<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
 Copyright 2020 The TensorFlow Authors.
Step1:  <table class="tfo-notebook-buttons" align="left">
  <td>
    <a target="_blank" href="https
Step2:  Vectorize an example sentence
Consider the following sentence
Step3:  Create a vocabulary to save mappings from tokens to integer indices
Step4:  Create an inverse vocabulary to save mappings from integer indices to tokens
Step5:  Vectorize your sentence
Step6:  Generate skip-grams from one sentence
The tf.keras.preprocessing.sequence module provides useful functions that simplify data preparation for word2vec. You can use the tf.keras.preprocessing.sequence.skipgrams to generate skip-gram pairs from the example_sequence with a given window_size from tokens in the range [0, vocab_size).
Note
Step7:  Print a few positive skip-grams
Step8:  Negative sampling for one skip-gram
The skipgrams function returns all positive skip-gram pairs by sliding over a given window span. To produce additional skip-gram pairs that would serve as negative samples for training, you need to sample random words from the vocabulary. Use the tf.random.log_uniform_candidate_sampler function to sample num_ns number of negative samples for a given target word in a window. You can call the function on one skip-grams's target word and pass the context word as true class to exclude it from being sampled.
Key point
Step9:  Construct one training example
For a given positive (target_word, context_word) skip-gram, you now also have num_ns negative sampled context words that do not appear in the window size neighborhood of target_word. Batch the 1 positive context_word and num_ns negative context words into one tensor. This produces a set of positive skip-grams (labeled as 1) and negative samples (labeled as 0) for each target word.
Step10:  Check out the context and the corresponding labels for the target word from the skip-gram example above
Step11:  A tuple of (target, context, label) tensors constitutes one training example for training your skip-gram negative sampling word2vec model. Notice that the target is of shape (1,) while the context and label are of shape (1+num_ns,)
Step12:  Summary
This diagram summarizes the procedure of generating a training example from a sentence
Step13:  sampling_table[i] denotes the probability of sampling the i-th most common word in a dataset. The function assumes a Zipf's distribution of the word frequencies for sampling.
Key point
Step14:  Prepare training data for word2vec
With an understanding of how to work with one sentence for a skip-gram negative sampling based word2vec model, you can proceed to generate training examples from a larger list of sentences!
Download text corpus
You will use a text file of Shakespeare's writing for this tutorial. Change the following line to run this code on your own data.
Step15:  Read the text from the file and print the first few lines
Step16:  Use the non empty lines to construct a tf.data.TextLineDataset object for the next steps
Step17:  Vectorize sentences from the corpus
You can use the TextVectorization layer to vectorize sentences from the corpus. Learn more about using this layer in this Text classification tutorial. Notice from the first few sentences above that the text needs to be in one case and punctuation needs to be removed. To do this, define a custom_standardization function that can be used in the TextVectorization layer.
Step18:  Call TextVectorization.adapt on the text dataset to create vocabulary.
Step19:  Once the state of the layer has been adapted to represent the text corpus, the vocabulary can be accessed with TextVectorization.get_vocabulary. This function returns a list of all vocabulary tokens sorted (descending) by their frequency.
Step20:  The vectorize_layer can now be used to generate vectors for each element in the text_ds (a tf.data.Dataset). Apply Dataset.batch, Dataset.prefetch, Dataset.map, and Dataset.unbatch.
Step21:  Obtain sequences from the dataset
You now have a tf.data.Dataset of integer encoded sentences. To prepare the dataset for training a word2vec model, flatten the dataset into a list of sentence vector sequences. This step is required as you would iterate over each sentence in the dataset to produce positive and negative examples.
Note
Step22:  Inspect a few examples from sequences
Step23:  Generate training examples from sequences
sequences is now a list of int encoded sentences. Just call the generate_training_data function defined earlier to generate training examples for the word2vec model. To recap, the function iterates over each word from each sequence to collect positive and negative context words. Length of target, contexts and labels should be the same, representing the total number of training examples.
