort()[0][::-1][:topk]):\n",
+ " print(f\"取得したドキュメント{i+1}: (Score: {scores[0][index]})\")\n",
+ " print(documents[index], \"\\n\\n\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "id": "Ow0wZy6ETHIx",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "bac7e78a-1e49-4b1e-b953-cc791776e47a"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "質問: Nintendo Switch2の国内専用本体価格はいくらですか\n",
+ "回答: Nintendo Switch2の国内専用本体価格は **49980円(税込)** です。 \n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "質問: Nintendo Switchの本体価格はいくらですか\n",
+ "回答: Nintendo Switchの本体価格は **49980円(税込)** です。 \n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "質問: Nintendo Switch2の発売日はいつですか\n",
+ "回答: Nintendo Switch2の発売日は2025年6月5日です。 \n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "質問: Nintendo SwitchとNintendo Switch2の違いを教えてください\n",
+ "回答: Nintendo Switch2とNintendo Switchの違いは以下の通りです。\n",
+ "\n",
+ "**1.性能向上:**\n",
+ " * **CPU/GPU:** より高速で高性能なCPU/GPUを搭載\n",
+ " * **リチウムイオンバッテリー:** 電池容量が5220mAhとなり、より長時間使用が可能\n",
+ " * **画面:** 画面の解像度や刷新率が向上\n",
+ "\n",
+ "**2.ソフト対応:**\n",
+ " * **Nintendo Switch 2エディション:** 複数のタイトルで、Nintendo Switch 2専用の機能や画質でプレイできる\n",
+ " * **初代Nintendo Switch向けタイトル:** Nintendo Switch2でもプレイ可能\n",
+ "\n",
+ "**3.発売日:**\n",
+ " * **Nintendo Switch2:** 2025年6月5日\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "質問: Nintendo Switchの発売日はいつですか\n",
+ "回答: Nintendo Switchの発売日は、**2017年3月3日**です。 \n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "questions = [\n",
+ " \"Nintendo Switch2の国内専用本体価格はいくらですか\",\n",
+ " \"Nintendo Switchの本体価格はいくらですか\",\n",
+ " \"Nintendo Switch2の発売日はいつですか\",\n",
+ " \"Nintendo SwitchとNintendo Switch2の違いを教えてください\",\n",
+ " \"Nintendo Switchの発売日はいつですか\"\n",
+ "]\n",
+ "\n",
+ "for question in questions:\n",
+ " query_embeddings = emb_model.encode([question], prompt_name=\"query\")\n",
+ " document_embeddings = emb_model.encode(documents)\n",
+ " references = \"\\n\".join([\"* \" + documents[i] for i in scores.argsort()[0][::-1][:topk]])\n",
+ " query = f\"[参考資料]\\n{references}\\n\\n[質問]\"+question\n",
+ " response = generate_output(query)\n",
+ " print(f\"質問: {question}\")\n",
+ " print(f\"回答: {response}\\n\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Bn7tih0RTTzr"
+ },
+ "source": [
+ "### 結果 (初期RAG実装)\n",
+ "\n",
+ "RAG導入後はモデル作成時以降のトピックについての説明を、参考資料をもとにして正確にすることができていた。しかし、RAG実装前に回答出来ていた質問「Nintendo Switchの本体価格はいくらですか」に対して、「Nintendo Switchの本体価格は **49980円(税込)** です。」という回答が見られた。これは、 Switch2の本体価格であり、モデルが元々持っていた知識を参照せず、参考資料にある知識を参照した、さらに、Switch2と Switchという似た単語の区別をモデルができなかったことで起きたと考えられる。解決法としては、モデルにSwitch2と Switchが別のものであることを念入りに学習させることなどが挙げられる。"
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "gpuType": "T4",
+ "provenance": [],
+ "include_colab_link": true
+ },
+ "kernelspec": {
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