Datasets:
itazap
/
transformers_code_embeddings_v3

Dataset card Data Studio Files
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1
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24
102
embedding
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2.56k
2.56k
tokens
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4
448
xlm_roberta/modeling_xlm_roberta.py:XLMRobertaSelfAttention
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[ "ALL_ATTENTION_FUNCTIONS", "Dropout", "EncoderDecoderCache", "False", "Linear", "Model", "Module", "None", "The", "ValueError", "__init__", "_attn_implementation", "a", "all_head_size", "and", "attention", "attention_head_size", "attention_interface", "attention_mask", "attenti...
xlm_roberta/modeling_xlm_roberta.py:XLMRobertaCrossAttention
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[ "ALL_ATTENTION_FUNCTIONS", "Dropout", "False", "Linear", "Model", "Module", "None", "The", "True", "ValueError", "__init__", "_attn_implementation", "a", "all_head_size", "and", "attention", "attention_head_size", "attention_interface", "attention_mask", "attention_probs_dropou...
xlm_roberta/modeling_xlm_roberta.py:XLMRobertaSelfOutput
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[ "Dropout", "LayerNorm", "Linear", "Model", "Module", "__init__", "class", "config", "def", "dense", "dropout", "eps", "forward", "hidden_dropout_prob", "hidden_size", "hidden_states", "input_tensor", "layer_norm_eps", "nn", "return", "self", "super" ]
xlm_roberta/modeling_xlm_roberta.py:XLMRobertaAttention
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[ "False", "Model", "ModelCrossAttention", "ModelSelfAttention", "ModelSelfOutput", "Module", "None", "__init__", "attention_class", "attention_mask", "attention_output", "attn_weights", "cache_position", "class", "config", "def", "else", "encoder_attention_mask", "encoder_hidden_s...
xlm_roberta/modeling_xlm_roberta.py:XLMRobertaIntermediate
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xlm_roberta/modeling_xlm_roberta.py:XLMRobertaOutput
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[ "Dropout", "LayerNorm", "Linear", "Model", "Module", "__init__", "class", "config", "def", "dense", "dropout", "eps", "forward", "hidden_dropout_prob", "hidden_size", "hidden_states", "input_tensor", "intermediate_size", "layer_norm_eps", "nn", "return", "self", "super" ]
xlm_roberta/modeling_xlm_roberta.py:XLMRobertaLayer
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[ "False", "GradientCheckpointingLayer", "If", "Model", "ModelAttention", "ModelIntermediate", "ModelOutput", "None", "True", "ValueError", "_", "__init__", "add_cross_attention", "and", "apply_chunking_to_forward", "are", "attention", "attention_mask", "attention_output", "be", ...
xlm_roberta/modeling_xlm_roberta.py:XLMRobertaLMHead
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[ "LayerNorm", "Linear", "Model", "Module", "Parameter", "__init__", "bias", "class", "config", "decoder", "def", "dense", "eps", "features", "forward", "gelu", "hidden_size", "kwargs", "layer_norm", "layer_norm_eps", "nn", "return", "self", "super", "torch", "vocab_s...
xlm_roberta/modeling_xlm_roberta.py:XLMRobertaPreTrainedModel
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[ "Model", "ModelConfig", "ModelCrossAttention", "ModelEmbeddings", "ModelLMHead", "ModelLayer", "ModelSelfAttention", "PreTrainedModel", "True", "_can_record_outputs", "_init_weights", "_supports_attention_backend", "_supports_flash_attn", "_supports_flex_attn", "_supports_sdpa", "arang...
xlm_roberta/modeling_xlm_roberta.py:XLMRobertaEncoder
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[ "BaseModelOutputWithPastAndCrossAttentions", "Model", "ModelLayer", "Module", "ModuleList", "None", "__init__", "attention_mask", "cache_position", "class", "config", "def", "else", "encoder_attention_mask", "encoder_hidden_states", "enumerate", "for", "forward", "hidden_states",...
