Datasets:
itazap
/
transformers_code_embeddings_v3

Dataset card Data Studio Files
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1
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24
102
embedding
listlengths
2.56k
2.56k
tokens
listlengths
4
448
tapas/modeling_tapas.py:reduce_mean
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[ "Model_mean", "_segment_Model", "def", "index", "mean", "name", "return", "segmented_Model_mean", "values" ]
tapas/modeling_tapas.py:reduce_max
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[ "Model_max", "_segment_Model", "amax", "def", "index", "name", "return", "segmented_Model_max", "values" ]
tapas/modeling_tapas.py:reduce_min
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tapas/modeling_tapas.py:compute_column_logits
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[ "CLOSE_ENOUGH_TO_LOG_ZERO", "EPSILON_ZERO_DIVISION", "Model_column_logits", "_", "allow_empty_column_selection", "as_tensor", "bs", "bsj", "cell_count", "cell_index", "cell_logits", "cell_logits_index", "cell_mask", "column_index", "column_logits", "column_output_bias", "column_outpu...
tapas/modeling_tapas.py:_single_column_cell_selection_loss
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tapas/modeling_tapas.py:compute_token_logits
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tapas/modeling_tapas.py:_calculate_aggregate_mask
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tapas/modeling_tapas.py:_calculate_aggregation_loss_known
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tapas/modeling_tapas.py:_calculate_aggregation_loss_unknown
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[ "Categorical", "_calculate_aggregation_loss_unknown", "aggregate_mask", "aggregation_ops_total_mass", "categorical", "def", "dim", "dist_aggregation", "distributions", "log", "logits", "logits_aggregation", "probs", "return", "sum", "torch" ]
tapas/modeling_tapas.py:_calculate_aggregation_loss
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[ "_calculate_aggregation_loss", "_calculate_aggregation_loss_known", "_calculate_aggregation_loss_unknown", "aggregate_mask", "aggregation_labels", "aggregation_loss_weight", "def", "if", "logits_aggregation", "num_aggregation_labels", "per_example_aggregation_loss", "return", "use_answer_as_...
tapas/modeling_tapas.py:_calculate_expected_result
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[ "AverageApproximationFunction", "EPSILON_ZERO_DIVISION", "FIRST_ORDER", "Invalid", "RATIO", "RelaxedBernoulli", "RelaxedOneHotCategorical", "SECOND_ORDER", "True", "ValueError", "_calculate_expected_result", "aggregation_op_only_probs", "aggregation_temperature", "all_results", "average_...
tapas/modeling_tapas.py:huber_loss
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[ "Model_loss", "abs", "def", "delta", "errors", "input", "return", "target", "torch", "where" ]
tapas/modeling_tapas.py:_calculate_regression_loss
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[ "EPSILON_ZERO_DIVISION", "None", "_calculate_expected_result", "_calculate_regression_loss", "abs", "aggregate_mask", "answer", "answer_loss_cutoff", "answer_loss_importance", "answer_masked", "config", "def", "delta", "detach", "dist_per_cell", "dtype", "else", "expected_result", ...
smolvlm/modeling_smolvlm.py:SmolVLMPreTrainedModel
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[ "ModelConfig", "ModelDecoderLayer", "ModelPreTrainedModel", "ModelVisionAttention", "PreTrainedModel", "True", "_no_split_modules", "_skip_keys_device_placement", "_supports_attention_backend", "_supports_flash_attn", "_supports_flex_attn", "_supports_sdpa", "base_model_prefix", "class", ...
smolvlm/modeling_smolvlm.py:SmolVLMVisionEmbeddings
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[ "Conv2d", "Embedding", "ModelVisionEmbeddings", "Module", "None", "True", "_", "__init__", "arange", "batch_size", "boundaries", "bucket_coords_h", "bucket_coords_w", "bucketize", "clamp", "class", "config", "def", "device", "dim", "dtype", "embed_dim", "embeddings", "f...
smolvlm/modeling_smolvlm.py:eager_attention_forward
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smolvlm/modeling_smolvlm.py:SmolVLMVisionAttention
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smolvlm/modeling_smolvlm.py:SmolVLMVisionMLP
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smolvlm/modeling_smolvlm.py:SmolVLMEncoderLayer
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smolvlm/modeling_smolvlm.py:SmolVLMEncoder
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smolvlm/modeling_smolvlm.py:SmolVLMVisionTransformer
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[ "BaseModelOutput", "False", "LayerNorm", "ModelEncoder", "ModelEncoderLayer", "ModelPreTrainedModel", "ModelVisionAttention", "ModelVisionConfig", "ModelVisionEmbeddings", "ModelVisionTransformer", "None", "True", "__init__", "_can_record_outputs", "_supports_flash_attn", "_supports_fl...
