[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-05-07 UTC."],[],[],null,["# mediapipe_model_maker.text_classifier.AverageWordEmbeddingModelOptions\n\n\u003cbr /\u003e\n\n|-----------------------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/google/mediapipe/blob/master/mediapipe/model_maker/python/text/text_classifier/model_options.py#L25-L41) |\n\nConfigurable model options for an Average Word Embedding classifier. \n\n mediapipe_model_maker.text_classifier.AverageWordEmbeddingModelOptions(\n seq_len: int = 256,\n wordvec_dim: int = 16,\n do_lower_case: bool = True,\n vocab_size: int = 10000,\n dropout_rate: float = 0.2\n )\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Attributes ---------- ||\n|-----------------|--------------------------------------------------------------------------------|\n| `seq_len` | Length of the sequence to feed into the model. |\n| `wordvec_dim` | Dimension of the word embedding. |\n| `do_lower_case` | Whether to convert all uppercase characters to lowercase during preprocessing. |\n| `vocab_size` | Number of words to generate the vocabulary from data. |\n| `dropout_rate` | The rate for dropout. |\n\n\u003cbr /\u003e\n\nMethods\n-------\n\n### `__eq__`\n\n __eq__(\n other\n )\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Class Variables --------------- ||\n|---------------|---------|\n| do_lower_case | `True` |\n| dropout_rate | `0.2` |\n| seq_len | `256` |\n| vocab_size | `10000` |\n| wordvec_dim | `16` |\n\n\u003cbr /\u003e"]]