xlm_roberta/modeling_xlm_roberta.py:XLMRobertaPooler
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[ "Linear", "Model", "Module", "Tanh", "__init__", "activation", "class", "config", "def", "dense", "first_token_tensor", "forward", "hidden_size", "hidden_states", "nn", "pooled_output", "return", "self", "super" ]
xlm_roberta/modeling_xlm_roberta.py:XLMRobertaModel
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[ "BaseModelOutputWithPoolingAndCrossAttentions", "DynamicCache", "EncoderDecoderCache", "False", "Model", "ModelEmbeddings", "ModelEncoder", "ModelLayer", "ModelPooler", "ModelPreTrainedModel", "None", "True", "ValueError", "You", "__init__", "_create_attention_masks", "_no_split_modu...
xlm_roberta/modeling_xlm_roberta.py:XLMRobertaForCausalLM
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[ "CausalLMOutputWithCrossAttentions", "False", "GenerationMixin", "Model", "ModelLMHead", "ModelModel", "ModelPreTrainedModel", "None", "True", "__init__", "_tied_weights_keys", "add", "add_pooling_layer", "attention_mask", "attentions", "auto_docstring", "bias", "can_return_tuple",...
xlm_roberta/modeling_xlm_roberta.py:XLMRobertaForMaskedLM
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[ "CrossEntropyLoss", "False", "If", "MaskedLMOutput", "Model", "ModelLMHead", "ModelModel", "ModelPreTrainedModel", "None", "True", "__init__", "_tied_weights_keys", "add_pooling_layer", "attention", "attention_mask", "attentions", "auto_docstring", "bi", "bias", "can_return_tup...
xlm_roberta/modeling_xlm_roberta.py:XLMRobertaClassificationHead
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[ "Dropout", "Linear", "Model", "Module", "None", "__init__", "class", "classifier_dropout", "config", "def", "dense", "dropout", "else", "features", "forward", "hidden_dropout_prob", "hidden_size", "if", "is", "kwargs", "nn", "not", "num_labels", "out_proj", "return", ...
xlm_roberta/modeling_xlm_roberta.py:XLMRobertaForSequenceClassification
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[ "BCEWithLogitsLoss", "CrossEntropyLoss", "False", "MSELoss", "Model", "ModelClassificationHead", "ModelModel", "ModelPreTrainedModel", "None", "SequenceClassifierOutput", "True", "__init__", "add_pooling_layer", "and", "attention_mask", "attentions", "auto_docstring", "can_return_t...
xlm_roberta/modeling_xlm_roberta.py:XLMRobertaForMultipleChoice
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[ "CrossEntropyLoss", "Dropout", "False", "Linear", "Model", "ModelModel", "ModelPreTrainedModel", "MultipleChoiceModelOutput", "None", "True", "__init__", "add_pooling_layer", "attention_mask", "attentions", "auto_docstring", "can_return_tuple", "class", "classifier", "config", ...
xlm_roberta/modeling_xlm_roberta.py:XLMRobertaForTokenClassification
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[ "CrossEntropyLoss", "Dropout", "False", "Linear", "Model", "ModelModel", "ModelPreTrainedModel", "None", "TokenClassifierOutput", "True", "__init__", "add_pooling_layer", "attention_mask", "attentions", "auto_docstring", "can_return_tuple", "class", "classifier", "classifier_drop...
xlm_roberta/modeling_xlm_roberta.py:XLMRobertaForQuestionAnswering
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[ "CrossEntropyLoss", "False", "Linear", "Model", "ModelModel", "ModelPreTrainedModel", "None", "QuestionAnsweringModelOutput", "True", "__init__", "add_pooling_layer", "and", "attention_mask", "attentions", "auto_docstring", "can_return_tuple", "clamp", "class", "config", "conti...
lasr/modeling_lasr.py:LasrEncoderSubsampling
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[ "Conv1d", "Linear", "ModelEncoderSubsampling", "Module", "ReLU", "__init__", "act_fn", "class", "config", "conv_0", "conv_1", "def", "dense_0", "dense_1", "forward", "hidden_size", "hidden_states", "input_features", "kernel_size", "nn", "num_mel_bins", "return", "self", ...
lasr/modeling_lasr.py:LasrEncoderRotaryEmbedding
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[ "False", "ModelEncoderRotaryEmbedding", "Module", "None", "ROPE_INIT_FUNCTIONS", "Tensor", "__init__", "and", "arange", "attention_factor", "attention_scaling", "base", "cat", "class", "clone", "compute_default_rope_parameters", "config", "cos", "cpu", "def", "default", "de...
