Datasets:
itazap
/
transformers_code_embeddings_v3

Dataset card Data Studio Files
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1
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24
102
embedding
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2.56k
2.56k
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4
448
conditional_detr/modeling_conditional_detr.py:ConditionalDetrPreTrainedModel
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[ "Conv2d", "Embedding", "False", "GroupNorm", "LayerNorm", "Linear", "ModelConfig", "ModelConvEncoder", "ModelDecoderLayer", "ModelEncoderLayer", "ModelLearnedPositionEmbedding", "ModelMHAttentionMap", "ModelPreTrainedModel", "None", "PreTrainedModel", "_init_weights", "_is_hf_initial...
conditional_detr/modeling_conditional_detr.py:ConditionalDetrEncoder
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[ "BaseModelOutput", "False", "ModelEncoder", "ModelEncoderLayer", "ModelPreTrainedModel", "ModuleList", "None", "True", "_", "__init__", "_prepare_4d_attention_mask", "all_attentions", "attention_mask", "attentions", "class", "config", "def", "dropout", "dropout_probability", "d...
conditional_detr/modeling_conditional_detr.py:ConditionalDetrDecoder
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[ "False", "LayerNorm", "MLP", "ModelDecoder", "ModelDecoderLayer", "ModelDecoderOutput", "ModelPreTrainedModel", "ModuleList", "None", "_", "__init__", "_prepare_4d_attention_mask", "all_cross_attentions", "all_hidden_states", "all_self_attns", "and", "attention_mask", "attentions",...
conditional_detr/modeling_conditional_detr.py:ConditionalDetrModel
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[ "BaseModelOutput", "Conv2d", "Embedding", "False", "ModelConvEncoder", "ModelConvModel", "ModelDecoder", "ModelEncoder", "ModelModel", "ModelModelOutput", "ModelPreTrainedModel", "None", "True", "__init__", "and", "attention_mask", "attentions", "auto_docstring", "backbone", "b...
conditional_detr/modeling_conditional_detr.py:ConditionalDetrMLPPredictionHead
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[ "Linear", "ModelMLPPredictionHead", "Module", "ModuleList", "__init__", "class", "def", "else", "enumerate", "for", "forward", "functional", "h", "hidden_dim", "i", "if", "in", "input_dim", "k", "layer", "layers", "n", "nn", "num_layers", "output_dim", "relu", "re...
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", "class", "class_lab...
conditional_detr/modeling_conditional_detr.py:ConditionalDetrForSegmentation
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[ "BaseModelOutput", "Model", "ModelForObjectDetection", "ModelForSegmentation", "ModelMHAttentionMap", "ModelMaskHeadSmallConv", "ModelPreTrainedModel", "ModelSegmentationOutput", "None", "__init__", "and", "attention_mask", "attentions", "auto_docstring", "auxiliary_loss", "auxiliary_o...
conditional_detr/modeling_conditional_detr.py:_expand
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[ "_expand", "def", "flatten", "int", "length", "repeat", "return", "tensor", "unsqueeze" ]
conditional_detr/modeling_conditional_detr.py:ConditionalDetrMaskHeadSmallConv
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[ "Conv2d", "GroupNorm", "ModelMaskHeadSmallConv", "Module", "The", "ValueError", "__init__", "_expand", "a", "adapter1", "adapter2", "adapter3", "as", "attention", "bbox_mask", "be", "bias", "by", "cat", "class", "constant_", "context_dim", "cur_fpn", "def", "dim", "...
conditional_detr/modeling_conditional_detr.py:ConditionalDetrMHAttentionMap
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[ "Dropout", "Linear", "ModelMHAttentionMap", "Module", "None", "__init__", "bias", "bnchw", "bqnc", "bqnhw", "class", "conv2d", "def", "dim", "dropout", "dtype", "einsum", "finfo", "flatten", "float", "forward", "functional", "hidden_dim", "if", "is", "k", "k_linea...
