Datasets:
itazap
/
transformers_code_embeddings_v3

Dataset card Data Studio Files
xet
 Community
1
identifier
stringlengths
24
102
embedding
listlengths
2.56k
2.56k
tokens
listlengths
4
448
visual_bert/modeling_visual_bert.py:VisualBertForPreTrainingOutput
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[ "ModelForPreTrainingOutput", "ModelOutput", "None", "attentions", "class", "hidden_states", "loss", "prediction_logits", "r", "seq_relationship_logits" ]
visual_bert/modeling_visual_bert.py:VisualBertModel
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[ "BaseModelOutputWithPooling", "ModelEmbeddings", "ModelEncoder", "ModelLayer", "ModelModel", "ModelPooler", "ModelPreTrainedModel", "Model_attention_mask", "Model_embedding_output", "Model_embeds", "Model_input_shape", "Model_token_type_ids", "None", "True", "__init__", "add_pooling_la...
visual_bert/modeling_visual_bert.py:VisualBertForPreTraining
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[ "CrossEntropyLoss", "Found", "Model", "ModelForPreTraining", "ModelForPreTrainingOutput", "ModelModel", "ModelPreTrainedModel", "ModelPreTrainingHeads", "Model_attention_mask", "Model_embeds", "Model_token_type_ids", "None", "The", "ValueError", "__init__", "_tied_weights_keys", "and...
visual_bert/modeling_visual_bert.py:VisualBertForMultipleChoice
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[ "CrossEntropyLoss", "Dropout", "Linear", "Model", "ModelForMultipleChoice", "ModelModel", "ModelPreTrainedModel", "Model_attention_mask", "Model_embeds", "Model_token_type_ids", "MultipleChoiceModelOutput", "None", "_", "__init__", "attention_mask", "attentions", "auto_docstring", ...
visual_bert/modeling_visual_bert.py:VisualBertForQuestionAnswering
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[ "Dropout", "KLDivLoss", "Linear", "LogSoftmax", "Model", "ModelForQuestionAnswering", "ModelModel", "ModelPreTrainedModel", "Model_attention_mask", "Model_embeds", "Model_token_type_ids", "None", "SequenceClassifierOutput", "__init__", "attention_mask", "attentions", "auto_docstring"...
visual_bert/modeling_visual_bert.py:VisualBertForVisualReasoning
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[ "CrossEntropyLoss", "Dropout", "Linear", "Model", "ModelForModelReasoning", "ModelModel", "ModelPreTrainedModel", "Model_attention_mask", "Model_embeds", "Model_token_type_ids", "None", "SequenceClassifierOutput", "__init__", "attention_mask", "attentions", "auto_docstring", "class",...
visual_bert/modeling_visual_bert.py:VisualBertRegionToPhraseAttention
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[ "Dropout", "Linear", "ModelRegionToPhraseAttention", "Module", "The", "ValueError", "_", "__init__", "a", "all_head_size", "attention", "attention_head_size", "attention_mask", "attention_probs_dropout_prob", "attention_scores", "batch_size", "class", "config", "def", "dropout"...
visual_bert/modeling_visual_bert.py:VisualBertForRegionToPhraseAlignment
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[ "Dropout", "KLDivLoss", "LogSoftmax", "Model", "ModelForRegionToPhraseAlignment", "ModelModel", "ModelPreTrainedModel", "ModelPreTrainingHeads", "ModelRegionToPhraseAttention", "Model_attention_mask", "Model_embeds", "Model_features", "Model_token_type_ids", "None", "SequenceClassifierOu...
blenderbot_small/modeling_blenderbot_small.py:shift_tokens_right
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[ "Model_tokens_right", "Modeled_input_ids", "None", "clone", "decoder_start_token_id", "def", "if", "input_ids", "is", "masked_fill_", "new_zeros", "pad_token_id", "return", "shape" ]
blenderbot_small/modeling_blenderbot_small.py:BlenderbotSmallLearnedPositionalEmbedding
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[ "Embedding", "ModelLearnedPositionalEmbedding", "None", "__init__", "arange", "class", "def", "device", "dtype", "embedding_dim", "forward", "if", "input_ids_shape", "is", "long", "nn", "num_embeddings", "past_key_values_length", "position_ids", "return", "self", "seq_len",...
blenderbot_small/modeling_blenderbot_small.py:eager_attention_forward
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[ "Model_attention_forward", "None", "attention_mask", "attn_output", "attn_weights", "contiguous", "def", "dim", "dropout", "functional", "if", "is", "key", "kwargs", "matmul", "module", "nn", "not", "p", "query", "return", "scaling", "shape", "size", "softmax", "tor...
