Datasets:
itazap
/
transformers_code_embeddings_v3

Dataset card Data Studio Files
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1
identifier
stringlengths
24
102
embedding
listlengths
2.56k
2.56k
tokens
listlengths
4
448
arcee/modeling_arcee.py:ArceeDecoderLayer
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[ "False", "GradientCheckpointingLayer", "ModelAttention", "ModelDecoderLayer", "ModelMLP", "ModelRMSNorm", "None", "Tensor", "_", "__init__", "attention_mask", "cache_position", "class", "config", "def", "eps", "forward", "hidden_size", "hidden_states", "input_layernorm", "kwa...
arcee/modeling_arcee.py:ArceePreTrainedModel
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[ "ModelAttention", "ModelConfig", "ModelDecoderLayer", "ModelPreTrainedModel", "PreTrainedModel", "True", "_can_compile_fullgraph", "_can_record_outputs", "_no_split_modules", "_skip_keys_device_placement", "_supports_attention_backend", "_supports_flash_attn", "_supports_flex_attn", "_supp...
arcee/modeling_arcee.py:ArceeModel
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[ "BaseModelOutputWithPast", "DynamicCache", "Embedding", "False", "ModelDecoderLayer", "ModelModel", "ModelPreTrainedModel", "ModelRMSNorm", "ModelRotaryEmbedding", "ModuleList", "None", "ValueError", "You", "__init__", "and", "arange", "attention_mask", "auto_docstring", "cache_p...
arcee/modeling_arcee.py:ArceeForCausalLM
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[ "CausalLMOutputWithPast", "GenerationMixin", "Linear", "ModelForCausalLM", "ModelModel", "ModelPreTrainedModel", "None", "__init__", "_pp_plan", "_tied_weights_keys", "_tp_plan", "attention_mask", "attentions", "auto_docstring", "cache_position", "can_return_tuple", "class", "colwi...
arcee/modeling_arcee.py:ArceeForSequenceClassification
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[ "GenericForSequenceClassification", "ModelForSequenceClassification", "ModelPreTrainedModel", "class", "pass" ]
arcee/modeling_arcee.py:ArceeForQuestionAnswering
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[ "GenericForQuestionAnswering", "ModelForQuestionAnswering", "ModelPreTrainedModel", "base_model_prefix", "class", "transformer" ]
arcee/modeling_arcee.py:ArceeForTokenClassification
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[ "GenericForTokenClassification", "ModelForTokenClassification", "ModelPreTrainedModel", "class", "pass" ]
ernie4_5/modeling_ernie4_5.py:Ernie4_5RotaryEmbedding
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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", ...
ernie4_5/modeling_ernie4_5.py:Ernie4_5MLP
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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" ]
ernie4_5/modeling_ernie4_5.py:rotate_half
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[ "Model_half", "def", "dim", "flatten", "return", "stack", "torch", "x", "x1", "x2" ]
ernie4_5/modeling_ernie4_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" ]
ernie4_5/modeling_ernie4_5.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", ...
ernie4_5/modeling_ernie4_5.py:apply_rotary_pos_emb
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[ "Model_rotary_pos_emb", "cos", "def", "dim", "float", "k", "k_embed", "q", "q_embed", "repeat_interleave", "return", "rotate_half", "shape", "sin", "unsqueeze", "unsqueeze_dim" ]
ernie4_5/modeling_ernie4_5.py:Ernie4_5Attention
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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...
ernie4_5/modeling_ernie4_5.py:Ernie4_5RMSNorm
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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", ...
ernie4_5/modeling_ernie4_5.py:Ernie4_5DecoderLayer
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[ "False", "GradientCheckpointingLayer", "ModelAttention", "ModelDecoderLayer", "ModelMLP", "ModelRMSNorm", "None", "Tensor", "_", "__init__", "attention_mask", "cache_position", "class", "config", "def", "eps", "forward", "hidden_size", "hidden_states", "input_layernorm", "kwa...
