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
glm4v_moe/modeling_glm4v_moe.py:Glm4vMoeModel
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[ "False", "H_grid", "Image", "M", "ModelModel", "ModelModelOutputWithPast", "ModelPreTrainedModel", "ModelTextDecoderLayer", "ModelTextModel", "ModelVisionBlock", "ModelVisionModel", "Multimodal", "None", "Please", "RoPE", "T_grid", "Tensor", "True", "ValueError", "Video", "W_...
glm4v_moe/modeling_glm4v_moe.py:load_balancing_loss_func
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[ "Model_balancing_loss_func", "None", "_", "attention_mask", "batch_size", "cat", "compute_device", "concatenated_gate_logits", "def", "device", "dim", "else", "expand", "expert_attention_mask", "expert_mask", "float", "for", "functional", "gate_logits", "if", "in", "is", ...
glm4v_moe/modeling_glm4v_moe.py:Glm4vMoeForConditionalGeneration
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xlm_roberta/modeling_xlm_roberta.py:XLMRobertaEmbeddings
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xlm_roberta/modeling_xlm_roberta.py:eager_attention_forward
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xlm_roberta/modeling_xlm_roberta.py:XLMRobertaSelfAttention
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xlm_roberta/modeling_xlm_roberta.py:XLMRobertaCrossAttention
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xlm_roberta/modeling_xlm_roberta.py:XLMRobertaSelfOutput
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xlm_roberta/modeling_xlm_roberta.py:XLMRobertaAttention
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xlm_roberta/modeling_xlm_roberta.py:XLMRobertaIntermediate
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xlm_roberta/modeling_xlm_roberta.py:XLMRobertaOutput
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xlm_roberta/modeling_xlm_roberta.py:XLMRobertaLayer
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[ "False", "GradientCheckpointingLayer", "If", "Model", "ModelAttention", "ModelIntermediate", "ModelOutput", "None", "True", "ValueError", "_", "__init__", "add_cross_attention", "and", "apply_chunking_to_forward", "are", "attention", "attention_mask", "attention_output", "be", ...
xlm_roberta/modeling_xlm_roberta.py:XLMRobertaLMHead
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[ "LayerNorm", "Linear", "Model", "Module", "Parameter", "__init__", "bias", "class", "config", "decoder", "def", "dense", "eps", "features", "forward", "gelu", "hidden_size", "kwargs", "layer_norm", "layer_norm_eps", "nn", "return", "self", "super", "torch", "vocab_s...
xlm_roberta/modeling_xlm_roberta.py:XLMRobertaPreTrainedModel
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[ "Model", "ModelConfig", "ModelCrossAttention", "ModelEmbeddings", "ModelLMHead", "ModelLayer", "ModelSelfAttention", "PreTrainedModel", "True", "_can_record_outputs", "_init_weights", "_supports_attention_backend", "_supports_flash_attn", "_supports_flex_attn", "_supports_sdpa", "arang...
xlm_roberta/modeling_xlm_roberta.py:XLMRobertaEncoder
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xlm_roberta/modeling_xlm_roberta.py:XLMRobertaPooler
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xlm_roberta/modeling_xlm_roberta.py:XLMRobertaModel
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xlm_roberta/modeling_xlm_roberta.py:XLMRobertaForCausalLM
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[ "CausalLMOutputWithCrossAttentions", "False", "GenerationMixin", "Model", "ModelLMHead", "ModelModel", "ModelPreTrainedModel", "None", "True", "__init__", "_tied_weights_keys", "add", "add_pooling_layer", "attention_mask", "attentions", "auto_docstring", "bias", "can_return_tuple",...
xlm_roberta/modeling_xlm_roberta.py:XLMRobertaForMaskedLM
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xlm_roberta/modeling_xlm_roberta.py:XLMRobertaClassificationHead
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xlm_roberta/modeling_xlm_roberta.py:XLMRobertaForSequenceClassification
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xlm_roberta/modeling_xlm_roberta.py:XLMRobertaForMultipleChoice
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xlm_roberta/modeling_xlm_roberta.py:XLMRobertaForTokenClassification
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xlm_roberta/modeling_xlm_roberta.py:XLMRobertaForQuestionAnswering
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evolla/modeling_evolla.py:create_position_ids_from_input_ids
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[ "Model_position_ids_from_input_ids", "cumsum", "def", "dim", "incremental_indices", "input_ids", "int", "long", "mask", "ne", "padding_idx", "return", "torch", "type_as" ]
evolla/modeling_evolla.py:EvollaSaProtEmbeddings
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evolla/modeling_evolla.py:EvollaSaProtRotaryEmbedding
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evolla/modeling_evolla.py:rotate_half
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[ "Model_half", "cat", "def", "dim", "return", "shape", "torch", "x", "x1", "x2" ]
evolla/modeling_evolla.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" ]
evolla/modeling_evolla.py:eager_attention_forward
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evolla/modeling_evolla.py:EvollaSaProtSelfAttention
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[ "ALL_ATTENTION_FUNCTIONS", "False", "Linear", "ModelSaProtSelfAttention", "Module", "None", "The", "ValueError", "__init__", "_attn_implementation", "a", "absolute", "all_head_size", "and", "apply_rotary_pos_emb", "attention", "attention_head_size", "attention_interface", "attent...