blenderbot_small/modeling_blenderbot_small.py:BlenderbotSmallAttention
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[ "ALL_ATTENTION_FUNCTIONS", "EncoderDecoderCache", "False", "Instantiating", "Linear", "ModelAttention", "Module", "None", "Please", "True", "ValueError", "__class__", "__init__", "__name__", "_attn_implementation", "a", "and", "attention_interface", "attention_mask", "attn_outp...
blenderbot_small/modeling_blenderbot_small.py:BlenderbotSmallEncoderLayer
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[ "ACT2FN", "False", "GradientCheckpointingLayer", "LayerNorm", "Linear", "ModelAttention", "ModelEncoderLayer", "None", "__init__", "activation_dropout", "activation_fn", "activation_function", "and", "any", "attention_dropout", "attention_mask", "attn_weights", "clamp", "clamp_va...
blenderbot_small/modeling_blenderbot_small.py:BlenderbotSmallDecoderLayer
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[ "ACT2FN", "False", "GradientCheckpointingLayer", "LayerNorm", "Linear", "ModelAttention", "ModelDecoderLayer", "None", "True", "__init__", "activation_dropout", "activation_fn", "activation_function", "attention_dropout", "attention_mask", "cache_position", "class", "config", "cr...
blenderbot_small/modeling_blenderbot_small.py:BlenderbotSmallPreTrainedModel
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[ "ModelConfig", "ModelForConditionalGeneration", "ModelPreTrainedModel", "PreTrainedModel", "True", "_can_compile_fullgraph", "_init_weights", "_supports_flash_attn", "_supports_flex_attn", "_supports_sdpa", "attention_mask", "base_model_prefix", "class", "config", "decoder_input_ids", ...
blenderbot_small/modeling_blenderbot_small.py:BlenderbotSmallEncoder
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[ "BaseModelOutput", "Embedding", "False", "LayerNorm", "ModelEncoder", "ModelEncoderLayer", "ModelLearnedPositionalEmbedding", "ModelPreTrainedModel", "ModuleList", "None", "True", "_", "__init__", "all_attentions", "and", "attention_mask", "attentions", "class", "config", "crea...
blenderbot_small/modeling_blenderbot_small.py:BlenderbotSmallDecoder
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[ "BaseModelOutputWithPastAndCrossAttentions", "DynamicCache", "Embedding", "EncoderDecoderCache", "False", "LayerNorm", "ModelDecoder", "ModelDecoderLayer", "ModelLearnedPositionalEmbedding", "ModelPreTrainedModel", "ModuleList", "None", "Setting", "True", "ValueError", "You", "__init...
blenderbot_small/modeling_blenderbot_small.py:BlenderbotSmallModel
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[ "BaseModelOutput", "Embedding", "ModelDecoder", "ModelEncoder", "ModelModel", "ModelPreTrainedModel", "None", "Seq2SeqModelOutput", "__init__", "_tied_weights_keys", "and", "attention_mask", "attentions", "auto_docstring", "cache_position", "class", "config", "cross_attentions", ...
blenderbot_small/modeling_blenderbot_small.py:BlenderbotSmallForConditionalGeneration
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[ "CrossEntropyLoss", "False", "GenerationMixin", "Linear", "ModelForConditionalGeneration", "ModelModel", "ModelPreTrainedModel", "None", "Seq2SeqLMOutput", "True", "__init__", "_keys_to_ignore_on_load_missing", "_resize_final_logits_bias", "_tied_weights_keys", "and", "attention_mask",...
blenderbot_small/modeling_blenderbot_small.py:BlenderbotSmallDecoderWrapper
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[ "ModelDecoder", "ModelDecoderWrapper", "ModelPreTrainedModel", "__init__", "args", "class", "config", "decoder", "def", "forward", "kwargs", "post_init", "return", "self", "super" ]
blenderbot_small/modeling_blenderbot_small.py:BlenderbotSmallForCausalLM
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[ "CausalLMOutputWithCrossAttentions", "CrossEntropyLoss", "False", "GenerationMixin", "Linear", "ModelDecoderWrapper", "ModelForCausalLM", "ModelPreTrainedModel", "None", "True", "__init__", "_tied_weights_keys", "attention_mask", "attentions", "auto_docstring", "cache_position", "cla...
dots1/modeling_dots1.py:Dots1RMSNorm
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[ "ModelRMSNorm", "Module", "Parameter", "True", "__init__", "class", "def", "dtype", "eps", "extra_repr", "f", "float32", "forward", "hidden_size", "hidden_states", "input_dtype", "keepdim", "mean", "nn", "ones", "pow", "return", "rsqrt", "self", "shape", "super", ...
dots1/modeling_dots1.py:Dots1RotaryEmbedding
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[ "False", "ModelRotaryEmbedding", "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", "device", ...
