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
swin2sr/modeling_swin2sr.py:UpsampleOneStep
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[ "Conv2d", "ModelOneStep", "Module", "PixelShuffle", "__init__", "class", "conv", "def", "forward", "in_channels", "nn", "out_channels", "pixel_shuffle", "return", "scale", "self", "super", "x" ]
swin2sr/modeling_swin2sr.py:PixelShuffleUpsampler
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[ "Conv2d", "LeakyReLU", "ModelShuffleUpsampler", "Module", "True", "Upsample", "__init__", "activation", "class", "config", "conv_before_upsample", "def", "embed_dim", "final_convolution", "forward", "inplace", "nn", "num_channels_out", "num_features", "return", "self", "seq...
swin2sr/modeling_swin2sr.py:NearestConvUpsampler
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[ "Conv2d", "LeakyReLU", "Model", "ModelConvUpsampler", "Module", "The", "True", "ValueError", "__init__", "activation", "an", "at", "class", "config", "conv", "conv_before_upsample", "conv_hr", "conv_up1", "conv_up2", "def", "embed_dim", "factor", "final_convolution", "f...
swin2sr/modeling_swin2sr.py:PixelShuffleAuxUpsampler
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[ "Conv2d", "LeakyReLU", "ModelShuffleAuxUpsampler", "Module", "Sequential", "True", "Upsample", "__init__", "activation", "aux", "bicubic", "class", "config", "conv_after_aux", "conv_aux", "conv_before_upsample", "conv_bicubic", "def", "embed_dim", "final_convolution", "forwar...
swin2sr/modeling_swin2sr.py:Swin2SRForImageSuperResolution
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[ "Conv2d", "False", "ImageSuperResolutionOutput", "Model", "ModelForImageSuperResolution", "ModelModel", "ModelPreTrainedModel", "NearestConvUpsampler", "None", "PixelShuffleAuxUpsampler", "PixelShuffleUpsampler", "UpsampleOneStep", "__init__", "align_corners", "attentions", "auto_docst...
vipllava/modeling_vipllava.py:VipLlavaModelOutputWithPast
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[ "BaseModelOutputWithPast", "ModelModelOutputWithPast", "None", "class", "image_hidden_states", "r" ]
vipllava/modeling_vipllava.py:VipLlavaCausalLMOutputWithPast
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[ "ModelCausalLMOutputWithPast", "ModelOutput", "None", "attentions", "class", "hidden_states", "image_hidden_states", "logits", "loss", "past_key_values", "r" ]
vipllava/modeling_vipllava.py:VipLlavaMultiModalProjector
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[ "ACT2FN", "LayerNorm", "Linear", "ModelMultiModalProjector", "Module", "__init__", "act", "class", "config", "def", "else", "eps", "forward", "hidden_size", "hidden_states", "if", "int", "isinstance", "len", "linear_1", "linear_2", "nn", "num_feature_layers", "projector...
vipllava/modeling_vipllava.py:VipLlavaPreTrainedModel
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[ "ModelConfig", "ModelPreTrainedModel", "PreTrainedModel", "True", "_can_compile_fullgraph", "_skip_keys_device_placement", "_supports_attention_backend", "_supports_flash_attn", "_supports_flex_attn", "_supports_sdpa", "base_model_prefix", "class", "config", "image", "input_modalities", ...
vipllava/modeling_vipllava.py:VipLlavaModel
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[ "AutoModel", "Image", "ModelModel", "ModelModelOutputWithPast", "ModelMultiModalProjector", "ModelPreTrainedModel", "None", "Obtains", "True", "ValueError", "You", "__init__", "all", "and", "apply", "attention_mask", "attentions", "auto_docstring", "can_return_tuple", "cat", ...
vipllava/modeling_vipllava.py:VipLlavaForConditionalGeneration
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[ "GenerationMixin", "Linear", "ModelCausalLMOutputWithPast", "ModelForConditionalGeneration", "ModelModel", "ModelPreTrainedModel", "None", "__init__", "_tied_weights_keys", "attention_mask", "attentions", "auto_docstring", "can_return_tuple", "class", "config", "def", "else", "embe...