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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regnet/modeling_regnet.py:RegNetEmbeddings
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regnet/modeling_regnet.py:RegNetShortCut
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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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[ "Model_reverse", "Model_size", "Models", "contiguous", "def", "height", "num_channels", "permute", "return", "shape", "view", "width" ]
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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[ "False", "ModelAttention", "ModelSelfAttention", "ModelSelfOutput", "Module", "None", "__init__", "attention_mask", "attention_output", "class", "config", "def", "dim", "forward", "hidden_states", "nn", "num_heads", "output", "output_attentions", "outputs", "return", "self"...
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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[ "False", "Identity", "LayerNorm", "ModelAttention", "ModelDropPath", "ModelIntermediate", "ModelLayer", "ModelOutput", "Module", "None", "_", "__init__", "always_partition", "attention", "attention_output", "attention_outputs", "attention_windows", "attn_mask", "batch_size", "c...
swin/modeling_swin.py:SwinStage
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swin/modeling_swin.py:SwinEncoder
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[ "False", "ModelEncoder", "ModelEncoderOutput", "ModelPatchMerging", "ModelStage", "Module", "ModuleList", "None", "True", "_", "__init__", "all_hidden_states", "all_reshaped_hidden_states", "all_self_attentions", "always_partition", "and", "attentions", "batch_size", "class", "...
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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[ "Conv2d", "False", "Model", "ModelForMaskedImageModeling", "ModelMaskedImageModelingOutput", "ModelModel", "ModelPreTrainedModel", "None", "PixelShuffle", "Sequential", "True", "__init__", "add_pooling_layer", "attentions", "auto_docstring", "batch_size", "bool_masked_pos", "class"...
swin/modeling_swin.py:SwinForImageClassification
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swin/modeling_swin.py:SwinBackbone
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jamba/modeling_jamba.py:JambaRMSNorm
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[ "ModelRMSNorm", "Module", "Parameter", "True", "__init__", "class", "def", "eps", "extra_repr", "f", "float32", "forward", "hidden_size", "hidden_states", "keepdim", "mean", "nn", "ones", "pow", "return", "rsqrt", "self", "shape", "super", "to", "torch", "tuple", ...
jamba/modeling_jamba.py:HybridMambaAttentionDynamicCache
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[ "Any", "False", "ModelMambaAttentionDynamicCache", "None", "_", "__getitem__", "__init__", "__len__", "append", "batch_size", "beam_idx", "cache_kwargs", "cache_position", "cat", "class", "config", "conv_kernel_size", "conv_states", "def", "device", "dim", "dtype", "else"...
jamba/modeling_jamba.py:rotate_half
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[ "Model_half", "cat", "def", "dim", "return", "shape", "torch", "x", "x1", "x2" ]
jamba/modeling_jamba.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" ]
jamba/modeling_jamba.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" ]
jamba/modeling_jamba.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", ...
jamba/modeling_jamba.py:JambaAttention
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[ "ALL_ATTENTION_FUNCTIONS", "Linear", "ModelAttention", "Module", "None", "True", "__init__", "_attn_implementation", "attention_dropout", "attention_interface", "attention_mask", "attn_output", "attn_weights", "cache_position", "class", "config", "contiguous", "def", "dropout", ...
jamba/modeling_jamba.py:JambaMambaMixer
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[ "A", "ACT2FN", "A_log", "B", "C", "CUDA", "Conv1d", "D", "False", "Fast", "HybridMambaAttentionDynamicCache", "Linear", "Make", "Mamba", "ModelMambaMixer", "ModelRMSNorm", "Module", "None", "Parameter", "The", "To", "True", "ValueError", "_", "__init__", "a", "act...