dots1/modeling_dots1.py:rotate_half
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[ "Model_half", "cat", "def", "dim", "return", "shape", "torch", "x", "x1", "x2" ]
dots1/modeling_dots1.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" ]
dots1/modeling_dots1.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" ]
dots1/modeling_dots1.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", ...
dots1/modeling_dots1.py:Dots1Attention
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[ "ALL_ATTENTION_FUNCTIONS", "Linear", "ModelAttention", "ModelRMSNorm", "Module", "None", "Tensor", "True", "__init__", "_attn_implementation", "apply_rotary_pos_emb", "attention_dropout", "attention_interface", "attention_mask", "attn_output", "attn_weights", "cache_kwargs", "cache...
dots1/modeling_dots1.py:Dots1MLP
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[ "ACT2FN", "Linear", "ModelMLP", "Module", "None", "__init__", "act_fn", "class", "config", "def", "down_proj", "else", "forward", "gate_proj", "hidden_act", "hidden_size", "if", "intermediate_size", "is", "nn", "return", "self", "super", "up_proj", "x" ]
dots1/modeling_dots1.py:Dots1TopkRouter
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[ "F", "ModelTopkRouter", "Module", "Parameter", "__init__", "class", "config", "def", "e_score_correction_bias", "empty", "float32", "forward", "hidden_size", "hidden_states", "linear", "n_routed_experts", "nn", "register_buffer", "return", "router_logits", "self", "super", ...
dots1/modeling_dots1.py:Dots1NaiveMoe
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[ "ACT2FN", "ModelNaiveMoe", "Module", "None", "Parameter", "__init__", "act_fn", "chunk", "class", "config", "continue", "current_hidden_states", "current_state", "def", "dim", "down_proj", "dtype", "empty", "expert_hit", "expert_idx", "expert_mask", "final_hidden_states", ...
dots1/modeling_dots1.py:Dots1MoE
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dots1/modeling_dots1.py:Dots1DecoderLayer
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[ "False", "GradientCheckpointingLayer", "ModelAttention", "ModelDecoderLayer", "ModelMLP", "ModelMoE", "ModelRMSNorm", "None", "Tensor", "_", "__init__", "attention_mask", "attention_type", "cache_position", "class", "config", "def", "else", "eps", "first_k_dense_replace", "fo...
dots1/modeling_dots1.py:Dots1PreTrainedModel
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dots1/modeling_dots1.py:Dots1Model
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[ "BaseModelOutputWithPast", "DynamicCache", "Embedding", "False", "ModelDecoderLayer", "ModelModel", "ModelPreTrainedModel", "ModelRMSNorm", "ModelRotaryEmbedding", "ModuleList", "None", "ValueError", "You", "__init__", "and", "arange", "attention_mask", "attention_type", "auto_do...
dots1/modeling_dots1.py:Dots1ForCausalLM
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[ "CausalLMOutputWithPast", "GenerationMixin", "Linear", "ModelForCausalLM", "ModelModel", "ModelPreTrainedModel", "None", "__init__", "_pp_plan", "_tied_weights_keys", "_tp_plan", "attention_mask", "attentions", "auto_docstring", "cache_position", "can_return_tuple", "class", "colwi...
depth_anything/modeling_depth_anything.py:DepthAnythingReassembleLayer
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[ "Conv2d", "ConvTranspose2d", "Identity", "ModelReassembleLayer", "Module", "__init__", "channels", "class", "config", "def", "elif", "factor", "forward", "hidden_state", "if", "in_channels", "int", "kernel_size", "nn", "out_channels", "padding", "projection", "reassemble_...
depth_anything/modeling_depth_anything.py:DepthAnythingReassembleStage
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depth_anything/modeling_depth_anything.py:DepthAnythingPreActResidualLayer
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[ "Conv2d", "ModelPreActResidualLayer", "Module", "ReLU", "__init__", "activation1", "activation2", "class", "config", "convolution1", "convolution2", "def", "forward", "fusion_hidden_size", "hidden_state", "kernel_size", "nn", "padding", "residual", "return", "self", "stride...
depth_anything/modeling_depth_anything.py:DepthAnythingFeatureFusionLayer
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[ "Conv2d", "False", "ModelFeatureFusionLayer", "ModelPreActResidualLayer", "Module", "None", "True", "__init__", "align_corners", "bilinear", "class", "config", "def", "else", "forward", "functional", "fusion_hidden_size", "hidden_state", "if", "interpolate", "is", "kernel_s...
depth_anything/modeling_depth_anything.py:DepthAnythingFeatureFusionStage
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[ "ModelFeatureFusionLayer", "ModelFeatureFusionStage", "Module", "ModuleList", "None", "_", "__init__", "append", "class", "config", "def", "else", "enumerate", "for", "forward", "fused_hidden_state", "fused_hidden_states", "hidden_state", "hidden_states", "idx", "if", "in",...