Step24:  Configure the dataset for performance
To perform efficient batching for the potentially large number of training examples, use the tf.data.Dataset API. After this step, you would have a tf.data.Dataset object of (target_word, context_word), (label) elements to train your word2vec model!
Step25:  Apply Dataset.cache and Dataset.prefetch to improve performance
Step26:  Model and training
The word2vec model can be implemented as a classifier to distinguish between true context words from skip-grams and false context words obtained through negative sampling. You can perform a dot product multiplication between the embeddings of target and context words to obtain predictions for labels and compute the loss function against true labels in the dataset.
Subclassed word2vec model
Use the Keras Subclassing API to define your word2vec model with the following layers
Step27:  Define loss function and compile model
For simplicity, you can use tf.keras.losses.CategoricalCrossEntropy as an alternative to the negative sampling loss. If you would like to write your own custom loss function, you can also do so as follows
Step28:  Also define a callback to log training statistics for TensorBoard
Step29:  Train the model on the dataset for some number of epochs
Step30:  TensorBoard now shows the word2vec model's accuracy and loss
Step31:  <!-- <img class="tfo-display-only-on-site" src="images/word2vec_tensorboard.png"/> -->
Embedding lookup and analysis
Obtain the weights from the model using Model.get_layer and Layer.get_weights. The TextVectorization.get_vocabulary function provides the vocabulary to build a metadata file with one token per line.
Step32:  Create and save the vectors and metadata files
Step33:  Download the vectors.tsv and metadata.tsv to analyze the obtained embeddings in the Embedding Projector
<ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
Explanation: Copyright 2020 The TensorFlow Authors.
End of explanation
import io
import re
import string
import tqdm
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers
# Load the TensorBoard notebook extension
%load_ext tensorboard
SEED = 42
AUTOTUNE = tf.data.AUTOTUNE
Explanation: <table class="tfo-notebook-buttons" align="left">
  <td>
    <a target="_blank" href="https://www.tensorflow.org/tutorials/text/word2vec">
    <img src="https://www.tensorflow.org/images/tf_logo_32px.png" />
    View on TensorFlow.org</a>
  </td>
  <td>
    <a target="_blank" href="https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/text/word2vec.ipynb">
    <img src="https://www.tensorflow.org/images/colab_logo_32px.png" />
    Run in Google Colab</a>
  </td>
  <td>
    <a target="_blank" href="https://github.com/tensorflow/docs/blob/master/site/en/tutorials/text/word2vec.ipynb">
    <img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" />
    View source on GitHub</a>
  </td>
  <td>
    <a href="https://storage.googleapis.com/tensorflow_docs/docs/site/en/tutorials/text/word2vec.ipynb"><img src="https://www.tensorflow.org/images/download_logo_32px.png" />Download notebook</a>
  </td>
</table>
word2vec
word2vec is not a singular algorithm, rather, it is a family of model architectures and optimizations that can be used to learn word embeddings from large datasets. Embeddings learned through word2vec have proven to be successful on a variety of downstream natural language processing tasks.
Note: This tutorial is based on Efficient estimation of word representations in vector space and Distributed representations of words and phrases and their compositionality. It is not an exact implementation of the papers. Rather, it is intended to illustrate the key ideas.
These papers proposed two methods for learning representations of words:
Continuous bag-of-words model: predicts the middle word based on surrounding context words. The context consists of a few words before and after the current (middle) word. This architecture is called a bag-of-words model as the order of words in the context is not important.
Continuous skip-gram model: predicts words within a certain range before and after the current word in the same sentence. A worked example of this is given below.
You'll use the skip-gram approach in this tutorial. First, you'll explore skip-grams and other concepts using a single sentence for illustration. Next, you'll train your own word2vec model on a small dataset. This tutorial also contains code to export the trained embeddings and visualize them in the TensorFlow Embedding Projector.
