Datasets:
itazap
/
transformers_code_embeddings_v3

Dataset card Data Studio Files
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1
identifier
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24
117
embedding
listlengths
2.56k
2.56k
tokens
listlengths
4
444
videomt/modeling_videomt.py:eager_attention_forward
[ 0.000041531115130055696, 0.03166968375444412, 0.025335747748613358, -0.010858177207410336, 0.00008880589302862063, 0.035741500556468964, 0.0592675507068634, -0.02182946167886257, 0.02262120321393013, 0.011593366973102093, 0.024204688146710396, 0.03144347295165062, 0.002544885268434882, -0....
[ "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", "...
videomt/modeling_videomt.py:VideomtAttention
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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", "be", "by", "class", "config", "contiguous", ...
videomt/modeling_videomt.py:VideomtSwiGLUFFN
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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", ...
videomt/modeling_videomt.py:VideomtDropPath
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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", ...
videomt/modeling_videomt.py:VideomtLayer
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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...
videomt/modeling_videomt.py:VideomtLayerScale
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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" ]
videomt/modeling_videomt.py:VideomtForUniversalSegmentationOutput
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[ "ModelForUniversalSegmentationOutput", "ModelOutput", "None", "attentions", "class", "class_queries_logits", "hidden_states", "last_hidden_state", "loss", "masks_queries_logits", "r" ]
videomt/modeling_videomt.py:sample_point
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[ "False", "Model_point", "True", "add_dim", "def", "dim", "functional", "grid_Model", "if", "input_features", "kwargs", "nn", "point_coordinates", "point_features", "return", "squeeze", "torch", "unsqueeze" ]
videomt/modeling_videomt.py:pair_wise_dice_loss
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[ "Model_wise_dice_loss", "None", "T", "def", "denominator", "flatten", "inputs", "labels", "loss", "matmul", "numerator", "return", "sigmoid", "sum", "torch" ]
videomt/modeling_videomt.py:pair_wise_sigmoid_cross_entropy_loss
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[ "BCEWithLogitsLoss", "Model_wise_sigmoid_cross_entropy_loss", "T", "criterion", "cross_entropy_loss_neg", "cross_entropy_loss_pos", "def", "height_and_width", "inputs", "labels", "loss", "loss_neg", "loss_pos", "matmul", "nn", "none", "ones_like", "r", "reduction", "return", ...
videomt/modeling_videomt.py:VideomtHungarianMatcher
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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_...
videomt/modeling_videomt.py:dice_loss
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[ "Model_loss", "def", "denominator", "flatten", "inputs", "labels", "loss", "num_masks", "numerator", "probs", "r", "return", "sigmoid", "sum" ]
videomt/modeling_videomt.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" ]
videomt/modeling_videomt.py:VideomtLoss
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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...
videomt/modeling_videomt.py:VideomtPreTrainedModel
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[ "Conv2d", "ConvTranspose2d", "Embedding", "False", "Linear", "Model", "ModelAttention", "ModelConfig", "ModelEmbeddings", "ModelForUniversalSegmentation", "ModelLayer", "ModelLayerScale", "ModelLoss", "ModelPreTrainedModel", "None", "PreTrainedModel", "True", "_", "_calculate_fan...
videomt/modeling_videomt.py:VideomtLayerNorm2d
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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" ]
videomt/modeling_videomt.py:VideomtScaleLayer
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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", ...
videomt/modeling_videomt.py:VideomtScaleBlock
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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" ]
videomt/modeling_videomt.py:VideomtMaskHead
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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" ]
videomt/modeling_videomt.py:VideomtForUniversalSegmentation
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[ "Embedding", "EomtForUniversalSegmentation", "Flatten", "LayerNorm", "Linear", "ModelEmbeddings", "ModelForUniversalSegmentation", "ModelForUniversalSegmentationOutput", "ModelLayer", "ModelLoss", "ModelMaskHead", "ModelPreTrainedModel", "ModelScaleBlock", "ModuleList", "None", "Tensor...
sew_d/modeling_sew_d.py:_compute_mask_indices
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[ "False", "None", "ValueError", "_", "_compute_mask_indices", "and", "append", "arange", "array", "attention_mask", "batch_size", "be", "bigger", "bool", "broadcast_to", "but", "choice", "compute_num_masked_span", "concatenate", "def", "detach", "dtype", "dummy_mask_idx", ...