depth_anything/modeling_depth_anything.py:DepthAnythingPreTrainedModel
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[ "Model", "ModelConfig", "ModelPreTrainedModel", "PreTrainedModel", "True", "base_model_prefix", "class", "config", "image", "input_modalities", "main_input_name", "pixel_values", "supports_gradient_checkpointing" ]
depth_anything/modeling_depth_anything.py:DepthAnythingNeck
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[ "ModelFeatureFusionStage", "ModelNeck", "ModelReassembleStage", "Module", "ModuleList", "None", "The", "TypeError", "ValueError", "__init__", "a", "be", "channel", "class", "config", "convs", "def", "enumerate", "equal", "feature", "features", "for", "forward", "fusion_...
depth_anything/modeling_depth_anything.py:DepthAnythingDepthEstimationHead
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[ "Conv2d", "Model", "ModelModelEstimationHead", "Model_estimation_type", "Module", "ReLU", "Sigmoid", "True", "Unknown", "ValueError", "__init__", "activation1", "activation2", "align_corners", "bilinear", "class", "config", "conv1", "conv2", "conv3", "def", "dim", "elif",...
depth_anything/modeling_depth_anything.py:DepthAnythingForDepthEstimation
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[ "DPTViTEmbeddings", "ModelEstimatorOutput", "ModelForModelEstimation", "ModelModelEstimationHead", "ModelNeck", "ModelPreTrainedModel", "None", "_", "__init__", "_no_split_modules", "attentions", "auto_docstring", "backbone", "class", "config", "def", "else", "feature_maps", "for...
swiftformer/modeling_swiftformer.py:SwiftFormerPatchEmbedding
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[ "BatchNorm2d", "Conv2d", "ModelPatchEmbedding", "Module", "ReLU", "Sequential", "__init__", "batch_norm_eps", "class", "config", "def", "embed_dims", "eps", "forward", "in_chs", "kernel_size", "nn", "num_channels", "out_chs", "padding", "patch_embedding", "return", "self"...
swiftformer/modeling_swiftformer.py:drop_path
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[ "False", "Model_path", "Model_prob", "def", "device", "div", "dtype", "floor_", "if", "input", "keep_prob", "ndim", "not", "or", "output", "rand", "random_tensor", "return", "shape", "torch", "training" ]
swiftformer/modeling_swiftformer.py:SwiftFormerDropPath
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[ "ModelDropPath", "Module", "__init__", "class", "config", "def", "drop_path", "drop_path_rate", "drop_prob", "extra_repr", "f", "forward", "hidden_states", "nn", "p", "return", "self", "super", "training" ]
swiftformer/modeling_swiftformer.py:SwiftFormerEmbeddings
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[ "BatchNorm2d", "Conv2d", "Iterable", "ModelEmbeddings", "Module", "__init__", "abc", "batch_norm_eps", "class", "collections", "config", "def", "down_pad", "down_patch_size", "down_stride", "else", "embed_dim", "embed_dims", "eps", "forward", "if", "in_chans", "index", ...
swiftformer/modeling_swiftformer.py:SwiftFormerConvEncoder
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[ "BatchNorm2d", "Conv2d", "Dropout", "GELU", "ModelConvEncoder", "Module", "Parameter", "True", "__init__", "act", "batch_norm_eps", "class", "config", "def", "depth_wise_conv", "dim", "drop_conv_encoder_rate", "drop_path", "eps", "forward", "groups", "hidden_dim", "input"...
swiftformer/modeling_swiftformer.py:SwiftFormerMlp
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[ "ACT2CLS", "BatchNorm2d", "Conv2d", "Dropout", "ModelMlp", "Module", "__init__", "act", "act_layer", "batch_norm_eps", "class", "config", "def", "drop", "drop_mlp_rate", "eps", "fc1", "fc2", "forward", "hidden_act", "hidden_features", "in_features", "int", "mlp_ratio", ...
swiftformer/modeling_swiftformer.py:SwiftFormerEfficientAdditiveAttention
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[ "Linear", "ModelEfficientAdditiveAttention", "Module", "Parameter", "__init__", "class", "config", "def", "dim", "final", "forward", "functional", "global_queries", "key", "nn", "normalize", "out", "proj", "query", "query_weight", "randn", "repeat", "return", "scale_fac...
swiftformer/modeling_swiftformer.py:SwiftFormerLocalRepresentation
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[ "BatchNorm2d", "Conv2d", "GELU", "Identity", "ModelLocalRepresentation", "Module", "Parameter", "True", "__init__", "act", "batch_norm_eps", "class", "config", "def", "depth_wise_conv", "dim", "drop_path", "eps", "forward", "groups", "input", "kernel_size", "layer_scale",...