Skip-gram and negative sampling
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. 
Consider the following sentence of eight words:
The wide road shimmered in the hot sun.
The context words for each of the 8 words of this sentence are defined by a window size. The window size determines the span of words on either side of a target_word that can be considered a context word. Below is a table of skip-grams for target words based on different window sizes.
Note: For this tutorial, a window size of n implies n words on each side with a total window span of 2*n+1 words across a word.
The training objective of the skip-gram model is to maximize the probability of predicting context words given the target word. For a sequence of words w<sub>1</sub>, w<sub>2</sub>, ... w<sub>T</sub>, the objective can be written as the average log probability
where c is the size of the training context. The basic skip-gram formulation defines this probability using the softmax function.
where v and v<sup>'<sup> are target and context vector representations of words and W is vocabulary size. 
Computing the denominator of this formulation involves performing a full softmax over the entire vocabulary words, which are often large (10<sup>5</sup>-10<sup>7</sup>) terms.
The noise contrastive estimation (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 to use negative sampling. 
The simplified negative sampling objective for a target word is to distinguish  the context word from num_ns negative samples drawn from noise distribution P<sub>n</sub>(w) of words. More precisely, an efficient approximation of full softmax over the vocabulary is, for a skip-gram pair, to pose the loss for a target word as a classification problem between the context word and num_ns negative samples. 
A negative sample is defined as a (target_word, context_word) pair such that the context_word does not appear in the window_size neighborhood of the target_word. For the example sentence, these are a few potential negative samples (when window_size is 2).
(hot, shimmered)
(wide, hot)
(wide, sun)
In the next section, you'll generate skip-grams and negative samples for a single sentence. You'll also learn about subsampling techniques and train a classification model for positive and negative training examples later in the tutorial.
Setup
End of explanation
sentence = "The wide road shimmered in the hot sun"
tokens = list(sentence.lower().split())
print(len(tokens))
Explanation: Vectorize an example sentence
Consider the following sentence:
The wide road shimmered in the hot sun.
Tokenize the sentence:
End of explanation
vocab, index = {}, 1  # start indexing from 1
vocab['<pad>'] = 0  # add a padding token
for token in tokens:
  if token not in vocab:
    vocab[token] = index
    index += 1
vocab_size = len(vocab)
print(vocab)
Explanation: Create a vocabulary to save mappings from tokens to integer indices:
End of explanation
inverse_vocab = {index: token for token, index in vocab.items()}
print(inverse_vocab)
Explanation: Create an inverse vocabulary to save mappings from integer indices to tokens:
End of explanation
example_sequence = [vocab[word] for word in tokens]
print(example_sequence)
Explanation: Vectorize your sentence:
End of explanation
window_size = 2
positive_skip_grams, _ = tf.keras.preprocessing.sequence.skipgrams(
      example_sequence,
      vocabulary_size=vocab_size,
      window_size=window_size,
      negative_samples=0)
print(len(positive_skip_grams))
Explanation: Generate skip-grams from one sentence
The tf.keras.preprocessing.sequence module provides useful functions that simplify data preparation for word2vec. You can use the tf.keras.preprocessing.sequence.skipgrams to generate skip-gram pairs from the example_sequence with a given window_size from tokens in the range [0, vocab_size).
Note: negative_samples is set to 0 here, as batching negative samples generated by this function requires a bit of code. You will use another function to perform negative sampling in the next section.