ernie4_5/modeling_ernie4_5.py:Ernie4_5PreTrainedModel
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[ "ModelAttention", "ModelConfig", "ModelDecoderLayer", "ModelPreTrainedModel", "PreTrainedModel", "True", "_can_compile_fullgraph", "_can_record_outputs", "_no_split_modules", "_skip_keys_device_placement", "_supports_attention_backend", "_supports_flash_attn", "_supports_flex_attn", "_supp...
ernie4_5/modeling_ernie4_5.py:Ernie4_5Model
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[ "BaseModelOutputWithPast", "DynamicCache", "Embedding", "False", "ModelDecoderLayer", "ModelModel", "ModelPreTrainedModel", "ModelRMSNorm", "ModelRotaryEmbedding", "ModuleList", "None", "ValueError", "You", "__init__", "and", "arange", "attention_mask", "auto_docstring", "cache_p...
ernie4_5/modeling_ernie4_5.py:Ernie4_5ForCausalLM
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[ "CausalLMOutputWithPast", "GenerationMixin", "Linear", "ModelForCausalLM", "ModelModel", "ModelPreTrainedModel", "None", "__init__", "_pp_plan", "_tied_weights_keys", "_tp_plan", "attention_mask", "attentions", "auto_docstring", "cache_position", "can_return_tuple", "class", "colwi...
vjepa2/modeling_vjepa2.py:VJEPA2WithMaskedInputPredictorOutput
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[ "FloatTensor", "Model", "ModelOutput", "None", "attentions", "class", "hidden_states", "last_hidden_state", "masked_hidden_state", "r", "target_hidden_state", "torch" ]
vjepa2/modeling_vjepa2.py:VJEPA2WithMaskedInputModelOutput
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[ "FloatTensor", "Model", "ModelOutput", "ModelWithMaskedInputPredictorOutput", "None", "attentions", "class", "def", "hidden_states", "if", "isinstance", "last_hidden_state", "list", "masked_hidden_state", "output", "predictor_output", "r", "return", "self", "super", "to_tuple...
vjepa2/modeling_vjepa2.py:VJEPA2PatchEmbeddings3D
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[ "Conv3d", "Model", "Module", "__init__", "class", "config", "crop_size", "def", "flatten", "forward", "frames_per_clip", "hidden_size", "in_channels", "in_chans", "kernel_size", "nn", "num_patches", "out_channels", "patch_size", "pixel_values_videos", "proj", "return", "s...
vjepa2/modeling_vjepa2.py:VJEPA2Embeddings
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[ "Model", "ModelPatchEmbeddings3D", "Module", "__init__", "class", "config", "def", "dtype", "embeddings", "forward", "hidden_size", "if", "nn", "num_frames", "num_patches", "patch_embeddings", "patch_size", "permute", "pixel_values_videos", "proj", "repeat", "return", "se...
vjepa2/modeling_vjepa2.py:eager_attention_forward
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vjepa2/modeling_vjepa2.py:rotate_queries_or_keys
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vjepa2/modeling_vjepa2.py:VJEPA2RopeAttention
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vjepa2/modeling_vjepa2.py:drop_path
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vjepa2/modeling_vjepa2.py:VJEPA2DropPath
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vjepa2/modeling_vjepa2.py:VJEPA2MLP
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vjepa2/modeling_vjepa2.py:VJEPA2Layer
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[ "False", "GradientCheckpointingLayer", "Identity", "LayerNorm", "Model", "ModelDropPath", "ModelMLP", "ModelRopeAttention", "None", "__init__", "attention", "attention_output", "class", "config", "def", "drop_path", "drop_path_rate", "else", "eps", "forward", "hidden_size", ...
vjepa2/modeling_vjepa2.py:VJEPA2Encoder
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[ "BaseModelOutput", "False", "LayerNorm", "Model", "ModelEmbeddings", "ModelLayer", "Module", "ModuleList", "None", "__init__", "all_hidden_states", "all_self_attentions", "attentions", "can_return_tuple", "class", "config", "def", "drop_path_rate", "drop_path_rates", "else", ...