lasr/modeling_lasr.py:rotate_half
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[ "Model_half", "cat", "def", "dim", "return", "shape", "torch", "x", "x1", "x2" ]
lasr/modeling_lasr.py:apply_rotary_pos_emb
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[ "Model_rotary_pos_emb", "cos", "def", "k", "k_embed", "q", "q_embed", "return", "rotate_half", "sin", "unsqueeze", "unsqueeze_dim" ]
lasr/modeling_lasr.py:repeat_kv
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[ "Model_kv", "None", "batch", "def", "expand", "head_dim", "hidden_states", "if", "n_rep", "num_key_value_heads", "reshape", "return", "shape", "slen" ]
lasr/modeling_lasr.py:eager_attention_forward
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[ "Model_attention_forward", "None", "attention_mask", "attn_output", "attn_weights", "causal_mask", "contiguous", "def", "dim", "dropout", "dtype", "float32", "functional", "if", "is", "key", "key_states", "kwargs", "matmul", "module", "nn", "not", "num_key_value_groups", ...
lasr/modeling_lasr.py:LasrEncoderAttention
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[ "ALL_ATTENTION_FUNCTIONS", "False", "Linear", "ModelEncoderAttention", "Module", "None", "Tensor", "__init__", "_attn_implementation", "apply_rotary_pos_emb", "attention_dropout", "attention_interface", "attention_mask", "attn_output", "attn_weights", "class", "config", "contiguous...
lasr/modeling_lasr.py:LasrEncoderConvolutionModule
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[ "ACT2FN", "BatchNorm1d", "Conv1d", "ModelEncoderConvolutionModule", "Module", "None", "__init__", "activation", "all", "all_masked_rows", "attention_mask", "batch_norm_momentum", "bool", "channels", "class", "config", "conv_kernel_size", "def", "depthwise_conv", "dim", "else"...
lasr/modeling_lasr.py:LasrEncoderFeedForward
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[ "ACT2FN", "Linear", "ModelEncoderFeedForward", "Module", "__init__", "activation", "activation_dropout", "class", "config", "def", "dropout", "forward", "functional", "hidden_act", "hidden_size", "hidden_states", "intermediate_size", "linear1", "linear2", "nn", "p", "return...
lasr/modeling_lasr.py:LasrEncoderBlock
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[ "False", "GradientCheckpointingLayer", "LayerNorm", "ModelEncoderAttention", "ModelEncoderBlock", "ModelEncoderConvolutionModule", "ModelEncoderFeedForward", "None", "_", "__init__", "attention_mask", "attn_output", "class", "config", "conv", "conv_output", "conv_residual_weights", ...
lasr/modeling_lasr.py:LasrPreTrainedModel
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[ "False", "ModelCTCConfig", "ModelEncoderAttention", "ModelEncoderBlock", "ModelPreTrainedModel", "None", "PreTrainedModel", "True", "_", "_can_compile_fullgraph", "_can_record_outputs", "_get_output_attention_mask", "_get_subsampling_output_length", "_init_weights", "_no_split_modules", ...
