Datasets:
itazap
/
transformers_code_embeddings_v3

Dataset card Data Studio Files
xet
 Community
1
identifier
stringlengths
24
117
embedding
listlengths
2.56k
2.56k
tokens
listlengths
4
444
nemotron/modeling_nemotron.py:eager_attention_forward
[ -0.000020655716070905328, 0.020133469253778458, 0.01408211700618267, -0.01877615600824356, 0, 0.03845718875527382, 0.05293518677353859, -0.03461147099733353, 0.020359687507152557, 0.007861101999878883, 0.02805112488567829, 0.020246578380465508, 0.002516683656722307, -0.01832371950149536, ...
[ "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",...
nemotron/modeling_nemotron.py:NemotronAttention
[ -0.0001267861807718873, 0.03065408021211624, 0.02614612691104412, -0.008001616224646568, -0.000686758488882333, 0.03133027255535126, 0.0403461791574955, 0.0022539764177054167, -0.0006938021397218108, 0.011269882321357727, 0.01679212413728237, 0.024230247363448143, -0.0014157789992168546, -...
[ "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...
nemotron/modeling_nemotron.py:NemotronDecoderLayer
[ -0.0001658348337514326, 0.04380862042307854, 0.008693979121744633, -0.0002752152504399419, -0.0007550777518190444, 0.03477591648697853, 0.040647175163030624, -0.03342100977897644, 0.005560759454965591, -0.01078279223293066, -0.004318762104958296, 0.02630775421857834, -0.002342857886105776, ...
[ "False", "GradientCheckpointingLayer", "ModelAttention", "ModelDecoderLayer", "ModelLayerNorm1P", "ModelMLP", "None", "Tensor", "_", "__init__", "attention_mask", "class", "config", "def", "eps", "forward", "hidden_size", "hidden_states", "input_layernorm", "kwargs", "layer_i...
nemotron/modeling_nemotron.py:NemotronPreTrainedModel
[ -0.00023169608903117478, 0.03961826115846634, 0.0033392535988241434, 0.01675286516547203, -0.0008949482580646873, 0.032600171864032745, 0.02060149610042572, -0.024336932227015495, -0.0016342533053830266, 0.008546225726604462, -0.001896016881801188, -0.007357677444815636, -0.00226390082389116...
[ "ModelAttention", "ModelConfig", "ModelDecoderLayer", "ModelLayerNorm1P", "ModelPreTrainedModel", "PreTrainedModel", "True", "_can_compile_fullgraph", "_can_record_outputs", "_init_weights", "_no_split_modules", "_skip_keys_device_placement", "_supports_attention_backend", "_supports_flash...
nemotron/modeling_nemotron.py:NemotronModel
[ -0.00009034540562424809, 0.04693049192428589, -0.008083721622824669, -0.007578488439321518, -0.0005789123242720962, 0.03727493807673454, 0.03929586708545685, -0.022230233997106552, 0.011171253398060799, 0.0012841328280046582, 0.017065633088350296, 0.0022454780992120504, -0.002273546531796455...
[ "BaseModelOutputWithPast", "DynamicCache", "Embedding", "False", "ModelDecoderLayer", "ModelLayerNorm1P", "ModelModel", "ModelPreTrainedModel", "ModelRotaryEmbedding", "ModuleList", "None", "ValueError", "You", "__init__", "and", "arange", "attention_mask", "auto_docstring", "cap...
nemotron/modeling_nemotron.py:NemotronForCausalLM
[ -0.00024259707424789667, 0.03989216685295105, 0.009576386772096157, -0.0069981287233531475, -0.0011262170737609267, 0.028445836156606674, 0.041025467216968536, -0.008669747039675713, 0.00552483880892396, 0.02209935523569584, 0.02708587609231472, 0.0034707318991422653, 0.0007153957849368453, ...
[ "CausalLMOutputWithPast", "GenerationMixin", "Linear", "ModelForCausalLM", "ModelModel", "ModelPreTrainedModel", "None", "__init__", "_tied_weights_keys", "attention_mask", "attentions", "auto_docstring", "can_return_tuple", "class", "config", "def", "else", "embed_tokens", "forw...
nemotron/modeling_nemotron.py:NemotronForSequenceClassification
[ -0.00028425012715160847, 0.024524668231606483, -0.011053371243178844, 0.024064110592007637, -0.001129804295487702, 0.035002343356609344, 0.03246928006410599, -0.004317723214626312, 0.010650384239852428, -0.012319903820753098, 0.027518289163708687, 0.010477675125002861, -0.005785749293863773,...
[ "GenericForSequenceClassification", "ModelForSequenceClassification", "ModelPreTrainedModel", "class" ]
nemotron/modeling_nemotron.py:NemotronForQuestionAnswering
[ -0.00012994326243642718, 0.03438930958509445, 0.020229004323482513, 0.02775869145989418, -0.0006462043384090066, 0.03506360948085785, 0.04203137755393982, 0.024387190118432045, -0.001411815988831222, 0.014497453346848488, 0.0045515261590480804, 0.009945927187800407, -0.0032731653191149235, ...
[ "GenericForQuestionAnswering", "ModelForQuestionAnswering", "ModelPreTrainedModel", "base_model_prefix", "class", "transformer" ]
nemotron/modeling_nemotron.py:NemotronForTokenClassification
[ -0.0002579738211352378, 0.02935168705880642, -0.005044821184128523, -0.020637905225157738, -0.0010963886743411422, 0.04081718996167183, 0.04815511032938957, -0.001949135446920991, 0.005962061695754528, -0.018115494400262833, 0.039670638740062714, 0.024880141019821167, -0.006076716352254152, ...
[ "GenericForTokenClassification", "ModelForTokenClassification", "ModelPreTrainedModel", "class" ]
aimv2/modeling_aimv2.py:Aimv2Output
[ -0.00008383870590478182, 0.015956569463014603, 0.016181308776140213, 0.010843724943697453, -0.0005126890609972179, 0.033486321568489075, 0.04427386075258255, -0.009439096786081791, 0.015282347798347473, 0.005309492349624634, 0.02247404120862484, 0.018765823915600777, -0.003216597018763423, ...
