Fix notebook failure with Keras 3.

PiperOrigin-RevId: 613736720

docs/tutorials/word2vec.ipynb

@@ -86,7 +86,7 @@
         "id": "xP00WlaMWBZC"
       },
       "source": [
-        "## Skip-gram and negative sampling "
+        "## Skip-gram and negative sampling"
       ]
     },
     {
@@ -95,7 +95,7 @@
         "id": "Zr2wjv0bW236"
       },
       "source": [
-        "While a bag-of-words model predicts a word given the neighboring context, a skip-gram model predicts the context (or neighbors) of a word, given the word itself. The model is trained on skip-grams, which are n-grams that allow tokens to be skipped (see the diagram below for an example). The context of a word can be represented through a set of skip-gram pairs of `(target_word, context_word)` where `context_word` appears in the neighboring context of `target_word`. "
+        "While a bag-of-words model predicts a word given the neighboring context, a skip-gram model predicts the context (or neighbors) of a word, given the word itself. The model is trained on skip-grams, which are n-grams that allow tokens to be skipped (see the diagram below for an example). The context of a word can be represented through a set of skip-gram pairs of `(target_word, context_word)` where `context_word` appears in the neighboring context of `target_word`."
       ]
     },
     {
@@ -189,7 +189,7 @@
         "id": "Y5VWYtmFzHkU"
       },
       "source": [
-        "The [noise contrastive estimation](https://www.tensorflow.org/api_docs/python/tf/nn/nce_loss) (NCE) loss function is an efficient approximation for a full softmax. With an objective to learn word embeddings instead of modeling the word distribution, the NCE loss can be [simplified](https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf) to use negative sampling. "
+        "The [noise contrastive estimation](https://www.tensorflow.org/api_docs/python/tf/nn/nce_loss) (NCE) loss function is an efficient approximation for a full softmax. With an objective to learn word embeddings instead of modeling the word distribution, the NCE loss can be [simplified](https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf) to use negative sampling."
       ]
     },
     {
@@ -447,7 +447,7 @@
         "id": "_ua9PkMTISF0"
       },
       "source": [
-        "### Negative sampling for one skip-gram "
+        "### Negative sampling for one skip-gram"
       ]
     },
     {
@@ -630,7 +630,7 @@
         "id": "iLKwNAczHsKg"
       },
       "source": [
-        "### Skip-gram sampling table "
+        "### Skip-gram sampling table"
       ]
     },
     {
@@ -639,7 +639,7 @@
         "id": "TUUK3uDtFNFE"
       },
       "source": [
-        "A large dataset means larger vocabulary with higher number of more frequent words such as stopwords. Training examples obtained from sampling commonly occurring words (such as `the`, `is`, `on`) don't add much useful information  for the model to learn from. [Mikolov et al.](https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf) suggest subsampling of frequent words as a helpful practice to improve embedding quality. "
+        "A large dataset means larger vocabulary with higher number of more frequent words such as stopwords. Training examples obtained from sampling commonly occurring words (such as `the`, `is`, `on`) don't add much useful information  for the model to learn from. [Mikolov et al.](https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf) suggest subsampling of frequent words as a helpful practice to improve embedding quality."
       ]
     },
     {
@@ -815,7 +815,7 @@
         "id": "sOsbLq8a37dr"
       },
       "source": [
-        "Read the text from the file and print the first few lines: "
+        "Read the text from the file and print the first few lines:"
       ]
     },
     {
@@ -1178,11 +1178,9 @@
         "    super(Word2Vec, self).__init__()\n",
         "    self.target_embedding = layers.Embedding(vocab_size,\n",
         "                                      embedding_dim,\n",
-        "                                      input_length=1,\n",
         "                                      name=\"w2v_embedding\")\n",
         "    self.context_embedding = layers.Embedding(vocab_size,\n",
-        "                                       embedding_dim,\n",
-        "                                       input_length=num_ns+1)\n",
+        "                                       embedding_dim)\n",
         "\n",
         "  def call(self, pair):\n",
         "    target, context = pair\n",
@@ -1222,7 +1220,7 @@
         "      return tf.nn.sigmoid_cross_entropy_with_logits(logits=x_logit, labels=y_true)\n",
         "```\n",
         "\n",
-        "It's time to build your model! Instantiate your word2vec class with an embedding dimension of 128 (you could experiment with different values). Compile the model with the `tf.keras.optimizers.Adam` optimizer. "
+        "It's time to build your model! Instantiate your word2vec class with an embedding dimension of 128 (you could experiment with different values). Compile the model with the `tf.keras.optimizers.Adam` optimizer."
       ]
     },
     {
@@ -1424,8 +1422,8 @@
   ],
   "metadata": {
     "colab": {
-      "collapsed_sections": [],
       "name": "word2vec.ipynb",
+      "provenance": [],
       "toc_visible": true
     },
     "kernelspec": {