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
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4
448
conditional_detr/modeling_conditional_detr.py:ConditionalDetrEncoder
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[ "BaseModelOutput", "ModelEncoder", "ModelEncoderLayer", "ModelPreTrainedModel", "ModelSelfAttention", "ModuleList", "None", "_", "__init__", "_can_record_outputs", "attention_mask", "attentions", "capture_outputs", "class", "config", "create_bidirectional_mask", "def", "dropout", ...
conditional_detr/modeling_conditional_detr.py:gen_sine_position_embeddings
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[ "Model_sine_position_embeddings", "None", "arange", "cat", "cos", "d_model", "def", "device", "dim", "dim_t", "div", "dtype", "flatten", "float32", "floor", "math", "pi", "pos", "pos_tensor", "pos_x", "pos_y", "return", "rounding_mode", "scale", "sin", "stack", "t...
conditional_detr/modeling_conditional_detr.py:ConditionalDetrDecoder
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[ "LayerNorm", "ModelDecoder", "ModelDecoderCrossAttention", "ModelDecoderLayer", "ModelDecoderOutput", "ModelDecoderSelfAttention", "ModelMLPPredictionHead", "ModelPreTrainedModel", "ModuleList", "None", "OutputRecorder", "_", "__init__", "_can_record_outputs", "and", "attention_mask", ...
conditional_detr/modeling_conditional_detr.py:ConditionalDetrModel
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[ "BaseModelOutput", "Conv2d", "Embedding", "False", "ModelConvEncoder", "ModelDecoder", "ModelEncoder", "ModelLearnedPositionEmbedding", "ModelModel", "ModelModelOutput", "ModelPreTrainedModel", "ModelSinePositionEmbedding", "None", "True", "_", "__init__", "attention_mask", "attent...
conditional_detr/modeling_conditional_detr.py:inverse_sigmoid
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[ "Model_sigmoid", "clamp", "def", "eps", "log", "max", "min", "return", "torch", "x", "x1", "x2" ]
conditional_detr/modeling_conditional_detr.py:ConditionalDetrForObjectDetection
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[ "Linear", "ModelForObjectDetection", "ModelMLPPredictionHead", "ModelModel", "ModelObjectDetectionOutput", "ModelPreTrainedModel", "None", "__init__", "_set_aux_loss", "a", "append", "auto_docstring", "auxiliary_loss", "auxiliary_outputs", "b", "bbox_predictor", "can_return_tuple", ...
conditional_detr/modeling_conditional_detr.py:ConditionalDetrForSegmentation
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[ "Model", "ModelForObjectDetection", "ModelForSegmentation", "ModelMHAttentionMap", "ModelMaskHeadSmallConv", "ModelPreTrainedModel", "ModelSegmentationOutput", "None", "__init__", "_checkpoint_conversion_mapping", "activation_function", "adapter1", "adapter2", "adapter3", "attention_mask...
flaubert/modeling_flaubert.py:create_sinusoidal_embeddings
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[ "False", "FloatTensor", "Model_sinusoidal_embeddings", "array", "cos", "def", "detach_", "dim", "for", "in", "j", "n_pos", "np", "out", "pos", "position_enc", "power", "range", "requires_grad", "return", "sin", "torch" ]
flaubert/modeling_flaubert.py:get_masks
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[ "False", "Model_masks", "None", "alen", "arange", "assert", "attn_mask", "bs", "causal", "def", "device", "dtype", "else", "if", "is", "item", "lengths", "long", "mask", "not", "or", "padding_mask", "repeat", "return", "size", "slen", "torch" ]
flaubert/modeling_flaubert.py:MultiHeadAttention
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[ "EncoderDecoderCache", "False", "Linear", "ModelHeadAttention", "Module", "None", "True", "__init__", "and", "assert", "attention_dropout", "bs", "cache", "cache_position", "class", "config", "context", "contiguous", "cross_attention_cache", "curr_past_key_values", "current_s...
flaubert/modeling_flaubert.py:TransformerFFN
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[ "Linear", "ModelFFN", "Module", "__init__", "act", "apply_chunking_to_forward", "chunk_size_feed_forward", "class", "config", "def", "dim_hidden", "dropout", "else", "ff_chunk", "forward", "functional", "gelu", "gelu_activation", "if", "in_dim", "input", "lin1", "lin2", ...
flaubert/modeling_flaubert.py:FlaubertPredLayer
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[ "AdaptiveLogSoftmaxWithLoss", "False", "Linear", "ModelPredLayer", "Module", "None", "True", "__init__", "asm", "asm_cutoffs", "asm_div_value", "class", "config", "cross_entropy", "cutoffs", "def", "dim", "div_value", "else", "emb_dim", "forward", "functional", "head_bias...