[ "ModelOutput", "None", "class", "def", "else", "for", "if", "image_embeds", "in", "isinstance", "logits_per_image", "logits_per_text", "loss", "r", "return", "self", "text_embeds", "text_model_output", "to_tuple", "tuple", "v", "values", "vision_model_output" ]
aimv2/modeling_aimv2.py:Aimv2RMSNorm
[ -0.00010937952902168036, 0.04177592322230339, 0.032291658222675323, 0.04855039715766907, -0.0003740074171219021, 0.03861450031399727, 0.024952644482254982, -0.030033500865101814, 0.008581000380218029, 0.042001739144325256, 0.0195330660790205, 0.005617167800664902, 0.002427519764751196, 0.0...
[ "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", ...
aimv2/modeling_aimv2.py:Aimv2MLP
[ -0.00024346320424228907, 0.025046052411198616, 0.022045142948627472, 0.02608482912182808, -0.0009449979406781495, 0.060479868203401566, 0.03601091355085373, -0.005799834616482258, -0.00044544751290231943, -0.004155105445533991, 0.028162380680441856, -0.04709119349718094, -0.00169522536452859...
[ "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" ]
aimv2/modeling_aimv2.py:build_2d_sinusoidal_position_embedding
[ -0.000248516007559374, 0.022948678582906723, 0.040444206446409225, 0.012667215429246426, -0.0009727626456879079, -0.00477150734513998, 0.05203215032815933, 0.002229543635621667, 0.008179727010428905, 0.01522338017821312, 0.020790139213204384, 0.007838904857635498, -0.0003656735352706164, 0...
[ "False", "Model_2d_sinusoidal_position_embedding", "None", "ValueError", "arange", "be", "by", "cat", "cls_token", "cos", "def", "device", "dim", "divisible", "dtype", "emb_h", "emb_w", "embed_dim", "f", "flatten", "float32", "float64", "got", "grid_h", "grid_w", "h...
aimv2/modeling_aimv2.py:Aimv2VisionEmbeddings
[ -0.00003408453267184086, 0.02567027322947979, 0.00906340777873993, 0.04120754078030586, 0, 0.01356696430593729, 0.03220042958855629, -0.015649858862161636, 0.01463655848056078, 0.01981564797461033, 0.019928237423300743, 0.01643798127770424, 0.002167336642742157, -0.009964118711650372, -0...
[ "Conv2d", "Embedding", "False", "ModelRMSNorm", "ModelVisionEmbeddings", "Module", "_", "__init__", "arange", "build_2d_sinusoidal_position_embedding", "cat", "class", "config", "def", "device", "dim", "dtype", "else", "embed_dim", "expand", "flatten", "forward", "half", ...
aimv2/modeling_aimv2.py:Aimv2TextEmbeddings
[ -0.00016475887969136238, 0.022222960367798805, 0.012075231410562992, -0.012642143294215202, -0.0009354052017442882, 0.039003562182188034, 0.03469502925872803, 0.0028629067819565535, 0.011451627127826214, -0.004081768449395895, 0.03129355609416962, 0.023810314014554024, -0.0006023442838340998...
[ "Embedding", "False", "ModelTextEmbeddings", "Module", "None", "Sequence", "ValueError", "__init__", "and", "arange", "be", "class", "config", "def", "else", "embed_dim", "embeddings", "expand", "f", "forward", "got", "hidden_size", "if", "input_ids", "inputs_embeds",...
aimv2/modeling_aimv2.py:eager_attention_forward
[ 0.000041531115130055696, 0.03166968375444412, 0.025335747748613358, -0.010858177207410336, 0.00008880589302862063, 0.035741500556468964, 0.0592675507068634, -0.02182946167886257, 0.02262120321393013, 0.011593366973102093, 0.024204688146710396, 0.03144347295165062, 0.002544885268434882, -0....
[ "Model_attention_forward", "None", "attention_mask", "attn_output", "attn_weights", "contiguous", "def", "dim", "dropout", "dtype", "float32", "functional", "if", "is", "key", "kwargs", "matmul", "module", "nn", "not", "p", "query", "return", "scaling", "softmax", "...
aimv2/modeling_aimv2.py:Aimv2Attention
[ -0.00008876202628016472, 0.0362219363451004, 0.0425214022397995, -0.00009183793736156076, -0.0000949138484429568, 0.024410435929894447, 0.04297136515378952, -0.005821382626891136, 0.002446667989715934, 0.02553534135222435, 0.01934836432337761, 0.011249048635363579, -0.0026013425085693598, ...
[ "ALL_ATTENTION_FUNCTIONS", "False", "Linear", "ModelAttention", "Module", "None", "ValueError", "__init__", "_attn_implementation", "and", "attention_dropout", "attention_interface", "attention_mask", "attn_output", "attn_weights", "be", "by", "class", "config", "contiguous", ...
aimv2/modeling_aimv2.py:Aimv2EncoderLayer
[ -0.00007818741141818464, 0.028271155431866646, 0.033246878534555435, 0.030306678265333176, -0.0002862454566638917, 0.05088808014988899, 0.023860855028033257, -0.022616924718022346, 0.010121073573827744, 0.018432792276144028, 0.011478088796138763, 0.020807569846510887, 0.0023040990345180035, ...
[ "GradientCheckpointingLayer", "ModelAttention", "ModelEncoderLayer", "ModelMLP", "ModelRMSNorm", "None", "_", "__init__", "attention", "attention_mask", "attn_output", "class", "config", "def", "ffn", "forward", "hidden_size", "hidden_states", "kwargs", "mlp_output", "norm_hi...
aimv2/modeling_aimv2.py:Aimv2Encoder
[ -0.0000659062061458826, 0.016984468325972557, 0.024520622566342354, 0.036668453365564346, -0.00009358680836157873, 0.037343334406614304, 0.010348152369260788, -0.01653454825282097, 0.00792983453720808, 0.007479914929717779, 0.007479914929717779, 0.003908677492290735, -0.002151178428903222, ...