swiftformer/modeling_swiftformer.py:SwiftFormerEncoderBlock
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swiftformer/modeling_swiftformer.py:SwiftFormerStage
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[ "GradientCheckpointingLayer", "ModelEncoderBlock", "ModelStage", "ModuleList", "__init__", "append", "block", "block_dpr", "block_idx", "blocks", "class", "config", "def", "depth", "depths", "dim", "drop_path", "drop_path_rate", "else", "embed_dims", "for", "forward", "if...
swiftformer/modeling_swiftformer.py:SwiftFormerEncoder
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[ "BaseModelOutputWithNoAttention", "False", "ModelEmbeddings", "ModelEncoder", "ModelStage", "Module", "ModuleList", "None", "__init__", "all_hidden_states", "append", "block", "break", "class", "config", "def", "depths", "downsamples", "else", "embed_dims", "for", "forward"...
swiftformer/modeling_swiftformer.py:SwiftFormerPreTrainedModel
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[ "BatchNorm2d", "Conv2d", "LayerNorm", "Linear", "Model", "ModelConfig", "ModelConvEncoder", "ModelEfficientAdditiveAttention", "ModelEncoderBlock", "ModelLocalRepresentation", "ModelPreTrainedModel", "None", "PreTrainedModel", "True", "_init_weights", "_no_split_modules", "base_model...
swiftformer/modeling_swiftformer.py:SwiftFormerModel
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[ "BaseModelOutputWithNoAttention", "ModelEncoder", "ModelModel", "ModelPatchEmbedding", "ModelPreTrainedModel", "None", "__init__", "auto_docstring", "class", "config", "def", "else", "embedding_output", "encoder", "encoder_outputs", "forward", "hidden_states", "if", "is", "kwar...
swiftformer/modeling_swiftformer.py:SwiftFormerForImageClassification
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[ "BatchNorm2d", "Identity", "ImageClassifierOutputWithNoAttention", "Linear", "Model", "ModelForImageClassification", "ModelModel", "ModelPreTrainedModel", "None", "__init__", "auto_docstring", "batch_norm_eps", "class", "cls_out", "config", "def", "dist_head", "distillation_out", ...
moshi/modeling_moshi.py:MoshiConditionalGenerationGenerateOutput
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[ "ModelConditionalGenerationGenerateOutput", "ModelOutput", "None", "attentions", "audio_codes", "audio_sequences", "beam_indices", "class", "hidden_states", "logits", "past_key_values", "r", "scores", "sequences", "sequences_scores" ]
moshi/modeling_moshi.py:MoshiCausalLMOutputWithPast
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[ "ModelCausalLMOutputWithPast", "ModelOutput", "None", "attentions", "class", "hidden_states", "last_hidden_state", "logits", "loss", "past_key_values", "r" ]
moshi/modeling_moshi.py:MoshiConditionalGenerationOutputWithPast
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[ "ModelConditionalGenerationOutputWithPast", "ModelOutput", "None", "attentions", "audio_logits", "class", "depth_attentions", "depth_hidden_states", "depth_loss", "depth_past_key_values", "hidden_states", "last_hidden_state", "logits", "loss", "past_key_values", "r" ]
moshi/modeling_moshi.py:MoshiUnconditionalInput
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[ "ModelOutput", "ModelUnconditionalInput", "Model_audio_codes", "None", "attention_mask", "class", "input_ids", "r", "user_audio_codes" ]
moshi/modeling_moshi.py:MoshiRMSNorm
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moshi/modeling_moshi.py:MoshiFlexibleLinear
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moshi/modeling_moshi.py:MoshiLinear
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moshi/modeling_moshi.py:MoshiRotaryEmbedding
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[ "False", "ModelRotaryEmbedding", "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", "device", ...
moshi/modeling_moshi.py:rotate_half
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[ "Model_half", "cat", "def", "dim", "return", "shape", "torch", "x", "x1", "x2" ]
moshi/modeling_moshi.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" ]
moshi/modeling_moshi.py:MoshiGatingMLP
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[ "ACT2FN", "False", "Linear", "ModelFlexibleLinear", "ModelGatingMLP", "Module", "None", "_", "__init__", "activation_fn", "batch_size", "class", "config", "def", "else", "fc1", "fc2", "ffn_dim", "forward", "hidden_act", "hidden_size", "hidden_states", "if", "is", "lay...
moshi/modeling_moshi.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" ]
moshi/modeling_moshi.py:MoshiAttention
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[ "False", "Instantiating", "ModelAttention", "ModelLinear", "ModelRotaryEmbedding", "Module", "None", "Please", "True", "ValueError", "_", "__class__", "__init__", "__name__", "a", "and", "apply_rotary_pos_emb", "attention_dropout", "attention_mask", "attn_output", "attn_weigh...