smolvlm/modeling_smolvlm.py:SmolVLMBaseModelOutputWithPast
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[ "ModelBaseModelOutputWithPast", "ModelOutput", "None", "attentions", "class", "hidden_states", "image_hidden_states", "last_hidden_state", "past_key_values", "r" ]
smolvlm/modeling_smolvlm.py:SmolVLMSimpleMLP
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smolvlm/modeling_smolvlm.py:SmolVLMConnector
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smolvlm/modeling_smolvlm.py:SmolVLMModel
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smolvlm/modeling_smolvlm.py:SmolVLMCausalLMOutputWithPast
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smolvlm/modeling_smolvlm.py:SmolVLMForConditionalGeneration
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bart/modeling_bart.py:shift_tokens_right
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bart/modeling_bart.py:BartLearnedPositionalEmbedding
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[ "Embedding", "ModelLearnedPositionalEmbedding", "None", "__init__", "arange", "bsz", "class", "def", "device", "dtype", "else", "embedding_dim", "expand", "forward", "if", "input_ids", "is", "long", "nn", "num_embeddings", "offset", "past_key_values_length", "position_ids...
bart/modeling_bart.py:BartScaledWordEmbedding
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[ "Embedding", "ModelScaledWordEmbedding", "__init__", "class", "def", "embed_scale", "embedding_dim", "forward", "input_ids", "nn", "num_embeddings", "padding_idx", "return", "self", "super" ]
bart/modeling_bart.py:eager_attention_forward
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bart/modeling_bart.py:BartAttention
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bart/modeling_bart.py:BartEncoderLayer
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bart/modeling_bart.py:BartDecoderLayer
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bart/modeling_bart.py:BartClassificationHead
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bart/modeling_bart.py:BartPreTrainedModel
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bart/modeling_bart.py:PretrainedBartModel
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bart/modeling_bart.py:BartPretrainedModel
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bart/modeling_bart.py:BartEncoder
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[ "BaseModelOutput", "False", "LayerNorm", "ModelEncoder", "ModelEncoderLayer", "ModelLearnedPositionalEmbedding", "ModelPreTrainedModel", "ModelScaledWordEmbedding", "ModuleList", "None", "True", "__init__", "all_attentions", "and", "attention_mask", "attentions", "class", "config",...
bart/modeling_bart.py:BartDecoder
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[ "BaseModelOutputWithPastAndCrossAttentions", "DynamicCache", "EncoderDecoderCache", "False", "LayerNorm", "ModelDecoder", "ModelDecoderLayer", "ModelLearnedPositionalEmbedding", "ModelPreTrainedModel", "ModelScaledWordEmbedding", "ModuleList", "None", "Setting", "True", "ValueError", "...
bart/modeling_bart.py:BartModel
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[ "BaseModelOutput", "If", "ModelDecoder", "ModelEncoder", "ModelModel", "ModelPreTrainedModel", "ModelScaledWordEmbedding", "None", "Please", "Seq2SeqModelOutput", "ValueError", "__init__", "_tied_weights_keys", "and", "are", "attention_mask", "attentions", "auto_docstring", "be",...
bart/modeling_bart.py:BartForConditionalGeneration
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[ "CrossEntropyLoss", "False", "GenerationMixin", "Linear", "ModelForConditionalGeneration", "ModelModel", "ModelPreTrainedModel", "None", "Seq2SeqLMOutput", "True", "__init__", "_keys_to_ignore_on_load_missing", "_resize_final_logits_bias", "_tied_weights_keys", "and", "attention_mask",...
bart/modeling_bart.py:BartForSequenceClassification
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[ "All", "BCEWithLogitsLoss", "CrossEntropyLoss", "False", "MSELoss", "ModelClassificationHead", "ModelForSequenceClassification", "ModelModel", "ModelPreTrainedModel", "None", "NotImplementedError", "Passing", "Seq2SeqSequenceClassifierOutput", "__class__", "__init__", "__name__", "an...
bart/modeling_bart.py:BartForQuestionAnswering
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[ "CrossEntropyLoss", "False", "Linear", "ModelForQuestionAnswering", "ModelModel", "ModelPreTrainedModel", "None", "Seq2SeqQuestionAnsweringModelOutput", "__init__", "and", "attention_mask", "auto_docstring", "cache_position", "clamp", "class", "config", "contiguous", "cross_attenti...