[ "BaseModelOutput", "False", "ModelEncoder", "ModelEncoderLayer", "Module", "ModuleList", "None", "_", "__init__", "attention_mask", "auto_docstring", "class", "config", "def", "encoder_layer", "for", "forward", "gradient_checkpointing", "hidden_states", "in", "inputs_embeds",...
aimv2/modeling_aimv2.py:Aimv2AttentionPoolingHead
[ -0.0002045001310762018, 0.025499049574136734, 0.051900722086429596, 0.014441940002143383, -0.0006452331435866654, 0.013426491059362888, 0.03136608749628067, 0, -0.0030322433449327946, 0.020196150988340378, 0.011000696569681168, 0.01337007712572813, -0.0014385526301339269, -0.02132442779839...
[ "F", "Linear", "ModelAttentionPoolingHead", "Module", "Parameter", "__init__", "attn_output", "batch_size", "class", "cls_token", "config", "def", "dim", "expand", "forward", "hidden_dim", "hidden_size", "hidden_states", "k_proj", "key", "mean", "nn", "num_attention_heads...
aimv2/modeling_aimv2.py:Aimv2PreTrainedModel
[ -0.00023903712281025946, 0.03973010927438736, 0.005080430768430233, 0.020093388855457306, -0.0012701076921075583, 0.03653343394398689, 0.02614424005150795, -0.011131281033158302, 0.0013771392405033112, 0.006050850264728069, 0.009019192308187485, -0.0031538628973066807, -0.003182404674589634,...
[ "Model", "ModelAttentionPoolingHead", "ModelConfig", "ModelEncoderLayer", "ModelPreTrainedModel", "ModelTextEmbeddings", "ModelVisionEmbeddings", "Parameter", "PreTrainedModel", "True", "_init_weights", "_no_split_modules", "_supports_flash_attn", "_supports_flex_attn", "_supports_sdpa",...
aimv2/modeling_aimv2.py:Aimv2VisionModel
[ -0.000011222913599340245, 0.03453284874558449, -0.003643888281658292, 0.05269623175263405, 0.0002873065823223442, 0.024105722084641457, 0.03632676228880882, -0.024890560656785965, 0.006783238146454096, 0.030496541410684586, 0.024890560656785965, 0.012893758714199066, 0.0009670319268479943, ...
[ "BaseModelOutputWithPooling", "False", "ModelAttention", "ModelAttentionPoolingHead", "ModelEncoder", "ModelEncoderLayer", "ModelPreTrainedModel", "ModelRMSNorm", "ModelVisionConfig", "ModelVisionEmbeddings", "ModelVisionModel", "None", "__init__", "_can_record_outputs", "attentions", ...
aimv2/modeling_aimv2.py:Aimv2TextModel
[ 0.00008618299762019888, 0.04098985716700554, 0.022958800196647644, 0.033822230994701385, 0.00027648554532788694, 0.0318063385784626, 0.02553466521203518, -0.017695074900984764, 0.011479400098323822, 0.014671233482658863, 0.029118478298187256, 0.011983374133706093, -0.00007568354340037331, ...
[ "BaseModelOutputWithPooling", "False", "ModelAttention", "ModelEncoder", "ModelEncoderLayer", "ModelPreTrainedModel", "ModelRMSNorm", "ModelTextEmbeddings", "ModelTextModel", "None", "_", "__init__", "_can_record_outputs", "arange", "argmax", "attention_mask", "attentions", "auto_d...
aimv2/modeling_aimv2.py:_get_vector_norm
[ -0.000019035393052035943, 0.0236134584993124, 0.046335846185684204, 0.026175295934081078, 0.00016272540960926563, 0.019603626802563667, 0.03163312375545502, -0.03230142965912819, 0.00824243389070034, 0.050122909247875214, 0.029850976541638374, 0.012976264581084251, -0.0005882480181753635, ...
[ "True", "_get_vector_norm", "def", "dim", "keepdim", "normed_tensor", "pow", "return", "square_tensor", "sum", "sum_tensor", "tensor", "torch" ]
aimv2/modeling_aimv2.py:Aimv2Model
[ -0.00004366358189145103, 0.0444890633225441, 0.027027105912566185, 0.02480265311896801, 0, 0.028139332309365273, 0.02580365724861622, -0.020687414333224297, 0.007007027510553598, 0.025358766317367554, 0.043821729719638824, 0.026915883645415306, -0.0016196799697354436, 0.002794469241052866,...
[ "False", "Linear", "ModelModel", "ModelOutput", "ModelPreTrainedModel", "ModelTextModel", "ModelVisionModel", "None", "Parameter", "True", "__init__", "_from_config", "_get_vector_norm", "_supports_flash_attn", "attention_mask", "auto_docstring", "can_return_tuple", "clamp", "cla...
xlnet/modeling_xlnet.py:XLNetRelativeAttention
[ -0.00026705750497058034, 0.03418336063623428, 0.028062691912055016, -0.0189394298940897, -0.001198149868287146, 0.04018854722380638, 0.03141173720359802, -0.011201979592442513, 0.005889700725674629, 0.028062691912055016, 0.00941197294741869, 0.009931651875376701, 0.0011909321183338761, 0.0...
[ "Dropout", "False", "FloatTensor", "LayerNorm", "Model", "Module", "None", "Parameter", "The", "True", "ValueError", "__init__", "a", "ac", "arange", "attention", "attn_mask", "attn_mask_g", "attn_mask_h", "attn_out", "attn_prob", "attn_prob_g", "attn_prob_h", "attn_sco...
xlnet/modeling_xlnet.py:XLNetFeedForward
[ -0.0002293171128258109, 0.060539718717336655, 0.020065248012542725, 0.029237933456897736, -0.0009817639365792274, 0.04219435155391693, 0.03256303071975708, -0.010032624006271362, 0.000659286743029952, 0.007911440916359425, 0.02511022426187992, -0.020753199234604836, 0.003611744614318013, -...
