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
kosmos2/modeling_kosmos2.py:Kosmos2TextModel
[ 0.00007629470201209188, 0.05625055730342865, 0.022544866427779198, 0.006026845425367355, 0.00009286154818255454, 0.022879691794514656, 0.025335073471069336, -0.010658588260412216, 0.004603840410709381, 0.0040178969502449036, 0.02477703057229519, 0.022544866427779198, -0.0017857319908216596, ...
[ "ModelPreTrainedModel", "ModelTextConfig", "ModelTextModel", "ModelTextTransformer", "None", "__init__", "attention_mask", "auto_docstring", "cache_position", "can_return_tuple", "class", "config", "def", "embed_tokens", "encoder_attention_mask", "encoder_hidden_states", "forward", ...
kosmos2/modeling_kosmos2.py:Kosmos2TextForCausalLM
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[ "CausalLMOutputWithCrossAttentions", "False", "GenerationMixin", "Linear", "ModelPreTrainedModel", "ModelTextConfig", "ModelTextForCausalLM", "ModelTextTransformer", "None", "__init__", "_tied_weights_keys", "and", "attention_mask", "attentions", "auto_docstring", "batch_size", "bool...
kosmos2/modeling_kosmos2.py:Kosmos2ImageToTextProjection
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[ "False", "Linear", "ModelImageToTextProjection", "ModelTextAttention", "Module", "None", "Parameter", "__init__", "add_inner_attn_layernorm", "attention_dropout", "attention_heads", "attention_mask", "attn_weights", "cat", "class", "config", "def", "dense", "dim", "dropout", ...
kosmos2/modeling_kosmos2.py:Kosmos2Model
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[ "False", "FutureWarning", "ModelConfig", "ModelImageToTextProjection", "ModelModel", "ModelModelOutput", "ModelOutput", "ModelPreTrainedModel", "ModelTextModel", "ModelVisionModel", "None", "Please", "True", "__init__", "a", "access", "and", "attention_mask", "attentions", "aut...
kosmos2/modeling_kosmos2.py:Kosmos2ForConditionalGeneration
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[ "GenerationMixin", "Make", "ModelConfig", "ModelForConditionalGeneration", "ModelForConditionalGenerationModelOutput", "ModelImageToTextProjection", "ModelPreTrainedModel", "ModelTextForCausalLM", "ModelVisionModel", "None", "ValueError", "__init__", "_tied_weights_keys", "allowed", "alo...
lightglue/modeling_lightglue.py:LightGlueKeypointMatchingOutput
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[ "ModelKeypointMatchingOutput", "ModelOutput", "None", "attentions", "class", "hidden_states", "keypoints", "loss", "mask", "matches", "matching_scores", "prune", "r" ]
lightglue/modeling_lightglue.py:LightGluePositionalEncoder
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[ "False", "Linear", "ModelPositionalEncoder", "Module", "__init__", "class", "config", "cos", "cosines", "def", "descriptor_dim", "dim", "else", "embeddings", "forward", "if", "keypoints", "nn", "num_attention_heads", "output", "output_hidden_states", "projected_keypoints", ...
lightglue/modeling_lightglue.py:rotate_half
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[ "Model_half", "def", "dim", "flatten", "return", "rot_x", "stack", "torch", "x", "x1", "x2" ]
lightglue/modeling_lightglue.py:apply_rotary_pos_emb
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[ "Model_rotary_pos_emb", "cos", "def", "float", "k", "k_embed", "q", "q_embed", "return", "rotate_half", "sin", "unsqueeze", "unsqueeze_dim" ]
lightglue/modeling_lightglue.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" ]
lightglue/modeling_lightglue.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", "key_states", "kwargs", "matmul", "module", "nn", "not", "num_key_value_groups", "p", "query",...
lightglue/modeling_lightglue.py:LightGlueAttention
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[ "ALL_ATTENTION_FUNCTIONS", "Linear", "ModelAttention", "Module", "None", "Tensor", "True", "__init__", "_attn_implementation", "apply_rotary_pos_emb", "attention_dropout", "attention_interface", "attention_mask", "attn_output", "attn_weights", "class", "config", "contiguous", "co...