bart/modeling_bart.py:BartDecoderWrapper
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[ "ModelDecoder", "ModelDecoderWrapper", "ModelPreTrainedModel", "__init__", "args", "class", "config", "decoder", "def", "forward", "kwargs", "post_init", "return", "self", "super" ]
bart/modeling_bart.py:BartForCausalLM
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swin2sr/modeling_swin2sr.py:Swin2SREncoderOutput
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[ "ModelEncoderOutput", "ModelOutput", "None", "attentions", "class", "hidden_states", "last_hidden_state" ]
swin2sr/modeling_swin2sr.py:window_partition
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[ "Model_partition", "Model_size", "Models", "batch_size", "contiguous", "def", "height", "input_feature", "num_channels", "permute", "return", "shape", "view", "width" ]
swin2sr/modeling_swin2sr.py:window_reverse
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[ "Model_reverse", "Model_size", "Models", "contiguous", "def", "height", "num_channels", "permute", "return", "shape", "view", "width" ]
swin2sr/modeling_swin2sr.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" ]
swin2sr/modeling_swin2sr.py:Swin2SRDropPath
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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" ]
swin2sr/modeling_swin2sr.py:Swin2SREmbeddings
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swin2sr/modeling_swin2sr.py:Swin2SRPatchEmbeddings
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[ "Conv2d", "Iterable", "LayerNorm", "ModelPatchEmbeddings", "Module", "None", "True", "_", "__init__", "abc", "class", "collections", "config", "def", "else", "embed_dim", "embeddings", "flatten", "forward", "height", "if", "image_size", "is", "isinstance", "kernel_siz...
swin2sr/modeling_swin2sr.py:Swin2SRPatchUnEmbeddings
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swin2sr/modeling_swin2sr.py:Swin2SRPatchMerging
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swin2sr/modeling_swin2sr.py:Swin2SRSelfAttention
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[ "Dropout", "False", "Iterable", "Linear", "ModelSelfAttention", "Module", "None", "Parameter", "ReLU", "Sequential", "The", "True", "ValueError", "__init__", "a", "abc", "abs", "all_head_size", "arange", "attention", "attention_head_size", "attention_mask", "attention_pro...
swin2sr/modeling_swin2sr.py:Swin2SRSelfOutput
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[ "Dropout", "Linear", "ModelSelfOutput", "Module", "__init__", "attention_probs_dropout_prob", "class", "config", "def", "dense", "dim", "dropout", "forward", "hidden_states", "input_tensor", "nn", "return", "self", "super" ]
swin2sr/modeling_swin2sr.py:Swin2SRAttention
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[ "False", "Iterable", "ModelAttention", "ModelSelfAttention", "ModelSelfOutput", "Module", "None", "__init__", "abc", "attention_mask", "attention_output", "class", "collections", "config", "def", "dim", "else", "forward", "hidden_states", "if", "isinstance", "nn", "num_he...
swin2sr/modeling_swin2sr.py:Swin2SRIntermediate
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[ "ACT2FN", "Linear", "ModelIntermediate", "Module", "__init__", "class", "config", "def", "dense", "dim", "else", "forward", "hidden_act", "hidden_states", "if", "int", "intermediate_act_fn", "isinstance", "mlp_ratio", "nn", "return", "self", "str", "super" ]
swin2sr/modeling_swin2sr.py:Swin2SROutput
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[ "Dropout", "Linear", "ModelOutput", "Module", "__init__", "class", "config", "def", "dense", "dim", "dropout", "forward", "hidden_dropout_prob", "hidden_states", "int", "mlp_ratio", "nn", "return", "self", "super" ]
swin2sr/modeling_swin2sr.py:Swin2SRLayer
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[ "False", "Identity", "Iterable", "LayerNorm", "ModelAttention", "ModelDropPath", "ModelIntermediate", "ModelLayer", "ModelOutput", "Module", "None", "_", "__init__", "_compute_window_shift", "abc", "attention", "attention_output", "attention_outputs", "attention_windows", "attn...
swin2sr/modeling_swin2sr.py:Swin2SRStage
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[ "Conv2d", "False", "GradientCheckpointingLayer", "LeakyReLU", "ModelLayer", "ModelPatchEmbeddings", "ModelPatchUnEmbeddings", "ModelStage", "ModuleList", "Sequential", "True", "_", "__init__", "class", "config", "conv", "def", "depth", "dim", "drop_path", "elif", "else", ...
swin2sr/modeling_swin2sr.py:Swin2SREncoder
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[ "False", "ModelEncoder", "ModelEncoderOutput", "ModelStage", "Module", "ModuleList", "None", "True", "__init__", "all_hidden_states", "all_input_dimensions", "all_self_attentions", "attentions", "class", "config", "cpu", "def", "depth", "depths", "device", "dim", "dpr", "...