lasr/modeling_lasr.py:LasrEncoder
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lasr/modeling_lasr.py:LasrGenerateOutput
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[ "LongTensor", "ModelGenerateOutput", "ModelOutput", "None", "attentions", "class", "hidden_states", "logits", "sequences", "torch" ]
lasr/modeling_lasr.py:LasrForCTC
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llama4/modeling_llama4.py:Llama4TextExperts
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llama4/modeling_llama4.py:Llama4TextMLP
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llama4/modeling_llama4.py:Llama4TextL2Norm
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llama4/modeling_llama4.py:Llama4TextRMSNorm
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llama4/modeling_llama4.py:Llama4Router
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llama4/modeling_llama4.py:Llama4TextMoe
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llama4/modeling_llama4.py:Llama4TextRotaryEmbedding
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llama4/modeling_llama4.py:apply_rotary_emb
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llama4/modeling_llama4.py:repeat_kv
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llama4/modeling_llama4.py:eager_attention_forward
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llama4/modeling_llama4.py:vision_eager_attention_forward
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llama4/modeling_llama4.py:Llama4TextAttention
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llama4/modeling_llama4.py:Llama4TextDecoderLayer
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llama4/modeling_llama4.py:Llama4PreTrainedModel
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llama4/modeling_llama4.py:Llama4TextModel
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llama4/modeling_llama4.py:Llama4ForCausalLM
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llama4/modeling_llama4.py:Llama4CausalLMOutputWithPast
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llama4/modeling_llama4.py:Llama4VisionMLP2
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llama4/modeling_llama4.py:Llama4MultiModalProjector
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llama4/modeling_llama4.py:pixel_shuffle
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llama4/modeling_llama4.py:Llama4VisionPixelShuffleMLP
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llama4/modeling_llama4.py:reshape_for_broadcast
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llama4/modeling_llama4.py:vision_apply_rotary_emb
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llama4/modeling_llama4.py:Llama4VisionAttention
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llama4/modeling_llama4.py:Llama4VisionMLP
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llama4/modeling_llama4.py:Llama4VisionEncoderLayer
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llama4/modeling_llama4.py:Llama4VisionEncoder
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llama4/modeling_llama4.py:Llama4UnfoldConvolution
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llama4/modeling_llama4.py:Llama4VisionRotaryEmbedding
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llama4/modeling_llama4.py:Llama4VisionModel
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[ "BaseModelOutput", "LayerNorm", "ModelPreTrainedModel", "ModelUnfoldConvolution", "ModelVisionConfig", "ModelVisionEncoder", "ModelVisionEncoderLayer", "ModelVisionModel", "ModelVisionPixelShuffleMLP", "ModelVisionRotaryEmbedding", "None", "Parameter", "_", "__init__", "_no_split_modules...
llama4/modeling_llama4.py:Llama4ForConditionalGeneration
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[ "CrossEntropyLoss", "False", "GenerationMixin", "Image", "ModelCausalLMOutputWithPast", "ModelConfig", "ModelForCausalLM", "ModelForConditionalGeneration", "ModelMultiModalProjector", "ModelPreTrainedModel", "ModelTextDecoderLayer", "ModelVisionEncoderLayer", "ModelVisionModel", "None", ...
git/modeling_git.py:GitVisionModelOutput
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[ "ModelOutput", "ModelVisionModelOutput", "None", "attentions", "class", "hidden_states", "image_embeds", "last_hidden_state", "r" ]
git/modeling_git.py:token_type_ids_mask_function
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[ "Model_type_ids", "Model_type_ids_at_kv_idx", "Model_type_ids_at_q_idx", "Model_type_ids_mask_function", "None", "batch_idx", "def", "head_idx", "if", "image_group_ids", "image_group_ids_at_kv_idx", "image_group_ids_at_q_idx", "inner_mask", "is", "is_image_block", "kv_idx", "q_idx", ...
git/modeling_git.py:create_causal_mask_mapping
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[ "False", "Model_causal_mask_mapping", "Model_masks_for_generate", "None", "and", "attention_mask", "cache_position", "config", "cumsum", "def", "device", "dim", "else", "functional", "get_text_config", "if", "image_group_ids", "input_embeds", "int", "is", "is_first_iteration"...
git/modeling_git.py:GitEmbeddings
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[ "Dropout", "Embedding", "False", "LayerNorm", "ModelEmbeddings", "Module", "None", "__init__", "arange", "class", "config", "def", "dropout", "else", "embeddings", "eps", "expand", "forward", "hidden_dropout_prob", "hidden_size", "if", "input_ids", "input_shape", "input...
git/modeling_git.py:GitSelfAttention
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[ "Dropout", "Instantiating", "Linear", "ModelSelfAttention", "Module", "None", "Please", "The", "ValueError", "__class__", "__init__", "__name__", "a", "all_head_size", "and", "attention", "attention_head_size", "attention_mask", "attention_probs", "attention_probs_dropout_prob"...
git/modeling_git.py:GitSelfOutput
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[ "Dropout", "LayerNorm", "Linear", "ModelSelfOutput", "Module", "__init__", "class", "config", "def", "dense", "dropout", "eps", "forward", "hidden_dropout_prob", "hidden_size", "hidden_states", "input_tensor", "layer_norm_eps", "nn", "return", "self", "super" ]
git/modeling_git.py:GitAttention
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[ "False", "ModelAttention", "ModelSelfOutput", "Model_SELF_ATTENTION_CLASSES", "Module", "None", "__init__", "_attn_implementation", "attention_mask", "attention_output", "attn_output", "cache_position", "class", "config", "def", "forward", "hidden_states", "layer_idx", "nn", "o...
