# nlp_architect.nn.torch.modules package

## Submodules

## nlp_architect.nn.torch.modules.embedders module

class `nlp_architect.nn.torch.modules.embedders.``CNNLSTM`(word_vocab_size: int, num_labels: int, word_embedding_dims: int = 100, char_embedding_dims: int = 16, cnn_kernel_size: int = 3, cnn_num_filters: int = 128, lstm_hidden_size: int = 100, lstm_layers: int = 2, bidir: bool = True, dropout: float = 0.5, padding_idx: int = 0)

  Bases: `torch.nn.modules.module.Module`

  CNN-LSTM embedder (based on Ma and Hovy. 2016)

  Parameters: | word_vocab_size (int) – word vocabulary size num_labels (int) – number of labels (classifier) word_embedding_dims (int, optional) – word embedding dims char_embedding_dims (int, optional) – character embedding dims cnn_kernel_size (int, optional) – character CNN kernel size cnn_num_filters (int, optional) – character CNN number of filters lstm_hidden_size (int, optional) – LSTM embedder hidden size lstm_layers (int, optional) – num of LSTM layers bidir (bool, optional) – apply bi-directional LSTM dropout (float, optional) – dropout rate padding_idx (int, optinal) – padding number for embedding layers

  `forward`(words, word_chars, **kwargs)

    CNN-LSTM forward step

    Parameters: | words (torch.tensor) – words word_chars (torch.tensor) – word character tensors
    Returns: | logits of model
    Return type: | torch.tensor

  classmethod `from_config`(word_vocab_size: int, num_labels: int, config: str)

    Load a model from a configuration file A valid configuration file is a JSON file with fields as in class __init__

    Parameters: | word_vocab_size (int) – word vocabulary size num_labels (int) – number of labels (classifier) config (str) – path to configuration file
    Returns: | CNNLSTM module pre-configured
    Return type: | CNNLSTM

  `load_embeddings`(embeddings)

    Load pre-defined word embeddings

    Parameters: | embeddings (torch.tensor) – word embedding tensor

class `nlp_architect.nn.torch.modules.embedders.``IDCNN`(word_vocab_size: int, num_labels: int, word_embedding_dims: int = 100, char_embedding_dims: int = 16, char_cnn_filters: int = 128, char_cnn_kernel_size: int = 3, cnn_kernel_size: int = 3, cnn_num_filters: int = 128, input_dropout: float = 0.5, word_dropout: float = 0.5, hidden_dropout: float = 0.5, blocks: int = 1, dilations: List[T] = None, padding_idx: int = 0)

  Bases: `torch.nn.modules.module.Module`

  ID-CNN (iterated dilated) tagging model (based on Strubell et al 2017) with word character embedding (using CNN feature extractors)

  Parameters: | word_vocab_size (int) – word vocabulary size num_labels (int) – number of labels (classifier) word_embedding_dims (int, optional) – word embedding dims char_embedding_dims (int, optional) – character embedding dims char_cnn_filters (int, optional) – character CNN kernel size char_cnn_kernel_size (int, optional) – character CNN number of filters cnn_kernel_size (int, optional) – CNN embedder kernel size cnn_num_filters (int, optional) – CNN embedder number of filters input_dropout (float, optional) – input dropout rate word_dropout (float, optional) – pre embedder dropout rate hidden_dropout (float, optional) – pre classifier dropout rate blocks (int, optinal) – number of blocks dilations (List, optinal) – List of dilations per CNN layer padding_idx (int, optinal) – padding number for embedding layers

  `forward`(words, word_chars, **kwargs)

    IDCNN forward step

    Parameters: | words (torch.tensor) – words word_chars (torch.tensor) – word character tensors
    Returns: | logits of model
    Return type: | torch.tensor

  classmethod `from_config`(word_vocab_size: int, num_labels: int, config: str)

    Load a model from a configuration file A valid configuration file is a JSON file with fields as in class __init__

    Parameters: | word_vocab_size (int) – word vocabulary size num_labels (int) – number of labels (classifier) config (str) – path to configuration file
    Returns: | IDCNNEmbedder module pre-configured
    Return type: | IDCNN

  `load_embeddings`(embeddings)

    Load pre-defined word embeddings

    Parameters: | embeddings (torch.tensor) – word embedding tensor

## Module contents