[ "ACT2FN", "Dropout", "LayerNorm", "Linear", "Model", "Module", "__init__", "activation_function", "class", "config", "d_inner", "d_model", "def", "dropout", "else", "eps", "ff_activation", "forward", "if", "inp", "isinstance", "layer_1", "layer_2", "layer_norm", "laye...
xlnet/modeling_xlnet.py:XLNetLayer
[ -0.00027144388877786696, 0.039576880633831024, 0.03405452519655228, -0.0008736538584344089, -0.0009024161263369024, 0.030833151191473007, 0.024620501324534416, -0.02623118832707405, 0.00471701193600893, 0.0019558342173695564, 0.007276853546500206, 0.00606883829459548, 0.00604007625952363, ...
[ "Dropout", "False", "Model", "ModelFeedForward", "ModelRelativeAttention", "Module", "None", "__init__", "apply_chunking_to_forward", "attn_mask_g", "attn_mask_h", "chunk_size_feed_forward", "class", "config", "def", "dropout", "ff", "ff_chunk", "forward", "if", "is", "mems...
xlnet/modeling_xlnet.py:XLNetPoolerStartLogits
[ -0.0001693864178378135, 0.021113883703947067, 0.04427104815840721, 0.023611225187778473, -0.0009294081828556955, 0.018389511853456497, 0.041773706674575806, -0.03155731037259102, 0.007548781111836433, 0.01577865518629551, 0.020432790741324425, -0.010046122595667839, 0.0025824778713285923, ...
[ "Linear", "Model", "Module", "None", "__init__", "class", "config", "def", "dense", "else", "float16", "forward", "hidden_size", "hidden_states", "if", "is", "nn", "not", "p_mask", "return", "self", "squeeze", "super", "torch", "x" ]
xlnet/modeling_xlnet.py:XLNetPoolerEndLogits
[ -0.00040180140058510005, 0.026299728080630302, 0.030390797182917595, 0.028053043410182, -0.0016656494699418545, 0.04301466420292854, 0.03717028349637985, -0.03857293352484703, 0.003272854955866933, 0.030858347192406654, 0.030390797182917595, 0.00672104163095355, 0.0028053042478859425, -0.0...
[ "LayerNorm", "Linear", "Model", "Module", "None", "One", "Tanh", "__init__", "activation", "assert", "be", "cat", "class", "config", "def", "dense_0", "dense_1", "dim", "else", "eps", "expand", "float16", "forward", "gather", "hidden_size", "hidden_states", "hsz",...
xlnet/modeling_xlnet.py:XLNetPoolerAnswerClass
[ -0.00040695362258702517, 0.015650376677513123, 0.029081672430038452, 0.019270988181233406, -0.0016862114425748587, 0.042279377579689026, 0.03714044764637947, -0.023475566878914833, -0.0012044367613270879, 0.02067251317203045, 0.03387022018432617, 0.02055571973323822, 0.0016862114425748587, ...
[ "Linear", "Model", "Module", "None", "One", "Tanh", "__init__", "activation", "assert", "be", "cat", "class", "cls_index", "cls_token_state", "config", "def", "dense_0", "dense_1", "dim", "else", "expand", "forward", "gather", "hidden_size", "hidden_states", "hsz", ...
xlnet/modeling_xlnet.py:XLNetSequenceSummary
[ -0.0003531706752255559, 0.033088814467191696, 0.027845868840813637, 0.037749212235212326, -0.0016165749402716756, 0.026447748765349388, 0.019457153975963593, -0.008971262723207474, -0.0032185863237828016, -0.008563478477299213, 0.03495297208428383, 0.012000520713627338, 0.0033205323852598667...
[ "Dropout", "Identity", "Linear", "Model", "Module", "None", "NotImplementedError", "__init__", "activation", "activation_string", "and", "attn", "class", "cls_index", "config", "def", "dim", "dtype", "elif", "else", "expand", "first", "first_dropout", "forward", "full...
xlnet/modeling_xlnet.py:XLNetPreTrainedModel
[ -0.00011749005352612585, 0.05088290944695473, 0.011872678995132446, 0.001455816556699574, -0.0005017620278522372, 0.04183705896139145, 0.013229556381702423, -0.0038444865494966507, 0.0033215233124792576, 0.00004173988781985827, 0.0038444865494966507, 0.01051580160856247, 0.000727908278349787...
[ "Model", "ModelConfig", "ModelModel", "ModelRelativeAttention", "PreTrainedModel", "_init_weights", "base_model_prefix", "class", "config", "def", "elif", "for", "if", "in", "init", "initializer_range", "isinstance", "k", "mask_emb", "mean", "module", "no_grad", "normal_"...
xlnet/modeling_xlnet.py:XLNetModelOutput
[ -0.00018820232071448117, 0.0185836348682642, 0.0185836348682642, 0.013765654526650906, -0.001182985957711935, 0.05208006128668785, 0.0525389164686203, -0.01938663050532341, 0.023516327142715454, -0.005707011092454195, -0.00018103270849678665, 0.015715789049863815, -0.0011327987303957343, -...
[ "FloatTensor", "Model", "ModelOutput", "None", "attentions", "class", "hidden_states", "last_hidden_state", "mems", "r", "torch" ]
xlnet/modeling_xlnet.py:XLNetLMHeadModelOutput
[ -0.0001496366603532806, 0.016987323760986328, 0.007524586282670498, 0.005044893361628056, -0.0010118288919329643, 0.0643010139465332, 0.05312814190983772, -0.009120711125433445, 0.021319661289453506, -0.015619217418134212, 0.0031209932640194893, 0.011457893066108227, -0.001717258826829493, ...
[ "Model", "ModelOutput", "None", "attentions", "class", "hidden_states", "logits", "loss", "mems", "r" ]
xlnet/modeling_xlnet.py:XLNetForSequenceClassificationOutput
[ -0.0001496366603532806, 0.016987323760986328, 0.007524586282670498, 0.005044893361628056, -0.0010118288919329643, 0.0643010139465332, 0.05312814190983772, -0.009120711125433445, 0.021319661289453506, -0.015619217418134212, 0.0031209932640194893, 0.011457893066108227, -0.001717258826829493, ...