sew_d/modeling_sew_d.py:make_log_bucket_position
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[ "Model_log_bucket_position", "abs", "abs_pos", "bucket_pos", "bucket_size", "ceil", "def", "log", "log_pos", "max_position", "mid", "relative_pos", "return", "sign", "tensor", "torch", "type_as", "where" ]
sew_d/modeling_sew_d.py:build_relative_position
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[ "Model_relative_position", "None", "and", "arange", "bucket_size", "def", "device", "if", "k_ids", "key_size", "long", "make_log_bucket_position", "max_position", "q_ids", "query_size", "rel_pos_ids", "return", "to", "torch", "unsqueeze" ]
sew_d/modeling_sew_d.py:c2p_dynamic_expand
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[ "Model_dynamic_expand", "Model_pos", "def", "expand", "query_layer", "relative_pos", "return", "size" ]
sew_d/modeling_sew_d.py:p2c_dynamic_expand
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[ "Model_dynamic_expand", "c2p_pos", "def", "expand", "key_layer", "query_layer", "return", "size" ]
sew_d/modeling_sew_d.py:pos_dynamic_expand
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[ "Model_dynamic_expand", "Model_index", "def", "expand", "key_layer", "p2c_att", "return", "size" ]
sew_d/modeling_sew_d.py:get_mask
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[ "DropoutContext", "Model_mask", "None", "and", "bernoulli_", "bool", "def", "dropout", "else", "empty_like", "if", "input", "is", "isinstance", "local_context", "mask", "not", "return", "reuse_mask", "scale", "to", "torch" ]
sew_d/modeling_sew_d.py:SEWDNoLayerNormConvLayer
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[ "ACT2FN", "Conv1d", "GradientCheckpointingLayer", "Model", "__init__", "activation", "class", "config", "conv", "conv_dim", "conv_kernel", "conv_stride", "def", "else", "feat_extract_activation", "forward", "hidden_states", "if", "in_conv_dim", "kernel_size", "layer_id", "n...
sew_d/modeling_sew_d.py:SEWDLayerNormConvLayer
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[ "ACT2FN", "Conv1d", "GradientCheckpointingLayer", "LayerNorm", "Model", "True", "__init__", "activation", "class", "config", "conv", "conv_dim", "conv_kernel", "conv_stride", "def", "elementwise_affine", "else", "feat_extract_activation", "forward", "hidden_states", "if", "...
sew_d/modeling_sew_d.py:SEWDGroupNormConvLayer
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[ "ACT2FN", "Conv1d", "GradientCheckpointingLayer", "GroupNorm", "Model", "True", "__init__", "activation", "affine", "class", "config", "conv", "conv_dim", "conv_kernel", "conv_stride", "def", "else", "feat_extract_activation", "forward", "hidden_states", "if", "in_conv_dim"...
sew_d/modeling_sew_d.py:SEWDPositionalConvEmbedding
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[ "ACT2FN", "Conv1d", "GatheredParameters", "Model", "ModelSamePadLayer", "Module", "__init__", "activation", "class", "config", "conv", "deepspeed", "def", "dim", "else", "feat_extract_activation", "forward", "groups", "hasattr", "hidden_size", "hidden_states", "if", "is_d...
sew_d/modeling_sew_d.py:SEWDSamePadLayer
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sew_d/modeling_sew_d.py:SEWDUpsampling
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sew_d/modeling_sew_d.py:SEWDFeatureEncoder
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sew_d/modeling_sew_d.py:ContextPooler
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sew_d/modeling_sew_d.py:XSoftmax
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sew_d/modeling_sew_d.py:DropoutContext
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sew_d/modeling_sew_d.py:XDropout
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sew_d/modeling_sew_d.py:StableDropout
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sew_d/modeling_sew_d.py:SEWDSelfOutput
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sew_d/modeling_sew_d.py:DisentangledSelfAttention
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sew_d/modeling_sew_d.py:SEWDAttention
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sew_d/modeling_sew_d.py:SEWDIntermediate
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sew_d/modeling_sew_d.py:SEWDOutput
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sew_d/modeling_sew_d.py:SEWDLayer
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sew_d/modeling_sew_d.py:ConvLayer
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sew_d/modeling_sew_d.py:SEWDTransformerEncoder
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sew_d/modeling_sew_d.py:SEWDEncoder
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sew_d/modeling_sew_d.py:SEWDPreTrainedModel
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sew_d/modeling_sew_d.py:SEWDModel
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sew_d/modeling_sew_d.py:SEWDForCTC
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sew_d/modeling_sew_d.py:SEWDForSequenceClassification
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glm/modeling_glm.py:GlmMLP
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glm/modeling_glm.py:GlmRotaryEmbedding
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glm/modeling_glm.py:repeat_kv
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glm/modeling_glm.py:eager_attention_forward
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glm/modeling_glm.py:rotate_half
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glm/modeling_glm.py:apply_rotary_pos_emb
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glm/modeling_glm.py:GlmAttention
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glm/modeling_glm.py:GlmRMSNorm
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glm/modeling_glm.py:GlmDecoderLayer
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glm/modeling_glm.py:GlmPreTrainedModel