lightglue/modeling_lightglue.py:LightGlueMLP
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[ "ACT2FN", "LayerNorm", "Linear", "ModelMLP", "Module", "True", "__init__", "activation_fn", "class", "config", "def", "elementwise_affine", "fc1", "fc2", "forward", "hidden_act", "hidden_size", "hidden_states", "intermediate_size", "layer_norm", "nn", "return", "self", ...
lightglue/modeling_lightglue.py:LightGlueTransformerLayer
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[ "False", "ModelAttention", "ModelMLP", "ModelTransformerLayer", "Module", "None", "__init__", "all_attentions", "all_hidden_states", "attention_mask", "attention_output", "batch_size", "cat", "class", "config", "cross_attention", "cross_attention_hidden_states", "cross_attention_ou...
lightglue/modeling_lightglue.py:sigmoid_log_double_softmax
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[ "Model_log_double_softmax", "batch_size", "certainties", "contiguous", "def", "functional", "logModel", "log_softmax", "matchability0", "matchability1", "new_full", "nn", "num_keypoints_0", "num_keypoints_1", "return", "scores", "scores0", "scores1", "shape", "similarity", "s...
lightglue/modeling_lightglue.py:LightGlueMatchAssignmentLayer
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[ "Linear", "ModelMatchAssignmentLayer", "Module", "None", "__init__", "batch_size", "class", "config", "def", "descriptor_dim", "descriptors", "device", "dtype", "final_projection", "finfo", "forward", "functional", "get_matchability", "if", "is", "m_descriptors", "m_descrip...
lightglue/modeling_lightglue.py:LightGlueTokenConfidenceLayer
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[ "Linear", "ModelTokenConfidenceLayer", "Module", "__init__", "class", "config", "def", "descriptor_dim", "descriptors", "detach", "forward", "functional", "nn", "return", "self", "sigmoid", "squeeze", "super", "token" ]
lightglue/modeling_lightglue.py:LightGluePreTrainedModel
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[ "False", "Model", "ModelConfig", "ModelPreTrainedModel", "PreTrainedModel", "True", "_supports_flash_attn", "_supports_sdpa", "base_model_prefix", "class", "config", "image", "input_modalities", "main_input_name", "pixel_values", "supports_gradient_checkpointing" ]
lightglue/modeling_lightglue.py:get_matches_from_scores
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[ "Model_matches_from_scores", "None", "_", "arange", "batch_size", "def", "device", "exp", "gather", "indices", "indices0", "indices1", "matches", "matches0", "matches1", "matching_scores", "matching_scores0", "matching_scores1", "max", "max0", "max1", "mutual0", "mutual1"...
lightglue/modeling_lightglue.py:normalize_keypoints
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[ "Model_keypoints", "None", "def", "device", "dtype", "height", "keypoints", "max", "return", "scale", "shift", "size", "tensor", "torch", "values", "width" ]
lightglue/modeling_lightglue.py:LightGlueForKeypointMatching
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[ "AutoModelForKeypointDetection", "False", "Identity", "Input", "Linear", "ModelForKeypointMatching", "ModelKeypointMatchingOutput", "ModelMatchAssignmentLayer", "ModelPositionalEncoder", "ModelPreTrainedModel", "ModelTokenConfidenceLayer", "ModelTransformerLayer", "ModuleList", "None", "...
big_bird/modeling_big_bird.py:BigBirdEmbeddings
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[ "Dropout", "Embedding", "False", "LayerNorm", "ModelEmbeddings", "Module", "None", "__init__", "arange", "buffered_token_type_ids", "buffered_token_type_ids_expanded", "class", "config", "def", "device", "dropout", "dtype", "else", "embeddings", "eps", "expand", "forward", ...
big_bird/modeling_big_bird.py:BigBirdSelfAttention
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[ "Dropout", "False", "Linear", "ModelSelfAttention", "Module", "None", "The", "ValueError", "_", "__init__", "a", "all_head_size", "and", "attention", "attention_head_size", "attention_mask", "attention_probs", "attention_probs_dropout_prob", "attention_scores", "batch_size", ...
