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
eomt/modeling_eomt.py:EomtHungarianMatcher
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[ "All", "False", "ModelHungarianMatcher", "Module", "None", "ValueError", "__init__", "align_corners", "and", "append", "as_tensor", "assigned_indices", "batch_size", "be", "can", "class", "class_labels", "class_queries_logits", "cost_class", "cost_dice", "cost_mask", "cost_...
eomt/modeling_eomt.py:dice_loss
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[ "Model_loss", "def", "denominator", "flatten", "inputs", "labels", "loss", "num_masks", "numerator", "probs", "r", "return", "sigmoid", "sum" ]
eomt/modeling_eomt.py:sigmoid_cross_entropy_loss
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[ "BCEWithLogitsLoss", "Model_cross_entropy_loss", "criterion", "cross_entropy_loss", "def", "inputs", "labels", "loss", "mean", "nn", "none", "num_masks", "r", "reduction", "return", "sum" ]
eomt/modeling_eomt.py:EomtLoss
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[ "CrossEntropyLoss", "False", "ModelHungarianMatcher", "ModelLoss", "Module", "None", "PartialState", "Tensor", "_", "__init__", "_get_predictions_permutation_indices", "_get_targets_permutation_indices", "_max_by_axis", "_pad_images_to_max_in_batch", "_shared_state", "abs", "align_co...
eomt/modeling_eomt.py:EomtPatchEmbeddings
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[ "Conv2d", "Expected", "Iterable", "Make", "ModelPatchEmbeddings", "Module", "ValueError", "__init__", "abc", "but", "channel", "class", "collections", "config", "configuration", "def", "dimension", "else", "embeddings", "f", "flatten", "forward", "got", "hidden_size", ...
eomt/modeling_eomt.py:EomtEmbeddings
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[ "Dropout", "Embedding", "False", "ModelEmbeddings", "ModelPatchEmbeddings", "Module", "Parameter", "_", "__init__", "arange", "batch_size", "cat", "class", "cls_token", "cls_tokens", "config", "def", "dim", "dropout", "dtype", "embeddings", "expand", "forward", "hidden_...
eomt/modeling_eomt.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", "if", "is", "key", "kwargs", "matmul", "module", "nn", "not", "p", "query", "return", "scaling", "softmax", "...
eomt/modeling_eomt.py:EomtAttention
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[ "ALL_ATTENTION_FUNCTIONS", "False", "Linear", "ModelAttention", "Module", "None", "ValueError", "__init__", "_attn_implementation", "and", "attention_dropout", "attention_interface", "attention_mask", "attn_output", "attn_weights", "batch_size", "be", "by", "class", "config", ...
eomt/modeling_eomt.py:EomtLayerScale
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[ "ModelLayerScale", "Module", "Parameter", "__init__", "class", "config", "def", "forward", "hidden_size", "hidden_state", "lambda1", "layerscale_value", "nn", "ones", "return", "self", "super", "torch" ]
eomt/modeling_eomt.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" ]
eomt/modeling_eomt.py:EomtDropPath
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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" ]
eomt/modeling_eomt.py:EomtMLP
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[ "ACT2FN", "Linear", "ModelMLP", "Module", "__init__", "activation", "class", "config", "def", "else", "fc1", "fc2", "forward", "hidden_act", "hidden_features", "hidden_size", "hidden_state", "if", "in_features", "int", "isinstance", "mlp_ratio", "nn", "out_features", ...
eomt/modeling_eomt.py:EomtSwiGLUFFN
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[ "Linear", "ModelSwiGLUFFN", "Module", "__init__", "chunk", "class", "config", "def", "dim", "forward", "functional", "hidden", "hidden_features", "hidden_size", "hidden_state", "in_features", "int", "mlp_ratio", "nn", "out_features", "return", "self", "silu", "super", ...
eomt/modeling_eomt.py:EomtLayer
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[ "GradientCheckpointingLayer", "Identity", "LayerNorm", "ModelAttention", "ModelDropPath", "ModelLayer", "ModelLayerScale", "ModelMLP", "ModelSwiGLUFFN", "None", "_", "__init__", "attention", "attention_mask", "class", "config", "def", "drop_path", "drop_path_rate", "else", "e...
