Merge branch 'datawhalechina:main' into main

ch03/01_main-chapter-code/exercise-solutions.ipynb

@@ -5,7 +5,7 @@
    "id": "51c9672d-8d0c-470d-ac2d-1271f8ec3f14",
    "metadata": {},
    "source": [
-    "# Chapter 3 Exercise solutions"
+    "# Chapter 3 习题解答"
    ]
   },
   {
@@ -13,12 +13,12 @@
    "id": "33dfa199-9aee-41d4-a64b-7e3811b9a616",
    "metadata": {},
    "source": [
-    "# Exercise 3.1"
+    "# 3.1"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "id": "5fee2cf5-61c3-4167-81b5-44ea155bbaf2",
    "metadata": {},
    "outputs": [],
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 2,
    "id": "62ea289c-41cd-4416-89dd-dde6383a6f70",
    "metadata": {},
    "outputs": [],
@@ -72,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 3,
    "id": "7b035143-f4e8-45fb-b398-dec1bd5153d4",
    "metadata": {},
    "outputs": [],
@@ -103,7 +103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 4,
    "id": "7591d79c-c30e-406d-adfd-20c12eb448f6",
    "metadata": {},
    "outputs": [],
@@ -115,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 5,
    "id": "ddd0f54f-6bce-46cc-a428-17c2a56557d0",
    "metadata": {},
    "outputs": [
@@ -130,7 +130,7 @@
        "        [-0.5299, -0.1081]], grad_fn=<MmBackward0>)"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -141,7 +141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 6,
    "id": "340908f8-1144-4ddd-a9e1-a1c5c3d592f5",
    "metadata": {},
    "outputs": [
@@ -156,7 +156,7 @@
        "        [-0.5299, -0.1081]], grad_fn=<MmBackward0>)"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -170,15 +170,15 @@
    "id": "33543edb-46b5-4b01-8704-f7f101230544",
    "metadata": {},
    "source": [
-    "# Exercise 3.2"
+    "# 3.2"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0588e209-1644-496a-8dae-7630b4ef9083",
+   "id": "1fc1a301",
    "metadata": {},
    "source": [
-    "If we want to have an output dimension of 2, as earlier in single-head attention, we can have to change the projection dimension `d_out` to 1:"
+    "如果我们想要多头注意力机制的输出和之前单头注意力机制一样为 2，我们可以将输出维度 `d_out` 设置为 1："
    ]
   },
   {
@@ -227,7 +227,7 @@
    "id": "92bdabcb-06cf-4576-b810-d883bbd313ba",
    "metadata": {},
    "source": [
-    "# Exercise 3.3"
+    "# 3.3"
    ]
   },
   {
@@ -249,7 +249,7 @@
    "id": "375d5290-8e8b-4149-958e-1efb58a69191",
    "metadata": {},
    "source": [
-    "Optionally, the number of parameters is as follows:"
+    "上述实现的参数量为:"
    ]
   },
   {
@@ -280,7 +280,9 @@
    "id": "a56c1d47-9b95-4bd1-a517-580a6f779c52",
    "metadata": {},
    "source": [
-    "The GPT-2 model has 117M parameters in total, but as we can see, most of its parameters are not in the multi-head attention module itself."
+    "\n",
+    "\n",
+    "GPT-2 模型有 117M 的参数，但正如我们所见，绝大部分参数其实都不是来源于多头注意力机制（而是线性层）。"
    ]
   }
  ],
@@ -300,7 +302,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.9.18"
   }
  },
  "nbformat": 4,