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
444
unispeech_sat/modeling_unispeech_sat.py:UniSpeechSatGumbelVectorQuantizer
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[ "FloatTensor", "Linear", "ModelGumbelVectorQuantizer", "Module", "None", "Parameter", "True", "ValueError", "__init__", "_compute_perplexity", "argmax", "batch_size", "be", "by", "class", "codevector_dim", "codevector_idx", "codevector_probs", "codevector_soft_dist", "codevecto...
unispeech_sat/modeling_unispeech_sat.py:UniSpeechSatPreTrainedModel
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[ "Conv1d", "Model", "ModelConfig", "ModelFeatureProjection", "ModelGumbelVectorQuantizer", "ModelPositionalConvEmbedding", "ModelPreTrainedModel", "Modelform_", "None", "PreTrainedModel", "True", "_conv_out_length", "_get_feat_extract_output_lengths", "_get_feature_vector_attention_mask", ...
unispeech_sat/modeling_unispeech_sat.py:_compute_mask_indices
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unispeech_sat/modeling_unispeech_sat.py:UniSpeechSatModel
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[ "ModelBaseModelOutput", "ModelEncoder", "ModelEncoderStableLayerNorm", "ModelFeatureEncoder", "ModelFeatureProjection", "ModelModel", "ModelPreTrainedModel", "Modelform_", "None", "Parameter", "Tensor", "True", "__init__", "_compute_mask_indices", "_get_feature_vector_attention_mask", ...
unispeech_sat/modeling_unispeech_sat.py:UniSpeechSatForPreTraining
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[ "Dropout", "False", "FloatTensor", "LayerNorm", "Linear", "Model", "ModelForPreTraining", "ModelForPreTrainingOutput", "ModelGumbelVectorQuantizer", "ModelModel", "ModelPreTrainedModel", "None", "Parameter", "__init__", "_freeze_parameters", "attention_mask", "attentions", "auto_do...
unispeech_sat/modeling_unispeech_sat.py:UniSpeechSatForCTC
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unispeech_sat/modeling_unispeech_sat.py:UniSpeechSatForSequenceClassification
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[ "CrossEntropyLoss", "False", "Linear", "Model", "ModelForSequenceClassification", "ModelModel", "ModelPreTrainedModel", "None", "Parameter", "Sequence", "SequenceClassifierOutput", "True", "ValueError", "_HIDDEN_STATES_START_POSITION", "__init__", "_freeze_parameters", "_get_feature_...
unispeech_sat/modeling_unispeech_sat.py:UniSpeechSatForAudioFrameClassification
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[ "Audio", "CrossEntropyLoss", "False", "Linear", "Model", "ModelForAudioFrameClassification", "ModelModel", "ModelPreTrainedModel", "None", "Parameter", "TokenClassifierOutput", "True", "ValueError", "_HIDDEN_STATES_START_POSITION", "__init__", "_freeze_parameters", "adapters", "add...
unispeech_sat/modeling_unispeech_sat.py:AMSoftmaxLoss
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[ "CrossEntropyLoss", "Model", "Module", "Parameter", "True", "__init__", "bool", "class", "cos_theta", "def", "dim", "flatten", "forward", "functional", "hidden_states", "input_dim", "labels", "logits", "loss", "margin", "mm", "nn", "normalize", "num_labels", "one_hot"...
unispeech_sat/modeling_unispeech_sat.py:TDNNLayer
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[ "Detected", "Linear", "LoRA", "LoraLayer", "Model", "Module", "ReLU", "You", "__init__", "activation", "applied", "be", "bias", "class", "config", "conv1d", "def", "dilation", "due", "else", "exclude", "forward", "from", "functional", "hidden_states", "if", "in_co...
unispeech_sat/modeling_unispeech_sat.py:UniSpeechSatForXVector
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[ "AMSoftmaxLoss", "False", "Linear", "Model", "ModelForXVector", "ModelModel", "ModelPreTrainedModel", "ModuleList", "None", "Parameter", "TDNNLayer", "True", "XVectorOutput", "_HIDDEN_STATES_START_POSITION", "__init__", "_conv_out_length", "_freeze_parameters", "_get_feat_extract_o...