vjepa2/modeling_vjepa2.py:apply_masks
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vjepa2/modeling_vjepa2.py:VJEPA2PredictorEmbeddings
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vjepa2/modeling_vjepa2.py:VJEPA2Predictor
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vjepa2/modeling_vjepa2.py:VJEPA2PoolerSelfAttention
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vjepa2/modeling_vjepa2.py:VJEPA2PoolerCrossAttention
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vjepa2/modeling_vjepa2.py:VJEPA2PoolerSelfAttentionLayer
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vjepa2/modeling_vjepa2.py:VJEPA2PoolerCrossAttentionLayer
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vjepa2/modeling_vjepa2.py:VJEPA2AttentivePooler
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vjepa2/modeling_vjepa2.py:VJEPA2PreTrainedModel
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vjepa2/modeling_vjepa2.py:VJEPA2Model
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vjepa2/modeling_vjepa2.py:VJEPA2ForVideoClassification
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whisper/modeling_whisper.py:sinusoids
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whisper/modeling_whisper.py:shift_tokens_right
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whisper/modeling_whisper.py:_compute_mask_indices
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whisper/modeling_whisper.py:WhisperPositionalEmbedding
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whisper/modeling_whisper.py:eager_attention_forward
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whisper/modeling_whisper.py:WhisperAttention
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whisper/modeling_whisper.py:WhisperEncoderLayer
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whisper/modeling_whisper.py:WhisperDecoderLayer
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whisper/modeling_whisper.py:WhisperPreTrainedModel
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whisper/modeling_whisper.py:WhisperEncoder
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whisper/modeling_whisper.py:WhisperDecoder
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whisper/modeling_whisper.py:WhisperModel
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whisper/modeling_whisper.py:WhisperForConditionalGeneration
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whisper/modeling_whisper.py:WhisperDecoderWrapper
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whisper/modeling_whisper.py:WhisperForCausalLM
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whisper/modeling_whisper.py:WhisperForAudioClassification
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ibert/modeling_ibert.py:IBertEmbeddings
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ibert/modeling_ibert.py:IBertSelfAttention
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ibert/modeling_ibert.py:IBertSelfOutput
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ibert/modeling_ibert.py:IBertAttention
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ibert/modeling_ibert.py:IBertIntermediate
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ibert/modeling_ibert.py:IBertOutput
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ibert/modeling_ibert.py:IBertLayer
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ibert/modeling_ibert.py:IBertEncoder
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ibert/modeling_ibert.py:IBertPooler
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ibert/modeling_ibert.py:IBertPreTrainedModel
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ibert/modeling_ibert.py:IBertModel
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ibert/modeling_ibert.py:IBertForMaskedLM
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ibert/modeling_ibert.py:IBertLMHead
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ibert/modeling_ibert.py:IBertForSequenceClassification
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ibert/modeling_ibert.py:IBertForMultipleChoice
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ibert/modeling_ibert.py:IBertForTokenClassification
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ibert/modeling_ibert.py:IBertClassificationHead
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ibert/modeling_ibert.py:IBertForQuestionAnswering
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ibert/modeling_ibert.py:create_position_ids_from_input_ids
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deepseek_vl_hybrid/modeling_deepseek_vl_hybrid.py:BaseModelOutputWithHighResVisionEncodings