evolla/modeling_evolla.py:EvollaSaProtSelfOutput
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[ "Dropout", "Linear", "ModelSaProtSelfOutput", "Module", "__init__", "class", "config", "def", "dense", "dropout", "forward", "hidden_dropout_prob", "hidden_size", "hidden_states", "input_tensor", "nn", "return", "self", "super" ]
evolla/modeling_evolla.py:EvollaSaProtAttention
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[ "False", "LayerNorm", "ModelSaProtAttention", "ModelSaProtSelfAttention", "ModelSaProtSelfOutput", "Module", "None", "_", "__init__", "attention_mask", "attn_output", "class", "config", "def", "encoder_attention_mask", "encoder_hidden_states", "eps", "forward", "hidden_size", "...
evolla/modeling_evolla.py:gelu
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[ "Model", "def", "erf", "math", "return", "sqrt", "torch", "x" ]
evolla/modeling_evolla.py:EvollaSaProtIntermediate
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[ "Linear", "ModelSaProtIntermediate", "Module", "__init__", "class", "config", "def", "dense", "forward", "gelu", "hidden_size", "hidden_states", "intermediate_size", "nn", "return", "self", "super" ]
evolla/modeling_evolla.py:EvollaSaProtOutput
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[ "Dropout", "Linear", "ModelSaProtOutput", "Module", "__init__", "class", "config", "def", "dense", "dropout", "forward", "hidden_dropout_prob", "hidden_size", "hidden_states", "input_tensor", "intermediate_size", "nn", "return", "self", "super" ]
evolla/modeling_evolla.py:EvollaSaProtLayer
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[ "AttributeError", "GradientCheckpointingLayer", "If", "LayerNorm", "ModelSaProtAttention", "ModelSaProtIntermediate", "ModelSaProtLayer", "ModelSaProtOutput", "None", "True", "__init__", "add_cross_attention", "and", "are", "attention", "attention_mask", "attention_output", "attent...
evolla/modeling_evolla.py:EvollaSaProtEncoder
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[ "BaseModelOutputWithCrossAttentions", "False", "LayerNorm", "ModelSaProtEncoder", "ModelSaProtLayer", "Module", "ModuleList", "None", "_", "__init__", "attention_mask", "can_return_tuple", "class", "config", "def", "emb_layer_norm_after", "encoder_attention_mask", "encoder_hidden_s...
evolla/modeling_evolla.py:EvollaSaProtPooler
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[ "Linear", "ModelSaProtPooler", "Module", "Tanh", "__init__", "activation", "class", "config", "def", "dense", "first_token_tensor", "forward", "hidden_size", "hidden_states", "nn", "pooled_output", "return", "self", "super" ]
evolla/modeling_evolla.py:EvollaSaProtPreTrainedModel
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[ "ModelSaProtLayer", "ModelSaProtPreTrainedModel", "ModelSaProtRotaryEmbedding", "ModelSaProtSelfAttention", "OutputRecorder", "PreTrainedModel", "SaProtConfig", "True", "_", "_can_record_outputs", "_init_weights", "_no_split_modules", "_supports_attention_backend", "_supports_flash_attn", ...
evolla/modeling_evolla.py:EvollaSaProtProteinEncoder
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[ "BaseModelOutputWithPoolingAndCrossAttentions", "ModelSaProtEmbeddings", "ModelSaProtEncoder", "ModelSaProtPreTrainedModel", "ModelSaProtProteinEncoder", "ModelSaProtRotaryEmbedding", "None", "__init__", "arange", "attention_mask", "attentions", "batch_size", "capture_outputs", "class", ...