deberta/modeling_deberta.py:DebertaLayerNorm
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[ "ModelLayerNorm", "Module", "Parameter", "True", "__init__", "bias", "class", "def", "eps", "float", "forward", "hidden_states", "keepdim", "mean", "nn", "ones", "pow", "return", "self", "size", "sqrt", "super", "torch", "variance", "variance_epsilon", "weight", "...
deberta/modeling_deberta.py:DebertaSelfOutput
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[ "Dropout", "LayerNorm", "Linear", "ModelLayerNorm", "ModelSelfOutput", "Module", "__init__", "class", "config", "def", "dense", "dropout", "forward", "hidden_dropout_prob", "hidden_size", "hidden_states", "input_tensor", "layer_norm_eps", "nn", "return", "self", "super" ]
deberta/modeling_deberta.py:build_relative_position
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[ "Model_relative_position", "None", "arange", "def", "device", "dtype", "k_ids", "key_layer", "key_size", "long", "q_ids", "query_layer", "query_size", "rel_pos_ids", "repeat", "return", "size", "torch", "unsqueeze", "view" ]
deberta/modeling_deberta.py:c2p_dynamic_expand
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[ "Model_dynamic_expand", "Model_pos", "def", "expand", "query_layer", "relative_pos", "return", "size" ]
deberta/modeling_deberta.py:p2c_dynamic_expand
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[ "Model_dynamic_expand", "c2p_pos", "def", "expand", "key_layer", "query_layer", "return", "size" ]
deberta/modeling_deberta.py:pos_dynamic_expand
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[ "Model_dynamic_expand", "Model_index", "def", "expand", "key_layer", "p2c_att", "return", "size" ]
deberta/modeling_deberta.py:scaled_size_sqrt
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[ "Model_size_sqrt", "def", "dtype", "float", "query_layer", "return", "scale_factor", "size", "sqrt", "tensor", "torch" ]
deberta/modeling_deberta.py:build_rpos
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[ "Model_relative_position", "Model_rpos", "def", "else", "if", "key_layer", "query_layer", "relative_pos", "return", "size" ]
deberta/modeling_deberta.py:compute_attention_span
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[ "Model_attention_span", "def", "key_layer", "max", "max_relative_positions", "min", "query_layer", "return", "size", "tensor", "torch" ]
deberta/modeling_deberta.py:uneven_size_corrected
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[ "Model_size_corrected", "def", "dim", "else", "gather", "if", "index", "key_layer", "p2c_att", "pos_dynamic_expand", "pos_index", "query_layer", "relative_pos", "return", "size", "torch", "unsqueeze" ]
deberta/modeling_deberta.py:DisentangledSelfAttention
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[ "Dropout", "False", "Linear", "ModelSelfAttention", "Model_att_bias", "Module", "None", "Parameter", "Relative", "The", "ValueError", "__init__", "a", "all_head_size", "and", "att_span", "attention", "attention_head_size", "attention_mask", "attention_probs", "attention_probs...
deberta/modeling_deberta.py:DebertaEmbeddings
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[ "Dropout", "Embedding", "False", "LayerNorm", "Linear", "ModelEmbeddings", "ModelLayerNorm", "Module", "None", "True", "__init__", "arange", "class", "config", "def", "device", "dim", "dropout", "dtype", "else", "embed_proj", "embedding_size", "embeddings", "expand", ...
deberta/modeling_deberta.py:DebertaAttention
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[ "DisentangledSelfAttention", "False", "ModelAttention", "ModelSelfOutput", "Module", "None", "__init__", "att_matrix", "attention_mask", "attention_output", "class", "config", "def", "else", "forward", "hidden_states", "if", "is", "nn", "output", "output_attentions", "query...
deberta/modeling_deberta.py:DebertaIntermediate
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deberta/modeling_deberta.py:DebertaOutput
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[ "Dropout", "LayerNorm", "Linear", "ModelLayerNorm", "ModelOutput", "Module", "__init__", "class", "config", "def", "dense", "dropout", "forward", "hidden_dropout_prob", "hidden_size", "hidden_states", "input_tensor", "intermediate_size", "layer_norm_eps", "nn", "return", "s...
