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
444
t5gemma/modeling_t5gemma.py:T5GemmaRMSNorm
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[ "ModelRMSNorm", "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", "weight",...
t5gemma/modeling_t5gemma.py:T5GemmaMLP
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[ "ACT2FN", "Dropout", "Linear", "ModelMLP", "Module", "__init__", "act_fn", "class", "config", "def", "down_proj", "dropout", "dropout_rate", "forward", "gate_proj", "hidden_activation", "hidden_size", "hidden_states", "intermediate_size", "nn", "return", "self", "super", ...
t5gemma/modeling_t5gemma.py:T5GemmaRotaryEmbedding
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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", "deprecate...
t5gemma/modeling_t5gemma.py:rotate_half
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[ "Model_half", "cat", "def", "dim", "return", "shape", "torch", "x", "x1", "x2" ]
t5gemma/modeling_t5gemma.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" ]
t5gemma/modeling_t5gemma.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" ]
t5gemma/modeling_t5gemma.py:eager_attention_forward
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[ "Model_attention_forward", "None", "attention_mask", "attn_output", "attn_weights", "contiguous", "def", "dim", "dropout", "dtype", "float32", "functional", "head_dim", "if", "is", "key", "key_states", "kwargs", "matmul", "module", "nn", "not", "num_key_value_groups", "...
t5gemma/modeling_t5gemma.py:T5GemmaSelfAttention
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[ "ALL_ATTENTION_FUNCTIONS", "Linear", "ModelSelfAttention", "Module", "None", "Tensor", "__init__", "_attn_implementation", "apply_rotary_pos_emb", "attention_dropout", "attention_interface", "attention_mask", "attn_logit_softcapping", "attn_output", "attn_weights", "class", "config",...
t5gemma/modeling_t5gemma.py:T5GemmaCrossAttention
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[ "ALL_ATTENTION_FUNCTIONS", "False", "Linear", "ModelCrossAttention", "Module", "None", "True", "__init__", "_attn_implementation", "attention_dropout", "attention_interface", "attention_mask", "attn_logit_softcapping", "attn_output", "attn_weights", "class", "config", "contiguous",...
t5gemma/modeling_t5gemma.py:T5GemmaEncoderLayer
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[ "Dropout", "GradientCheckpointingLayer", "ModelEncoderLayer", "ModelMLP", "ModelRMSNorm", "ModelSelfAttention", "None", "Tensor", "_", "__init__", "attention_mask", "attention_type", "class", "config", "def", "dropout", "dropout_rate", "eps", "forward", "hidden_size", "hidden...
t5gemma/modeling_t5gemma.py:T5GemmaDecoderLayer
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[ "Dropout", "False", "GradientCheckpointingLayer", "ModelCrossAttention", "ModelDecoderLayer", "ModelMLP", "ModelRMSNorm", "ModelSelfAttention", "None", "Tensor", "_", "__init__", "attention_mask", "attention_type", "class", "config", "cross_attn", "def", "dropout", "dropout_rat...
t5gemma/modeling_t5gemma.py:T5GemmaClassificationHead
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[ "Dropout", "Linear", "ModelClassificationHead", "Module", "__init__", "class", "classifier_dropout_rate", "def", "dropout", "forward", "hidden_size", "hidden_states", "nn", "num_labels", "out_proj", "p", "return", "self", "super" ]
t5gemma/modeling_t5gemma.py:T5GemmaLMHead
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[ "Linear", "ModelLMHead", "Module", "__init__", "class", "def", "forward", "hidden_size", "hidden_states", "logits", "nn", "out_proj", "return", "self", "super", "vocab_size" ]
t5gemma/modeling_t5gemma.py:T5GemmaPreTrainedModel
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[ "ModelClassificationHead", "ModelConfig", "ModelDecoderLayer", "ModelEncoderLayer", "ModelLMHead", "ModelPreTrainedModel", "None", "PreTrainedModel", "RMSNorm", "True", "__class__", "__name__", "_can_compile_fullgraph", "_can_record_outputs", "_init_weights", "_no_split_modules", "_s...
t5gemma/modeling_t5gemma.py:make_default_2d_attention_mask
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[ "Model_default_2d_attention_mask", "None", "attention_mask", "def", "device", "dtype", "else", "hidden_states", "if", "is", "long", "not", "ones", "pad_token_id", "return", "shape", "to", "token_ids", "torch" ]
t5gemma/modeling_t5gemma.py:T5GemmaEncoder
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[ "BaseModelOutput", "Dropout", "Embedding", "False", "ModelEncoder", "ModelEncoderLayer", "ModelPreTrainedModel", "ModelRMSNorm", "ModelRotaryEmbedding", "ModelSelfAttention", "ModuleList", "None", "ValueError", "You", "__init__", "_can_record_outputs", "arange", "attention_mask", ...