flaubert/modeling_flaubert.py:FlaubertSquadHeadOutput
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[ "ModelOutput", "ModelSquadHeadOutput", "None", "class", "cls_logits", "end_top_index", "end_top_log_probs", "loss", "r", "start_top_index", "start_top_log_probs" ]
flaubert/modeling_flaubert.py:FlaubertPoolerStartLogits
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[ "Linear", "ModelPoolerStartLogits", "Module", "None", "__init__", "class", "config", "def", "dense", "else", "float16", "forward", "hidden_size", "hidden_states", "if", "is", "nn", "not", "p_mask", "return", "self", "squeeze", "super", "torch", "x" ]
flaubert/modeling_flaubert.py:FlaubertPoolerEndLogits
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[ "LayerNorm", "Linear", "ModelPoolerEndLogits", "Module", "None", "One", "Tanh", "__init__", "activation", "assert", "be", "cat", "class", "config", "def", "dense_0", "dense_1", "dim", "else", "eps", "expand", "float16", "forward", "gather", "hidden_size", "hidden_st...
flaubert/modeling_flaubert.py:FlaubertPoolerAnswerClass
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[ "Linear", "ModelPoolerAnswerClass", "Module", "None", "One", "Tanh", "__init__", "activation", "assert", "be", "cat", "class", "cls_index", "cls_token_state", "config", "def", "dense_0", "dense_1", "dim", "else", "expand", "forward", "gather", "hidden_size", "hidden_s...
flaubert/modeling_flaubert.py:FlaubertSQuADHead
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[ "BCEWithLogitsLoss", "CrossEntropyLoss", "False", "ModelPoolerAnswerClass", "ModelPoolerEndLogits", "ModelPoolerStartLogits", "ModelSQuADHead", "ModelSquadHeadOutput", "Module", "None", "__init__", "and", "answer_class", "auto_docstring", "bh", "bl", "blh", "class", "cls_index", ...
flaubert/modeling_flaubert.py:FlaubertSequenceSummary
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[ "Dropout", "Identity", "Linear", "ModelSequenceSummary", "Module", "None", "NotImplementedError", "__init__", "activation", "activation_string", "and", "attn", "class", "cls_index", "config", "def", "dim", "dtype", "elif", "else", "expand", "first", "first_dropout", "fo...
flaubert/modeling_flaubert.py:FlaubertPreTrainedModel
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[ "Embedding", "False", "LayerNorm", "Linear", "ModelConfig", "ModelModel", "ModelPreTrainedModel", "None", "PreTrainedModel", "_init_weights", "_is_hf_initialized", "and", "arange", "attention_mask", "attns_list", "base_model_prefix", "bias", "class", "config", "copy_", "creat...
flaubert/modeling_flaubert.py:FlaubertModel
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[ "BaseModelOutput", "DynamicCache", "Embedding", "EncoderDecoderCache", "False", "FloatTensor", "LayerNorm", "ModelModel", "ModelPreTrainedModel", "ModuleList", "MultiHeadAttention", "None", "TransformerFFN", "__init__", "_slen", "a", "and", "append", "arange", "assert", "atte...
flaubert/modeling_flaubert.py:FlaubertWithLMHeadModel
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[ "GenerationMixin", "MaskedLMOutput", "ModelModel", "ModelPreTrainedModel", "ModelPredLayer", "ModelWithLMHeadModel", "None", "Tensor", "__init__", "_tied_weights_keys", "attention_mask", "attentions", "auto_docstring", "cache", "cat", "class", "config", "def", "device", "dim", ...
flaubert/modeling_flaubert.py:FlaubertForSequenceClassification
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[ "BCEWithLogitsLoss", "CrossEntropyLoss", "MSELoss", "ModelForSequenceClassification", "ModelModel", "ModelPreTrainedModel", "ModelSequenceSummary", "None", "SequenceClassifierOutput", "Tensor", "__init__", "and", "attention_mask", "attentions", "auto_docstring", "cache", "class", "...
flaubert/modeling_flaubert.py:FlaubertForTokenClassification
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[ "CrossEntropyLoss", "Dropout", "Linear", "ModelForTokenClassification", "ModelModel", "ModelPreTrainedModel", "None", "Tensor", "TokenClassifierOutput", "__init__", "attention_mask", "attentions", "auto_docstring", "cache", "class", "classifier", "config", "def", "dropout", "el...
flaubert/modeling_flaubert.py:FlaubertForQuestionAnsweringSimple
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[ "CrossEntropyLoss", "Linear", "ModelForQuestionAnsweringSimple", "ModelModel", "ModelPreTrainedModel", "None", "QuestionAnsweringModelOutput", "Tensor", "__init__", "and", "attention_mask", "attentions", "auto_docstring", "cache", "clamp", "class", "config", "contiguous", "def", ...
