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
tokens
listlengths
4
448
m2m_100/modeling_m2m_100.py:M2M100SinusoidalPositionalEmbedding
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[ "False", "ModelSinusoidalPositionalEmbedding", "Module", "None", "__init__", "arange", "bsz", "cat", "class", "contiguous", "cos", "create_position_ids_from_input_ids", "create_position_ids_from_inputs_embeds", "cumsum", "def", "detach", "device", "dim", "dtype", "else", "emb...
m2m_100/modeling_m2m_100.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...
m2m_100/modeling_m2m_100.py:M2M100Attention
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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...
m2m_100/modeling_m2m_100.py:M2M100EncoderLayer
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[ "ACT2FN", "False", "GradientCheckpointingLayer", "LayerNorm", "Linear", "ModelAttention", "ModelEncoderLayer", "__init__", "activation_dropout", "activation_fn", "activation_function", "attention_dropout", "attention_mask", "attn_weights", "clamp", "clamp_value", "class", "config",...
m2m_100/modeling_m2m_100.py:M2M100DecoderLayer
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[ "ACT2FN", "False", "GradientCheckpointingLayer", "LayerNorm", "Linear", "ModelAttention", "ModelDecoderLayer", "None", "True", "__init__", "activation_dropout", "activation_fn", "activation_function", "attention_dropout", "attention_mask", "cache_position", "class", "config", "cr...
m2m_100/modeling_m2m_100.py:M2M100PreTrainedModel
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[ "False", "ModelConfig", "ModelDecoderLayer", "ModelEncoderLayer", "ModelPreTrainedModel", "ModelSinusoidalPositionalEmbedding", "PreTrainedModel", "True", "_can_compile_fullgraph", "_init_weights", "_no_split_modules", "_supports_flash_attn", "_supports_flex_attn", "_supports_sdpa", "bas...
m2m_100/modeling_m2m_100.py:M2M100Encoder
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[ "BaseModelOutput", "False", "LayerNorm", "ModelEncoder", "ModelEncoderLayer", "ModelPreTrainedModel", "ModelScaledWordEmbedding", "ModelSinusoidalPositionalEmbedding", "ModuleList", "None", "_", "__init__", "all_attentions", "and", "attention_mask", "attentions", "class", "config",...
m2m_100/modeling_m2m_100.py:M2M100Decoder
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[ "BaseModelOutputWithPastAndCrossAttentions", "DynamicCache", "EncoderDecoderCache", "False", "LayerNorm", "ModelDecoder", "ModelDecoderLayer", "ModelPreTrainedModel", "ModelScaledWordEmbedding", "ModelSinusoidalPositionalEmbedding", "ModuleList", "None", "Setting", "True", "ValueError", ...
m2m_100/modeling_m2m_100.py:M2M100Model
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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_...
m2m_100/modeling_m2m_100.py:M2M100ForConditionalGeneration
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[ "CrossEntropyLoss", "GenerationMixin", "Linear", "ModelForConditionalGeneration", "ModelModel", "ModelPreTrainedModel", "None", "Seq2SeqLMOutput", "__init__", "_tied_weights_keys", "attention_mask", "auto_docstring", "base_model_prefix", "cache_position", "class", "config", "cross_at...
xcodec/modeling_xcodec.py:XcodecOutput
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[ "ModelOutput", "None", "audio_codes", "audio_values", "class" ]
xcodec/modeling_xcodec.py:XcodecEncoderOutput
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[ "ModelEncoderOutput", "ModelOutput", "None", "audio_codes", "class" ]
xcodec/modeling_xcodec.py:XcodecDecoderOutput
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[ "ModelDecoderOutput", "ModelOutput", "None", "audio_values", "class" ]
xcodec/modeling_xcodec.py:ResidualUnit
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[ "Conv1d", "ELU", "ModelUnit", "Module", "__init__", "activation", "class", "config", "conv1", "conv2", "def", "dilation", "forward", "groups", "hidden_state", "in_channels", "kernel_size", "nn", "out_channels", "output_tensor", "padding", "return", "self", "stride", "...
xcodec/modeling_xcodec.py:SemanticEncoderBlock
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[ "Conv1d", "ModelEncoderBlock", "Module", "ModuleList", "ResidualUnit", "__init__", "block_dilations", "class", "config", "conv", "def", "dilation", "else", "for", "forward", "hidden_state", "if", "in", "in_channels", "kernel", "kernel_size", "nn", "out_channels", "paddi...
xcodec/modeling_xcodec.py:SemanticEncoder
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[ "Conv1d", "ModelEncoder", "ModelEncoderBlock", "Model_hidden_size", "Module", "ModuleList", "Number", "ValueError", "__init__", "block", "channel_ratios", "class", "config", "conv", "conv_blocks", "def", "enumerate", "for", "forward", "hidden_state", "i", "if", "in", "i...
xcodec/modeling_xcodec.py:SemanticDecoderBlock
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[ "Conv1d", "ConvTranspose1d", "ModelDecoderBlock", "Module", "ModuleList", "ResidualUnit", "__init__", "block_dilations", "class", "config", "conv", "def", "dilation", "else", "for", "forward", "hidden_state", "if", "in", "in_channels", "kernel_size", "nn", "out_channels",...
xcodec/modeling_xcodec.py:SemanticDecoder
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[ "Conv1d", "ModelDecoder", "ModelDecoderBlock", "Model_hidden_size", "Module", "ModuleList", "__init__", "block", "channel_ratios", "class", "config", "conv1", "conv2", "conv_blocks", "def", "else", "enumerate", "for", "forward", "hidden_state", "i", "if", "in", "in_chan...