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deepseek_vl_hybrid/modeling_deepseek_vl_hybrid.py:DeepseekVLHybridBaseModelOutputWithPast
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deepseek_vl_hybrid/modeling_deepseek_vl_hybrid.py:DeepseekVLHybridCausalLMOutputWithPast
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deepseek_vl_hybrid/modeling_deepseek_vl_hybrid.py:DeepseekVLHybridLayerNorm
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deepseek_vl_hybrid/modeling_deepseek_vl_hybrid.py:DeepseekVLSamVisionNeck
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deepseek_vl_hybrid/modeling_deepseek_vl_hybrid.py:DeepseekVLSamVisionProj
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deepseek_vl_hybrid/modeling_deepseek_vl_hybrid.py:DeepseekVLHybridAligner
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deepseek_vl_hybrid/modeling_deepseek_vl_hybrid.py:DeepseekVLHybridPreTrainedModel
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deepseek_vl_hybrid/modeling_deepseek_vl_hybrid.py:DeepseekVLHybridModel
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deepseek_vl_hybrid/modeling_deepseek_vl_hybrid.py:DeepseekVLHybridForConditionalGeneration
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fastspeech2_conformer/modeling_fastspeech2_conformer.py:FastSpeech2ConformerModelOutput
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fastspeech2_conformer/modeling_fastspeech2_conformer.py:FastSpeech2ConformerWithHifiGanOutput
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fastspeech2_conformer/modeling_fastspeech2_conformer.py:length_regulator
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fastspeech2_conformer/modeling_fastspeech2_conformer.py:FastSpeech2ConformerDurationPredictor
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fastspeech2_conformer/modeling_fastspeech2_conformer.py:FastSpeech2ConformerBatchNormConvLayer
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fastspeech2_conformer/modeling_fastspeech2_conformer.py:FastSpeech2ConformerSpeechDecoderPostnet
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fastspeech2_conformer/modeling_fastspeech2_conformer.py:FastSpeech2ConformerPredictorLayer
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[ "Conv1d", "Dropout", "LayerNorm", "ModelPredictorLayer", "Module", "ReLU", "__init__", "activation", "class", "conv", "def", "dropout", "dropout_rate", "forward", "hidden_states", "input_channels", "kernel_size", "layer_norm", "nn", "num_chans", "padding", "return", "self...
fastspeech2_conformer/modeling_fastspeech2_conformer.py:FastSpeech2ConformerVariancePredictor
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[ "Linear", "ModelPredictorLayer", "ModelVariancePredictor", "Module", "ModuleList", "None", "__init__", "append", "class", "config", "conv_layers", "def", "dropout_rate", "else", "encoder_hidden_states", "for", "forward", "hidden_size", "hidden_states", "idx", "if", "in", ...
fastspeech2_conformer/modeling_fastspeech2_conformer.py:FastSpeech2ConformerVarianceEmbedding
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[ "Conv1d", "Dropout", "ModelVarianceEmbedding", "Module", "__init__", "class", "conv", "def", "dropout", "dropout_rate", "forward", "hidden_states", "in_channels", "kernel_size", "nn", "out_channels", "padding", "return", "self", "super", "transpose" ]
fastspeech2_conformer/modeling_fastspeech2_conformer.py:FastSpeech2ConformerAttention
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[ "Dropout", "False", "Linear", "ModelAttention", "Module", "None", "Parameter", "Tensor", "_", "__init__", "attention_dropout_rate", "attention_mask", "attn_output", "attn_weights", "bsz", "bsz_pos", "but", "cat", "class", "config", "contiguous", "def", "device", "dim", ...
fastspeech2_conformer/modeling_fastspeech2_conformer.py:FastSpeech2ConformerConvolutionModule
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[ "ACT2FN", "BatchNorm1d", "Conv1d", "ModelConvolutionModule", "Module", "None", "__init__", "activation", "all", "all_masked_rows", "attention_mask", "bool", "channels", "class", "config", "conv_kernel_size", "def", "depthwise_conv", "dim", "else", "forward", "functional", ...
fastspeech2_conformer/modeling_fastspeech2_conformer.py:FastSpeech2ConformerEncoderLayer
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[ "Dropout", "False", "LayerNorm", "Linear", "ModelAttention", "ModelConvolutionModule", "ModelEncoderLayer", "ModelMultiLayeredConv1d", "Module", "None", "__init__", "attention_mask", "attention_output", "attention_scores", "cat", "class", "concat_after", "concat_linear", "config"...
fastspeech2_conformer/modeling_fastspeech2_conformer.py:FastSpeech2ConformerMultiLayeredConv1d
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