swin2sr/modeling_swin2sr.py:Swin2SRPreTrainedModel
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swin2sr/modeling_swin2sr.py:Swin2SRModel
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swin2sr/modeling_swin2sr.py:Upsample
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swin2sr/modeling_swin2sr.py:UpsampleOneStep
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swin2sr/modeling_swin2sr.py:PixelShuffleUpsampler
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swin2sr/modeling_swin2sr.py:NearestConvUpsampler
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swin2sr/modeling_swin2sr.py:PixelShuffleAuxUpsampler
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swin2sr/modeling_swin2sr.py:Swin2SRForImageSuperResolution
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cohere/modeling_cohere.py:CohereLayerNorm
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cohere/modeling_cohere.py:CohereRotaryEmbedding
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cohere/modeling_cohere.py:CohereMLP
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cohere/modeling_cohere.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" ]
cohere/modeling_cohere.py:eager_attention_forward
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cohere/modeling_cohere.py:rotate_half
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[ "Model_half", "def", "dim", "flatten", "return", "rot_x", "stack", "torch", "x", "x1", "x2" ]
cohere/modeling_cohere.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" ]
cohere/modeling_cohere.py:CohereAttention
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cohere/modeling_cohere.py:CohereDecoderLayer
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cohere/modeling_cohere.py:CoherePreTrainedModel
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cohere/modeling_cohere.py:CohereModel
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cohere/modeling_cohere.py:CohereForCausalLM
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pegasus_x/modeling_pegasus_x.py:DimensionInfo
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[ "ModelInfo", "batch_size", "block_size", "class", "dim_per_head", "global_len", "hidden_dim", "int", "num_blocks", "num_heads", "padded_seq_len", "seq_len" ]
pegasus_x/modeling_pegasus_x.py:shift_tokens_right
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pegasus_x/modeling_pegasus_x.py:PegasusXScaledWordEmbedding
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[ "Embedding", "ModelScaledWordEmbedding", "__init__", "class", "def", "embed_scale", "embedding_dim", "forward", "input_ids", "nn", "num_embeddings", "padding_idx", "return", "self", "super" ]
pegasus_x/modeling_pegasus_x.py:PegasusXSinusoidalPositionalEmbedding
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pegasus_x/modeling_pegasus_x.py:eager_attention_forward
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pegasus_x/modeling_pegasus_x.py:PegasusXAttention
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[ "ALL_ATTENTION_FUNCTIONS", "EncoderDecoderCache", "False", "Instantiating", "Linear", "ModelAttention", "Module", "None", "Please", "True", "ValueError", "__class__", "__init__", "__name__", "_attn_implementation", "a", "and", "attention_interface", "attention_mask", "attn_outp...
pegasus_x/modeling_pegasus_x.py:PegasusXGlobalLocalAttention
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pegasus_x/modeling_pegasus_x.py:PegasusXEncoderLayer
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pegasus_x/modeling_pegasus_x.py:PegasusXDecoderLayer
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pegasus_x/modeling_pegasus_x.py:PegasusXPreTrainedModel
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pegasus_x/modeling_pegasus_x.py:PegasusXEncoder
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pegasus_x/modeling_pegasus_x.py:PegasusXDecoder
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pegasus_x/modeling_pegasus_x.py:PegasusXModel
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[ "BaseModelOutput", "ModelDecoder", "ModelEncoder", "ModelModel", "ModelPreTrainedModel", "ModelScaledWordEmbedding", "None", "Seq2SeqModelOutput", "__init__", "_tied_weights_keys", "and", "attention_mask", "attentions", "auto_docstring", "cache_position", "class", "config", "cross_...
pegasus_x/modeling_pegasus_x.py:PegasusXForConditionalGeneration
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[ "CrossEntropyLoss", "False", "GenerationMixin", "Linear", "ModelForConditionalGeneration", "ModelModel", "ModelPreTrainedModel", "None", "Seq2SeqLMOutput", "__init__", "_tied_weights_keys", "and", "attention_mask", "auto_docstring", "base_model_prefix", "cache_position", "class", "...
pegasus_x/modeling_pegasus_x.py:PegasusXDecoderWrapper
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[ "ModelDecoder", "ModelDecoderWrapper", "ModelPreTrainedModel", "__init__", "args", "class", "config", "decoder", "def", "forward", "kwargs", "post_init", "return", "self", "super" ]
xlm_roberta/modeling_xlm_roberta.py:XLMRobertaEmbeddings
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[ "Dropout", "Embedding", "False", "LayerNorm", "Model", "Module", "None", "__init__", "arange", "batch_size", "buffered_token_type_ids", "class", "config", "create_position_ids_from_input_ids", "create_position_ids_from_inputs_embeds", "cumsum", "def", "device", "dim", "dropout"...
xlm_roberta/modeling_xlm_roberta.py:eager_attention_forward
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[ "Model_attention_forward", "None", "attention_mask", "attn_output", "attn_weights", "contiguous", "def", "dim", "dropout", "functional", "if", "is", "key", "kwargs", "matmul", "module", "nn", "not", "p", "query", "return", "scaling", "shape", "size", "softmax", "tor...