evolla/modeling_evolla.py:EvollaSequenceCompressorAttention
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[ "LayerNorm", "Linear", "ModelSequenceCompressorAttention", "Module", "None", "True", "__init__", "amax", "attn", "bool", "bs", "cat", "chunk", "class", "def", "detach", "device", "dim", "dim_head", "forward", "h", "heads", "inner_dim", "k", "keepdim", "kv_input", ...
evolla/modeling_evolla.py:EvollaFeedForward
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[ "GELU", "LayerNorm", "Linear", "ModelFeedForward", "Module", "__init__", "activation", "class", "def", "dim", "fc1", "fc2", "forward", "inner_dim", "int", "mult", "nn", "norm", "return", "self", "super", "x" ]
evolla/modeling_evolla.py:EvollaSequenceCompressorResampler
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[ "LayerNorm", "Linear", "ModelFeedForward", "ModelSequenceCompressorAttention", "ModelSequenceCompressorResampler", "Module", "ModuleList", "None", "Parameter", "True", "_", "__init__", "append", "attn", "b", "bs", "cat", "class", "config", "def", "device", "dim", "dim_hea...
evolla/modeling_evolla.py:EvollaProteinEncoderModelOutput
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[ "ModelOutput", "ModelProteinEncoderModelOutput", "None", "attentions", "class", "hidden_states", "last_hidden_state", "r", "sequence_compressor_output" ]
evolla/modeling_evolla.py:EvollaProteinEncoder
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[ "ModelProteinEncoder", "ModelProteinEncoderModelOutput", "ModelSaProtProteinEncoder", "ModelSequenceCompressorResampler", "Module", "__init__", "attention_mask", "can_return_tuple", "class", "config", "def", "forward", "input_ids", "kwargs", "last_hidden_state", "model", "nn", "pro...
evolla/modeling_evolla.py:EvollaSequenceAlignerCrossAttention
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[ "Dropout", "Linear", "ModelFeedForward", "ModelRMSNorm", "ModelSequenceAlignerCrossAttention", "Module", "None", "Parameter", "Softmax", "T", "True", "_", "__init__", "aligner_attention_probs_dropout_prob", "aligner_enable_bias", "aligner_ffn_mult", "all_head_size", "amax", "and"...
evolla/modeling_evolla.py:EvollaRMSNorm
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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", ...
evolla/modeling_evolla.py:EvollaRotaryEmbedding
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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...
evolla/modeling_evolla.py:EvollaMLP
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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" ]
evolla/modeling_evolla.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" ]
evolla/modeling_evolla.py:EvollaAttention
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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", "class", "config", "contiguous", "co...
evolla/modeling_evolla.py:EvollaDecoderLayer
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[ "False", "GradientCheckpointingLayer", "ModelAttention", "ModelDecoderLayer", "ModelMLP", "ModelRMSNorm", "ModelSequenceAlignerCrossAttention", "None", "Tensor", "_", "__init__", "adapter", "aligner_num_add_layers", "attention_mask", "class", "config", "def", "eps", "forward", ...
evolla/modeling_evolla.py:EvollaPreTrainedModel
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[ "False", "ModelAttention", "ModelConfig", "ModelDecoderLayer", "ModelPreTrainedModel", "ModelSaProtLayer", "ModelSequenceAlignerCrossAttention", "ModelSequenceCompressorResampler", "PreTrainedModel", "True", "_can_compile_fullgraph", "_can_record_outputs", "_init_weights", "_no_split_modul...
evolla/modeling_evolla.py:EvollaModel
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[ "BaseModelOutputWithPast", "DynamicCache", "Embedding", "False", "ModelDecoderLayer", "ModelModel", "ModelPreTrainedModel", "ModelProteinEncoder", "ModelRMSNorm", "ModelRotaryEmbedding", "ModuleList", "None", "ValueError", "You", "__init__", "and", "arange", "attention_mask", "au...
evolla/modeling_evolla.py:EvollaForProteinText2Text
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[ "CausalLMOutputWithPast", "GenerationMixin", "Linear", "ModelForProteinText2Text", "ModelModel", "ModelPreTrainedModel", "None", "__init__", "attention_mask", "attentions", "auto_docstring", "can_return_tuple", "class", "config", "def", "else", "forward", "get_input_embeddings", ...
cohere_asr/modeling_cohere_asr.py:CohereAsrDecoderMLP
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[ "ACT2FN", "Linear", "ModelDecoderMLP", "Module", "__init__", "activation_fn", "class", "config", "def", "fc1", "fc2", "forward", "hidden_act", "hidden_size", "hidden_states", "intermediate_size", "nn", "return", "self", "super" ]
cohere_asr/modeling_cohere_asr.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_asr/modeling_cohere_asr.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",...