git/modeling_git.py:GitIntermediate
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[ "ACT2FN", "Linear", "ModelIntermediate", "Module", "__init__", "class", "config", "def", "dense", "else", "forward", "hidden_act", "hidden_size", "hidden_states", "if", "intermediate_act_fn", "intermediate_size", "isinstance", "nn", "return", "self", "str", "super" ]
git/modeling_git.py:GitOutput
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[ "Dropout", "LayerNorm", "Linear", "ModelOutput", "Module", "__init__", "class", "config", "def", "dense", "dropout", "eps", "forward", "hidden_dropout_prob", "hidden_size", "hidden_states", "input_tensor", "intermediate_size", "layer_norm_eps", "nn", "return", "self", "su...
git/modeling_git.py:GitLayer
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[ "False", "GradientCheckpointingLayer", "ModelAttention", "ModelIntermediate", "ModelLayer", "ModelOutput", "None", "__init__", "apply_chunking_to_forward", "attention", "attention_mask", "attention_output", "cache_position", "chunk_size_feed_forward", "class", "config", "def", "fee...
git/modeling_git.py:GitEncoder
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[ "BaseModelOutputWithPast", "DynamicCache", "False", "ModelEncoder", "ModelLayer", "Module", "ModuleList", "None", "Setting", "True", "__init__", "all_hidden_states", "all_self_attentions", "and", "attention_mask", "attentions", "cache_position", "checkpointing", "class", "confi...
git/modeling_git.py:GitPreTrainedModel
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[ "Embedding", "False", "LayerNorm", "Linear", "Model", "ModelConfig", "ModelEmbeddings", "ModelPreTrainedModel", "ModelVisionEmbeddings", "None", "PreTrainedModel", "True", "_init_weights", "_is_hf_initialized", "and", "arange", "base_model_prefix", "bias", "class", "class_embed...
git/modeling_git.py:GitVisionEmbeddings
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[ "Conv2d", "Embedding", "False", "Input", "ModelVisionEmbeddings", "Module", "Parameter", "ValueError", "_", "__init__", "align_corners", "and", "arange", "batch_size", "bicubic", "cat", "class", "class_embedding", "class_embeds", "class_pos_embed", "config", "def", "dim",...
git/modeling_git.py:GitVisionMLP
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[ "ACT2FN", "Linear", "ModelVisionMLP", "Module", "__init__", "activation_fn", "class", "config", "def", "fc1", "fc2", "forward", "hidden_act", "hidden_size", "hidden_states", "intermediate_size", "nn", "return", "self", "super" ]
git/modeling_git.py:eager_attention_forward
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[ "Model_attention_forward", "None", "attention_mask", "attn_output", "attn_weights", "contiguous", "def", "dim", "dropout", "dtype", "float32", "functional", "if", "is", "key", "kwargs", "matmul", "module", "nn", "not", "p", "query", "return", "scaling", "softmax", "...
git/modeling_git.py:GitVisionAttention
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[ "ALL_ATTENTION_FUNCTIONS", "False", "Linear", "ModelVisionAttention", "Module", "None", "ValueError", "__init__", "_attn_implementation", "and", "attention_dropout", "attention_interface", "attention_mask", "attn_output", "attn_weights", "batch_size", "be", "by", "causal_attentio...
git/modeling_git.py:GitVisionEncoderLayer
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[ "False", "GradientCheckpointingLayer", "LayerNorm", "ModelVisionAttention", "ModelVisionEncoderLayer", "ModelVisionMLP", "__init__", "attention_mask", "attn_weights", "causal_attention_mask", "class", "config", "def", "embed_dim", "eps", "forward", "hidden_size", "hidden_states", ...
git/modeling_git.py:GitVisionEncoder
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[ "BaseModelOutput", "False", "ModelVisionEncoder", "ModelVisionEncoderLayer", "Module", "ModuleList", "None", "_", "__init__", "all_attentions", "attention_mask", "attentions", "can_return_tuple", "causal_attention_mask", "class", "config", "def", "else", "encoder_layer", "encod...
