Datasets:
itazap
/
transformers_code_embeddings_v3

Dataset card Data Studio Files
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1
identifier
stringlengths
24
102
embedding
listlengths
2.56k
2.56k
tokens
listlengths
4
448
xlstm/modeling_xlstm.py:xLSTMForCausalLM
[ -0.00038795583532191813, 0.037243761122226715, 0.01335153728723526, 0.00813975278288126, -0.0020056585781276226, 0.012121790088713169, 0.02693731151521206, 0.014171368442475796, -0.0020642178133130074, 0.02084713615477085, 0.012707384303212166, -0.025531886145472527, 0.0006258533103391528, ...
[ "CrossEntropyLoss", "GenerationMixin", "Linear", "ModelCausalLMOutput", "ModelForCausalLM", "ModelModel", "ModelPreTrainedModel", "Model_outputs", "None", "__init__", "and", "auto_docstring", "backbone", "cache_params", "can_return_tuple", "class", "config", "contiguous", "def", ...
beit/modeling_beit.py:BeitModelOutputWithPooling
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[ "BaseModelOutputWithPooling", "ModelModelOutputWithPooling", "class", "r" ]
beit/modeling_beit.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" ]
beit/modeling_beit.py:BeitDropPath
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[ "ModelDropPath", "Module", "None", "__init__", "class", "def", "drop_path", "drop_prob", "extra_repr", "f", "forward", "hidden_states", "nn", "p", "return", "self", "super", "training" ]
beit/modeling_beit.py:BeitEmbeddings
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[ "Dropout", "False", "Iterable", "ModelEmbeddings", "ModelPatchEmbeddings", "Module", "None", "Parameter", "_", "__init__", "abc", "align_corners", "and", "batch_size", "bicubic", "bool_masked_pos", "cat", "class", "class_pos_embed", "cls_token", "cls_tokens", "collections",...
beit/modeling_beit.py:BeitPatchEmbeddings
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[ "Conv2d", "Iterable", "Make", "ModelPatchEmbeddings", "Module", "ValueError", "__init__", "abc", "batch_size", "channel", "class", "collections", "config", "configuration", "def", "dimension", "dtype", "else", "embeddings", "flatten", "forward", "height", "hidden_size", ...
beit/modeling_beit.py:BeitSelfAttention
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[ "Dropout", "False", "Linear", "ModelRelativePositionBias", "ModelSelfAttention", "Module", "None", "The", "ValueError", "_", "__init__", "a", "all_head_size", "and", "attention", "attention_head_size", "attention_probs", "attention_probs_dropout_prob", "attention_scores", "batc...
beit/modeling_beit.py:BeitSdpaSelfAttention
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[ "False", "If", "ModelSdpaSelfAttention", "ModelSelfAttention", "None", "The", "True", "_", "__class__", "__name__", "all_head_size", "attention", "attention_head_size", "attention_probs_dropout_prob", "attn_bias", "attn_implementation", "attn_mask", "batch_size", "be", "class",...
beit/modeling_beit.py:BeitSelfOutput
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[ "Dropout", "Linear", "ModelSelfOutput", "Module", "None", "__init__", "class", "config", "def", "dense", "dropout", "forward", "gamma", "hidden_dropout_prob", "hidden_size", "hidden_states", "input_tensor", "nn", "return", "self", "super" ]
beit/modeling_beit.py:BeitAttention
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[ "False", "ModelAttention", "ModelSelfOutput", "Model_SELF_ATTENTION_CLASSES", "Module", "None", "__init__", "_attn_implementation", "attention", "attention_output", "class", "config", "def", "forward", "hidden_states", "interpolate_pos_encoding", "nn", "output", "output_attention...
beit/modeling_beit.py:BeitIntermediate
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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" ]
beit/modeling_beit.py:BeitOutput
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[ "Dropout", "Linear", "ModelOutput", "Module", "__init__", "class", "config", "def", "dense", "dropout", "forward", "hidden_dropout_prob", "hidden_size", "hidden_states", "intermediate_size", "nn", "return", "self", "super" ]
beit/modeling_beit.py:BeitLayer
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[ "False", "GradientCheckpointingLayer", "Identity", "LayerNorm", "ModelAttention", "ModelDropPath", "ModelIntermediate", "ModelLayer", "ModelOutput", "None", "Parameter", "True", "__init__", "attention", "attention_output", "chunk_size_feed_forward", "class", "config", "def", "d...
