# tf.feature_column.categorical_column_with_vocabulary_file

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/feature_column/feature_column_v2.py#L1390-L1525) |

A `CategoricalColumn` with a vocabulary file. (deprecated)

> [!WARNING]
> **Warning:** tf.feature_column is not recommended for new code. Instead, feature preprocessing can be done directly using either [Keras preprocessing
> layers](https://www.tensorflow.org/guide/migrate/migrating_feature_columns) or through the one-stop utility [`tf.keras.utils.FeatureSpace`](https://www.tensorflow.org/api_docs/python/tf/keras/utils/FeatureSpace) built on top of them. See the [migration guide](https://tensorflow.org/guide/migrate) for details.

    tf.feature_column.categorical_column_with_vocabulary_file(
        key,
        vocabulary_file,
        vocabulary_size=None,
        dtype=https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/dtypes#string,
        default_value=None,
        num_oov_buckets=0,
        file_format=None
    )

> [!WARNING]
> **Deprecated:** THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use Keras preprocessing layers instead, either directly or via the [`tf.keras.utils.FeatureSpace`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/keras/utils/FeatureSpace) utility. Each of `tf.feature_column.*` has a functional equivalent in `tf.keras.layers` for feature preprocessing when training a Keras model.

Use this when your inputs are in string or integer format, and you have a
vocabulary file that maps each value to an integer ID. By default,
out-of-vocabulary values are ignored. Use either (but not both) of
`num_oov_buckets` and `default_value` to specify how to include
out-of-vocabulary values.

For input dictionary `features`, `features[key]` is either `Tensor` or
`SparseTensor`. If `Tensor`, missing values can be represented by `-1` for int
and `''` for string, which will be dropped by this feature column.

Example with `num_oov_buckets`:
File `'/us/states.txt'` contains 50 lines, each with a 2-character U.S. state
abbreviation. All inputs with values in that file are assigned an ID 0-49,
corresponding to its line number. All other values are hashed and assigned an
ID 50-54.

    states = categorical_column_with_vocabulary_file(
        key='states', vocabulary_file='/us/states.txt', vocabulary_size=50,
        num_oov_buckets=5)
    columns = [states, ...]
    features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
    linear_prediction = linear_model(features, columns)

Example with `default_value`:
File `'/us/states.txt'` contains 51 lines - the first line is `'XX'`, and the
other 50 each have a 2-character U.S. state abbreviation. Both a literal
`'XX'` in input, and other values missing from the file, will be assigned
ID 0. All others are assigned the corresponding line number 1-50.

    states = categorical_column_with_vocabulary_file(
        key='states', vocabulary_file='/us/states.txt', vocabulary_size=51,
        default_value=0)
    columns = [states, ...]
    features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
    linear_prediction, _, _ = linear_model(features, columns)

And to make an embedding with either:

    columns = [embedding_column(states, 3),...]
    features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
    dense_tensor = input_layer(features, columns)

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| ## Args ||
|---|---|
| `key` | A unique string identifying the input feature. It is used as the column name and the dictionary key for feature parsing configs, feature `Tensor` objects, and feature columns. |
| `vocabulary_file` | The vocabulary file name. |
| `vocabulary_size` | Number of the elements in the vocabulary. This must be no greater than length of `vocabulary_file`, if less than length, later values are ignored. If None, it is set to the length of `vocabulary_file`. |
| `dtype` | The type of features. Only string and integer types are supported. |
| `default_value` | The integer ID value to return for out-of-vocabulary feature values, defaults to `-1`. This can not be specified with a positive `num_oov_buckets`. |
| `num_oov_buckets` | Non-negative integer, the number of out-of-vocabulary buckets. All out-of-vocabulary inputs will be assigned IDs in the range `[vocabulary_size, vocabulary_size+num_oov_buckets)` based on a hash of the input value. A positive `num_oov_buckets` can not be specified with `default_value`. |
| `file_format` | The format of the vocabulary file. The format is 'text' by default unless `vocabulary_file` is a string which ends in 'tfrecord.gz'. Accepted alternative value for `file_format` is 'tfrecord_gzip'. |

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| ## Returns ||
|---|---|
| A `CategoricalColumn` with a vocabulary file. ||

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| ## Raises ||
|---|---|
| `ValueError` | `vocabulary_file` is missing or cannot be opened. |
| `ValueError` | `vocabulary_size` is missing or \< 1. |
| `ValueError` | `num_oov_buckets` is a negative integer. |
| `ValueError` | `num_oov_buckets` and `default_value` are both specified. |
| `ValueError` | `dtype` is neither string nor integer. |

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