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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/google-ai-edge/mediapipe/blob/master/mediapipe/tasks/python/text/text_embedder.py#L144-L174) |

Options for the text embedder task.

    mp.tasks.text.TextEmbedderOptions(
        base_options: https://developers.google.com/edge/api/mediapipe/python/mp/tasks/BaseOptions,
        l2_normalize: Optional[bool] = None,
        quantize: Optional[bool] = None
    )

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| ## Attributes ||
|---|---|
| `base_options` | Base options for the text embedder task. |
| `l2_normalize` | Whether to normalize the returned feature vector with L2 norm. Use this option only if the model does not already contain a native L2_NORMALIZATION TF Lite Op. In most cases, this is already the case and L2 norm is thus achieved through TF Lite inference. |
| `quantize` | Whether the returned embedding should be quantized to bytes via scalar quantization. Embeddings are implicitly assumed to be unit-norm and therefore any dimension is guaranteed to have a value in \[-1.0, 1.0\]. Use the l2_normalize option if this is not the case. |

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## Methods

### `to_ctypes`

[View source](https://github.com/google-ai-edge/mediapipe/blob/master/mediapipe/tasks/python/text/text_embedder.py#L163-L174)

    to_ctypes() -> _MpTextEmbedderOptionsC

Generates a ctypes MpTextEmbedderOptionsC object.

### `__eq__`

    __eq__(
        other
    )

Return self==value.

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| ## Class Variables ||
|---|---|
| l2_normalize | `None` |
| quantize | `None` |

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