[ "Model", "ModelOutput", "None", "attentions", "class", "hidden_states", "logits", "loss", "mems", "r" ]
xlnet/modeling_xlnet.py:XLNetForTokenClassificationOutput
[ -0.0001496366603532806, 0.016987323760986328, 0.007524586282670498, 0.005044893361628056, -0.0010118288919329643, 0.0643010139465332, 0.05312814190983772, -0.009120711125433445, 0.021319661289453506, -0.015619217418134212, 0.0031209932640194893, 0.011457893066108227, -0.001717258826829493, ...
[ "Model", "ModelOutput", "None", "attentions", "class", "hidden_states", "logits", "loss", "mems", "r" ]
xlnet/modeling_xlnet.py:XLNetForMultipleChoiceOutput
[ -0.0001496366603532806, 0.016987323760986328, 0.007524586282670498, 0.005044893361628056, -0.0010118288919329643, 0.0643010139465332, 0.05312814190983772, -0.009120711125433445, 0.021319661289453506, -0.015619217418134212, 0.0031209932640194893, 0.011457893066108227, -0.001717258826829493, ...
[ "Model", "ModelOutput", "None", "attentions", "class", "hidden_states", "logits", "loss", "mems", "r" ]
xlnet/modeling_xlnet.py:XLNetForQuestionAnsweringSimpleOutput
[ -0.00019971498113591224, 0.025907421484589577, 0.015934210270643234, 0.009686624631285667, -0.001239486737176776, 0.06648807227611542, 0.054336804896593094, -0.007967105135321617, 0.019946422427892685, -0.0033387329895049334, 0.006763441953808069, 0.019946422427892685, 0.00010970890434691682...
[ "Model", "ModelOutput", "None", "attentions", "class", "end_logits", "hidden_states", "loss", "mems", "r", "start_logits" ]
xlnet/modeling_xlnet.py:XLNetForQuestionAnsweringOutput
[ -0.0001869448460638523, 0.019484993070364, 0.026549728587269783, 0.010540127754211426, -0.0010397693840786815, 0.06745681911706924, 0.05742945522069931, -0.005070656072348356, 0.016636310145258904, -0.009229733608663082, 0.011679601855576038, 0.027575254440307617, -0.000794070481788367, -0...
[ "Model", "ModelOutput", "None", "attentions", "class", "cls_logits", "end_top_index", "end_top_log_probs", "hidden_states", "loss", "mems", "r", "start_top_index", "start_top_log_probs" ]
xlnet/modeling_xlnet.py:XLNetModel
[ -0.00030731107108294964, 0.032434795051813126, 0.015987364575266838, -0.005319536663591862, -0.001114227226935327, 0.050607483834028244, 0.017827635630965233, -0.013169447891414165, 0.005233273841440678, -0.008683783933520317, 0.004744451493024826, 0.00966142863035202, 0.0015383524587377906,...
[ "BERT", "Dropout", "Embedding", "FloatTensor", "Model", "ModelLayer", "ModelOutput", "ModelPreTrainedModel", "ModuleList", "None", "Parameter", "Please", "Unsupported", "ValueError", "You", "_", "__init__", "added", "and", "arange", "assert", "att_stream", "attention", ...
xlnet/modeling_xlnet.py:XLNetLMHeadModel
[ -0.0002722240169532597, 0.036894358694553375, 0.022432681173086166, -0.0020639204885810614, -0.0011671826941892505, 0.0428156778216362, 0.022432681173086166, -0.02562108263373375, -0.000238418419030495, -0.005380427464842796, 0.0017223061295226216, 0.02562108263373375, 0.0006049422663636506,...
[ "CrossEntropyLoss", "False", "GenerationMixin", "Linear", "Model", "ModelModel", "ModelOutput", "ModelPreTrainedModel", "None", "__init__", "_reorder_cache", "_tied_weights_keys", "attention_mask", "attentions", "attn_type", "auto_docstring", "beam_idx", "cat", "class", "config...
xlnet/modeling_xlnet.py:XLNetForSequenceClassification
[ -0.00037938953028060496, 0.036074522882699966, 0.016649780794978142, 0.016071662306785583, -0.001488652196712792, 0.047868117690086365, 0.014741993509232998, 0.010984229855239391, -0.0071975612081587315, -0.0015970492968335748, 0.026940269395709038, 0.0060124206356704235, 0.00047333360998891...
[ "BCEWithLogitsLoss", "CrossEntropyLoss", "Linear", "MSELoss", "Model", "ModelModel", "ModelOutput", "ModelPreTrainedModel", "ModelSequenceSummary", "None", "__init__", "and", "attention_mask", "attentions", "auto_docstring", "class", "config", "d_model", "def", "dtype", "elif...
xlnet/modeling_xlnet.py:XLNetForTokenClassification
[ -0.0002464179997332394, 0.0408451110124588, 0.018266841769218445, 0.008679586462676525, -0.0009147603414021432, 0.03721443563699722, 0.02530127763748169, 0.007715187966823578, 0.0023684492334723473, 0.007318082731217146, 0.02552819438278675, 0.008055564016103745, 0.0009360337862744927, 0.0...
[ "CrossEntropyLoss", "Linear", "Model", "ModelModel", "ModelOutput", "ModelPreTrainedModel", "None", "__init__", "attention_mask", "attentions", "auto_docstring", "class", "classifier", "config", "def", "else", "forward", "hidden_size", "hidden_states", "if", "input_ids", "i...
xlnet/modeling_xlnet.py:XLNetForMultipleChoice
[ -0.0002541665453463793, 0.046411167830228806, 0.020475516095757484, 0.025253135710954666, -0.001087761833332479, 0.04481862857937813, 0.035490892827510834, 0.006483913399279118, -0.00022128487762529403, 0.008474588394165039, 0.02400185540318489, 0.005261070095002651, -0.0002950464840978384, ...