deberta/modeling_deberta.py:DebertaLayer
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[ "False", "GradientCheckpointingLayer", "ModelAttention", "ModelIntermediate", "ModelLayer", "ModelOutput", "None", "__init__", "att_matrix", "attention", "attention_mask", "attention_output", "class", "config", "def", "else", "forward", "hidden_states", "if", "intermediate", ...
deberta/modeling_deberta.py:DebertaEncoder
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deberta/modeling_deberta.py:DebertaPreTrainedModel
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deberta/modeling_deberta.py:DebertaModel
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deberta/modeling_deberta.py:LegacyDebertaPredictionHeadTransform
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deberta/modeling_deberta.py:LegacyDebertaLMPredictionHead
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deberta/modeling_deberta.py:LegacyDebertaOnlyMLMHead
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[ "ModelModelLMPredictionHead", "ModelModelOnlyMLMHead", "Module", "__init__", "class", "config", "def", "forward", "nn", "prediction_scores", "predictions", "return", "self", "sequence_output", "super" ]
deberta/modeling_deberta.py:DebertaLMPredictionHead
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deberta/modeling_deberta.py:DebertaOnlyMLMHead
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[ "ModelLMPredictionHead", "ModelOnlyMLMHead", "Module", "__init__", "class", "config", "def", "forward", "lm_head", "nn", "prediction_scores", "return", "self", "sequence_output", "super", "word_embeddings" ]
deberta/modeling_deberta.py:DebertaForMaskedLM
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deberta/modeling_deberta.py:ContextPooler
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[ "ACT2FN", "Dropout", "Linear", "ModelPooler", "Model_token", "Module", "__init__", "class", "config", "def", "dense", "dropout", "forward", "hidden_size", "hidden_states", "nn", "output_dim", "pooled_output", "pooler_dropout", "pooler_hidden_act", "pooler_hidden_size", "pro...
deberta/modeling_deberta.py:DebertaForSequenceClassification
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[ "BCEWithLogitsLoss", "ContextPooler", "CrossEntropyLoss", "Dropout", "Linear", "LogSoftmax", "MSELoss", "Model", "ModelForSequenceClassification", "ModelModel", "ModelPreTrainedModel", "None", "SequenceClassifierOutput", "__init__", "attention_mask", "attentions", "auto_docstring", ...
deberta/modeling_deberta.py:DebertaForTokenClassification
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[ "CrossEntropyLoss", "Dropout", "Linear", "Model", "ModelForTokenClassification", "ModelModel", "ModelPreTrainedModel", "None", "TokenClassifierOutput", "__init__", "attention_mask", "attentions", "auto_docstring", "class", "classifier", "config", "def", "dropout", "else", "forw...
deberta/modeling_deberta.py:DebertaForQuestionAnswering
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[ "CrossEntropyLoss", "Linear", "Model", "ModelForQuestionAnswering", "ModelModel", "ModelPreTrainedModel", "None", "QuestionAnsweringModelOutput", "__init__", "and", "attention_mask", "attentions", "auto_docstring", "clamp", "class", "config", "contiguous", "def", "dim", "else",...
rembert/modeling_rembert.py:RemBertEmbeddings
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[ "Dropout", "Embedding", "False", "LayerNorm", "ModelEmbeddings", "Module", "None", "__init__", "arange", "class", "config", "def", "device", "dropout", "dtype", "else", "embeddings", "eps", "expand", "forward", "hidden_dropout_prob", "if", "input_embedding_size", "input...
rembert/modeling_rembert.py:RemBertPooler
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[ "Linear", "ModelPooler", "Module", "Tanh", "__init__", "activation", "class", "config", "def", "dense", "first_token_tensor", "forward", "hidden_size", "hidden_states", "nn", "pooled_output", "return", "self", "super" ]
rembert/modeling_rembert.py:RemBertSelfAttention
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[ "Dropout", "EncoderDecoderCache", "False", "Linear", "ModelSelfAttention", "Module", "None", "The", "True", "ValueError", "__init__", "a", "all_head_size", "and", "attention", "attention_head_size", "attention_mask", "attention_probs", "attention_probs_dropout_prob", "attention...