cohere_asr/modeling_cohere_asr.py:CohereAsrSelfAttention
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[ "ALL_ATTENTION_FUNCTIONS", "Linear", "ModelSelfAttention", "Module", "None", "True", "__init__", "_attn_implementation", "attention_dropout", "attention_interface", "attention_mask", "attn_output", "attn_weights", "class", "config", "contiguous", "def", "dropout", "eager_attentio...
cohere_asr/modeling_cohere_asr.py:CohereAsrCrossAttention
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[ "ALL_ATTENTION_FUNCTIONS", "False", "Linear", "ModelCrossAttention", "Module", "None", "True", "__init__", "_attn_implementation", "and", "attention_dropout", "attention_interface", "attention_mask", "attn_output", "attn_weights", "bsz", "class", "config", "contiguous", "cross_...
cohere_asr/modeling_cohere_asr.py:CohereAsrDecoderLayer
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[ "GradientCheckpointingLayer", "LayerNorm", "ModelCrossAttention", "ModelDecoderLayer", "ModelDecoderMLP", "ModelSelfAttention", "None", "_", "__init__", "attention_mask", "class", "config", "def", "encoder_attention_mask", "encoder_attn", "encoder_hidden_states", "encoder_position_id...
cohere_asr/modeling_cohere_asr.py:CohereAsrPreTrainedModel
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cohere_asr/modeling_cohere_asr.py:CohereAsrDecoder
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cohere_asr/modeling_cohere_asr.py:CohereAsrModel
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cohere_asr/modeling_cohere_asr.py:shift_tokens_right
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cohere_asr/modeling_cohere_asr.py:CohereAsrForConditionalGeneration
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csm/modeling_csm.py:CsmOutputWithPast
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csm/modeling_csm.py:CsmRMSNorm
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csm/modeling_csm.py:CsmRotaryEmbedding
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csm/modeling_csm.py:CsmMLP
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csm/modeling_csm.py:rotate_half
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csm/modeling_csm.py:apply_rotary_pos_emb
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csm/modeling_csm.py:repeat_kv
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csm/modeling_csm.py:eager_attention_forward
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csm/modeling_csm.py:CsmAttention
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csm/modeling_csm.py:CsmDecoderLayer
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csm/modeling_csm.py:CsmPreTrainedModel
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csm/modeling_csm.py:CsmDepthDecoderModel
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csm/modeling_csm.py:CsmCodebooksHead
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csm/modeling_csm.py:CsmDepthDecoderForCausalLM
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csm/modeling_csm.py:CsmBackboneModelEmbeddings
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csm/modeling_csm.py:CsmBackboneModel
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csm/modeling_csm.py:CsmForConditionalGeneration
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unispeech_sat/modeling_unispeech_sat.py:UniSpeechSatForPreTrainingOutput
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unispeech_sat/modeling_unispeech_sat.py:UniSpeechSatSamePadLayer
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unispeech_sat/modeling_unispeech_sat.py:UniSpeechSatPositionalConvEmbedding
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unispeech_sat/modeling_unispeech_sat.py:UniSpeechSatNoLayerNormConvLayer
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unispeech_sat/modeling_unispeech_sat.py:UniSpeechSatLayerNormConvLayer
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unispeech_sat/modeling_unispeech_sat.py:UniSpeechSatGroupNormConvLayer
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unispeech_sat/modeling_unispeech_sat.py:UniSpeechSatFeatureEncoder
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unispeech_sat/modeling_unispeech_sat.py:UniSpeechSatFeatureProjection
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unispeech_sat/modeling_unispeech_sat.py:eager_attention_forward
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unispeech_sat/modeling_unispeech_sat.py:UniSpeechSatAttention
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unispeech_sat/modeling_unispeech_sat.py:UniSpeechSatFeedForward
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unispeech_sat/modeling_unispeech_sat.py:UniSpeechSatEncoderLayer
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unispeech_sat/modeling_unispeech_sat.py:UniSpeechSatEncoder
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unispeech_sat/modeling_unispeech_sat.py:UniSpeechSatAttnAdapterLayer
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unispeech_sat/modeling_unispeech_sat.py:UniSpeechSatEncoderLayerStableLayerNorm
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unispeech_sat/modeling_unispeech_sat.py:UniSpeechSatEncoderStableLayerNorm
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