Datasets:
itazap
/
transformers_code_embeddings_v3

Dataset card Data Studio Files
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1
identifier
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24
117
embedding
listlengths
2.56k
2.56k
tokens
listlengths
4
444
nystromformer/modeling_nystromformer.py:NystromformerForMaskedLM
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[ "CrossEntropyLoss", "MaskedLMOutput", "Model", "ModelForMaskedLM", "ModelModel", "ModelOnlyMLMHead", "ModelPreTrainedModel", "None", "__init__", "_tied_weights_keys", "attention_mask", "attentions", "auto_docstring", "bias", "class", "cls", "config", "decoder", "def", "else", ...
nystromformer/modeling_nystromformer.py:NystromformerClassificationHead
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[ "ACT2FN", "Dropout", "Linear", "ModelClassificationHead", "Module", "__init__", "class", "config", "def", "dense", "dropout", "features", "forward", "hidden_act", "hidden_dropout_prob", "hidden_size", "kwargs", "nn", "num_labels", "out_proj", "return", "self", "super", ...
nystromformer/modeling_nystromformer.py:NystromformerForSequenceClassification
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nystromformer/modeling_nystromformer.py:NystromformerForMultipleChoice
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nystromformer/modeling_nystromformer.py:NystromformerForTokenClassification
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nystromformer/modeling_nystromformer.py:NystromformerForQuestionAnswering
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convnextv2/modeling_convnextv2.py:ConvNextV2GRN
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convnextv2/modeling_convnextv2.py:ConvNextV2LayerNorm
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[ "LayerNorm", "ModelLayerNorm", "NotImplementedError", "Unsupported", "__init__", "channels_first", "channels_last", "class", "data", "data_format", "def", "else", "eps", "f", "features", "format", "forward", "if", "in", "kwargs", "nn", "normalized_shape", "not", "permut...
convnextv2/modeling_convnextv2.py:ConvNextV2Embeddings
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convnextv2/modeling_convnextv2.py:ConvNextV2DropPath
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convnextv2/modeling_convnextv2.py:ConvNextV2Layer
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[ "ACT2FN", "Identity", "Linear", "Model2d", "ModelDropPath", "ModelGRN", "ModelLayer", "ModelLayerNorm", "Module", "__init__", "act", "class", "config", "def", "dim", "drop_path", "dwModel", "else", "eps", "features", "forward", "grn", "groups", "hidden_act", "if", "...
convnextv2/modeling_convnextv2.py:ConvNextV2Stage
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convnextv2/modeling_convnextv2.py:ConvNextV2PreTrainedModel
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convnextv2/modeling_convnextv2.py:ConvNextV2Encoder
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convnextv2/modeling_convnextv2.py:ConvNextV2Model
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[ "BaseModelOutputWithPoolingAndNoAttention", "LayerNorm", "ModelEmbeddings", "ModelEncoder", "ModelModel", "ModelPreTrainedModel", "None", "__init__", "auto_docstring", "can_return_tuple", "class", "config", "def", "embedding_output", "embeddings", "encoder", "encoder_outputs", "eps...
convnextv2/modeling_convnextv2.py:ConvNextV2ForImageClassification
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convnextv2/modeling_convnextv2.py:ConvNextV2Backbone
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[ "BackboneMixin", "BackboneOutput", "False", "ModelBackbone", "ModelEmbeddings", "ModelEncoder", "ModelLayerNorm", "ModelPreTrainedModel", "ModuleDict", "__init__", "append", "auto_docstring", "can_return_tuple", "channels", "channels_first", "class", "config", "data_format", "def...
speech_encoder_decoder/modeling_speech_encoder_decoder.py:shift_tokens_right
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speech_encoder_decoder/modeling_speech_encoder_decoder.py:SpeechEncoderDecoderModel
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audio_spectrogram_transformer/modeling_audio_spectrogram_transformer.py:ASTPatchEmbeddings
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audio_spectrogram_transformer/modeling_audio_spectrogram_transformer.py:ASTEmbeddings
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[ "ASTPatchEmbeddings", "Dropout", "Model", "Module", "Parameter", "__init__", "batch_size", "cat", "class", "cls_token", "cls_tokens", "config", "def", "dim", "distillation_token", "distillation_tokens", "dropout", "embeddings", "expand", "forward", "frequency_out_dimension", ...
audio_spectrogram_transformer/modeling_audio_spectrogram_transformer.py:eager_attention_forward
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audio_spectrogram_transformer/modeling_audio_spectrogram_transformer.py:ASTAttention
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audio_spectrogram_transformer/modeling_audio_spectrogram_transformer.py:ASTMLP
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audio_spectrogram_transformer/modeling_audio_spectrogram_transformer.py:ASTLayer
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audio_spectrogram_transformer/modeling_audio_spectrogram_transformer.py:ASTPreTrainedModel
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[ "ASTAttention", "ASTConfig", "ASTEmbeddings", "ASTLayer", "Model", "PreTrainedModel", "True", "_can_compile_fullgraph", "_can_record_outputs", "_init_weights", "_input_embed_layer", "_no_split_modules", "_supports_attention_backend", "_supports_flash_attn", "_supports_flex_attn", "_sup...