flaubert/modeling_flaubert.py:FlaubertForQuestionAnsweringOutput
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[ "ModelForQuestionAnsweringOutput", "ModelOutput", "None", "attentions", "class", "cls_logits", "end_top_index", "end_top_log_probs", "hidden_states", "loss", "r", "start_top_index", "start_top_log_probs" ]
flaubert/modeling_flaubert.py:FlaubertForQuestionAnswering
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[ "ModelForQuestionAnswering", "ModelForQuestionAnsweringOutput", "ModelModel", "ModelPreTrainedModel", "ModelSQuADHead", "None", "Tensor", "__init__", "attention_mask", "attentions", "auto_docstring", "cache", "class", "cls_index", "cls_logits", "config", "def", "else", "end_posit...
flaubert/modeling_flaubert.py:FlaubertForMultipleChoice
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[ "CrossEntropyLoss", "Linear", "Model", "ModelForMultipleChoice", "ModelModel", "ModelPreTrainedModel", "ModelSequenceSummary", "MultipleChoiceModelOutput", "None", "Please", "Tensor", "The", "__init__", "attention", "attention_mask", "attentions", "auto_docstring", "be", "cache",...
regnet/modeling_regnet.py:RegNetConvLayer
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[ "ACT2FN", "BatchNorm2d", "Conv2d", "Identity", "ModelConvLayer", "Module", "None", "__init__", "activation", "class", "convolution", "def", "else", "forward", "groups", "hidden_state", "if", "in_channels", "is", "kernel_size", "nn", "normalization", "not", "out_channels...
regnet/modeling_regnet.py:RegNetEmbeddings
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[ "Make", "ModelConvLayer", "ModelEmbeddings", "Module", "ValueError", "__init__", "activation", "channel", "class", "config", "configuration", "def", "dimension", "embedder", "embedding_size", "forward", "hidden_act", "hidden_state", "if", "in", "kernel_size", "match", "nn...
regnet/modeling_regnet.py:RegNetShortCut
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[ "BatchNorm2d", "Conv2d", "ModelShortCut", "Module", "__init__", "class", "convolution", "def", "forward", "hidden_state", "in_channels", "input", "kernel_size", "nn", "normalization", "out_channels", "return", "self", "stride", "super" ]
regnet/modeling_regnet.py:RegNetSELayer
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regnet/modeling_regnet.py:RegNetXLayer
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regnet/modeling_regnet.py:RegNetYLayer
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regnet/modeling_regnet.py:RegNetStage
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regnet/modeling_regnet.py:RegNetEncoder
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regnet/modeling_regnet.py:RegNetPreTrainedModel
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regnet/modeling_regnet.py:RegNetModel
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regnet/modeling_regnet.py:RegNetForImageClassification
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glm4_moe/modeling_glm4_moe.py:Glm4MoeRotaryEmbedding
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glm4_moe/modeling_glm4_moe.py:repeat_kv
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glm4_moe/modeling_glm4_moe.py:eager_attention_forward
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glm4_moe/modeling_glm4_moe.py:rotate_half
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glm4_moe/modeling_glm4_moe.py:apply_rotary_pos_emb
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glm4_moe/modeling_glm4_moe.py:Glm4MoeAttention
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glm4_moe/modeling_glm4_moe.py:Glm4MoeMLP
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glm4_moe/modeling_glm4_moe.py:Glm4MoeTopkRouter
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glm4_moe/modeling_glm4_moe.py:Glm4MoeRMSNorm
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glm4_moe/modeling_glm4_moe.py:Glm4MoeNaiveMoe
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glm4_moe/modeling_glm4_moe.py:Glm4MoeMoE
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glm4_moe/modeling_glm4_moe.py:Glm4MoeDecoderLayer
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glm4_moe/modeling_glm4_moe.py:Glm4MoePreTrainedModel
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glm4_moe/modeling_glm4_moe.py:Glm4MoeModel
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glm4_moe/modeling_glm4_moe.py:Glm4MoeForCausalLM
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swin/modeling_swin.py:SwinEncoderOutput
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swin/modeling_swin.py:SwinModelOutput
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swin/modeling_swin.py:SwinMaskedImageModelingOutput
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swin/modeling_swin.py:SwinImageClassifierOutput
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swin/modeling_swin.py:window_partition
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swin/modeling_swin.py:window_reverse
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swin/modeling_swin.py:SwinEmbeddings
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swin/modeling_swin.py:SwinPatchEmbeddings
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swin/modeling_swin.py:SwinPatchMerging
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swin/modeling_swin.py:drop_path
