# BlazingText Hyperparameters
When you start a training job with a `CreateTrainingJob` request, you specify a training algorithm. You can also specify algorithm-specific hyperparameters as string-to-string maps. The hyperparameters for the BlazingText algorithm depend on which mode you use: Word2Vec (unsupervised) and Text Classification (supervised).
## Word2Vec Hyperparameters
The following table lists the hyperparameters for the BlazingText Word2Vec training algorithm provided by Amazon SageMaker AI.
| Parameter Name | Description |
| --- | --- |
| mode | The Word2vec architecture used for training.
**Required**
Valid values: `batch_skipgram`, `skipgram`, or `cbow` |
| batch\_size | The size of each batch when `mode` is set to `batch_skipgram`. Set to a number between 10 and 20.
**Optional**
Valid values: Positive integer
Default value: 11 |
| buckets | The number of hash buckets to use for subwords.
**Optional**
Valid values: positive integer
Default value: 2000000 |
| epochs | The number of complete passes through the training data.
**Optional**
Valid values: Positive integer
Default value: 5 |
| evaluation | Whether the trained model is evaluated using the [WordSimilarity-353 Test](http://www.gabrilovich.com/resources/data/wordsim353/wordsim353.html).
**Optional**
Valid values: (Boolean) `True` or `False`
Default value: `True` |
| learning\_rate | The step size used for parameter updates.
**Optional**
Valid values: Positive float
Default value: 0.05 |
| min\_char | The minimum number of characters to use for subwords/character n-grams.
**Optional**
Valid values: positive integer
Default value: 3 |
| min\_count | Words that appear less than `min_count` times are discarded.
**Optional**
Valid values: Non-negative integer
Default value: 5 |
| max\_char | The maximum number of characters to use for subwords/character n-grams
**Optional**
Valid values: positive integer
Default value: 6 |
| negative\_samples | The number of negative samples for the negative sample sharing strategy.
**Optional**
Valid values: Positive integer
Default value: 5 |
| sampling\_threshold | The threshold for the occurrence of words. Words that appear with higher frequency in the training data are randomly down-sampled.
**Optional**
Valid values: Positive fraction. The recommended range is (0, 1e-3]
Default value: 0.0001 |
| subwords | Whether to learn subword embeddings on not.
**Optional**
Valid values: (Boolean) `True` or `False`
Default value: `False` |
| vector\_dim | The dimension of the word vectors that the algorithm learns.
**Optional**
Valid values: Positive integer
Default value: 100 |
| window\_size | The size of the context window. The context window is the number of words surrounding the target word used for training.
**Optional**
Valid values: Positive integer
Default value: 5 |
## Text Classification Hyperparameters
The following table lists the hyperparameters for the Text Classification training algorithm provided by Amazon SageMaker AI.
**Note**
Although some of the parameters are common between the Text Classification and Word2Vec modes, they might have different meanings depending on the context.
| Parameter Name | Description |
| --- | --- |
| mode | The training mode.
**Required**
Valid values: `supervised` |
| buckets | The number of hash buckets to use for word n-grams.
**Optional**
Valid values: Positive integer
Default value: 2000000 |
| early\_stopping | Whether to stop training if validation accuracy doesn't improve after a `patience` number of epochs. Note that a validation channel is required if early stopping is used.
**Optional**
Valid values: (Boolean) `True` or `False`
Default value: `False` |
| epochs | The maximum number of complete passes through the training data.
**Optional**
Valid values: Positive integer
Default value: 5 |
| learning\_rate | The step size used for parameter updates.
**Optional**
Valid values: Positive float
Default value: 0.05 |
| min\_count | Words that appear less than `min_count` times are discarded.
**Optional**
Valid values: Non-negative integer
Default value: 5 |
| min\_epochs | The minimum number of epochs to train before early stopping logic is invoked.
**Optional**
Valid values: Positive integer
Default value: 5 |
| patience | The number of epochs to wait before applying early stopping when no progress is made on the validation set. Used only when `early_stopping` is `True`.
**Optional**
Valid values: Positive integer
Default value: 4 |
| vector\_dim | The dimension of the embedding layer.
**Optional**
Valid values: Positive integer
Default value: 100 |
| word\_ngrams | The number of word n-gram features to use.
**Optional**
Valid values: Positive integer
Default value: 2 |