# tf.keras.datasets.imdb.get_word_index

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/keras-team/keras/tree/v2.13.1/keras/datasets/imdb.py#L172-L217) |

Retrieves a dict mapping words to their index in the IMDB dataset.

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

\`tf.compat.v1.keras.datasets.imdb.get_word_index\`

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    tf.keras.datasets.imdb.get_word_index(
        path='imdb_word_index.json'
    )

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| ## Args ||
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| `path` | where to cache the data (relative to `~/.keras/dataset`). |

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| ## Returns ||
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| The word index dictionary. Keys are word strings, values are their index. ||

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#### Example:

    # Use the default parameters to keras.datasets.imdb.load_data
    start_char = 1
    oov_char = 2
    index_from = 3
    # Retrieve the training sequences.
    (x_train, _), _ = keras.datasets.imdb.load_data(
        start_char=start_char, oov_char=oov_char, index_from=index_from
    )
    # Retrieve the word index file mapping words to indices
    word_index = keras.datasets.imdb.get_word_index()
    # Reverse the word index to obtain a dict mapping indices to words
    # And add `index_from` to indices to sync with `x_train`
    inverted_word_index = dict(
        (i + index_from, word) for (word, i) in word_index.items()
    )
    # Update `inverted_word_index` to include `start_char` and `oov_char`
    inverted_word_index[start_char] = "[START]"
    inverted_word_index[oov_char] = "[OOV]"
    # Decode the first sequence in the dataset
    decoded_sequence = " ".join(inverted_word_index[i] for i in x_train[0])