audio_spectrogram_transformer/modeling_audio_spectrogram_transformer.py:ASTModel
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[ "ASTEmbeddings", "ASTLayer", "ASTPreTrainedModel", "BaseModelOutputWithPooling", "False", "LayerNorm", "Model", "ModuleList", "None", "_", "__init__", "attention_mask", "auto_docstring", "capture_outputs", "class", "config", "create_bidirectional_mask", "def", "embedding_output",...
audio_spectrogram_transformer/modeling_audio_spectrogram_transformer.py:ASTMLPHead
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audio_spectrogram_transformer/modeling_audio_spectrogram_transformer.py:ASTForAudioClassification
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swin/modeling_swin.py:SwinDropPath
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swin/modeling_swin.py:SwinEncoderOutput
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[ "ModelEncoderOutput", "ModelOutput", "None", "attentions", "class", "hidden_states", "last_hidden_state", "r", "reshaped_hidden_states" ]
swin/modeling_swin.py:SwinModelOutput
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swin/modeling_swin.py:SwinMaskedImageModelingOutput
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[ "ModelMaskedImageModelingOutput", "ModelOutput", "None", "attentions", "class", "hidden_states", "loss", "r", "reconstruction", "reshaped_hidden_states" ]
swin/modeling_swin.py:SwinImageClassifierOutput
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[ "ModelImageClassifierOutput", "ModelOutput", "None", "attentions", "class", "hidden_states", "logits", "loss", "r", "reshaped_hidden_states" ]
swin/modeling_swin.py:SwinEmbeddings
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[ "Dropout", "False", "LayerNorm", "ModelEmbeddings", "ModelPatchEmbeddings", "Module", "None", "Parameter", "_", "__init__", "align_corners", "and", "batch_size", "bicubic", "bool_masked_pos", "class", "config", "def", "dim", "dropout", "else", "embed_dim", "embeddings", ...
swin/modeling_swin.py:SwinPatchEmbeddings
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swin/modeling_swin.py:SwinPatchMerging
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swin/modeling_swin.py:SwinRelativePositionBias
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swin/modeling_swin.py:eager_attention_forward
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swin/modeling_swin.py:SwinAttention
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swin/modeling_swin.py:SwinMLP
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swin/modeling_swin.py:window_partition
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[ "Model_partition", "Model_size", "Models", "batch_size", "contiguous", "def", "height", "input_feature", "num_channels", "return", "shape", "transpose", "view", "width" ]
swin/modeling_swin.py:window_reverse
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[ "Model_reverse", "Model_size", "Models", "contiguous", "def", "height", "num_channels", "return", "shape", "transpose", "view", "width" ]
swin/modeling_swin.py:SwinLayer
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[ "Dropout", "False", "GradientCheckpointingLayer", "Identity", "LayerNorm", "ModelAttention", "ModelDropPath", "ModelLayer", "ModelMLP", "None", "True", "_", "__init__", "always_partition", "arange", "attention", "attention_output", "attention_windows", "attn_mask", "attn_weight...
swin/modeling_swin.py:SwinStage
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swin/modeling_swin.py:SwinPreTrainedModel
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swin/modeling_swin.py:SwinEncoder
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swin/modeling_swin.py:SwinModel
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swin/modeling_swin.py:SwinForMaskedImageModeling
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swin/modeling_swin.py:SwinForImageClassification
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swin/modeling_swin.py:SwinBackbone
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nllb_moe/modeling_nllb_moe.py:NllbMoeScaledWordEmbedding
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nllb_moe/modeling_nllb_moe.py:NllbMoeSinusoidalPositionalEmbedding
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nllb_moe/modeling_nllb_moe.py:NllbMoeTop2Router
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nllb_moe/modeling_nllb_moe.py:NllbMoeDenseActDense
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nllb_moe/modeling_nllb_moe.py:NllbMoeExperts
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nllb_moe/modeling_nllb_moe.py:NllbMoeSparseMLP
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nllb_moe/modeling_nllb_moe.py:eager_attention_forward
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nllb_moe/modeling_nllb_moe.py:NllbMoeAttention
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nllb_moe/modeling_nllb_moe.py:NllbMoeEncoderLayer
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nllb_moe/modeling_nllb_moe.py:NllbMoeDecoderLayer
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nllb_moe/modeling_nllb_moe.py:NllbMoePreTrainedModel
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nllb_moe/modeling_nllb_moe.py:NllbMoeEncoder
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nllb_moe/modeling_nllb_moe.py:NllbMoeDecoder
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nllb_moe/modeling_nllb_moe.py:NllbMoeModel
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nllb_moe/modeling_nllb_moe.py:load_balancing_loss_func
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nllb_moe/modeling_nllb_moe.py:shift_tokens_right
