Datasets:
itazap
/
transformers_code_embeddings_v3

Dataset card Data Studio Files
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1
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24
117
embedding
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2.56k
2.56k
tokens
listlengths
4
448
lxmert/modeling_lxmert.py:LxmertLMPredictionHead
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[ "Linear", "ModelLMPredictionHead", "ModelPredictionHeadTransform", "Module", "Parameter", "__init__", "bias", "class", "config", "decoder", "def", "forward", "hidden_size", "hidden_states", "nn", "return", "self", "super", "torch", "transform", "vocab_size", "zeros" ]
lxmert/modeling_lxmert.py:LxmertVisualAnswerHead
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[ "GeLU", "LayerNorm", "Linear", "ModelVisualAnswerHead", "Module", "Sequential", "__init__", "class", "config", "def", "eps", "forward", "hid_dim", "hidden_size", "hidden_states", "logit_fc", "nn", "num_labels", "return", "self", "super" ]
lxmert/modeling_lxmert.py:LxmertVisualObjHead
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[ "ModelPredictionHeadTransform", "ModelVisualObjHead", "Module", "ModuleDict", "__init__", "class", "config", "decoder_dict", "def", "for", "forward", "hidden_states", "if", "in", "key", "nn", "num", "num_attr_labels", "num_object_labels", "output", "return", "self", "shap...
lxmert/modeling_lxmert.py:LxmertPreTrainingHeads
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lxmert/modeling_lxmert.py:LxmertPreTrainedModel
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[ "Model", "ModelConfig", "ModelLMPredictionHead", "ModelPreTrainedModel", "PreTrainedModel", "_init_weights", "base_model_prefix", "bias", "class", "config", "def", "if", "image", "init", "input_modalities", "isinstance", "module", "no_grad", "self", "super", "text", "torch"...
lxmert/modeling_lxmert.py:LxmertModel
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lxmert/modeling_lxmert.py:LxmertForPreTraining
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lxmert/modeling_lxmert.py:LxmertForQuestionAnswering
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dpt/modeling_dpt.py:BaseModelOutputWithIntermediateActivations
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[ "ModelModelOutputWithIntermediateActivations", "ModelOutput", "None", "class", "intermediate_activations", "last_hidden_states", "r" ]
dpt/modeling_dpt.py:BaseModelOutputWithPoolingAndIntermediateActivations
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[ "ModelModelOutputWithPoolingAndIntermediateActivations", "ModelOutput", "None", "attentions", "class", "hidden_states", "intermediate_activations", "last_hidden_state", "pooler_output", "r" ]
dpt/modeling_dpt.py:DPTViTHybridEmbeddings
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[ "BaseModelOutputWithIntermediateActivations", "Conv2d", "Expected", "False", "Input", "Iterable", "Make", "Model", "Module", "None", "Parameter", "ValueError", "__init__", "_resize_pos_embed", "abc", "backbone", "backbone_featmap_shape", "backbone_output", "batch_size", "biline...
dpt/modeling_dpt.py:DPTViTEmbeddings
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[ "BaseModelOutputWithIntermediateActivations", "Dropout", "Model", "ModelViTPatchEmbeddings", "Module", "Parameter", "_", "__init__", "_resize_pos_embed", "batch_size", "bilinear", "cat", "class", "cls_token", "cls_tokens", "config", "def", "dim", "dropout", "embeddings", "exp...
dpt/modeling_dpt.py:DPTViTPatchEmbeddings
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[ "Conv2d", "Iterable", "Make", "Model", "Module", "ValueError", "__init__", "abc", "batch_size", "channel", "class", "collections", "config", "configuration", "def", "dimension", "else", "embeddings", "flatten", "forward", "height", "hidden_size", "if", "image_size", "...
dpt/modeling_dpt.py:eager_attention_forward
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dpt/modeling_dpt.py:DPTSelfAttention
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dpt/modeling_dpt.py:DPTViTSelfOutput
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dpt/modeling_dpt.py:DPTViTAttention
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dpt/modeling_dpt.py:DPTViTIntermediate
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dpt/modeling_dpt.py:DPTViTOutput
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dpt/modeling_dpt.py:DPTViTLayer
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[ "GradientCheckpointingLayer", "LayerNorm", "Model", "ModelViTAttention", "ModelViTIntermediate", "ModelViTOutput", "__init__", "attention", "attention_output", "chunk_size_feed_forward", "class", "config", "def", "eps", "forward", "hidden_size", "hidden_states", "hidden_states_norm...
dpt/modeling_dpt.py:DPTViTEncoder
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dpt/modeling_dpt.py:DPTReassembleStage
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[ "ACT2FN", "Hybrid", "Identity", "Linear", "Model", "ModelReassembleLayer", "Module", "ModuleList", "None", "Readout", "Sequential", "ValueError", "_", "__init__", "_get_backbone_hidden_size", "_init_reassemble_Model", "_init_reassemble_Model_hybrid", "add", "and", "append", "...
dpt/modeling_dpt.py:_get_backbone_hidden_size
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[ "None", "_get_backbone_hidden_size", "and", "backbone_config", "config", "def", "else", "hasattr", "hidden_size", "if", "is", "not", "return" ]
dpt/modeling_dpt.py:DPTReassembleLayer
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[ "Conv2d", "ConvTranspose2d", "Identity", "Model", "Module", "__init__", "_get_backbone_hidden_size", "channels", "class", "config", "def", "elif", "factor", "forward", "hidden_size", "hidden_state", "if", "in_channels", "int", "kernel_size", "nn", "out_channels", "padding...