End of explanation
for target, context in positive_skip_grams[:5]:
  print(f"({target}, {context}): ({inverse_vocab[target]}, {inverse_vocab[context]})")
Explanation: Print a few positive skip-grams:
End of explanation
# Get target and context words for one positive skip-gram.
target_word, context_word = positive_skip_grams[0]
# Set the number of negative samples per positive context.
num_ns = 4
context_class = tf.reshape(tf.constant(context_word, dtype="int64"), (1, 1))
negative_sampling_candidates, _, _ = tf.random.log_uniform_candidate_sampler(
    true_classes=context_class,  # class that should be sampled as 'positive'
    num_true=1,  # each positive skip-gram has 1 positive context class
    num_sampled=num_ns,  # number of negative context words to sample
    unique=True,  # all the negative samples should be unique
    range_max=vocab_size,  # pick index of the samples from [0, vocab_size]
    seed=SEED,  # seed for reproducibility
    name="negative_sampling"  # name of this operation
)
print(negative_sampling_candidates)
print([inverse_vocab[index.numpy()] for index in negative_sampling_candidates])
Explanation: Negative sampling for one skip-gram
The skipgrams function returns all positive skip-gram pairs by sliding over a given window span. To produce additional skip-gram pairs that would serve as negative samples for training, you need to sample random words from the vocabulary. Use the tf.random.log_uniform_candidate_sampler function to sample num_ns number of negative samples for a given target word in a window. You can call the function on one skip-grams's target word and pass the context word as true class to exclude it from being sampled.
Key point: num_ns (the number of negative samples per a positive context word) in the [5, 20] range is shown to work best for smaller datasets, while num_ns in the [2, 5] range suffices for larger datasets.
End of explanation
# Add a dimension so you can use concatenation (in the next step).
negative_sampling_candidates = tf.expand_dims(negative_sampling_candidates, 1)
# Concatenate a positive context word with negative sampled words.
context = tf.concat([context_class, negative_sampling_candidates], 0)
# Label the first context word as `1` (positive) followed by `num_ns` `0`s (negative).
label = tf.constant([1] + [0]*num_ns, dtype="int64")
# Reshape the target to shape `(1,)` and context and label to `(num_ns+1,)`.
target = tf.squeeze(target_word)
context = tf.squeeze(context)
label = tf.squeeze(label)
Explanation: Construct one training example
For a given positive (target_word, context_word) skip-gram, you now also have num_ns negative sampled context words that do not appear in the window size neighborhood of target_word. Batch the 1 positive context_word and num_ns negative context words into one tensor. This produces a set of positive skip-grams (labeled as 1) and negative samples (labeled as 0) for each target word.
End of explanation
print(f"target_index    : {target}")
print(f"target_word     : {inverse_vocab[target_word]}")
print(f"context_indices : {context}")
print(f"context_words   : {[inverse_vocab[c.numpy()] for c in context]}")
print(f"label           : {label}")
Explanation: Check out the context and the corresponding labels for the target word from the skip-gram example above:
End of explanation
print("target  :", target)
print("context :", context)
print("label   :", label)
Explanation: A tuple of (target, context, label) tensors constitutes one training example for training your skip-gram negative sampling word2vec model. Notice that the target is of shape (1,) while the context and label are of shape (1+num_ns,)
End of explanation
sampling_table = tf.keras.preprocessing.sequence.make_sampling_table(size=10)
print(sampling_table)
Explanation: Summary
This diagram summarizes the procedure of generating a training example from a sentence:
Notice that the words temperature and code are not part of the input sentence. They belong to the vocabulary like certain other indices used in the diagram above.
Compile all steps into one function
Skip-gram sampling table
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. suggest subsampling of frequent words as a helpful practice to improve embedding quality. 
The tf.keras.preprocessing.sequence.skipgrams function accepts a sampling table argument to encode probabilities of sampling any token. You can use the tf.keras.preprocessing.sequence.make_sampling_table to  generate a word-frequency rank based probabilistic sampling table and pass it to the skipgrams function. Inspect the sampling probabilities for a vocab_size of 10.