[ "CrossEntropyLoss", "Linear", "Model", "ModelModel", "ModelOutput", "ModelPreTrainedModel", "ModelSequenceSummary", "None", "__init__", "attention_mask", "attentions", "auto_docstring", "class", "config", "d_model", "def", "else", "flat_attention_mask", "flat_input_ids", "flat_...
xlnet/modeling_xlnet.py:XLNetForQuestionAnsweringSimple
[ -0.00030248388065956533, 0.036668162792921066, 0.011957009322941303, 0.018675709143280983, -0.0009750656317919493, 0.05830465629696846, 0.018675709143280983, 0.015601050108671188, 0.0015230952994897962, 0.023914018645882607, 0.01571492664515972, 0.018675709143280983, 0.0013593981275334954, ...
[ "CrossEntropyLoss", "Linear", "Model", "ModelModel", "ModelOutput", "ModelPreTrainedModel", "None", "__init__", "and", "attention_mask", "attentions", "auto_docstring", "clamp", "class", "config", "contiguous", "def", "dim", "else", "end_logits", "end_loss", "end_positions"...
xlnet/modeling_xlnet.py:XLNetForQuestionAnswering
[ -0.00021201890194788575, 0.03837985545396805, 0.02373192273080349, 0.027819719165563583, -0.0008125912863761187, 0.0704009160399437, 0.03974245488643646, 0.014647933654487133, 0.0042581199668347836, 0.0063304053619503975, 0.021233825013041496, 0.015329232439398766, 0.0032361713238060474, 0...
[ "BCEWithLogitsLoss", "CrossEntropyLoss", "Model", "ModelModel", "ModelOutput", "ModelPoolerAnswerClass", "ModelPoolerEndLogits", "ModelPoolerStartLogits", "ModelPreTrainedModel", "None", "__init__", "and", "answer_class", "attention_mask", "attentions", "auto_docstring", "bh", "bl"...
aya_vision/modeling_aya_vision.py:AyaVisionMultiModalProjector
[ -0.0003842599398922175, 0.04872559756040573, 0.023098690435290337, 0.03539500758051872, -0.0011779182823374867, 0.029074471443891525, 0.017007991671562195, -0.06527391076087952, 0.0004848139360547066, 0.027695445343852043, 0.031717605888843536, -0.008101779967546463, -0.002197823254391551, ...
[ "ACT2FN", "LayerNorm", "Linear", "ModelMultiModalProjector", "Module", "__init__", "act", "adapter_layer_norm_eps", "alignment_intermediate_size", "batch_size", "channels", "chunk", "class", "config", "def", "dim", "downsample_factor", "eps", "feature_dim", "forward", "gate",...
aya_vision/modeling_aya_vision.py:AyaVisionPreTrainedModel
[ -0.0003886564518325031, 0.03285247087478638, 0.017364876344799995, 0.019476821646094322, -0.0020532794296741486, 0.029801884666085243, 0.024874012917280197, -0.03144450858235359, -0.004810540471225977, 0.014900942333042622, 0.015135602094233036, 0.006981149781495333, -0.004047893453389406, ...
[ "False", "ModelConfig", "ModelPreTrainedModel", "PreTrainedModel", "True", "_can_compile_fullgraph", "_skip_keys_device_placement", "_supports_attention_backend", "_supports_flash_attn", "_supports_flex_attn", "_supports_sdpa", "base_model_prefix", "class", "config", "image", "input_mo...
aya_vision/modeling_aya_vision.py:AyaVisionCausalLMOutputWithPast
[ -0.0002024024142883718, 0.026021139696240425, 0.029543651267886162, 0.009260798804461956, -0.0011860071681439877, 0.03317979350686073, 0.037270452827215195, -0.03158898279070854, 0.017839821055531502, -0.004545177333056927, 0.019657891243696213, 0.019430631771683693, -0.000909035443328321, ...
[ "ModelCausalLMOutputWithPast", "ModelOutput", "None", "attentions", "class", "hidden_states", "image_hidden_states", "logits", "loss", "past_key_values", "r" ]
aya_vision/modeling_aya_vision.py:AyaVisionModelOutputWithPast
[ -0.00011365362297510728, 0.024697195738554, 0.014547663740813732, 0.017366977408528328, -0.0007929322309792042, 0.028869781643152237, 0.04307912662625313, -0.0383426807820797, 0.016577569767832756, -0.008232398889958858, 0.02762928418815136, 0.026163239032030106, -0.0024387072771787643, 0....
[ "BaseModelOutputWithPast", "ModelModelOutputWithPast", "None", "class", "image_hidden_states", "r" ]
aya_vision/modeling_aya_vision.py:AyaVisionModel
[ -0.00019367155618965626, 0.04038183391094208, -0.003267002757638693, 0.018620513379573822, -0.0006344715366140008, 0.03454890474677086, 0.02983769029378891, -0.03275415673851967, 0, 0.02871597185730934, 0.02871597185730934, 0.013741041533648968, 0.00009639761265134439, 0.0050196866504848, ...
[ "AutoModel", "Image", "ModelModel", "ModelModelOutputWithPast", "ModelMultiModalProjector", "ModelPreTrainedModel", "None", "Obtains", "True", "ValueError", "You", "__init__", "all", "and", "apply", "attention_mask", "attentions", "auto_docstring", "can_return_tuple", "cat", ...
aya_vision/modeling_aya_vision.py:AyaVisionForConditionalGeneration
[ -0.0002158705028705299, 0.04223339632153511, 0.013647227548062801, 0.01875791698694229, -0.0007722196751274168, 0.027631424367427826, 0.032348982989788055, -0.03706654533743858, -0.0018112061079591513, 0.027631424367427826, 0.03998693823814392, 0.010782994329929352, 0.0019796905107796192, ...
[ "GenerationMixin", "Linear", "ModelCausalLMOutputWithPast", "ModelForConditionalGeneration", "ModelModel", "ModelPreTrainedModel", "None", "__init__", "_tied_weights_keys", "attention_mask", "attentions", "auto_docstring", "can_return_tuple", "class", "config", "def", "else", "embe...
pe_audio/modeling_pe_audio.py:Snake1d
[ -0.00017833536549005657, 0.05198211222887039, 0.04565385356545448, 0.002952244831249118, -0.0007098100613802671, 0.009209874086081982, 0.044297799468040466, -0.018758760765194893, 0.008362339809536934, 0.02689509280025959, 0.027347110211849213, -0.04181169718503952, 0.0027968634385615587, ...