eomt/modeling_eomt.py:EomtLayerNorm2d
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[ "F", "LayerNorm", "ModelLayerNorm2d", "True", "__init__", "affine", "bias", "class", "def", "elementwise_affine", "eps", "forward", "hidden_state", "layer_norm", "nn", "normalized_shape", "num_channels", "permute", "return", "self", "super", "weight" ]
eomt/modeling_eomt.py:EomtScaleLayer
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[ "ACT2FN", "Conv2d", "ConvTranspose2d", "ModelLayerNorm2d", "ModelScaleLayer", "Module", "__init__", "activation", "class", "config", "conv1", "conv2", "def", "forward", "groups", "hidden_act", "hidden_size", "hidden_states", "kernel_size", "layernorm2d", "nn", "padding", ...
eomt/modeling_eomt.py:EomtScaleBlock
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[ "ModelScaleBlock", "ModelScaleLayer", "Module", "ModuleList", "_", "__init__", "block", "class", "config", "def", "for", "forward", "hidden_states", "in", "nn", "num_blocks", "num_upscale_blocks", "range", "return", "self", "super" ]
eomt/modeling_eomt.py:EomtMaskHead
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[ "ACT2FN", "Linear", "ModelMaskHead", "Module", "__init__", "activation", "class", "config", "def", "fc1", "fc2", "fc3", "forward", "hidden_act", "hidden_size", "hidden_states", "nn", "return", "self", "super" ]
eomt/modeling_eomt.py:EomtPreTrainedModel
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[ "Conv2d", "ConvTranspose2d", "Embedding", "False", "LayerNorm", "Linear", "Model", "ModelAttention", "ModelConfig", "ModelEmbeddings", "ModelForUniversalSegmentation", "ModelLayer", "ModelLayerScale", "ModelLoss", "ModelPreTrainedModel", "None", "PreTrainedModel", "True", "_", ...
eomt/modeling_eomt.py:EomtForUniversalSegmentation
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[ "Embedding", "F", "LayerNorm", "Linear", "ModelEmbeddings", "ModelForUniversalSegmentation", "ModelForUniversalSegmentationOutput", "ModelLayer", "ModelLoss", "ModelMaskHead", "ModelPreTrainedModel", "ModelScaleBlock", "ModuleList", "None", "Tensor", "_", "__init__", "_disable_atte...
chameleon/modeling_chameleon.py:ChameleonVQVAEModelOutput
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[ "BaseModelOutputWithPooling", "ModelVQVAEModelOutput", "None", "class", "embedding_loss", "image_tokens", "quantized_last_hidden_state", "r" ]
chameleon/modeling_chameleon.py:ChameleonRMSNorm
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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", ...
chameleon/modeling_chameleon.py:ChameleonRotaryEmbedding
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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", ...
chameleon/modeling_chameleon.py:rotate_half
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[ "Model_half", "cat", "def", "dim", "return", "shape", "torch", "x", "x1", "x2" ]
chameleon/modeling_chameleon.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" ]
chameleon/modeling_chameleon.py:ChameleonMLP
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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" ]
chameleon/modeling_chameleon.py:ChameleonLayerNorm
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[ "F", "LayerNorm", "ModelLayerNorm", "None", "__init__", "args", "bias", "class", "def", "eps", "forward", "hidden_size", "hidden_states", "kwargs", "layer_norm", "nn", "normalized_shape", "return", "self", "super", "weight" ]
chameleon/modeling_chameleon.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" ]
chameleon/modeling_chameleon.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", ...
chameleon/modeling_chameleon.py:ChameleonAttention
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[ "ALL_ATTENTION_FUNCTIONS", "False", "Instantiating", "Linear", "ModelAttention", "ModelLayerNorm", "Module", "None", "Please", "True", "ValueError", "_", "__class__", "__init__", "__name__", "_attn_implementation", "a", "and", "apply_rotary_pos_emb", "attention_dropout", "att...
chameleon/modeling_chameleon.py:ChameleonDecoderLayer
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[ "False", "GradientCheckpointingLayer", "ModelAttention", "ModelDecoderLayer", "ModelMLP", "ModelRMSNorm", "None", "__init__", "attention_mask", "cache_position", "class", "config", "def", "eps", "forward", "hidden_size", "hidden_states", "if", "input_layernorm", "kwargs", "la...