rembert/modeling_rembert.py:RemBertSelfOutput
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[ "Dropout", "LayerNorm", "Linear", "ModelSelfOutput", "Module", "__init__", "class", "config", "def", "dense", "dropout", "eps", "forward", "hidden_dropout_prob", "hidden_size", "hidden_states", "input_tensor", "layer_norm_eps", "nn", "return", "self", "super" ]
rembert/modeling_rembert.py:RemBertAttention
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rembert/modeling_rembert.py:RemBertIntermediate
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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" ]
rembert/modeling_rembert.py:RemBertOutput
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rembert/modeling_rembert.py:RemBertLayer
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[ "False", "GradientCheckpointingLayer", "If", "ModelAttention", "ModelIntermediate", "ModelLayer", "ModelOutput", "None", "True", "ValueError", "__init__", "add_cross_attention", "and", "apply_chunking_to_forward", "are", "attention", "attention_mask", "attention_output", "be", ...
rembert/modeling_rembert.py:RemBertEncoder
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rembert/modeling_rembert.py:RemBertPredictionHeadTransform
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[ "ACT2FN", "LayerNorm", "Linear", "ModelPredictionHeadTransform", "Module", "__init__", "class", "config", "def", "dense", "else", "eps", "forward", "hidden_act", "hidden_size", "hidden_states", "if", "isinstance", "layer_norm_eps", "nn", "return", "self", "str", "super"...
rembert/modeling_rembert.py:RemBertLMPredictionHead
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rembert/modeling_rembert.py:RemBertOnlyMLMHead
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[ "ModelLMPredictionHead", "ModelOnlyMLMHead", "Module", "__init__", "class", "config", "def", "forward", "nn", "prediction_scores", "predictions", "return", "self", "sequence_output", "super" ]
rembert/modeling_rembert.py:RemBertPreTrainedModel
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rembert/modeling_rembert.py:RemBertModel
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rembert/modeling_rembert.py:RemBertForMaskedLM
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[ "CrossEntropyLoss", "False", "If", "MaskedLMOutput", "Model", "ModelForMaskedLM", "ModelModel", "ModelOnlyMLMHead", "ModelPreTrainedModel", "None", "__init__", "add_pooling_layer", "attention", "attention_mask", "attentions", "auto_docstring", "bi", "class", "cls", "config", ...
rembert/modeling_rembert.py:RemBertForCausalLM
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[ "CausalLMOutputWithCrossAttentions", "False", "GenerationMixin", "Model", "ModelForCausalLM", "ModelModel", "ModelOnlyMLMHead", "ModelPreTrainedModel", "None", "True", "__init__", "add", "add_pooling_layer", "attention_mask", "attentions", "auto_docstring", "class", "cls", "confi...
rembert/modeling_rembert.py:RemBertForSequenceClassification
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[ "BCEWithLogitsLoss", "CrossEntropyLoss", "Dropout", "Linear", "MSELoss", "Model", "ModelForSequenceClassification", "ModelModel", "ModelPreTrainedModel", "None", "SequenceClassifierOutput", "__init__", "and", "attention_mask", "attentions", "auto_docstring", "class", "classifier", ...
rembert/modeling_rembert.py:RemBertForMultipleChoice
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rembert/modeling_rembert.py:RemBertForTokenClassification
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[ "CrossEntropyLoss", "Dropout", "False", "Linear", "Model", "ModelForTokenClassification", "ModelModel", "ModelPreTrainedModel", "None", "TokenClassifierOutput", "__init__", "add_pooling_layer", "attention_mask", "attentions", "auto_docstring", "class", "classifier", "classifier_dro...
rembert/modeling_rembert.py:RemBertForQuestionAnswering
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[ "CrossEntropyLoss", "False", "Linear", "Model", "ModelForQuestionAnswering", "ModelModel", "ModelPreTrainedModel", "None", "QuestionAnsweringModelOutput", "__init__", "add_pooling_layer", "and", "attention_mask", "attentions", "auto_docstring", "clamp_", "class", "config", "def",...
glm_ocr/modeling_glm_ocr.py:GlmOcrRMSNorm
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[ "ModelRMSNorm", "Module", "Parameter", "True", "__init__", "class", "def", "dtype", "eps", "extra_repr", "f", "float32", "forward", "hidden_size", "hidden_states", "input_dtype", "keepdim", "mean", "nn", "ones", "pow", "return", "rsqrt", "self", "shape", "super", ...