big_bird/modeling_big_bird.py:BigBirdBlockSparseAttention
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[ "Key", "Linear", "Make", "ModelBlockSparseAttention", "Model_block_sparse_attention", "Module", "None", "Query", "The", "Value", "ValueError", "_", "_Model_block_rand_mask", "_Model_block_rand_mask_with_head", "__init__", "_create_rand_mask_from_inputs", "_get_rand_attn_plan", "_ge...
big_bird/modeling_big_bird.py:BigBirdSelfOutput
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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" ]
big_bird/modeling_big_bird.py:BigBirdAttention
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[ "False", "ModelAttention", "ModelBlockSparseAttention", "ModelSelfAttention", "ModelSelfOutput", "Module", "None", "ValueError", "__init__", "attention_mask", "attention_output", "attention_type", "attn_weights", "band_mask", "be", "block_sparse", "but", "cache_position", "can", ...
big_bird/modeling_big_bird.py:BigBirdIntermediate
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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" ]
big_bird/modeling_big_bird.py:BigBirdOutput
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[ "Dropout", "LayerNorm", "Linear", "ModelOutput", "Module", "__init__", "class", "config", "def", "dense", "dropout", "eps", "forward", "hidden_dropout_prob", "hidden_size", "hidden_states", "input_tensor", "intermediate_size", "layer_norm_eps", "nn", "return", "self", "su...
big_bird/modeling_big_bird.py:BigBirdLayer
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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", "attenti...
big_bird/modeling_big_bird.py:BigBirdEncoder
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[ "BaseModelOutputWithPastAndCrossAttentions", "DynamicCache", "False", "ModelEncoder", "ModelLayer", "Module", "ModuleList", "None", "Setting", "True", "ValueError", "__init__", "add_cross_attention", "all_cross_attentions", "all_hidden_states", "all_self_attentions", "and", "attent...
big_bird/modeling_big_bird.py:BigBirdPredictionHeadTransform
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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"...
big_bird/modeling_big_bird.py:BigBirdLMPredictionHead
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big_bird/modeling_big_bird.py:BigBirdOnlyMLMHead
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big_bird/modeling_big_bird.py:BigBirdOnlyNSPHead
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big_bird/modeling_big_bird.py:BigBirdPreTrainingHeads
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big_bird/modeling_big_bird.py:BigBirdPreTrainedModel
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big_bird/modeling_big_bird.py:BigBirdForPreTrainingOutput
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big_bird/modeling_big_bird.py:BigBirdForQuestionAnsweringModelOutput
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[ "ModelForQuestionAnsweringModelOutput", "ModelOutput", "None", "attentions", "class", "end_logits", "hidden_states", "loss", "pooler_output", "r", "start_logits" ]
big_bird/modeling_big_bird.py:BigBirdModel
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big_bird/modeling_big_bird.py:BigBirdForPreTraining
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big_bird/modeling_big_bird.py:BigBirdForMaskedLM
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big_bird/modeling_big_bird.py:BigBirdForCausalLM
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big_bird/modeling_big_bird.py:BigBirdClassificationHead
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big_bird/modeling_big_bird.py:BigBirdForSequenceClassification
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big_bird/modeling_big_bird.py:BigBirdForMultipleChoice
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big_bird/modeling_big_bird.py:BigBirdForTokenClassification
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big_bird/modeling_big_bird.py:BigBirdForQuestionAnsweringHead
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big_bird/modeling_big_bird.py:BigBirdForQuestionAnswering
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helium/modeling_helium.py:HeliumRMSNorm
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helium/modeling_helium.py:HeliumRotaryEmbedding
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helium/modeling_helium.py:HeliumMLP
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helium/modeling_helium.py:repeat_kv
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helium/modeling_helium.py:eager_attention_forward
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helium/modeling_helium.py:rotate_half
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[ "Model_half", "def", "dim", "flatten", "return", "stack", "torch", "x", "x1", "x2" ]
helium/modeling_helium.py:apply_rotary_pos_emb
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helium/modeling_helium.py:HeliumAttention
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helium/modeling_helium.py:HeliumDecoderLayer
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helium/modeling_helium.py:HeliumPreTrainedModel
