diff --git a/labs/l2-basic/nlp-l2-basic.ipynb b/labs/l2-basic/nlp-l2-basic.ipynb
index 2604cc2f93ee61ce670556afadb46154778f8535..bcc64500b52dafcbe92b528eb6af20089c4408c9 100644
--- a/labs/l2-basic/nlp-l2-basic.ipynb
+++ b/labs/l2-basic/nlp-l2-basic.ipynb
@@ -110,7 +110,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Problem 1: Build the vocabulary and frequency table"
+    "## Problem 1: Build the vocabulary and frequency table (2 points)"
    ]
   },
   {
@@ -175,7 +175,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Problem 2: Preprocess the data"
+    "## Problem 2: Preprocess the data (2 points)"
    ]
   },
   {
@@ -251,7 +251,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Problem 3: Generate the training examples"
+    "## Problem 3: Generate the training examples (8 points)"
    ]
   },
   {
@@ -372,7 +372,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Problem 4: Implement the model"
+    "## Problem 4: Implement the model (2 points)"
    ]
   },
   {
@@ -456,7 +456,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Problem 5: Train the model"
+    "## Problem 5: Train the model (2 points)"
    ]
   },
   {
@@ -544,7 +544,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Problem 6: Analyse the embeddings (reflection)"
+    "## Problem 6: Analyse the embeddings (6 points)"
    ]
   },
   {
@@ -618,8 +618,7 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "pygments_lexer": "ipython3"
   }
  },
  "nbformat": 4,