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nllb_moe/modeling_nllb_moe.py:NllbMoeForConditionalGeneration
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pi0/modeling_pi0.py:PI0TimestepEmbeddings
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pi0/modeling_pi0.py:PI0ActionTimeEmbedding
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pi0/modeling_pi0.py:PI0PreTrainedModel
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pi0/modeling_pi0.py:PI0Model
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pi0/modeling_pi0.py:PI0ForConditionalGeneration
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pegasus/modeling_pegasus.py:shift_tokens_right
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pegasus/modeling_pegasus.py:PegasusSinusoidalPositionalEmbedding
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pegasus/modeling_pegasus.py:eager_attention_forward
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pegasus/modeling_pegasus.py:PegasusAttention
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pegasus/modeling_pegasus.py:PegasusEncoderLayer
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pegasus/modeling_pegasus.py:PegasusDecoderLayer
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pegasus/modeling_pegasus.py:PegasusPreTrainedModel
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pegasus/modeling_pegasus.py:PegasusEncoder
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pegasus/modeling_pegasus.py:PegasusDecoder
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pegasus/modeling_pegasus.py:PegasusModel
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pegasus/modeling_pegasus.py:PegasusForConditionalGeneration
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pegasus/modeling_pegasus.py:PegasusDecoderWrapper
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pegasus/modeling_pegasus.py:PegasusForCausalLM
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table_transformer/modeling_table_transformer.py:TableTransformerDecoderOutput
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table_transformer/modeling_table_transformer.py:TableTransformerModelOutput
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table_transformer/modeling_table_transformer.py:TableTransformerObjectDetectionOutput
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table_transformer/modeling_table_transformer.py:TableTransformerFrozenBatchNorm2d
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table_transformer/modeling_table_transformer.py:replace_batch_norm
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table_transformer/modeling_table_transformer.py:TableTransformerConvEncoder
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table_transformer/modeling_table_transformer.py:TableTransformerConvModel
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table_transformer/modeling_table_transformer.py:TableTransformerSinePositionEmbedding
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[ "False", "ModelSinePositionEmbedding", "Module", "None", "__init__", "and", "arange", "cat", "class", "cos", "cumsum", "def", "device", "dim", "dim_t", "div", "dtype", "embedding_dim", "flatten", "float", "float32", "floor", "forward", "if", "int64", "is", "math",...
table_transformer/modeling_table_transformer.py:TableTransformerLearnedPositionEmbedding
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[ "Embedding", "ModelLearnedPositionEmbedding", "Module", "None", "__init__", "arange", "cat", "class", "column_embeddings", "def", "device", "dim", "embedding_dim", "forward", "height", "height_values", "nn", "permute", "pixel_mask", "pixel_values", "pos", "repeat", "retur...
table_transformer/modeling_table_transformer.py:build_position_encoding
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[ "ModelLearnedPositionEmbedding", "ModelSinePositionEmbedding", "Model_position_encoding", "True", "config", "d_model", "def", "elif", "else", "if", "learned", "n_steps", "normalize", "position_embedding", "position_embedding_type", "return", "sine" ]
table_transformer/modeling_table_transformer.py:TableTransformerAttention
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[ "Attention", "False", "Linear", "ModelAttention", "Module", "None", "ValueError", "__init__", "_shape", "and", "attention_mask", "attn_output", "attn_probs", "attn_weights", "attn_weights_reshaped", "batch_size", "be", "bmm", "bool", "but", "by", "class", "contiguous", ...
table_transformer/modeling_table_transformer.py:TableTransformerEncoderLayer
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[ "ACT2FN", "False", "LayerNorm", "Linear", "ModelAttention", "ModelEncoderLayer", "Module", "None", "__init__", "activation_dropout", "activation_fn", "activation_function", "all", "attention_dropout", "attention_mask", "attn_weights", "clamp", "clamp_value", "class", "config", ...
table_transformer/modeling_table_transformer.py:TableTransformerDecoderLayer
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[ "ACT2FN", "False", "GradientCheckpointingLayer", "LayerNorm", "Linear", "ModelAttention", "ModelDecoderLayer", "None", "__init__", "activation_dropout", "activation_fn", "activation_function", "attention_dropout", "attention_mask", "class", "config", "cross_attn_weights", "d_model"...
table_transformer/modeling_table_transformer.py:TableTransformerPreTrainedModel
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[ "ModelConfig", "ModelConvEncoder", "ModelDecoderLayer", "ModelEncoderLayer", "ModelLearnedPositionEmbedding", "ModelPreTrainedModel", "PreTrainedModel", "_init_weights", "_no_split_modules", "base_model_prefix", "class", "column_embeddings", "config", "def", "if", "image", "init", ...