doge/modeling_doge.py:DogeRMSNorm
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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", ...
doge/modeling_doge.py:DogeRotaryEmbedding
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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", "deprecate...
doge/modeling_doge.py:rotate_half
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[ "Model_half", "cat", "def", "dim", "return", "shape", "torch", "x", "x1", "x2" ]
doge/modeling_doge.py:apply_rotary_pos_emb
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[ "Model_rotary_pos_emb", "cos", "def", "k", "k_embed", "q", "q_embed", "return", "rotate_half", "sin", "unsqueeze", "unsqueeze_dim" ]
doge/modeling_doge.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" ]
doge/modeling_doge.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",...
doge/modeling_doge.py:flex_attention_forward
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[ "BlockMask", "Model_attention_forward", "None", "True", "attention_mask", "attention_weights", "attn_output", "batch_idx", "block_mask", "causal_mask", "compile_friendly_Model_attention", "contiguous", "def", "dtype", "else", "enable_gqa", "head_idx", "if", "is", "isinstance", ...
doge/modeling_doge.py:DogeAttention
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[ "A", "ALL_ATTENTION_FUNCTIONS", "BlockMask", "F", "False", "Linear", "ModelAttention", "ModelRMSNorm", "Module", "None", "Parameter", "Tensor", "True", "__init__", "_attn_implementation", "active_mask", "and", "apply_rotary_pos_emb", "attention_dropout", "attention_interface", ...
doge/modeling_doge.py:DogeMLP
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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" ]
doge/modeling_doge.py:DogeCDMoE
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doge/modeling_doge.py:DogeDecoderLayer
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[ "F", "False", "GradientCheckpointingLayer", "ModelAttention", "ModelCDMoE", "ModelDecoderLayer", "ModelMLP", "ModelRMSNorm", "None", "Parameter", "Tensor", "__init__", "attention_mask", "class", "config", "def", "dropout", "else", "eps", "forward", "hidden_dropout", "hidden...
doge/modeling_doge.py:DogePreTrainedModel
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doge/modeling_doge.py:DogeModel
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[ "DynamicCache", "Embedding", "False", "ModelDecoderLayer", "ModelModel", "ModelPreTrainedModel", "ModelRMSNorm", "ModelRotaryEmbedding", "ModuleList", "MoeModelOutputWithPast", "None", "ValueError", "You", "__init__", "and", "arange", "attention_mask", "auto_docstring", "capture_...
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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[ "GenericForSequenceClassification", "ModelForSequenceClassification", "ModelPreTrainedModel", "class", "pass" ]
ijepa/modeling_ijepa.py:IJepaPatchEmbeddings
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ijepa/modeling_ijepa.py:IJepaEmbeddings
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[ "Dropout", "False", "Input", "Model", "ModelPatchEmbeddings", "Module", "None", "Parameter", "ValueError", "_", "__init__", "align_corners", "and", "batch_size", "bicubic", "bool_masked_pos", "class", "config", "def", "dim", "doesn", "dropout", "else", "embeddings", "...