beit/modeling_beit.py:BeitRelativePositionBias
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[ "False", "ModelRelativePositionBias", "Module", "None", "Parameter", "__init__", "align_corners", "arange", "bilinear", "cat", "class", "compile_compatible_method_lru_cache", "config", "contiguous", "coords", "coords_flatten", "def", "dim_size", "dtype", "flatten", "forward",...
beit/modeling_beit.py:BeitEncoder
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[ "BaseModelOutput", "False", "ModelEncoder", "ModelLayer", "ModelRelativePositionBias", "Module", "ModuleList", "None", "True", "__init__", "all_hidden_states", "all_self_attentions", "attentions", "class", "config", "cpu", "def", "device", "dim_size", "dpr", "drop_path_rate",...
beit/modeling_beit.py:BeitPreTrainedModel
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[ "Model", "ModelConfig", "ModelEmbeddings", "ModelLayer", "ModelPreTrainedModel", "ModelRelativePositionBias", "None", "PreTrainedModel", "True", "_init_weights", "_keys_to_ignore_on_load_unexpected", "_no_split_modules", "_supports_sdpa", "base_model_prefix", "class", "cls_token", "c...
beit/modeling_beit.py:BeitModel
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[ "False", "Identity", "LayerNorm", "ModelEmbeddings", "ModelEncoder", "ModelModel", "ModelModelOutputWithPooling", "ModelPooler", "ModelPreTrainedModel", "None", "True", "_", "__init__", "add_pooling_layer", "attentions", "auto_docstring", "bool_masked_pos", "class", "config", "...
beit/modeling_beit.py:BeitPooler
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[ "LayerNorm", "ModelPooler", "Module", "None", "__init__", "class", "config", "def", "else", "eps", "forward", "hidden_size", "hidden_states", "if", "is", "layer_norm_eps", "layernorm", "mean", "nn", "not", "patch_tokens", "pooled_output", "return", "self", "super", ...
beit/modeling_beit.py:BeitForMaskedImageModeling
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[ "CrossEntropyLoss", "False", "LayerNorm", "Linear", "MaskedLMOutput", "Model", "ModelForMaskedImageModeling", "ModelModel", "ModelPreTrainedModel", "None", "__init__", "add_pooling_layer", "attentions", "auto_docstring", "bool_masked_pos", "class", "config", "def", "else", "eps...
beit/modeling_beit.py:BeitForImageClassification
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[ "False", "Identity", "ImageClassifierOutput", "Linear", "Model", "ModelForImageClassification", "ModelModel", "ModelPreTrainedModel", "None", "True", "__init__", "add_pooling_layer", "attentions", "auto_docstring", "class", "classifier", "config", "def", "else", "forward", "h...
beit/modeling_beit.py:BeitConvModule
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[ "BatchNorm2d", "Conv2d", "ModelConvModule", "Module", "ReLU", "__init__", "activation", "bn", "class", "conv", "def", "dilation", "forward", "in_channels", "input", "int", "kernel_size", "nn", "out_channels", "output", "padding", "return", "self", "str", "super" ]
beit/modeling_beit.py:BeitPyramidPoolingBlock
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[ "AdaptiveAvgPool2d", "ModelConvModule", "ModelPyramidPoolingBlock", "Module", "__init__", "add_module", "channels", "class", "def", "enumerate", "for", "forward", "hidden_state", "i", "in", "in_channels", "input", "kernel_size", "layer", "layers", "nn", "pool_scale", "ret...
beit/modeling_beit.py:BeitPyramidPoolingModule
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[ "ModelPyramidPoolingBlock", "ModelPyramidPoolingModule", "Module", "__init__", "add_module", "align_corners", "append", "bilinear", "block", "blocks", "channels", "class", "def", "enumerate", "for", "forward", "functional", "i", "in", "in_channels", "interpolate", "mode", ...
beit/modeling_beit.py:BeitUperHead
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[ "Conv2d", "False", "ModelConvModule", "ModelPyramidPoolingModule", "ModelUperHead", "Module", "ModuleList", "__init__", "align_corners", "append", "bilinear", "bottleneck", "cat", "channels", "class", "classifier", "config", "def", "dim", "encoder_hidden_states", "enumerate",...
beit/modeling_beit.py:BeitFCNHead
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[ "Conv2d", "Identity", "ModelConvModule", "ModelFCNHead", "Module", "Sequential", "__init__", "append", "auxiliary_channels", "auxiliary_concat_input", "auxiliary_num_convs", "cat", "channels", "class", "classifier", "concat_input", "config", "conv_cat", "conv_padding", "convs",...