[ "Model", "Module", "Parameter", "__init__", "alpha", "class", "def", "forward", "hidden_dim", "hidden_states", "nn", "ones", "pow", "reciprocal", "reshape", "return", "self", "shape", "sin", "super", "torch" ]
pe_audio/modeling_pe_audio.py:PeAudioDacResidualUnit
[ -0.00009070267697097734, 0.011836478486657143, 0.005918239243328571, -0.03420685604214668, 0.00009689700527815148, 0.05051741003990173, 0.009514489211142063, -0.035792604088783264, 0.009344588033854961, 0.0009627757244743407, 0.013025789521634579, -0.026051579043269157, 0.0014370844000950456...
[ "Conv1d", "ModelDacResidualUnit", "Module", "Snake1d", "__init__", "class", "conv1", "conv2", "def", "dilation", "dimension", "forward", "hidden_state", "if", "kernel_size", "nn", "output_tensor", "pad", "padding", "return", "self", "shape", "snake1", "snake2", "super...
pe_audio/modeling_pe_audio.py:PeAudioDacEncoderBlock
[ -0.0001827643282013014, -0.017003322020173073, 0.01169691700488329, -0.019399764016270638, -0.00029242291930131614, 0.05477580428123474, 0.022366786375641823, -0.05979691818356514, 0.012210439890623093, 0.0006490361993201077, 0.024192646145820618, -0.02202443778514862, 0.0029384936206042767,...
[ "Conv1d", "ModelDacEncoderBlock", "ModelDacResidualUnit", "Module", "Snake1d", "__init__", "ceil", "class", "config", "conv1", "def", "dilation", "dimension", "encoder_hidden_size", "forward", "hidden_state", "kernel_size", "math", "nn", "padding", "res_unit1", "res_unit2",...
pe_audio/modeling_pe_audio.py:PeAudioDacEncoder
[ -0.00009215065801981837, 0.008183331228792667, 0.008352641947567463, 0.007195687852799892, -0.00009832342766458169, 0.03611953184008598, 0.0216717179864645, -0.046729642897844315, 0.014334939420223236, 0.009989308193325996, 0.02054298296570778, -0.01693103089928627, 0.0012345543364062905, ...
[ "Conv1d", "ModelDacEncoder", "ModelDacEncoderBlock", "Module", "ModuleList", "Snake1d", "__init__", "block", "class", "config", "conv1", "conv2", "d_model", "def", "downsampling_ratios", "encoder_hidden_size", "enumerate", "for", "forward", "hidden_size", "hidden_state", "i...
pe_audio/modeling_pe_audio.py:PeAudioEncoderEmbedder
[ -0.00024985562777146697, -0.004540233872830868, 0.026955854147672653, -0.0008887721924111247, -0.0010208387393504381, 0.026955854147672653, 0.02672741375863552, -0.05231263116002083, 0.008395149372518063, -0.005682431161403656, 0.02410036139190197, 0.012564169242978096, -0.002355781616643071...
[ "Conv1d", "False", "Linear", "ModelDacEncoder", "ModelEncoderEmbedder", "Module", "None", "__init__", "backends", "bottleneck", "class", "codebook_dim", "codec_features", "config", "cudnn", "dac_config", "dac_encoder", "data_proj", "def", "enabled", "flags", "forward", "h...
pe_audio/modeling_pe_audio.py:PeAudioContrastiveHead
[ -0.00023490145395044237, 0.033458445221185684, 0.06827331334352493, 0.030293457210063934, -0.0006640822975896299, 0.037075575441122055, 0.023850444704294205, -0.005086587741971016, 0.0051431055180728436, 0.03820592537522316, 0.00955148134380579, -0.0041823056526482105, -0.000784182280767709,...
[ "LayerNorm", "Linear", "ModelContrastiveHead", "Module", "__init__", "class", "def", "eps", "forward", "in_dim", "layer_norm", "nn", "normalized_shape", "out_dim", "proj", "return", "self", "super", "x" ]
pe_audio/modeling_pe_audio.py:PeAudioMaskedGroupNorm
[ 0, 0.007025959901511669, -0.0027991870883852243, 0.005570382345467806, 0, 0.01825070008635521, -0.005822309292852879, -0.04209977388381958, 0.016347253695130348, -0.009405269287526608, 0.02575252205133438, 0.03470991924405098, -0.0005248475936241448, -0.03224663808941841, 0.0001863209035...
[ "False", "GroupNorm", "ModelMaskedGroupNorm", "None", "True", "affine", "batch_size", "bias", "bool", "class", "def", "dim", "eps", "forward", "group_size", "grouped_shape", "hidden_size", "if", "is", "keepdim", "mask", "masked", "mean", "nn", "num_groups", "padding...
pe_audio/modeling_pe_audio.py:PeAudioConvBlock1d
[ 0.000043185493268538266, -0.006179733667522669, 0, -0.006803376600146294, 0.00046418869169428945, 0.03583111613988876, 0.00946803204715252, -0.04807719215750694, 0.019956570118665695, -0.009184557944536209, 0.026079609990119934, -0.005357658956199884, 0.0028630876913666725, -0.016554882749...
[ "Conv1d", "ModelConvBlock1d", "ModelMaskedGroupNorm", "Module", "None", "SiLU", "__init__", "activation", "class", "config", "def", "forward", "groupnorm", "hidden_size", "in_channels", "kernel_size", "nn", "num_channels", "num_groups", "out_channels", "padding", "padding_m...
pe_audio/modeling_pe_audio.py:PeAudioResnetBlock1d
[ 0.00009218462946591899, -0.011069179512560368, 0.03281421586871147, -0.02078983001410961, 0.000691823719535023, 0.0346122570335865, -0.008765441365540028, -0.0575372576713562, 0.01208057627081871, -0.0009833027143031359, 0.00041965956916101277, -0.014609069563448429, 0.0029358610045164824, ...