moshi/modeling_moshi.py:MoshiFlashAttention2
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[ "False", "ModelAttention", "ModelFlashAttention2", "None", "StaticCache", "The", "ValueError", "We", "_", "__init__", "_flash_attention_forward", "_flash_attn_uses_top_left_mask", "_is_quantized", "an", "and", "apply_rotary_pos_emb", "args", "at", "attention_dropout", "attentio...
moshi/modeling_moshi.py:MoshiSdpaAttention
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[ "False", "If", "ModelAttention", "ModelSdpaAttention", "None", "The", "True", "_", "__class__", "__name__", "and", "apply_rotary_pos_emb", "attention", "attention_dropout", "attention_mask", "attn_implementation", "attn_mask", "attn_output", "be", "bsz", "cache_kwargs", "ca...
moshi/modeling_moshi.py:MoshiDecoderLayer
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[ "False", "GradientCheckpointingLayer", "ModelDecoderLayer", "ModelGatingMLP", "ModelRMSNorm", "Model_ATTENTION_CLASSES", "None", "True", "__init__", "_attn_implementation", "attention_mask", "cache_position", "class", "config", "def", "else", "eps", "forward", "hidden_size", "h...
moshi/modeling_moshi.py:MoshiPreTrainedModel
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[ "MimiTransformerLayer", "ModelConfig", "ModelDecoderLayer", "ModelFlexibleLinear", "ModelPreTrainedModel", "PreTrainedModel", "True", "_init_weights", "_no_split_modules", "_supports_flash_attn", "_supports_sdpa", "audio", "base_model_prefix", "class", "config", "def", "if", "init"...
moshi/modeling_moshi.py:MoshiDepthDecoder
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[ "Attention", "AttentionMaskConverter", "BlockMask", "CausalLMOutputWithPast", "CrossEntropyLoss", "DynamicCache", "Embedding", "False", "Flash", "GenerationMixin", "Make", "Model", "ModelDecoderLayer", "ModelDepthConfig", "ModelDepthDecoder", "ModelFlexibleLinear", "ModelPreTrainedMo...
moshi/modeling_moshi.py:MoshiModel
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[ "Attention", "AttentionMaskConverter", "BaseModelOutputWithPast", "BlockMask", "DynamicCache", "Embedding", "False", "Flash", "Make", "Model", "ModelDecoderLayer", "ModelModel", "ModelPreTrainedModel", "ModelRMSNorm", "ModuleList", "None", "Setting", "StaticCache", "Tensor", "T...
moshi/modeling_moshi.py:MoshiForCausalLM
[ -0.00024000955454539508, 0.046759508550167084, 0.019313709810376167, -0.00047296000411733985, -0.0010235700756311417, 0.024960992857813835, 0.025525720790028572, -0.011181620880961418, 0.00022942089708521962, 0.01965254731476307, 0.02518688514828682, 0.002950705587863922, -0.0000966214938671...
[ "GenerationMixin", "Linear", "ModelCausalLMOutputWithPast", "ModelForCausalLM", "ModelModel", "ModelPreTrainedModel", "None", "__init__", "attention_mask", "attentions", "auto_docstring", "cache_position", "class", "config", "contiguous", "def", "device", "else", "float", "forw...
moshi/modeling_moshi.py:MoshiForConditionalGeneration
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[ "Any", "At", "AutoModel", "Check", "Embedding", "False", "GenerationMixin", "Make", "Model", "ModelConditionalGenerationGenerateOutput", "ModelConditionalGenerationOutputWithPast", "ModelConfig", "ModelDepthDecoder", "ModelForCausalLM", "ModelForConditionalGeneration", "ModelPreTrained...
luke/modeling_luke.py:BaseLukeModelOutputWithPooling
[ -0.00019867275841534138, 0.0184109415858984, 0.0393533892929554, 0.02450956590473652, -0.0010787660721689463, 0.06075610592961311, 0.0317588746547699, -0.001186642679385841, 0.01599450595676899, 0.003653421299532056, 0.015764368698000908, 0.012024646624922752, -0.002488353755325079, 0.0237...
[ "ModelModelModelOutputWithPooling", "ModelModelOutputWithPooling", "None", "class", "entity_hidden_states", "entity_last_hidden_state", "r" ]
luke/modeling_luke.py:BaseLukeModelOutput
[ -0.00017150506027974188, 0.03165803104639053, 0.02553439699113369, 0.016868876293301582, -0.0011337387841194868, 0.08134034276008606, 0.043212056159973145, -0.004968231078237295, 0.017908738926053047, 0.007972277700901031, 0.014615842141211033, 0.018024278804659843, -0.0010976324556395411, ...