ijepa/modeling_ijepa.py:eager_attention_forward
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ijepa/modeling_ijepa.py:IJepaAttention
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ijepa/modeling_ijepa.py:IJepaMLP
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ijepa/modeling_ijepa.py:IJepaLayer
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ijepa/modeling_ijepa.py:IJepaPreTrainedModel
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ijepa/modeling_ijepa.py:IJepaPooler
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ijepa/modeling_ijepa.py:IJepaModel
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ijepa/modeling_ijepa.py:IJepaForImageClassification
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encodec/modeling_encodec.py:EncodecOutput
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encodec/modeling_encodec.py:EncodecEncoderOutput
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encodec/modeling_encodec.py:EncodecDecoderOutput
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encodec/modeling_encodec.py:EncodecConv1d
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encodec/modeling_encodec.py:EncodecConvTranspose1d
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encodec/modeling_encodec.py:EncodecLSTM
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encodec/modeling_encodec.py:EncodecResnetBlock
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encodec/modeling_encodec.py:EncodecEncoder
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encodec/modeling_encodec.py:EncodecDecoder
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encodec/modeling_encodec.py:EncodecEuclideanCodebook
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encodec/modeling_encodec.py:EncodecVectorQuantization
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encodec/modeling_encodec.py:EncodecResidualVectorQuantizer
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encodec/modeling_encodec.py:EncodecPreTrainedModel
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encodec/modeling_encodec.py:EncodecModel
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xlstm/modeling_xlstm.py:small_init_method
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xlstm/modeling_xlstm.py:wang_init_method
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xlstm/modeling_xlstm.py:xLSTMPreTrainedModel
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xlstm/modeling_xlstm.py:xLSTMCache
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xlstm/modeling_xlstm.py:xLSTMOutput
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xlstm/modeling_xlstm.py:xLSTMModel
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xlstm/modeling_xlstm.py:xLSTMCausalLMOutput
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xlstm/modeling_xlstm.py:xLSTMForCausalLM
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minimax_m2/modeling_minimax_m2.py:MiniMaxM2TopKRouter
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minimax_m2/modeling_minimax_m2.py:MiniMaxM2Experts
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minimax_m2/modeling_minimax_m2.py:MiniMaxM2SparseMoeBlock
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minimax_m2/modeling_minimax_m2.py:MiniMaxM2RMSNorm
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minimax_m2/modeling_minimax_m2.py:MiniMaxM2RotaryEmbedding
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minimax_m2/modeling_minimax_m2.py:rotate_half
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minimax_m2/modeling_minimax_m2.py:MiniMaxM2Attention
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minimax_m2/modeling_minimax_m2.py:MiniMaxM2DecoderLayer
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minimax_m2/modeling_minimax_m2.py:MiniMaxM2PreTrainedModel
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minimax_m2/modeling_minimax_m2.py:load_balancing_loss_func
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minimax_m2/modeling_minimax_m2.py:MiniMaxM2ForCausalLM
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chinese_clip/modeling_chinese_clip.py:ChineseCLIPOutput
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chinese_clip/modeling_chinese_clip.py:ChineseCLIPTextEmbeddings
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chinese_clip/modeling_chinese_clip.py:ChineseCLIPVisionEmbeddings
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chinese_clip/modeling_chinese_clip.py:eager_attention_forward
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chinese_clip/modeling_chinese_clip.py:ChineseCLIPTextSelfAttention
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chinese_clip/modeling_chinese_clip.py:ChineseCLIPTextSelfOutput
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chinese_clip/modeling_chinese_clip.py:ChineseCLIPTextAttention
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chinese_clip/modeling_chinese_clip.py:ChineseCLIPVisionAttention
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chinese_clip/modeling_chinese_clip.py:ChineseCLIPTextIntermediate
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chinese_clip/modeling_chinese_clip.py:ChineseCLIPTextOutput
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chinese_clip/modeling_chinese_clip.py:ChineseCLIPVisionMLP
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chinese_clip/modeling_chinese_clip.py:ChineseCLIPTextLayer
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chinese_clip/modeling_chinese_clip.py:ChineseCLIPVisionLayer
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chinese_clip/modeling_chinese_clip.py:ChineseCLIPTextPooler
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chinese_clip/modeling_chinese_clip.py:ChineseCLIPPreTrainedModel
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chinese_clip/modeling_chinese_clip.py:ChineseCLIPTextEncoder
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chinese_clip/modeling_chinese_clip.py:ChineseCLIPVisionEncoder
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chinese_clip/modeling_chinese_clip.py:ChineseCLIPVisionModel
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chinese_clip/modeling_chinese_clip.py:ChineseCLIPTextModel
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chinese_clip/modeling_chinese_clip.py:contrastive_loss
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chinese_clip/modeling_chinese_clip.py:image_text_contrastive_loss
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chinese_clip/modeling_chinese_clip.py:_get_vector_norm
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chinese_clip/modeling_chinese_clip.py:ChineseCLIPModel
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segformer/modeling_segformer.py:SegFormerImageClassifierOutput
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segformer/modeling_segformer.py:SegformerOverlapPatchEmbeddings
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segformer/modeling_segformer.py:SegformerSequenceReduction
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