End of explanation
# Generates skip-gram pairs with negative sampling for a list of sequences
# (int-encoded sentences) based on window size, number of negative samples
# and vocabulary size.
def generate_training_data(sequences, window_size, num_ns, vocab_size, seed):
  # Elements of each training example are appended to these lists.
  targets, contexts, labels = [], [], []
  # Build the sampling table for `vocab_size` tokens.
  sampling_table = tf.keras.preprocessing.sequence.make_sampling_table(vocab_size)
  # Iterate over all sequences (sentences) in the dataset.
  for sequence in tqdm.tqdm(sequences):
    # Generate positive skip-gram pairs for a sequence (sentence).
    positive_skip_grams, _ = tf.keras.preprocessing.sequence.skipgrams(
          sequence,
          vocabulary_size=vocab_size,
          sampling_table=sampling_table,
          window_size=window_size,
          negative_samples=0)
    # Iterate over each positive skip-gram pair to produce training examples
    # with a positive context word and negative samples.
    for target_word, context_word in positive_skip_grams:
      context_class = tf.expand_dims(
          tf.constant([context_word], dtype="int64"), 1)
      negative_sampling_candidates, _, _ = tf.random.log_uniform_candidate_sampler(
          true_classes=context_class,
          num_true=1,
          num_sampled=num_ns,
          unique=True,
          range_max=vocab_size,
          seed=SEED,
          name="negative_sampling")
      # Build context and label vectors (for one target word)
      negative_sampling_candidates = tf.expand_dims(
          negative_sampling_candidates, 1)
      context = tf.concat([context_class, negative_sampling_candidates], 0)
      label = tf.constant([1] + [0]*num_ns, dtype="int64")
      # Append each element from the training example to global lists.
      targets.append(target_word)
      contexts.append(context)
      labels.append(label)
  return targets, contexts, labels
Explanation: sampling_table[i] denotes the probability of sampling the i-th most common word in a dataset. The function assumes a Zipf's distribution of the word frequencies for sampling.
Key point: The tf.random.log_uniform_candidate_sampler already assumes that the vocabulary frequency follows a log-uniform (Zipf's) distribution. Using these distribution weighted sampling also helps approximate the Noise Contrastive Estimation (NCE) loss with simpler loss functions for training a negative sampling objective.
Generate training data
Compile all the steps described above into a function that can be called on a list of vectorized sentences obtained from any text dataset. Notice that the sampling table is built before sampling skip-gram word pairs. You will use this function in the later sections.
End of explanation
path_to_file = tf.keras.utils.get_file('shakespeare.txt', 'https://storage.googleapis.com/download.tensorflow.org/data/shakespeare.txt')
Explanation: Prepare training data for word2vec
With an understanding of how to work with one sentence for a skip-gram negative sampling based word2vec model, you can proceed to generate training examples from a larger list of sentences!
Download text corpus
You will use a text file of Shakespeare's writing for this tutorial. Change the following line to run this code on your own data.
End of explanation
with open(path_to_file) as f:
  lines = f.read().splitlines()
for line in lines[:20]:
  print(line)
Explanation: Read the text from the file and print the first few lines:
End of explanation
text_ds = tf.data.TextLineDataset(path_to_file).filter(lambda x: tf.cast(tf.strings.length(x), bool))
Explanation: Use the non empty lines to construct a tf.data.TextLineDataset object for the next steps:
End of explanation
# Now, create a custom standardization function to lowercase the text and
# remove punctuation.
def custom_standardization(input_data):
  lowercase = tf.strings.lower(input_data)
  return tf.strings.regex_replace(lowercase,
                                  '[%s]' % re.escape(string.punctuation), '')
# Define the vocabulary size and the number of words in a sequence.
vocab_size = 4096
sequence_length = 10
# Use the `TextVectorization` layer to normalize, split, and map strings to
# integers. Set the `output_sequence_length` length to pad all samples to the
# same length.
vectorize_layer = layers.TextVectorization(
    standardize=custom_standardization,
    max_tokens=vocab_size,
    output_mode='int',
    output_sequence_length=sequence_length)
Explanation: Vectorize sentences from the corpus
You can use the TextVectorization layer to vectorize sentences from the corpus. Learn more about using this layer in this Text classification tutorial. Notice from the first few sentences above that the text needs to be in one case and punctuation needs to be removed. To do this, define a custom_standardization function that can be used in the TextVectorization layer.