beit/modeling_beit.py:BeitForSemanticSegmentation
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[ "BatchNorm2d", "ConvTranspose2d", "CrossEntropyLoss", "False", "GELU", "Identity", "MaxPool2d", "Model", "ModelFCNHead", "ModelForSemanticSegmentation", "ModelModel", "ModelPreTrainedModel", "ModelUperHead", "None", "One", "SemanticSegmenterOutput", "Sequential", "The", "True", ...
beit/modeling_beit.py:BeitBackbone
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[ "BackboneMixin", "BackboneOutput", "BatchNorm2d", "ConvTranspose2d", "GELU", "Identity", "MaxPool2d", "ModelBackbone", "ModelEmbeddings", "ModelEncoder", "ModelPreTrainedModel", "None", "One", "Sequential", "True", "ValueError", "_", "__init__", "_init_backbone", "a", "add_fp...
glmasr/modeling_glmasr.py:GlmAsrRotaryEmbedding
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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", ...
glmasr/modeling_glmasr.py:rotate_half
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[ "Model_half", "cat", "def", "dim", "return", "shape", "torch", "x", "x1", "x2" ]
glmasr/modeling_glmasr.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" ]
glmasr/modeling_glmasr.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", ...
glmasr/modeling_glmasr.py:apply_rotary_pos_emb
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[ "Model_rotary_pos_emb", "None", "cat", "cos", "def", "dim", "k", "k_embed", "k_pass", "k_rot", "position_ids", "q", "q_embed", "q_pass", "q_rot", "return", "rotary_dim", "rotate_half", "shape", "sin", "torch", "unsqueeze", "unsqueeze_dim" ]
glmasr/modeling_glmasr.py:GlmAsrAttention
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glmasr/modeling_glmasr.py:GlmAsrMLP
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glmasr/modeling_glmasr.py:GlmAsrEncoderLayer
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glmasr/modeling_glmasr.py:GlmAsrPreTrainedModel
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glmasr/modeling_glmasr.py:GlmAsrEncoder
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glmasr/modeling_glmasr.py:GlmAsrMultiModalProjector
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glmasr/modeling_glmasr.py:GlmAsrForConditionalGeneration
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granite/modeling_granite.py:rotate_half
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[ "Model_half", "cat", "def", "dim", "return", "shape", "torch", "x", "x1", "x2" ]
granite/modeling_granite.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" ]
granite/modeling_granite.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" ]
granite/modeling_granite.py:eager_attention_forward
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granite/modeling_granite.py:GraniteAttention
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granite/modeling_granite.py:GraniteRMSNorm
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granite/modeling_granite.py:GraniteMLP
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granite/modeling_granite.py:GraniteDecoderLayer
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granite/modeling_granite.py:GranitePreTrainedModel
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granite/modeling_granite.py:GraniteRotaryEmbedding
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granite/modeling_granite.py:GraniteModel
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granite/modeling_granite.py:GraniteForCausalLM
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olmoe/modeling_olmoe.py:OlmoeRMSNorm
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olmoe/modeling_olmoe.py:OlmoeRotaryEmbedding
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olmoe/modeling_olmoe.py:OlmoeMLP
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olmoe/modeling_olmoe.py:rotate_half
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[ "Model_half", "cat", "def", "dim", "return", "shape", "torch", "x", "x1", "x2" ]
olmoe/modeling_olmoe.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" ]
olmoe/modeling_olmoe.py:repeat_kv
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olmoe/modeling_olmoe.py:eager_attention_forward
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olmoe/modeling_olmoe.py:OlmoeAttention
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olmoe/modeling_olmoe.py:OlmoeExperts
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olmoe/modeling_olmoe.py:OlmoeTopKRouter
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olmoe/modeling_olmoe.py:OlmoeSparseMoeBlock
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olmoe/modeling_olmoe.py:OlmoeDecoderLayer
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olmoe/modeling_olmoe.py:OlmoePreTrainedModel
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olmoe/modeling_olmoe.py:OlmoeModel
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[ "DynamicCache", "Embedding", "False", "ModelDecoderLayer", "ModelModel", "ModelPreTrainedModel", "ModelRMSNorm", "ModelRotaryEmbedding", "ModuleList", "MoeModelOutputWithPast", "None", "ValueError", "You", "__init__", "and", "arange", "attention_mask", "auto_docstring", "cache_po...