t5gemma/modeling_t5gemma.py:T5GemmaDecoder
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[ "BaseModelOutputWithPastAndCrossAttentions", "Dropout", "DynamicCache", "Embedding", "EncoderDecoderCache", "False", "ModelCrossAttention", "ModelDecoder", "ModelDecoderLayer", "ModelPreTrainedModel", "ModelRMSNorm", "ModelRotaryEmbedding", "ModelSelfAttention", "ModuleList", "None", "...
t5gemma/modeling_t5gemma.py:T5GemmaModel
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[ "False", "ModelDecoder", "ModelEncoder", "ModelModel", "ModelPreTrainedModel", "None", "Seq2SeqModelOutput", "__init__", "attention_mask", "attentions", "auto_docstring", "can_return_tuple", "class", "config", "cross_attentions", "decoder", "decoder_attention_mask", "decoder_attent...
t5gemma/modeling_t5gemma.py:T5GemmaEncoderModel
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[ "ModelEncoder", "ModelEncoderModel", "ModelPreTrainedModel", "None", "__init__", "attention_mask", "auto_docstring", "can_return_tuple", "class", "config", "def", "encoder", "encoder_outputs", "forward", "get_input_embeddings", "if", "input_ids", "inputs_embeds", "is_encoder_deco...
t5gemma/modeling_t5gemma.py:T5GemmaForConditionalGeneration
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[ "DynamicCache", "EncoderDecoderCache", "False", "ForMaskedLM", "GenerationMixin", "Model", "ModelForConditionalGeneration", "ModelLMHead", "ModelModel", "ModelPreTrainedModel", "None", "Seq2SeqLMOutput", "StaticCache", "The", "True", "ValueError", "__init__", "_cache", "_pp_plan"...
t5gemma/modeling_t5gemma.py:T5GemmaForSequenceClassification
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[ "Cannot", "False", "If", "ModelClassificationHead", "ModelEncoderModel", "ModelForSequenceClassification", "ModelModel", "ModelPreTrainedModel", "None", "NotImplementedError", "Passing", "Please", "Results", "SequenceClassifierOutput", "ValueError", "__class__", "__init__", "__name...
t5gemma/modeling_t5gemma.py:T5GemmaForTokenClassification
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[ "False", "If", "ModelClassificationHead", "ModelEncoderModel", "ModelForTokenClassification", "ModelModel", "ModelPreTrainedModel", "None", "NotImplementedError", "Passing", "Please", "TokenClassifierOutput", "ValueError", "__class__", "__init__", "__name__", "_shift_right", "and",...
pvt/modeling_pvt.py:PvtPatchEmbeddings
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[ "Conv2d", "Dropout", "F", "False", "Iterable", "LayerNorm", "Make", "ModelPatchEmbeddings", "Module", "None", "Parameter", "ValueError", "_", "__init__", "abc", "and", "batch_size", "bilinear", "cat", "channel", "class", "cls_token", "collections", "config", "configur...
pvt/modeling_pvt.py:PvtSelfOutput
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[ "Dropout", "Linear", "ModelSelfOutput", "Module", "__init__", "class", "config", "def", "dense", "dropout", "forward", "hidden_dropout_prob", "hidden_size", "hidden_states", "nn", "return", "self", "super" ]
pvt/modeling_pvt.py:PvtEfficientSelfAttention
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[ "Conv2d", "Dropout", "False", "LayerNorm", "Linear", "ModelEfficientSelfAttention", "Module", "The", "ValueError", "__init__", "a", "all_head_size", "attention", "attention_head_size", "attention_probs", "attention_probs_dropout_prob", "attention_scores", "batch_size", "class", ...
pvt/modeling_pvt.py:PvtAttention
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[ "False", "ModelAttention", "ModelEfficientSelfAttention", "ModelSelfOutput", "Module", "__init__", "attention_output", "class", "config", "def", "forward", "height", "hidden_size", "hidden_states", "nn", "num_attention_heads", "output", "output_attentions", "outputs", "return",...
pvt/modeling_pvt.py:PvtFFN
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[ "ACT2FN", "Dropout", "Linear", "ModelFFN", "Module", "None", "__init__", "class", "config", "def", "dense1", "dense2", "dropout", "else", "forward", "hidden_act", "hidden_dropout_prob", "hidden_features", "hidden_states", "if", "in_features", "intermediate_act_fn", "is", ...
pvt/modeling_pvt.py:PvtDropPath
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[ "ModelDropPath", "Module", "__init__", "class", "def", "device", "div", "drop_prob", "dtype", "extra_repr", "f", "floor", "forward", "hidden_states", "if", "keep_prob", "ndim", "nn", "not", "or", "p", "rand", "random_tensor", "return", "self", "shape", "super", ...
pvt/modeling_pvt.py:PvtLayer
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[ "False", "Identity", "LayerNorm", "ModelAttention", "ModelDropPath", "ModelFFN", "ModelLayer", "Module", "__init__", "attention", "attention_output", "class", "config", "def", "drop_path", "else", "eps", "forward", "height", "hidden_features", "hidden_size", "hidden_states"...