git/modeling_git.py:GitVisionTransformer
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[ "BaseModelOutput", "False", "LayerNorm", "ModelVisionEmbeddings", "ModelVisionEncoder", "ModelVisionTransformer", "Module", "None", "__init__", "attentions", "auto_docstring", "class", "config", "def", "else", "embed_dim", "embeddings", "encoder", "encoder_outputs", "eps", "f...
git/modeling_git.py:GitVisionModel
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[ "False", "ModelPreTrainedModel", "ModelVisionConfig", "ModelVisionModel", "ModelVisionTransformer", "None", "__init__", "auto_docstring", "class", "config", "def", "else", "embeddings", "forward", "get_input_embeddings", "if", "image", "input_modalities", "interpolate_pos_encodin...
git/modeling_git.py:GitProjection
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[ "LayerNorm", "Linear", "ModelProjection", "Module", "Sequential", "__init__", "class", "config", "def", "embeddings", "eps", "forward", "hidden_size", "layer_norm_eps", "nn", "return", "self", "super", "vision_config", "visual_projection" ]
git/modeling_git.py:GitModel
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[ "BaseModelOutputWithPast", "Cache", "False", "ModelEmbeddings", "ModelEncoder", "ModelModel", "ModelPreTrainedModel", "ModelProjection", "ModelVisionModel", "None", "Parameter", "ParameterList", "_", "__init__", "and", "append", "arange", "attention_mask", "attentions", "auto_d...
git/modeling_git.py:GitForCausalLM
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[ "CausalLMOutputWithPast", "False", "GenerationMixin", "Linear", "Model", "ModelForCausalLM", "ModelModel", "ModelPreTrainedModel", "None", "__init__", "_tied_weights_keys", "attention", "attention_mask", "attentions", "auto_docstring", "cache_position", "class", "config", "contig...
lxmert/modeling_lxmert.py:GeLU
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[ "GeLU", "Module", "__init__", "class", "def", "forward", "gelu", "nn", "return", "self", "super", "x" ]
lxmert/modeling_lxmert.py:LxmertModelOutput
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[ "ModelModelOutput", "ModelOutput", "None", "class", "cross_encoder_attentions", "language_attentions", "language_hidden_states", "language_output", "pooled_output", "r", "vision_attentions", "vision_hidden_states", "vision_output" ]
lxmert/modeling_lxmert.py:LxmertForQuestionAnsweringOutput
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[ "ModelForQuestionAnsweringOutput", "ModelOutput", "None", "class", "cross_encoder_attentions", "language_attentions", "language_hidden_states", "loss", "question_answering_score", "r", "vision_attentions", "vision_hidden_states" ]
lxmert/modeling_lxmert.py:LxmertForPreTrainingOutput
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[ "ModelForPreTrainingOutput", "ModelOutput", "None", "class", "cross_encoder_attentions", "cross_relationship_score", "language_attentions", "language_hidden_states", "loss", "prediction_logits", "question_answering_score", "r", "vision_attentions", "vision_hidden_states" ]
lxmert/modeling_lxmert.py:LxmertEmbeddings
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[ "Dropout", "Embedding", "LayerNorm", "ModelEmbeddings", "Module", "None", "__init__", "arange", "class", "config", "def", "device", "dropout", "dtype", "else", "embeddings", "eps", "expand", "forward", "hidden_dropout_prob", "hidden_size", "if", "input_ids", "input_shap...
lxmert/modeling_lxmert.py:LxmertAttention
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[ "Dropout", "False", "Linear", "ModelAttention", "Module", "None", "The", "ValueError", "_", "__init__", "a", "attention", "attention_head_size", "attention_mask", "attention_probs", "attention_probs_dropout_prob", "attention_scores", "batch_size", "class", "config", "context"...
lxmert/modeling_lxmert.py:LxmertAttentionOutput
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[ "Dropout", "LayerNorm", "Linear", "ModelAttentionOutput", "Module", "__init__", "class", "config", "def", "dense", "dropout", "eps", "forward", "hidden_dropout_prob", "hidden_size", "hidden_states", "input_tensor", "nn", "return", "self", "super" ]
lxmert/modeling_lxmert.py:LxmertCrossAttentionLayer
[ -0.00004358119622338563, 0.04236180707812309, 0.05255582556128502, -0.010363918729126453, -0.0006512844702228904, 0.044853679835796356, 0.02842998318374157, -0.019934969022870064, 0.006739378906786442, 0.0013875191798433661, 0.011213419958949089, 0.04462714493274689, 0.0019396950956434011, ...