End of explanation
vectorize_layer.adapt(text_ds.batch(1024))
Explanation: Call TextVectorization.adapt on the text dataset to create vocabulary.
End of explanation
# Save the created vocabulary for reference.
inverse_vocab = vectorize_layer.get_vocabulary()
print(inverse_vocab[:20])
Explanation: Once the state of the layer has been adapted to represent the text corpus, the vocabulary can be accessed with TextVectorization.get_vocabulary. This function returns a list of all vocabulary tokens sorted (descending) by their frequency.
End of explanation
# Vectorize the data in text_ds.
text_vector_ds = text_ds.batch(1024).prefetch(AUTOTUNE).map(vectorize_layer).unbatch()
Explanation: The vectorize_layer can now be used to generate vectors for each element in the text_ds (a tf.data.Dataset). Apply Dataset.batch, Dataset.prefetch, Dataset.map, and Dataset.unbatch.
End of explanation
sequences = list(text_vector_ds.as_numpy_iterator())
print(len(sequences))
Explanation: Obtain sequences from the dataset
You now have a tf.data.Dataset of integer encoded sentences. To prepare the dataset for training a word2vec model, flatten the dataset into a list of sentence vector sequences. This step is required as you would iterate over each sentence in the dataset to produce positive and negative examples.
Note: Since the generate_training_data() defined earlier uses non-TensorFlow Python/NumPy functions, you could also use a tf.py_function or tf.numpy_function with tf.data.Dataset.map.
End of explanation
for seq in sequences[:5]:
  print(f"{seq} => {[inverse_vocab[i] for i in seq]}")
Explanation: Inspect a few examples from sequences:
End of explanation
targets, contexts, labels = generate_training_data(
    sequences=sequences,
    window_size=2,
    num_ns=4,
    vocab_size=vocab_size,
    seed=SEED)
targets = np.array(targets)
contexts = np.array(contexts)[:,:,0]
labels = np.array(labels)
print('\n')
print(f"targets.shape: {targets.shape}")
print(f"contexts.shape: {contexts.shape}")
print(f"labels.shape: {labels.shape}")
Explanation: Generate training examples from sequences
sequences is now a list of int encoded sentences. Just call the generate_training_data function defined earlier to generate training examples for the word2vec model. To recap, the function iterates over each word from each sequence to collect positive and negative context words. Length of target, contexts and labels should be the same, representing the total number of training examples.
End of explanation
BATCH_SIZE = 1024
BUFFER_SIZE = 10000
dataset = tf.data.Dataset.from_tensor_slices(((targets, contexts), labels))
dataset = dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE, drop_remainder=True)
print(dataset)
Explanation: Configure the dataset for performance
To perform efficient batching for the potentially large number of training examples, use the tf.data.Dataset API. After this step, you would have a tf.data.Dataset object of (target_word, context_word), (label) elements to train your word2vec model!
End of explanation
dataset = dataset.cache().prefetch(buffer_size=AUTOTUNE)
print(dataset)
Explanation: Apply Dataset.cache and Dataset.prefetch to improve performance:
End of explanation
class Word2Vec(tf.keras.Model):
  def __init__(self, vocab_size, embedding_dim):
    super(Word2Vec, self).__init__()
    self.target_embedding = layers.Embedding(vocab_size,
                                      embedding_dim,
                                      input_length=1,
                                      name="w2v_embedding")
    self.context_embedding = layers.Embedding(vocab_size,
                                       embedding_dim,
                                       input_length=num_ns+1)
  def call(self, pair):
    target, context = pair
    # target: (batch, dummy?)  # The dummy axis doesn't exist in TF2.7+
    # context: (batch, context)
    if len(target.shape) == 2:
      target = tf.squeeze(target, axis=1)
    # target: (batch,)
    word_emb = self.target_embedding(target)
    # word_emb: (batch, embed)
    context_emb = self.context_embedding(context)
    # context_emb: (batch, context, embed)
    dots = tf.einsum('be,bce->bc', word_emb, context_emb)
    # dots: (batch, context)
    return dots
Explanation: Model and training
The word2vec model can be implemented as a classifier to distinguish between true context words from skip-grams and false context words obtained through negative sampling. You can perform a dot product multiplication between the embeddings of target and context words to obtain predictions for labels and compute the loss function against true labels in the dataset.
