ez_transfer.model_zoo

bert

class easytransfer.model_zoo.modeling_bert.BertConfig(vocab_size, hidden_size, intermediate_size, num_hidden_layers, num_attention_heads, max_position_embeddings, type_vocab_size, hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, initializer_range=0.02, **kwargs)

Configuration for Bert.

Parameters:

• vocab_size -- Vocabulary size of inputs_ids in BertModel.
• hidden_size -- Size of the encoder layers and the pooler layer.
• num_hidden_layers -- Number of hidden layers in the Transformer encoder.
• num_attention_heads -- Number of attention heads for each attention layer in the Transformer encoder.
• intermediate_size -- The size of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
• hidden_dropout_prob -- The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
• attention_probs_dropout_prob -- The dropout ratio for the attention probabilities.
• max_position_embeddings -- The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
• type_vocab_size -- The vocabulary size of the token_type_ids passed into BertModel.
• initializer_range -- The stdev of the truncated_normal_initializer for initializing all weight matrices.

class easytransfer.model_zoo.modeling_bert.BertPreTrainedModel(config, **kwargs)

config_class

alias of BertConfig

call(inputs, masked_lm_positions=None, **kwargs)

Parameters:

• inputs -- [input_ids, input_mask, segment_ids]
• masked_lm_positions -- masked_lm_positions

Returns:

sequence_output, pooled_output

Examples:

google-bert-tiny-zh

google-bert-tiny-en

google-bert-small-zh

google-bert-small-en

google-bert-base-zh

google-bert-base-en

google-bert-large-zh

google-bert-large-en

pai-bert-tiny-zh

pai-bert-tiny-en

pai-bert-small-zh

pai-bert-small-en

pai-bert-base-zh

pai-bert-base-en

pai-bert-large-zh

pai-bert-large-en

model = model_zoo.get_pretrained_model('google-bert-base-zh')
outputs = model([input_ids, input_mask, segment_ids], mode=mode)

roberta

class easytransfer.model_zoo.modeling_bert.BertConfig(vocab_size, hidden_size, intermediate_size, num_hidden_layers, num_attention_heads, max_position_embeddings, type_vocab_size, hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, initializer_range=0.02, **kwargs)

Configuration for Bert.

Parameters:

• vocab_size -- Vocabulary size of inputs_ids in BertModel.
• hidden_size -- Size of the encoder layers and the pooler layer.
• num_hidden_layers -- Number of hidden layers in the Transformer encoder.
• num_attention_heads -- Number of attention heads for each attention layer in the Transformer encoder.
• intermediate_size -- The size of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
• hidden_dropout_prob -- The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
• attention_probs_dropout_prob -- The dropout ratio for the attention probabilities.
• max_position_embeddings -- The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
• type_vocab_size -- The vocabulary size of the token_type_ids passed into BertModel.
• initializer_range -- The stdev of the truncated_normal_initializer for initializing all weight matrices.

class easytransfer.model_zoo.modeling_roberta.RobertaPreTrainedModel(config, **kwargs)

config_class

alias of easytransfer.model_zoo.modeling_bert.BertConfig

call(inputs, masked_lm_positions=None, **kwargs)

Parameters:

• inputs -- [input_ids, input_mask, segment_ids]
• masked_lm_positions -- masked_lm_positions

Returns:

sequence_output, pooled_output

Examples:

hit-roberta-base-zh

hit-roberta-large-zh

pai-roberta-base-zh

pai-roberta-large-zh

model = model_zoo.get_pretrained_model('hit-roberta-base-zh')
outputs = model([input_ids, input_mask, segment_ids], mode=mode)

albert

class easytransfer.model_zoo.modeling_albert.AlbertConfig(vocab_size, embedding_size, hidden_size, intermediate_size, num_hidden_layers, num_attention_heads, max_position_embeddings, type_vocab_size, hidden_dropout_prob=0, attention_probs_dropout_prob=0, initializer_range=0.02, **kwargs)

Configuration for Albert.

Parameters:

• vocab_size -- Vocabulary size of inputs_ids in AlbertModel.
• embedding_size -- size of voc embeddings.
• hidden_size -- Size of the encoder layers and the pooler layer.
• num_hidden_layers -- Number of hidden layers in the Transformer encoder.
• num_attention_heads -- Number of attention heads for each attention layer in the Transformer encoder.
• intermediate_size -- The size of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
• hidden_dropout_prob -- The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
• attention_probs_dropout_prob -- The dropout ratio for the attention probabilities.
• max_position_embeddings -- The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
• type_vocab_size -- The vocabulary size of the token_type_ids passed into AlbertModel.
• initializer_range -- The stdev of the truncated_normal_initializer for initializing all weight matrices.

class easytransfer.model_zoo.modeling_albert.AlbertPreTrainedModel(config, **kwargs)

config_class

alias of AlbertConfig

call(inputs, masked_lm_positions=None, **kwargs)

Parameters:

• inputs -- [input_ids, input_mask, segment_ids]
• masked_lm_positions -- masked_lm_positions

Returns:

sequence_output, pooled_output

Examples:

google-albert-base-zh

google-albert-base-en

google-albert-large-zh

google-albert-large-en

google-albert-xlarge-zh

google-albert-xlarge-en

google-albert-xxlarge-zh

google-albert-xxlarge-en

pai-albert-base-zh

pai-albert-base-en

pai-albert-large-zh

pai-albert-large-en

pai-albert-xlarge-zh

pai-albert-xlarge-en

pai-albert-xxlarge-zh

pai-albert-xxlarge-en

model = model_zoo.get_pretrained_model('google-albert-base-zh')
outputs = model([input_ids, input_mask, segment_ids], mode=mode)

imagebert

class easytransfer.model_zoo.modeling_imagebert.ImageBertConfig(vocab_size, hidden_size, intermediate_size, num_hidden_layers, num_attention_heads, max_position_embeddings, type_vocab_size, hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, initializer_range=0.02, patch_type_vocab_size=2, patch_feature_size=2048, max_patch_position_embeddings=64, **kwargs)

Configuration for ImageBert.

Parameters:

• vocab_size -- Vocabulary size of inputs_ids in BertModel.
• hidden_size -- Size of the encoder layers and the pooler layer.
• num_hidden_layers -- Number of hidden layers in the Transformer encoder.
• num_attention_heads -- Number of attention heads for each attention layer in the Transformer encoder.
• intermediate_size -- The size of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
• hidden_dropout_prob -- The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
• attention_probs_dropout_prob -- The dropout ratio for the attention probabilities.
• max_position_embeddings -- The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
• type_vocab_size -- The vocabulary size of the token_type_ids passed into BertModel.
• initializer_range -- The stdev of the truncated_normal_initializer for initializing all weight matrices.
• patch_feature_size -- patch feature size
• max_patch_position_embeddings -- max_patch_position_embeddings

class easytransfer.model_zoo.modeling_imagebert.ImageBertPreTrainedModel(config, **kwargs)

config_class

alias of ImageBertConfig

mask_patch_features(patch_features, masked_patch_positions)

call(input_ids, input_mask=None, segment_ids=None, masked_lm_positions=None, image_feature=None, image_mask=None, masked_patch_positions=None, **kwargs)

Examples:

model = model_zoo.get_pretrained_model('icbu-imagebert-small-en')

mlm_logits, nsp_logits, mpm_logits, target_raw_patch_features =                 model(input_ids,
          input_mask=input_mask,
          segment_ids=token_type_ids,
          image_feature=image_feature,
          image_mask=image_mask,
          masked_lm_positions=lm_positions,
          masked_patch_positions=masked_patch_positions,
          output_features=False,
          mode=mode)