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glm/modeling_glm.py:GlmModel
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glm/modeling_glm.py:GlmForCausalLM
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glm/modeling_glm.py:GlmForSequenceClassification
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[ "GenericForSequenceClassification", "ModelForSequenceClassification", "ModelPreTrainedModel", "class", "pass" ]
glm/modeling_glm.py:GlmForTokenClassification
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[ "GenericForTokenClassification", "ModelForTokenClassification", "ModelPreTrainedModel", "class", "pass" ]
bert/modeling_bert.py:BertEmbeddings
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bert/modeling_bert.py:eager_attention_forward
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bert/modeling_bert.py:BertSelfAttention
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bert/modeling_bert.py:BertCrossAttention
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bert/modeling_bert.py:BertSelfOutput
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bert/modeling_bert.py:BertAttention
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bert/modeling_bert.py:BertIntermediate
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bert/modeling_bert.py:BertOutput
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bert/modeling_bert.py:BertLayer
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bert/modeling_bert.py:BertEncoder
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bert/modeling_bert.py:BertPooler
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bert/modeling_bert.py:BertPredictionHeadTransform
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bert/modeling_bert.py:BertLMPredictionHead
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bert/modeling_bert.py:BertOnlyMLMHead
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[ "ModelLMPredictionHead", "ModelOnlyMLMHead", "Module", "__init__", "class", "config", "def", "forward", "nn", "prediction_scores", "predictions", "return", "self", "sequence_output", "super" ]
bert/modeling_bert.py:BertOnlyNSPHead
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[ "Linear", "ModelOnlyNSPHead", "Module", "__init__", "class", "config", "def", "forward", "hidden_size", "nn", "pooled_output", "return", "self", "seq_relationship", "seq_relationship_score", "super" ]
bert/modeling_bert.py:BertPreTrainingHeads
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bert/modeling_bert.py:BertPreTrainedModel
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bert/modeling_bert.py:BertForPreTrainingOutput
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[ "ModelForPreTrainingOutput", "ModelOutput", "None", "attentions", "class", "hidden_states", "loss", "prediction_logits", "r", "seq_relationship_logits" ]
bert/modeling_bert.py:BertModel
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[ "BaseModelOutputWithPoolingAndCrossAttentions", "DynamicCache", "EncoderDecoderCache", "False", "ModelEmbeddings", "ModelEncoder", "ModelLayer", "ModelModel", "ModelPooler", "ModelPreTrainedModel", "None", "True", "ValueError", "You", "__init__", "_create_attention_masks", "_no_split...
bert/modeling_bert.py:BertForPreTraining
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bert/modeling_bert.py:BertLMHeadModel
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bert/modeling_bert.py:BertForMaskedLM
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[ "CrossEntropyLoss", "False", "If", "MaskedLMOutput", "Model", "ModelForMaskedLM", "ModelModel", "ModelOnlyMLMHead", "ModelPreTrainedModel", "None", "True", "__init__", "_tied_weights_keys", "add_pooling_layer", "attention", "attention_mask", "attentions", "auto_docstring", "bi", ...
bert/modeling_bert.py:BertForNextSentencePrediction
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[ "CrossEntropyLoss", "Model", "ModelForNextSentencePrediction", "ModelModel", "ModelOnlyNSPHead", "ModelPreTrainedModel", "NextSentencePredictorOutput", "None", "True", "__init__", "attention_mask", "attentions", "auto_docstring", "can_return_tuple", "class", "cls", "config", "def",...
bert/modeling_bert.py:BertForSequenceClassification
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bert/modeling_bert.py:BertForMultipleChoice
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bert/modeling_bert.py:BertForTokenClassification
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bert/modeling_bert.py:BertForQuestionAnswering
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nemotron/modeling_nemotron.py:_cast_if_autocast_enabled
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nemotron/modeling_nemotron.py:NemotronLayerNorm1P
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nemotron/modeling_nemotron.py:NemotronRotaryEmbedding
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nemotron/modeling_nemotron.py:rotate_half
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[ "Model_half", "cat", "def", "dim", "return", "shape", "torch", "x", "x1", "x2" ]
nemotron/modeling_nemotron.py:apply_rotary_pos_emb
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[ "Model_rotary_pos_emb", "cat", "cos", "def", "dim", "k", "k_embed", "k_pass", "q", "q_embed", "q_pass", "return", "rot_dim", "rotate_half", "shape", "sin", "torch", "unsqueeze", "unsqueeze_dim" ]
nemotron/modeling_nemotron.py:NemotronMLP
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[ "ACT2FN", "Linear", "ModelMLP", "Module", "__init__", "act_fn", "class", "config", "def", "down_proj", "forward", "hidden_act", "hidden_size", "intermediate_size", "nn", "return", "self", "super", "up_proj", "x" ]
nemotron/modeling_nemotron.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" ]