jamba/modeling_jamba.py:JambaMLP
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[ "ACT2FN", "Linear", "ModelMLP", "Module", "__init__", "act_fn", "class", "config", "def", "down_proj", "forward", "gate_proj", "hidden_act", "hidden_size", "intermediate_size", "nn", "return", "self", "super", "up_proj", "x" ]
jamba/modeling_jamba.py:JambaExperts
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jamba/modeling_jamba.py:JambaSparseMoeBlock
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jamba/modeling_jamba.py:JambaAttentionDecoderLayer
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[ "False", "GradientCheckpointingLayer", "ModelAttention", "ModelAttentionDecoderLayer", "ModelMLP", "ModelRMSNorm", "ModelSparseMoeBlock", "None", "_", "__init__", "attention_mask", "cache_position", "class", "config", "def", "else", "eps", "feed_forward", "ffn_layer_class", "fo...
jamba/modeling_jamba.py:JambaMambaDecoderLayer
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[ "GradientCheckpointingLayer", "ModelMLP", "ModelMambaDecoderLayer", "ModelMambaMixer", "ModelRMSNorm", "ModelSparseMoeBlock", "None", "__init__", "attention_mask", "cache_params", "class", "config", "def", "else", "eps", "feed_forward", "ffn_layer_class", "forward", "hidden_size"...
jamba/modeling_jamba.py:JambaPreTrainedModel
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[ "A", "A_log", "D", "Linear", "ModelAttention", "ModelAttentionDecoderLayer", "ModelConfig", "ModelExperts", "ModelMambaDecoderLayer", "ModelMambaMixer", "ModelPreTrainedModel", "None", "OutputRecorder", "PreTrainedModel", "True", "_can_record_outputs", "_init_weights", "_is_statefu...
jamba/modeling_jamba.py:JambaModel
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[ "ALL_DECODER_LAYER_TYPES", "Embedding", "False", "HybridMambaAttentionDynamicCache", "ModelMambaDecoderLayer", "ModelModel", "ModelPreTrainedModel", "ModelRMSNorm", "ModuleList", "MoeModelOutputWithPast", "None", "True", "ValueError", "You", "__init__", "_update_mamba_mask", "all", ...
jamba/modeling_jamba.py:load_balancing_loss_func
[ -0.00027690583374351263, 0.01840798556804657, 0.001643570140004158, -0.028812499716877937, -0.0009432663209736347, 0.05808233842253685, 0.038874007761478424, -0.011547867208719254, 0, -0.02252405695617199, 0.031556546688079834, -0.0061455233953893185, -0.0008396499906666577, 0.014349081553...
[ "Model_balancing_loss_func", "None", "_", "attention_mask", "batch_size", "cat", "compute_device", "concatenated_gate_logits", "def", "device", "dim", "else", "expand", "expert_attention_mask", "expert_mask", "float", "for", "functional", "gate_logits", "if", "in", "is", ...
jamba/modeling_jamba.py:JambaForCausalLM
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[ "GenerationMixin", "Linear", "ModelForCausalLM", "ModelModel", "ModelPreTrainedModel", "MoeCausalLMOutputWithPast", "None", "__init__", "_pp_plan", "_tied_weights_keys", "_tp_plan", "attention_mask", "attentions", "auto_docstring", "aux_loss", "cache_position", "can_return_tuple", ...
jamba/modeling_jamba.py:JambaForSequenceClassification
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[ "GenericForSequenceClassification", "ModelForSequenceClassification", "ModelPreTrainedModel", "class", "pass" ]
m2m_100/modeling_m2m_100.py:shift_tokens_right
[ -0.0001357423752779141, 0.026739485561847687, 0.00459761219099164, -0.04174518957734108, -0.0005782272783108056, 0.05189942196011543, 0.04039129242300987, -0.057089366018772125, 0.006572046782821417, 0.007784913759678602, 0.020759768784046173, -0.009872172959148884, -0.0006698974757455289, ...
[ "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" ]
m2m_100/modeling_m2m_100.py:M2M100ScaledWordEmbedding
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[ "Embedding", "ModelScaledWordEmbedding", "__init__", "class", "def", "embed_scale", "embedding_dim", "forward", "input_ids", "nn", "num_embeddings", "padding_idx", "return", "self", "super" ]