glm_ocr/modeling_glm_ocr.py:GlmOcrVisionMlp
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glm_ocr/modeling_glm_ocr.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" ]
glm_ocr/modeling_glm_ocr.py:eager_attention_forward
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glm_ocr/modeling_glm_ocr.py:rotate_half_llm
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[ "Model_half_llm", "def", "dim", "flatten", "return", "stack", "torch", "x", "x1", "x2" ]
glm_ocr/modeling_glm_ocr.py:apply_rotary_pos_emb
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glm_ocr/modeling_glm_ocr.py:GlmOcrTextAttention
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glm_ocr/modeling_glm_ocr.py:GlmOcrTextMLP
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glm_ocr/modeling_glm_ocr.py:GlmOcrTextDecoderLayer
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glm_ocr/modeling_glm_ocr.py:GlmOcrVisionRotaryEmbedding
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glm_ocr/modeling_glm_ocr.py:GlmOcrPreTrainedModel
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glm_ocr/modeling_glm_ocr.py:GlmOcrModelOutputWithPast
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[ "BaseModelOutputWithPast", "ModelModelOutputWithPast", "None", "class", "r", "rope_deltas" ]
glm_ocr/modeling_glm_ocr.py:rotate_half
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[ "Model_half", "cat", "def", "dim", "return", "shape", "torch", "x", "x1", "x2" ]
glm_ocr/modeling_glm_ocr.py:apply_rotary_pos_emb_vision
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glm_ocr/modeling_glm_ocr.py:GlmOcrVisionAttention
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glm_ocr/modeling_glm_ocr.py:GlmOcrVisionBlock
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glm_ocr/modeling_glm_ocr.py:GlmOcrVisionPatchMerger
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glm_ocr/modeling_glm_ocr.py:GlmOcrVisionPatchEmbed
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glm_ocr/modeling_glm_ocr.py:GlmOcrVisionModel
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glm_ocr/modeling_glm_ocr.py:GlmOcrTextRotaryEmbedding
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glm_ocr/modeling_glm_ocr.py:GlmOcrTextModel
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glm_ocr/modeling_glm_ocr.py:GlmOcrModel
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glm_ocr/modeling_glm_ocr.py:GlmOcrCausalLMOutputWithPast
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[ "CausalLMOutputWithPast", "ModelCausalLMOutputWithPast", "None", "class", "r", "rope_deltas" ]
glm_ocr/modeling_glm_ocr.py:GlmOcrForConditionalGeneration
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exaone4_5/modeling_exaone4_5.py:Exaone4_5_PatchEmbed
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exaone4_5/modeling_exaone4_5.py:Exaone4_5_VisionRotaryEmbedding
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exaone4_5/modeling_exaone4_5.py:Exaone4_5_RMSNorm
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exaone4_5/modeling_exaone4_5.py:Exaone4_5_PatchMerger
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exaone4_5/modeling_exaone4_5.py:rotate_half
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[ "Model_half", "cat", "def", "dim", "return", "shape", "torch", "x", "x1", "x2" ]
exaone4_5/modeling_exaone4_5.py:apply_rotary_pos_emb_vision
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exaone4_5/modeling_exaone4_5.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" ]
exaone4_5/modeling_exaone4_5.py:eager_attention_forward
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exaone4_5/modeling_exaone4_5.py:Exaone4_5_VisionAttention
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exaone4_5/modeling_exaone4_5.py:Exaone4_5_MLP
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exaone4_5/modeling_exaone4_5.py:Exaone4_5_VisionBlock
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exaone4_5/modeling_exaone4_5.py:apply_rotary_pos_emb
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exaone4_5/modeling_exaone4_5.py:Exaone4_5_Attention
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exaone4_5/modeling_exaone4_5.py:Exaone4_5_DecoderLayer
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exaone4_5/modeling_exaone4_5.py:Exaone4_5_PreTrainedModel
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exaone4_5/modeling_exaone4_5.py:Exaone4_5_VisionModel
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