dpt/modeling_dpt.py:DPTFeatureFusionStage
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[ "Model", "ModelFeatureFusionLayer", "Module", "ModuleList", "None", "_", "__init__", "append", "class", "config", "def", "else", "for", "forward", "fused_hidden_state", "fused_hidden_states", "hidden_state", "hidden_states", "if", "in", "is", "layer", "layers", "len", ...
dpt/modeling_dpt.py:DPTPreActResidualLayer
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dpt/modeling_dpt.py:DPTFeatureFusionLayer
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dpt/modeling_dpt.py:DPTPreTrainedModel
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dpt/modeling_dpt.py:DPTModel
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dpt/modeling_dpt.py:DPTViTPooler
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dpt/modeling_dpt.py:DPTNeck
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dpt/modeling_dpt.py:DPTDepthEstimationHead
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dpt/modeling_dpt.py:DPTForDepthEstimation
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dpt/modeling_dpt.py:DPTSemanticSegmentationHead
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dpt/modeling_dpt.py:DPTAuxiliaryHead
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dpt/modeling_dpt.py:DPTForSemanticSegmentation
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rwkv/modeling_rwkv.py:load_wkv_cuda_kernel
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rwkv/modeling_rwkv.py:RwkvLinearAttention
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rwkv/modeling_rwkv.py:rwkv_linear_attention_cpu
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rwkv/modeling_rwkv.py:rwkv_linear_attention
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rwkv/modeling_rwkv.py:RwkvSelfAttention
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rwkv/modeling_rwkv.py:RwkvFeedForward
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rwkv/modeling_rwkv.py:RwkvBlock
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rwkv/modeling_rwkv.py:RwkvPreTrainedModel
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rwkv/modeling_rwkv.py:RwkvOutput
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rwkv/modeling_rwkv.py:RwkvCausalLMOutput
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rwkv/modeling_rwkv.py:RwkvModel
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rwkv/modeling_rwkv.py:RwkvForCausalLM
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encodec/modeling_encodec.py:EncodecOutput
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[ "ModelOutput", "None", "audio_codes", "audio_values", "class", "r" ]
encodec/modeling_encodec.py:EncodecEncoderOutput
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encodec/modeling_encodec.py:EncodecDecoderOutput
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encodec/modeling_encodec.py:EncodecConv1d
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encodec/modeling_encodec.py:EncodecConvTranspose1d
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encodec/modeling_encodec.py:EncodecLSTM
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encodec/modeling_encodec.py:EncodecResnetBlock
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encodec/modeling_encodec.py:EncodecEncoder
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encodec/modeling_encodec.py:EncodecDecoder
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encodec/modeling_encodec.py:EncodecEuclideanCodebook
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encodec/modeling_encodec.py:EncodecVectorQuantization
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encodec/modeling_encodec.py:EncodecResidualVectorQuantizer
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encodec/modeling_encodec.py:EncodecPreTrainedModel
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encodec/modeling_encodec.py:EncodecModel
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falcon/modeling_falcon.py:FalconLinear
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falcon/modeling_falcon.py:rotate_half
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falcon/modeling_falcon.py:apply_rotary_pos_emb
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falcon/modeling_falcon.py:build_alibi_tensor
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falcon/modeling_falcon.py:FalconForTokenClassification
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falcon/modeling_falcon.py:FalconForQuestionAnswering
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conditional_detr/modeling_conditional_detr.py:ConditionalDetrFrozenBatchNorm2d
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conditional_detr/modeling_conditional_detr.py:replace_batch_norm
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conditional_detr/modeling_conditional_detr.py:ConditionalDetrLearnedPositionEmbedding
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conditional_detr/modeling_conditional_detr.py:ConditionalDetrDecoderLayer
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conditional_detr/modeling_conditional_detr.py:ConditionalDetrConvBlock
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[ "ACT2FN", "Conv2d", "GroupNorm", "ModelConvBlock", "Module", "__init__", "activation", "class", "conv", "def", "forward", "in_channels", "kernel_size", "min", "nn", "norm", "out_channels", "padding", "relu", "return", "self", "super", "x" ]
conditional_detr/modeling_conditional_detr.py:ConditionalDetrFPNFusionStage
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[ "Conv2d", "ModelConvBlock", "ModelFPNFusionStage", "Module", "__init__", "activation", "class", "current_channels", "def", "features", "forward", "fpn_adapter", "fpn_channels", "fpn_features", "functional", "interpolate", "kernel_size", "mode", "nearest", "nn", "output_channe...
conditional_detr/modeling_conditional_detr.py:ConditionalDetrMaskHeadSmallConv
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[ "Conv2d", "ModelConvBlock", "ModelFPNFusionStage", "ModelMaskHeadSmallConv", "Module", "ModuleList", "ValueError", "__init__", "activation_function", "attention_masks", "be", "by", "cat", "class", "conv1", "conv2", "def", "dim", "divisible", "expand", "f", "features", "fl...
conditional_detr/modeling_conditional_detr.py:ConditionalDetrMHAttentionMap
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[ "Linear", "ModelMHAttentionMap", "Module", "None", "_", "__init__", "attention_dropout", "attention_mask", "attn_weights", "attn_weights_shape", "batch_size", "bias", "class", "contiguous", "conv2d", "def", "dim", "dropout", "flatten", "forward", "functional", "head_dim", ...
conditional_detr/modeling_conditional_detr.py:ConditionalDetrPreTrainedModel
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[ "Conv2d", "Embedding", "False", "GroupNorm", "LayerNorm", "Linear", "ModelConfig", "ModelConvEncoder", "ModelDecoderLayer", "ModelEncoderLayer", "ModelLearnedPositionEmbedding", "ModelMHAttentionMap", "ModelMaskHeadSmallConv", "ModelPreTrainedModel", "None", "PreTrainedModel", "True"...