[ "False", "ModelAttention", "ModelAttentionOutput", "ModelCrossAttentionLayer", "Module", "None", "__init__", "att", "attention_output", "attention_probs", "class", "config", "ctx_att_mask", "ctx_tensor", "def", "else", "forward", "if", "input_tensor", "nn", "output", "outpu...
lxmert/modeling_lxmert.py:LxmertSelfAttentionLayer
[ 0.00020808607223443687, 0.04850973188877106, 0.036495640873909, -0.00008633358083898202, 0.0008004672708921134, 0.04465615749359131, 0.02482156828045845, -0.000683584890794009, 0.010257314890623093, 0.005553684197366238, 0.016661053523421288, 0.01904120296239853, 0.0034285499714314938, -0....
[ "False", "ModelAttention", "ModelAttentionOutput", "ModelSelfAttentionLayer", "Module", "__init__", "attention_mask", "attention_output", "attention_probs", "class", "config", "def", "else", "forward", "if", "input_tensor", "nn", "output", "output_attentions", "outputs", "ret...
lxmert/modeling_lxmert.py:LxmertIntermediate
[ -0.0002685948566067964, 0.0166170671582222, 0.039880964905023575, 0.010657843202352524, -0.001009916653856635, 0.04194376990199089, 0.03804735466837883, -0.021774088963866234, 0.00043333301437087357, 0.00015309907030314207, 0.018679875880479813, -0.020742684602737427, -0.0019195578061044216,...
[ "ACT2FN", "Linear", "ModelIntermediate", "Module", "__init__", "class", "config", "def", "dense", "forward", "hidden_act", "hidden_size", "hidden_states", "intermediate_act_fn", "intermediate_size", "nn", "return", "self", "super" ]
lxmert/modeling_lxmert.py:LxmertOutput
[ -0.00022269411419983953, 0.04059090092778206, 0.0478881411254406, 0.03283758461475372, -0.0011473200283944607, 0.05108068510890007, 0.035117968916893005, -0.02257583849132061, 0.0013468540273606777, 0.018471140414476395, 0.00974865723401308, 0.00664162915199995, 0.0016105237882584333, 0.00...
[ "Dropout", "LayerNorm", "Linear", "ModelOutput", "Module", "__init__", "class", "config", "def", "dense", "dropout", "eps", "forward", "hidden_dropout_prob", "hidden_size", "hidden_states", "input_tensor", "intermediate_size", "nn", "return", "self", "super" ]
lxmert/modeling_lxmert.py:LxmertLayer
[ 0.000020229741494404152, 0.01508610974997282, 0.026907315477728844, 0.011427165009081364, 0.00017327212844975293, 0.05539079010486603, 0.039404019713401794, -0.011258291080594063, 0.014860943891108036, 0.00021549072698689997, 0.01801326498389244, -0.010639085434377193, 0.0005558780976571143,...
[ "False", "ModelIntermediate", "ModelLayer", "ModelOutput", "ModelSelfAttentionLayer", "Module", "None", "__init__", "attention", "attention_mask", "attention_output", "class", "config", "def", "forward", "hidden_states", "intermediate", "intermediate_output", "layer_output", "n...
lxmert/modeling_lxmert.py:LxmertXLayer
[ -0.00006715262134093791, 0.0287641454488039, 0.013146113604307175, 0.02056187018752098, -0.00028792236116714776, 0.05977549031376839, 0.022471988573670387, -0.03460686281323433, 0.006797776557505131, 0.00589889707043767, 0.029887745156884193, 0.02483154833316803, 0.0025561887305229902, -0....
[ "False", "ModelCrossAttentionLayer", "ModelIntermediate", "ModelOutput", "ModelSelfAttentionLayer", "ModelXLayer", "Module", "__init__", "attention_probs", "class", "config", "cross_att", "ctx_att_mask", "def", "else", "forward", "if", "lang_att_output", "lang_attention_mask", ...