olmoe/modeling_olmoe.py:load_balancing_loss_func
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olmoe/modeling_olmoe.py:OlmoeForCausalLM
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patchtsmixer/modeling_patchtsmixer.py:PatchTSMixerGatedAttention
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patchtsmixer/modeling_patchtsmixer.py:PatchTSMixerBatchNorm
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patchtsmixer/modeling_patchtsmixer.py:PatchTSMixerPositionalEncoding
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patchtsmixer/modeling_patchtsmixer.py:PatchTSMixerNormLayer
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patchtsmixer/modeling_patchtsmixer.py:PatchTSMixerMLP
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[ "Dropout", "Linear", "ModelMLP", "Module", "__init__", "class", "config", "def", "dropout", "dropout1", "dropout2", "expansion_factor", "fc1", "fc2", "forward", "functional", "gelu", "in_features", "inputs", "nn", "num_hidden", "out_features", "return", "self", "super...
patchtsmixer/modeling_patchtsmixer.py:PatchTSMixerChannelFeatureMixerBlock
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patchtsmixer/modeling_patchtsmixer.py:eager_attention_forward
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patchtsmixer/modeling_patchtsmixer.py:PatchTSMixerAttention
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patchtsmixer/modeling_patchtsmixer.py:PatchMixerBlock
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patchtsmixer/modeling_patchtsmixer.py:FeatureMixerBlock
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patchtsmixer/modeling_patchtsmixer.py:PatchTSMixerLayer
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[ "FeatureMixerBlock", "ModelChannelFeatureMixerBlock", "ModelLayer", "ModelMixerBlock", "Model_mixer", "Module", "__init__", "channel_feature_mixer", "class", "config", "def", "feature_mixer", "forward", "hidden", "if", "mix_channel", "mode", "nn", "return", "self", "super" ]
patchtsmixer/modeling_patchtsmixer.py:PatchTSMixerBlock
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patchtsmixer/modeling_patchtsmixer.py:PatchTSMixerForPredictionHead
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[ "Dropout", "Flatten", "Linear", "ModelForPredictionHead", "Module", "None", "__init__", "base_forecast_block", "class", "config", "d_model", "def", "distribution_output", "dropout_layer", "else", "flatten", "for", "forecast", "forward", "get_parameter_projection", "head_dropo...
patchtsmixer/modeling_patchtsmixer.py:PatchTSMixerLinearHead
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patchtsmixer/modeling_patchtsmixer.py:PatchTSMixerPreTrainedModel
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patchtsmixer/modeling_patchtsmixer.py:PatchTSMixerPretrainHead
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[ "Dropout", "Linear", "ModelPretrainHead", "Model_length", "Module", "__init__", "base_pt_block", "class", "config", "d_model", "def", "dropout_layer", "forecast", "forward", "head_dropout", "hidden_features", "nn", "return", "self", "super" ]
patchtsmixer/modeling_patchtsmixer.py:random_masking
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patchtsmixer/modeling_patchtsmixer.py:forecast_masking
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[ "Model_mask_ratios", "Model_masking", "None", "ValueError", "_", "and", "append", "batch1", "batch2", "batch_size", "be", "bool", "def", "device", "elif", "f", "for", "greater", "if", "in", "inputs", "inputs_mask", "int", "is", "isinstance", "key", "lambda", "le...
patchtsmixer/modeling_patchtsmixer.py:PatchTSMixerPatchify
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patchtsmixer/modeling_patchtsmixer.py:PatchTSMixerMasking
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patchtsmixer/modeling_patchtsmixer.py:PatchTSMixerStdScaler
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patchtsmixer/modeling_patchtsmixer.py:PatchTSMixerMeanScaler
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patchtsmixer/modeling_patchtsmixer.py:PatchTSMixerNOPScaler
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patchtsmixer/modeling_patchtsmixer.py:PatchTSMixerEncoderOutput
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[ "ModelEncoderOutput", "ModelOutput", "None", "class", "hidden_states", "last_hidden_state", "r" ]
patchtsmixer/modeling_patchtsmixer.py:PatchTSMixerEncoder
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patchtsmixer/modeling_patchtsmixer.py:PatchTSMixerModelOutput
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[ "ModelModelOutput", "ModelOutput", "Model_input", "None", "class", "hidden_states", "last_hidden_state", "loc", "mask", "r", "scale" ]
patchtsmixer/modeling_patchtsmixer.py:PatchTSMixerModel
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patchtsmixer/modeling_patchtsmixer.py:PatchTSMixerForPreTrainingOutput
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patchtsmixer/modeling_patchtsmixer.py:PatchTSMixerForPretraining
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patchtsmixer/modeling_patchtsmixer.py:PatchTSMixerForPredictionOutput
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patchtsmixer/modeling_patchtsmixer.py:SamplePatchTSMixerPredictionOutput
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patchtsmixer/modeling_patchtsmixer.py:SamplePatchTSMixerRegressionOutput
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patchtsmixer/modeling_patchtsmixer.py:nll
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