chameleon/modeling_chameleon.py:ChameleonSwinDecoderLayer
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[ "False", "GradientCheckpointingLayer", "ModelAttention", "ModelMLP", "ModelRMSNorm", "ModelSwinDecoderLayer", "None", "__init__", "attention_mask", "cache_position", "class", "config", "def", "eps", "forward", "hidden_size", "hidden_states", "if", "input_layernorm", "kwargs", ...
chameleon/modeling_chameleon.py:ChameleonVQVAEVectorQuantizer
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[ "Embedding", "ModelVQVAEVectorQuantizer", "Module", "True", "__init__", "argmin", "bd", "beta", "bn", "class", "config", "contiguous", "def", "detach", "dim", "distances", "dn", "einsum", "embed_dim", "embedding", "embedding_dim", "forward", "getattr", "hidden_state", ...
chameleon/modeling_chameleon.py:ChameleonVQVAEEncoderConvDownsample
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chameleon/modeling_chameleon.py:ChameleonVQVAEEncoderResnetBlock
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chameleon/modeling_chameleon.py:ChameleonVQVAEEncoderAttnBlock
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chameleon/modeling_chameleon.py:ChameleonVQVAEEncoder
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[ "Conv2d", "GroupNorm", "Identity", "ModelVQVAEEncoder", "ModelVQVAEEncoderAttnBlock", "ModelVQVAEEncoderConvDownsample", "ModelVQVAEEncoderResnetBlock", "Module", "ModuleList", "None", "True", "__init__", "affine", "and", "append", "attn", "attn_1", "attn_resolutions", "attn_type...
chameleon/modeling_chameleon.py:ChameleonImageVocabularyMapping
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chameleon/modeling_chameleon.py:ChameleonPreTrainedModel
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[ "ModelAttention", "ModelConfig", "ModelDecoderLayer", "ModelPreTrainedModel", "ModelSwinDecoderLayer", "PreTrainedModel", "True", "_can_compile_fullgraph", "_can_record_outputs", "_no_split_modules", "_skip_keys_device_placement", "_supports_attention_backend", "_supports_flash_attn", "_su...
chameleon/modeling_chameleon.py:ChameleonVQVAE
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[ "Conv2d", "ModelPreTrainedModel", "ModelVQVAE", "ModelVQVAEConfig", "ModelVQVAEEncoder", "ModelVQVAEEncoderAttnBlock", "ModelVQVAEEncoderResnetBlock", "ModelVQVAEModelOutput", "ModelVQVAEVectorQuantizer", "__init__", "_can_record_outputs", "_no_split_modules", "attentions", "check_model_in...
chameleon/modeling_chameleon.py:ChameleonModel
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[ "BaseModelOutputWithPast", "DynamicCache", "Embedding", "False", "Image", "ModelDecoderLayer", "ModelImageVocabularyMapping", "ModelModel", "ModelPreTrainedModel", "ModelRMSNorm", "ModelRotaryEmbedding", "ModelSwinDecoderLayer", "ModelVQVAE", "ModuleList", "None", "Setting", "Tokeniz...
chameleon/modeling_chameleon.py:ChameleonForConditionalGeneration
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[ "CausalLMOutputWithPast", "False", "GenerationMixin", "Linear", "ModelForConditionalGeneration", "ModelModel", "ModelPreTrainedModel", "None", "True", "__init__", "_tied_weights_keys", "and", "attention_mask", "attentions", "auto_docstring", "cache_position", "can_return_tuple", "c...
fuyu/modeling_fuyu.py:FuyuPreTrainedModel
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fuyu/modeling_fuyu.py:FuyuModel
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fuyu/modeling_fuyu.py:FuyuForCausalLM
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aria/modeling_aria.py:AriaTextRMSNorm
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aria/modeling_aria.py:AriaProjectorMLP
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[ "ACT2FN", "Linear", "ModelProjectorMLP", "Module", "__init__", "act", "class", "def", "forward", "gelu_new", "hidden_features", "hidden_states", "in_features", "linear_in", "linear_out", "nn", "output_dim", "return", "self", "super" ]
aria/modeling_aria.py:AriaCrossAttention
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aria/modeling_aria.py:AriaProjector
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[ "KeyError", "LayerNorm", "ModelCrossAttention", "ModelProjector", "ModelProjectorMLP", "Module", "None", "Number", "Parameter", "__init__", "amongst", "attention_out", "attn_mask", "batch_size", "class", "config", "cross_attn", "def", "expand", "f", "feed_forward", "forward...