[ "ModelModelModelOutput", "ModelModelOutput", "None", "class", "entity_hidden_states", "entity_last_hidden_state", "r" ]
luke/modeling_luke.py:LukeMaskedLMOutput
[ -0.00023732711269985884, 0.02603817544877529, 0.032205112278461456, -0.002055645454674959, -0.001106336945667863, 0.07811452448368073, 0.03197670727968216, -0.007765771355479956, 0.008565189316868782, -0.001969993580132723, 0.023068908601999283, 0.03563118726015091, -0.0035402781795710325, ...
[ "ModelMaskedLMOutput", "ModelOutput", "None", "attentions", "class", "entity_hidden_states", "entity_logits", "hidden_states", "logits", "loss", "mep_loss", "mlm_loss", "r" ]
luke/modeling_luke.py:EntityClassificationOutput
[ -0.00015837239334359765, 0.01959594339132309, 0.01959594339132309, 0.008390217088162899, -0.0008129782509058714, 0.027366748079657555, 0.048426758497953415, -0.004166953731328249, 0.014640647917985916, -0.018694980069994926, 0.03311038762331009, 0.027254128828644753, -0.0020553215872496367, ...
[ "ModelClassificationOutput", "ModelOutput", "Model_hidden_states", "None", "attentions", "class", "hidden_states", "logits", "loss", "r" ]
luke/modeling_luke.py:EntityPairClassificationOutput
[ -0.0002146075712516904, 0.019183434545993805, 0.030875112861394882, -0.009364694356918335, -0.0009577528107911348, 0.030421067029237747, 0.05039907991886139, 0, 0.014415953308343887, -0.011918701231479645, 0.02746976912021637, 0.025880608707666397, -0.0020857728086411953, -0.01827534288167...
[ "ModelOutput", "ModelPairClassificationOutput", "Model_hidden_states", "None", "attentions", "class", "hidden_states", "logits", "loss", "r" ]
luke/modeling_luke.py:EntitySpanClassificationOutput
[ -0.00014456479402724653, 0.019632603973150253, 0.017714476212859154, -0.0027220493648201227, -0.0007051941356621683, 0.042650140821933746, 0.04716338589787483, -0.011395937763154507, 0.013426896184682846, -0.016586165875196457, 0.03745991364121437, 0.04738904535770416, -0.0011776741594076157...
[ "ModelOutput", "ModelSpanClassificationOutput", "Model_hidden_states", "None", "attentions", "class", "hidden_states", "logits", "loss", "r" ]
luke/modeling_luke.py:LukeSequenceClassifierOutput
[ -0.00030442987917922437, 0.020744210109114647, 0.01764976978302002, 0.022692561149597168, -0.0013466544914990664, 0.052032437175512314, 0.04080076515674591, -0.014039590023458004, 0.006532707251608372, 0.0071917083114385605, 0.026130829006433487, 0.03025674819946289, -0.004899530205875635, ...
[ "ModelOutput", "ModelSequenceClassifierOutput", "None", "attentions", "class", "entity_hidden_states", "hidden_states", "logits", "loss", "r" ]
luke/modeling_luke.py:LukeTokenClassifierOutput
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[ "ModelOutput", "ModelTokenClassifierOutput", "None", "attentions", "class", "entity_hidden_states", "hidden_states", "logits", "loss", "r" ]
luke/modeling_luke.py:LukeQuestionAnsweringModelOutput
[ -0.00016167385911103338, 0.032568007707595825, 0.04251934215426445, 0.009781710803508759, -0.0009894793620333076, 0.05654167756438255, 0.05428001284599304, 0.010573294013738632, 0.013400377705693245, 0.01786717027425766, 0.01594475284218788, 0.02883625589311123, -0.0030391153413802385, -0....
[ "ModelOutput", "ModelQuestionAnsweringModelOutput", "None", "attentions", "class", "end_logits", "entity_hidden_states", "hidden_states", "loss", "r", "start_logits" ]
luke/modeling_luke.py:LukeMultipleChoiceModelOutput
[ -0.0003017971757799387, 0.03145919367671013, 0.024635324254631996, 0.0183897502720356, -0.0014746497618034482, 0.05967994034290314, 0.044181663542985916, -0.014630838297307491, 0.0091948751360178, 0.011970685794949532, 0.025444936007261276, 0.014399521052837372, -0.005985342897474766, -0.0...