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helium/modeling_helium.py:HeliumModel
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helium/modeling_helium.py:HeliumForCausalLM
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helium/modeling_helium.py:HeliumForSequenceClassification
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helium/modeling_helium.py:HeliumForTokenClassification
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doge/modeling_doge.py:DogeRMSNorm
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doge/modeling_doge.py:DogeRotaryEmbedding
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doge/modeling_doge.py:rotate_half
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doge/modeling_doge.py:apply_rotary_pos_emb
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doge/modeling_doge.py:repeat_kv
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doge/modeling_doge.py:eager_attention_forward
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doge/modeling_doge.py:flex_attention_forward
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doge/modeling_doge.py:DogeAttention
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doge/modeling_doge.py:DogeMLP
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doge/modeling_doge.py:DogeCDMoE
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doge/modeling_doge.py:DogeDecoderLayer
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doge/modeling_doge.py:DogePreTrainedModel
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doge/modeling_doge.py:DogeModel
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doge/modeling_doge.py:load_balancing_loss_func
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doge/modeling_doge.py:DogeForCausalLM
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doge/modeling_doge.py:DogeForSequenceClassification
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pop2piano/modeling_pop2piano.py:Pop2PianoLayerNorm
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pop2piano/modeling_pop2piano.py:Pop2PianoDenseActDense
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pop2piano/modeling_pop2piano.py:Pop2PianoDenseGatedActDense
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pop2piano/modeling_pop2piano.py:Pop2PianoLayerFF
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pop2piano/modeling_pop2piano.py:Pop2PianoAttention
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pop2piano/modeling_pop2piano.py:Pop2PianoLayerSelfAttention
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pop2piano/modeling_pop2piano.py:Pop2PianoLayerCrossAttention
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pop2piano/modeling_pop2piano.py:Pop2PianoBlock
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pop2piano/modeling_pop2piano.py:Pop2PianoPreTrainedModel
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pop2piano/modeling_pop2piano.py:Pop2PianoStack
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pop2piano/modeling_pop2piano.py:Pop2PianoConcatEmbeddingToMel
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pop2piano/modeling_pop2piano.py:Pop2PianoForConditionalGeneration
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emu3/modeling_emu3.py:Emu3VQVAEModelOutput
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emu3/modeling_emu3.py:rotate_half
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emu3/modeling_emu3.py:apply_rotary_pos_emb
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emu3/modeling_emu3.py:repeat_kv
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emu3/modeling_emu3.py:eager_attention_forward
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emu3/modeling_emu3.py:Emu3Attention
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[ "ALL_ATTENTION_FUNCTIONS", "Linear", "ModelAttention", "Module", "None", "Tensor", "True", "__init__", "_attn_implementation", "apply_rotary_pos_emb", "attention_dropout", "attention_interface", "attention_mask", "attn_output", "attn_weights", "cache_kwargs", "cache_position", "cla...
emu3/modeling_emu3.py:Emu3RMSNorm
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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", ...
emu3/modeling_emu3.py:Emu3MLP
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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" ]
emu3/modeling_emu3.py:Emu3DecoderLayer
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[ "Dropout", "False", "GradientCheckpointingLayer", "ModelAttention", "ModelDecoderLayer", "ModelMLP", "ModelRMSNorm", "None", "Tensor", "_", "__init__", "attention_dropout", "attention_mask", "cache_position", "class", "config", "def", "dropout", "eps", "forward", "hidden_size...
emu3/modeling_emu3.py:Emu3VQVAEVectorQuantizer
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[ "Embedding", "ModelVQVAEVectorQuantizer", "Module", "True", "__init__", "argmin", "batch_size", "channels", "class", "codebook_size", "config", "contiguous", "data", "def", "dim", "distances", "embed_dim", "embedding", "embedding_sum", "forward", "height", "hidden_state", ...