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swin/modeling_swin.py:SwinDropPath
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swin/modeling_swin.py:SwinSelfAttention
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swin/modeling_swin.py:SwinSelfOutput
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swin/modeling_swin.py:SwinAttention
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swin/modeling_swin.py:SwinIntermediate
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swin/modeling_swin.py:SwinOutput
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swin/modeling_swin.py:SwinLayer
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swin/modeling_swin.py:SwinStage
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swin/modeling_swin.py:SwinEncoder
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swin/modeling_swin.py:SwinPreTrainedModel
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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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qwen3_5_moe/modeling_qwen3_5_moe.py:Qwen3_5MoeVisionRotaryEmbedding
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qwen3_5_moe/modeling_qwen3_5_moe.py:Qwen3_5MoeTextRotaryEmbedding
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qwen3_5_moe/modeling_qwen3_5_moe.py:Qwen3_5MoeDynamicCache
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qwen3_5_moe/modeling_qwen3_5_moe.py:Qwen3_5MoeRMSNormGated
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qwen3_5_moe/modeling_qwen3_5_moe.py:apply_mask_to_padding_states
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qwen3_5_moe/modeling_qwen3_5_moe.py:torch_causal_conv1d_update
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qwen3_5_moe/modeling_qwen3_5_moe.py:l2norm
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qwen3_5_moe/modeling_qwen3_5_moe.py:torch_chunk_gated_delta_rule
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qwen3_5_moe/modeling_qwen3_5_moe.py:torch_recurrent_gated_delta_rule
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qwen3_5_moe/modeling_qwen3_5_moe.py:Qwen3_5MoeGatedDeltaNet
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qwen3_5_moe/modeling_qwen3_5_moe.py:rotate_half
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qwen3_5_moe/modeling_qwen3_5_moe.py:apply_rotary_pos_emb
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qwen3_5_moe/modeling_qwen3_5_moe.py:repeat_kv
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qwen3_5_moe/modeling_qwen3_5_moe.py:eager_attention_forward
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qwen3_5_moe/modeling_qwen3_5_moe.py:Qwen3_5MoeAttention
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qwen3_5_moe/modeling_qwen3_5_moe.py:Qwen3_5MoeMLP
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qwen3_5_moe/modeling_qwen3_5_moe.py:Qwen3_5MoeExperts
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[ "ACT2FN", "Model_5MoeExperts", "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...
qwen3_5_moe/modeling_qwen3_5_moe.py:Qwen3_5MoeTopKRouter
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[ "F", "Model_5MoeTopKRouter", "Module", "Parameter", "True", "__init__", "class", "config", "def", "dim", "dtype", "float", "forward", "functional", "hidden_dim", "hidden_size", "hidden_states", "keepdim", "linear", "nn", "num_experts", "num_experts_per_tok", "reshape", ...
qwen3_5_moe/modeling_qwen3_5_moe.py:Qwen3_5MoeSparseMoeBlock
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[ "F", "Linear", "Model_5MoeExperts", "Model_5MoeMLP", "Model_5MoeSparseMoeBlock", "Model_5MoeTopKRouter", "Module", "_", "__init__", "batch_size", "class", "config", "def", "expert_output", "experts", "forward", "gate", "hidden_dim", "hidden_size", "hidden_states", "hidden_sta...
qwen3_5_moe/modeling_qwen3_5_moe.py:Qwen3_5MoeRMSNorm
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[ "Model_5MoeRMSNorm", "Module", "Parameter", "True", "__init__", "_norm", "class", "def", "dim", "eps", "extra_repr", "f", "float", "forward", "keepdim", "mean", "nn", "output", "pow", "return", "rsqrt", "self", "shape", "super", "torch", "tuple", "type_as", "wei...
qwen3_5_moe/modeling_qwen3_5_moe.py:Qwen3_5MoeDecoderLayer
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[ "GradientCheckpointingLayer", "Model_5MoeAttention", "Model_5MoeDecoderLayer", "Model_5MoeGatedDeltaNet", "Model_5MoeRMSNorm", "Model_5MoeSparseMoeBlock", "None", "Tensor", "_", "__init__", "attention_mask", "cache_params", "cache_position", "class", "config", "def", "elif", "eps",...
qwen3_5_moe/modeling_qwen3_5_moe.py:Qwen3_5MoePreTrainedModel
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[ "A_log", "Model_5MoeAttention", "Model_5MoeConfig", "Model_5MoeDecoderLayer", "Model_5MoeExperts", "Model_5MoeGatedDeltaNet", "Model_5MoePreTrainedModel", "Model_5MoeRMSNorm", "Model_5MoeSparseMoeBlock", "Model_5MoeTopKRouter", "Model_5MoeVisionBlock", "Model_5MoeVisionRotaryEmbedding", "Out...
qwen3_5_moe/modeling_qwen3_5_moe.py:Qwen3_5MoeVisionMLP
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[ "ACT2FN", "Linear", "Model_5MoeVisionMLP", "Module", "__init__", "act_fn", "class", "config", "def", "forward", "hidden_act", "hidden_size", "hidden_state", "intermediate_size", "linear_fc1", "linear_fc2", "nn", "return", "self", "super" ]