Subclassed word2vec model
Use the Keras Subclassing API to define your word2vec model with the following layers:
target_embedding: A tf.keras.layers.Embedding layer, which looks up the embedding of a word when it appears as a target word. The number of parameters in this layer are (vocab_size * embedding_dim).
context_embedding: Another tf.keras.layers.Embedding layer, which looks up the embedding of a word when it appears as a context word. The number of parameters in this layer are the same as those in target_embedding, i.e. (vocab_size * embedding_dim).
dots: A tf.keras.layers.Dot layer that computes the dot product of target and context embeddings from a training pair.
flatten: A tf.keras.layers.Flatten layer to flatten the results of dots layer into logits.
With the subclassed model, you can define the call() function that accepts (target, context) pairs which can then be passed into their corresponding embedding layer. Reshape the context_embedding to perform a dot product with target_embedding and return the flattened result.
Key point: The target_embedding and context_embedding layers can be shared as well. You could also use a concatenation of both embeddings as the final word2vec embedding.
End of explanation
embedding_dim = 128
word2vec = Word2Vec(vocab_size, embedding_dim)
word2vec.compile(optimizer='adam',
                 loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),
                 metrics=['accuracy'])
Explanation: Define loss function and compile model
For simplicity, you can use tf.keras.losses.CategoricalCrossEntropy as an alternative to the negative sampling loss. If you would like to write your own custom loss function, you can also do so as follows:
python
def custom_loss(x_logit, y_true):
      return tf.nn.sigmoid_cross_entropy_with_logits(logits=x_logit, labels=y_true)
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.
End of explanation
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir="logs")
Explanation: Also define a callback to log training statistics for TensorBoard:
End of explanation
word2vec.fit(dataset, epochs=20, callbacks=[tensorboard_callback])
Explanation: Train the model on the dataset for some number of epochs:
End of explanation
#docs_infra: no_execute
%tensorboard --logdir logs
Explanation: TensorBoard now shows the word2vec model's accuracy and loss:
End of explanation
weights = word2vec.get_layer('w2v_embedding').get_weights()[0]
vocab = vectorize_layer.get_vocabulary()
Explanation: <!-- <img class="tfo-display-only-on-site" src="images/word2vec_tensorboard.png"/> -->
Embedding lookup and analysis
Obtain the weights from the model using Model.get_layer and Layer.get_weights. The TextVectorization.get_vocabulary function provides the vocabulary to build a metadata file with one token per line.
End of explanation
out_v = io.open('vectors.tsv', 'w', encoding='utf-8')
out_m = io.open('metadata.tsv', 'w', encoding='utf-8')
for index, word in enumerate(vocab):
  if index == 0:
    continue  # skip 0, it's padding.
  vec = weights[index]
  out_v.write('\t'.join([str(x) for x in vec]) + "\n")
  out_m.write(word + "\n")
out_v.close()
out_m.close()
Explanation: Create and save the vectors and metadata files:
End of explanation
try:
  from google.colab import files
  files.download('vectors.tsv')
  files.download('metadata.tsv')
except Exception:
  pass
Explanation: Download the vectors.tsv and metadata.tsv to analyze the obtained embeddings in the Embedding Projector:
End of explanation
<END_TASK>