aria/modeling_aria.py:AriaSharedExpertsMLP
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aria/modeling_aria.py:sequential_experts_gemm
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aria/modeling_aria.py:AriaGroupedExpertsGemm
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aria/modeling_aria.py:AriaExperts
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aria/modeling_aria.py:AriaTextMoELayer
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aria/modeling_aria.py:rotate_half
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[ "Model_half", "cat", "def", "dim", "return", "shape", "torch", "x", "x1", "x2" ]
aria/modeling_aria.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" ]
aria/modeling_aria.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" ]
aria/modeling_aria.py:eager_attention_forward
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aria/modeling_aria.py:AriaTextAttention
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aria/modeling_aria.py:AriaTextDecoderLayer
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aria/modeling_aria.py:AriaTextPreTrainedModel
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[ "ModelGroupedExpertsGemm", "ModelTextAttention", "ModelTextConfig", "ModelTextDecoderLayer", "ModelTextPreTrainedModel", "PreTrainedModel", "True", "_can_record_outputs", "_init_weights", "_no_split_modules", "_skip_keys_device_placement", "_supports_attention_backend", "_supports_flash_attn...
aria/modeling_aria.py:AriaPreTrainedModel
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aria/modeling_aria.py:AriaTextRotaryEmbedding
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aria/modeling_aria.py:AriaTextModel
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aria/modeling_aria.py:AriaTextForCausalLM
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aria/modeling_aria.py:AriaCausalLMOutputWithPast
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aria/modeling_aria.py:AriaModelOutputWithPast
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aria/modeling_aria.py:AriaModel
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aria/modeling_aria.py:AriaForConditionalGeneration
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depth_pro/modeling_depth_pro.py:DepthProOutput
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depth_pro/modeling_depth_pro.py:DepthProDepthEstimatorOutput
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depth_pro/modeling_depth_pro.py:split_to_patches
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depth_pro/modeling_depth_pro.py:reshape_features
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depth_pro/modeling_depth_pro.py:merge_patches
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depth_pro/modeling_depth_pro.py:reconstruct_feature_maps
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depth_pro/modeling_depth_pro.py:DepthProPatchEncoder
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depth_pro/modeling_depth_pro.py:DepthProImageEncoder
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depth_pro/modeling_depth_pro.py:DepthProEncoder
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depth_pro/modeling_depth_pro.py:DepthProFeatureUpsampleBlock
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depth_pro/modeling_depth_pro.py:DepthProFeatureUpsample
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depth_pro/modeling_depth_pro.py:DepthProFeatureProjection
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depth_pro/modeling_depth_pro.py:DepthProNeck
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depth_pro/modeling_depth_pro.py:DepthProPreTrainedModel
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depth_pro/modeling_depth_pro.py:DepthProModel
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depth_pro/modeling_depth_pro.py:DepthProPreActResidualLayer
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depth_pro/modeling_depth_pro.py:DepthProFeatureFusionLayer
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depth_pro/modeling_depth_pro.py:DepthProFeatureFusionStage
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depth_pro/modeling_depth_pro.py:DepthProFovEncoder
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depth_pro/modeling_depth_pro.py:DepthProFovHead
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depth_pro/modeling_depth_pro.py:DepthProFovModel
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depth_pro/modeling_depth_pro.py:DepthProDepthEstimationHead
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depth_pro/modeling_depth_pro.py:DepthProForDepthEstimation
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mllama/modeling_mllama.py:_prepare_cross_attention_mask
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mllama/modeling_mllama.py:_prepare_aspect_ratio_attention_mask
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mllama/modeling_mllama.py:MllamaPrecomputedAspectRatioEmbedding
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mllama/modeling_mllama.py:MllamaPrecomputedPositionEmbedding
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mllama/modeling_mllama.py:MllamaVisionMLP
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mllama/modeling_mllama.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" ]
mllama/modeling_mllama.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", ...
mllama/modeling_mllama.py:MllamaVisionAttention
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