pvt/modeling_pvt.py:PvtEncoder
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[ "BaseModelOutput", "False", "LayerNorm", "ModelEncoder", "ModelLayer", "ModelPatchEmbeddings", "Module", "ModuleList", "None", "True", "__init__", "all_hidden_states", "all_self_attentions", "append", "attentions", "batch_size", "block", "block_layer", "blocks", "class", "cls...
pvt/modeling_pvt.py:PvtPreTrainedModel
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pvt/modeling_pvt.py:PvtModel
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pvt/modeling_pvt.py:PvtForImageClassification
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udop/modeling_udop.py:eager_attention_forward
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udop/modeling_udop.py:BaseModelOutputWithAttentionMask
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udop/modeling_udop.py:get_visual_bbox
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udop/modeling_udop.py:pad_sequence
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udop/modeling_udop.py:combine_image_text_embeddings
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udop/modeling_udop.py:UdopPatchEmbeddings
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udop/modeling_udop.py:UdopLayerNorm
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udop/modeling_udop.py:UdopDenseActDense
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udop/modeling_udop.py:UdopDenseGatedActDense
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udop/modeling_udop.py:UdopLayerFF
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udop/modeling_udop.py:UdopAttention
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udop/modeling_udop.py:UdopLayerSelfAttention
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udop/modeling_udop.py:UdopLayerCrossAttention
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udop/modeling_udop.py:UdopBlock
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udop/modeling_udop.py:UdopPreTrainedModel
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udop/modeling_udop.py:UdopCellEmbeddings
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udop/modeling_udop.py:RelativePositionBiasBase
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udop/modeling_udop.py:RelativePositionBias1D
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udop/modeling_udop.py:RelativePositionBiasHorizontal
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udop/modeling_udop.py:RelativePositionBiasVertical
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udop/modeling_udop.py:RelativePositionBiasAggregated
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udop/modeling_udop.py:create_relative_bias
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udop/modeling_udop.py:UdopStack
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udop/modeling_udop.py:UdopModel
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udop/modeling_udop.py:UdopForConditionalGeneration
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udop/modeling_udop.py:UdopEncoderModel
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univnet/modeling_univnet.py:UnivNetModelOutput
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univnet/modeling_univnet.py:UnivNetKernelPredictorResidualBlock
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univnet/modeling_univnet.py:UnivNetKernelPredictor
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univnet/modeling_univnet.py:UnivNetLvcResidualBlock
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univnet/modeling_univnet.py:UnivNetLvcBlock
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seed_oss/modeling_seed_oss.py:SeedOssRMSNorm
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seed_oss/modeling_seed_oss.py:rotate_half
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seed_oss/modeling_seed_oss.py:apply_rotary_pos_emb
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seed_oss/modeling_seed_oss.py:repeat_kv
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seed_oss/modeling_seed_oss.py:eager_attention_forward
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seed_oss/modeling_seed_oss.py:SeedOssAttention
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seed_oss/modeling_seed_oss.py:SeedOssDecoderLayer
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seed_oss/modeling_seed_oss.py:SeedOssPreTrainedModel
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seed_oss/modeling_seed_oss.py:SeedOssRotaryEmbedding
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seed_oss/modeling_seed_oss.py:SeedOssModel
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seed_oss/modeling_seed_oss.py:SeedOssForCausalLM
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seed_oss/modeling_seed_oss.py:SeedOssForSequenceClassification
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seed_oss/modeling_seed_oss.py:SeedOssForTokenClassification
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seed_oss/modeling_seed_oss.py:SeedOssForQuestionAnswering
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plbart/modeling_plbart.py:PLBartScaledWordEmbedding
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plbart/modeling_plbart.py:PLBartPreTrainedModel
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plbart/modeling_plbart.py:PLBartLearnedPositionalEmbedding
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plbart/modeling_plbart.py:eager_attention_forward
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plbart/modeling_plbart.py:PLBartAttention
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plbart/modeling_plbart.py:PLBartEncoderLayer
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plbart/modeling_plbart.py:PLBartEncoder
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plbart/modeling_plbart.py:PLBartDecoderLayer
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plbart/modeling_plbart.py:PLBartDecoder
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plbart/modeling_plbart.py:shift_tokens_right
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plbart/modeling_plbart.py:PLBartModel
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plbart/modeling_plbart.py:PLBartForConditionalGeneration
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plbart/modeling_plbart.py:PLBartClassificationHead
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plbart/modeling_plbart.py:PLBartForSequenceClassification
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plbart/modeling_plbart.py:PLBartDecoderWrapper
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plbart/modeling_plbart.py:PLBartForCausalLM
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pp_ocrv5_mobile_det/modeling_pp_ocrv5_mobile_det.py:PPOCRV5MobileDetPreTrainedModel
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pp_ocrv5_mobile_det/modeling_pp_ocrv5_mobile_det.py:PPOCRV5MobileDetSqueezeExcitationModule
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pp_ocrv5_mobile_det/modeling_pp_ocrv5_mobile_det.py:PPOCRV5MobileDetResidualSqueezeExcitationLayer
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pp_ocrv5_mobile_det/modeling_pp_ocrv5_mobile_det.py:PPOCRV5MobileDetNeck
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