xcodec/modeling_xcodec.py:XcodecEuclideanCodebook
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[ "F", "ModelEuclideanCodebook", "Module", "Tensor", "True", "__init__", "class", "clone", "cluster_size", "codebook_dim", "codebook_size", "config", "decode", "def", "dim", "dist", "embed", "embed_avg", "embed_ind", "embedding", "encode", "hidden_states", "indices", "ini...
xcodec/modeling_xcodec.py:XcodecVectorQuantization
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[ "ModelEuclideanCodebook", "ModelVectorQuantization", "Module", "__init__", "class", "codebook", "config", "decode", "def", "embed_in", "embed_ind", "encode", "hidden_states", "nn", "permute", "quantize", "return", "self", "super" ]
xcodec/modeling_xcodec.py:XcodecResidualVectorQuantization
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[ "ModelResidualVectorQuantization", "ModelVectorQuantization", "Module", "ModuleList", "None", "_", "__init__", "all_indices", "and", "append", "bandwidth", "bw_per_q", "class", "codebook_size", "codes", "config", "decode", "def", "device", "embeddings", "encode", "enumerate...
xcodec/modeling_xcodec.py:XcodecPreTrainedModel
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[ "AttributeError", "Conv1d", "ConvTranspose1d", "Embedding", "GroupNorm", "LayerNorm", "Linear", "Model", "ModelConfig", "ModelEuclideanCodebook", "ModelModel", "ModelPreTrainedModel", "None", "PreTrainedAudioTokenizerBase", "Snake1d", "Tensor", "True", "ValueError", "__class__", ...
xcodec/modeling_xcodec.py:XcodecModel
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[ "Audio", "AutoModel", "ConvTranspose1d", "F", "False", "Identity", "Linear", "ModelDecoderOutput", "ModelEncoderOutput", "ModelModel", "ModelOutput", "ModelPreTrainedModel", "ModelResidualVectorQuantization", "None", "Select", "SemanticDecoder", "SemanticEncoder", "Tanh", "This",...
qwen3_vl/modeling_qwen3_vl.py:BaseModelOutputWithDeepstackFeatures
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[ "ModelModelOutputWithDeepstackFeatures", "ModelModelOutputWithPooling", "None", "class", "deepstack_features", "r" ]
qwen3_vl/modeling_qwen3_vl.py:Qwen3VLVisionMLP
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qwen3_vl/modeling_qwen3_vl.py:Qwen3VLVisionPatchEmbed
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qwen3_vl/modeling_qwen3_vl.py:Qwen3VLVisionRotaryEmbedding
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qwen3_vl/modeling_qwen3_vl.py:Qwen3VLVisionPatchMerger
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[ "False", "GELU", "LayerNorm", "Linear", "ModelVisionPatchMerger", "Module", "__init__", "act_fn", "class", "config", "def", "else", "eps", "forward", "hidden_size", "if", "linear_fc1", "linear_fc2", "nn", "norm", "out_hidden_size", "return", "self", "spatial_merge_size"...
qwen3_vl/modeling_qwen3_vl.py:rotate_half
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[ "Model_half", "cat", "def", "dim", "return", "shape", "torch", "x", "x1", "x2" ]
qwen3_vl/modeling_qwen3_vl.py:apply_rotary_pos_emb_vision
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[ "Model_rotary_pos_emb_vision", "cos", "def", "dtype", "float", "k", "k_embed", "orig_q_dtype", "q", "q_embed", "return", "rotate_half", "sin", "unsqueeze" ]
qwen3_vl/modeling_qwen3_vl.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" ]
qwen3_vl/modeling_qwen3_vl.py:eager_attention_forward
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qwen3_vl/modeling_qwen3_vl.py:Qwen3VLVisionAttention
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qwen3_vl/modeling_qwen3_vl.py:Qwen3VLVisionBlock
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qwen3_vl/modeling_qwen3_vl.py:Qwen3VLTextRotaryEmbedding
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qwen3_vl/modeling_qwen3_vl.py:Qwen3VLTextRMSNorm
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qwen3_vl/modeling_qwen3_vl.py:apply_rotary_pos_emb
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qwen3_vl/modeling_qwen3_vl.py:Qwen3VLTextAttention
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qwen3_vl/modeling_qwen3_vl.py:Qwen3VLTextMLP
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qwen3_vl/modeling_qwen3_vl.py:Qwen3VLTextDecoderLayer
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qwen3_vl/modeling_qwen3_vl.py:Qwen3VLModelOutputWithPast
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qwen3_vl/modeling_qwen3_vl.py:Qwen3VLPreTrainedModel
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qwen3_vl/modeling_qwen3_vl.py:Qwen3VLVisionModel
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qwen3_vl/modeling_qwen3_vl.py:Qwen3VLTextModel
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qwen3_vl/modeling_qwen3_vl.py:Qwen3VLModel
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qwen3_vl/modeling_qwen3_vl.py:Qwen3VLCausalLMOutputWithPast
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qwen3_vl/modeling_qwen3_vl.py:Qwen3VLForConditionalGeneration
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falcon_mamba/modeling_falcon_mamba.py:FalconMambaCache
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falcon_mamba/modeling_falcon_mamba.py:rms_forward
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falcon_mamba/modeling_falcon_mamba.py:FalconMambaMixer
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falcon_mamba/modeling_falcon_mamba.py:FalconMambaRMSNorm
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falcon_mamba/modeling_falcon_mamba.py:FalconMambaBlock
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falcon_mamba/modeling_falcon_mamba.py:FalconMambaPreTrainedModel
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falcon_mamba/modeling_falcon_mamba.py:FalconMambaOutput
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falcon_mamba/modeling_falcon_mamba.py:FalconMambaCausalLMOutput
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falcon_mamba/modeling_falcon_mamba.py:FalconMambaModel
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falcon_mamba/modeling_falcon_mamba.py:FalconMambaForCausalLM
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