[ "ModelMultipleChoiceModelOutput", "ModelOutput", "None", "attentions", "class", "entity_hidden_states", "hidden_states", "logits", "loss", "r" ]
luke/modeling_luke.py:LukeEmbeddings
[ -0.00022853288101032376, 0.014285963028669357, 0.0055839973501861095, -0.007256362121552229, -0.0011196340201422572, 0.0333339124917984, 0.02471698261797428, -0.007426432799547911, 0.0038832875434309244, -0.014739485457539558, 0.021995848044753075, 0.01723385974764824, -0.0013180500827729702...
[ "Dropout", "Embedding", "LayerNorm", "ModelEmbeddings", "Module", "None", "__init__", "arange", "class", "config", "create_position_ids_from_input_ids", "create_position_ids_from_inputs_embeds", "def", "device", "dropout", "dtype", "else", "embeddings", "eps", "expand", "forw...
luke/modeling_luke.py:LukeEntityEmbeddings
[ -0.0002992511144839227, 0.03299694135785103, 0.00784542690962553, -0.008768417872488499, -0.0014854392502456903, 0.04407284036278725, 0.025151515379548073, 0.011652766726911068, -0.0016801328165456653, 0.023074785247445107, 0.016036976128816605, 0.025382263585925102, -0.002754552522674203, ...
[ "Dropout", "Embedding", "LayerNorm", "Linear", "ModelEntityEmbeddings", "Module", "None", "__init__", "clamp", "class", "config", "def", "dim", "dropout", "embeddings", "entity_emb_size", "entity_embedding_dense", "entity_embeddings", "entity_ids", "entity_vocab_size", "eps",...
luke/modeling_luke.py:LukeSelfAttention
[ -0.0002379106153966859, 0.039088357239961624, 0.045451581478118896, -0.009260759688913822, -0.0009658460621722043, 0.03181610628962517, 0.01795337349176407, -0.01016979105770588, 0.0007172827608883381, 0.018748776987195015, 0.021589500829577446, 0.03227062150835991, 0.000025189230655087158, ...
[ "Dropout", "False", "Linear", "ModelSelfAttention", "Module", "None", "The", "ValueError", "__init__", "a", "all_head_size", "and", "attention", "attention_head_size", "attention_mask", "attention_probs", "attention_probs_dropout_prob", "attention_scores", "cat", "class", "co...
luke/modeling_luke.py:LukeSelfOutput
[ -0.00012168083776487038, 0.04875698313117027, 0.03859927877783775, 0.020879726856946945, -0.0006630723946727812, 0.05620596557855606, 0.02370131015777588, -0.01952536590397358, 0.0035551965702325106, 0.017155233770608902, 0.01365646906197071, 0.003512872848659754, 0.0036962758749723434, -0...
[ "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" ]
luke/modeling_luke.py:LukeAttention
[ -0.00010630641918396577, 0.03058813512325287, 0.056003276258707047, -0.014169503934681416, -0.0005447106086649001, 0.05307941138744354, 0.031262874603271484, -0.029913395643234253, 0.005116765387356281, -0.002642724895849824, 0.021928993985056877, 0.04250850901007652, 0.0014619329012930393, ...
[ "False", "ModelAttention", "ModelSelfAttention", "ModelSelfOutput", "Module", "None", "__init__", "attention_mask", "attention_output", "cat", "class", "concat_hidden_states", "concat_self_outputs", "config", "def", "dim", "else", "entity_attention_output", "entity_hidden_states"...
luke/modeling_luke.py:LukeIntermediate
[ -0.00025367451598867774, 0.02240910567343235, 0.04047359153628349, 0.012862369418144226, -0.0009432404185645282, 0.03635763004422188, 0.03452831506729126, -0.018864808604121208, -0.001329111517407, -0.003772961674258113, 0.023209432139992714, -0.02103712037205696, -0.0013719861162826419, 0...
[ "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" ]
luke/modeling_luke.py:LukeOutput
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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...
luke/modeling_luke.py:LukeLayer
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[ "False", "GradientCheckpointingLayer", "ModelAttention", "ModelIntermediate", "ModelLayer", "ModelOutput", "None", "__init__", "apply_chunking_to_forward", "attention", "attention_mask", "attention_output", "cat", "chunk_size_feed_forward", "class", "concat_attention_output", "config...
luke/modeling_luke.py:LukeEncoder
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[ "BaseModelModelOutput", "False", "ModelEncoder", "ModelLayer", "Module", "ModuleList", "None", "True", "_", "__init__", "all_entity_hidden_states", "all_self_attentions", "all_word_hidden_states", "attention_mask", "attentions", "class", "config", "def", "else", "entity_hidden_...
luke/modeling_luke.py:LukePooler
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[ "Linear", "ModelPooler", "Module", "Tanh", "__init__", "activation", "class", "config", "def", "dense", "first_token_tensor", "forward", "hidden_size", "hidden_states", "nn", "pooled_output", "return", "self", "super" ]