[ "ModelConvBlock1d", "ModelResnetBlock1d", "Module", "None", "__init__", "block1", "block2", "class", "config", "def", "expand_as", "forward", "hidden_states", "if", "is", "nn", "not", "padding_mask", "residual", "return", "self", "super", "transpose", "unsqueeze" ]
pe_audio/modeling_pe_audio.py:PeAudioEncoderPatchEmbedder
[ -0.00019516093016136438, -0.00040621732478030026, 0.020007086917757988, 0.017972467467188835, -0.0008866135030984879, 0.024189358577132225, 0.0025150151923298836, -0.03549279645085335, 0.0029954113997519016, 0.0000816850078990683, -0.0035605833400040865, 0.015485711395740509, 0.0004697991826...
[ "ModelEncoderPatchEmbedder", "ModelResnetBlock1d", "Module", "None", "Parameter", "__init__", "cat", "class", "class_embedding", "config", "def", "dim", "expand", "forward", "hidden_size", "hidden_states", "if", "inputs_embeds", "is", "nn", "not", "padding_mask", "randn",...
pe_audio/modeling_pe_audio.py:repeat_kv
[ -0.0002073117793770507, -0.001958739012479782, -0.003831693669781089, -0.009035934694111347, -0.00046823869342915714, 0.03202609717845917, 0.010179723612964153, -0.0571894571185112, 0.011380702257156372, 0.052156783640384674, 0.006176461465656757, -0.02207513153553009, 0.0006791247869841754,...
[ "Model_kv", "None", "batch", "def", "expand", "head_dim", "hidden_states", "if", "n_rep", "num_key_value_heads", "reshape", "return", "shape", "slen" ]
pe_audio/modeling_pe_audio.py:eager_attention_forward
[ -0.000020655716070905328, 0.020133469253778458, 0.01408211700618267, -0.01877615600824356, 0, 0.03845718875527382, 0.05293518677353859, -0.03461147099733353, 0.020359687507152557, 0.007861101999878883, 0.02805112488567829, 0.020246578380465508, 0.002516683656722307, -0.01832371950149536, ...
[ "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",...
pe_audio/modeling_pe_audio.py:stack_freqs
[ -0.000022007403458701447, 0.02538098394870758, 0.04006325826048851, 0.022990847006440163, 0, 0.0022194134071469307, 0.05121723189949989, -0.02879546582698822, 0.026177696883678436, 0.03778693452477455, 0.0270882248878479, 0.0019633271731436253, 0.0036990223452448845, 0.003144168993458152, ...
[ "Model", "Model_freqs", "cos", "def", "dim", "freqs_cis", "narrow", "return", "sin", "size", "torch", "view" ]
pe_audio/modeling_pe_audio.py:apply_rotary_pos_emb
[ -0.00017032968753483146, 0.021573904901742935, 0.018263623118400574, 0.00450883200392127, -0.0008240033057518303, 0.01689385063946247, 0.05045325681567192, -0.0167797040194273, 0.01369771733880043, 0.03698383644223213, 0.011072320863604546, 0.004851274657994509, -0.0001658707915339619, -0....
[ "Model_rotary_pos_emb", "cos", "def", "flatten", "freqs_cis", "k", "k_", "q", "q_", "reshape", "return", "shape", "sin", "stack_freqs", "sum", "unsqueeze", "unsqueeze_dim" ]
pe_audio/modeling_pe_audio.py:PeAudioEncoderRMSNorm
[ -0.00013282190775498748, 0.04012284427881241, 0.031508900225162506, 0.05213702842593193, -0.0005667068180628121, 0.03898942843079567, 0.023234980180859566, -0.03241562843322754, 0.00867061410099268, 0.05145698040723801, 0.012467550113797188, 0.01598113216459751, 0.00256434828042984, 0.0170...
[ "ModelEncoderRMSNorm", "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", "sup...
pe_audio/modeling_pe_audio.py:PeAudioEncoderAttention
[ -0.00012435177632141858, 0.037026844918727875, 0.026867041364312172, 0.012191765941679478, -0.000578544451855123, 0.034317564219236374, 0.03273715078830719, -0.011119342409074306, 0.00341482344083488, 0.025625286623835564, 0.006970755290240049, 0.03589797765016556, -0.0011641443707048893, ...
[ "ALL_ATTENTION_FUNCTIONS", "False", "Linear", "ModelEncoderAttention", "ModelEncoderRMSNorm", "Module", "None", "Tensor", "__init__", "_attn_implementation", "apply_rotary_pos_emb", "attention_dropout", "attention_interface", "attention_mask", "attn_output", "attn_weights", "class", ...
pe_audio/modeling_pe_audio.py:PeAudioEncoderMLP
[ -0.00018339896632824093, 0.02336055599153042, 0.027368493378162384, 0.020497743040323257, -0.0007836951408535242, 0.062752865254879, 0.02416214346885681, -0.017291391268372536, 0, 0.007729595527052879, 0.020497743040323257, -0.029544232413172722, -0.0018465145258232951, 0.02038322947919368...
[ "ACT2FN", "Linear", "ModelEncoderMLP", "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" ]
pe_audio/modeling_pe_audio.py:PeAudioEncoderLayer
[ -0.00013346719788387418, 0.021609311923384666, 0.02206186205148697, 0.020364796742796898, -0.0004737643466796726, 0.05159081891179085, 0.030547194182872772, -0.02025165781378746, 0.006873118691146374, 0.0062791453674435616, 0.0026163107249885798, 0.02308010309934616, -0.0000936922078835778, ...
[ "False", "GradientCheckpointingLayer", "ModelEncoderAttention", "ModelEncoderLayer", "ModelEncoderMLP", "ModelEncoderRMSNorm", "None", "Tensor", "_", "__init__", "attention_mask", "class", "config", "def", "eps", "forward", "hidden_size", "hidden_states", "input_layernorm", "kw...
pe_audio/modeling_pe_audio.py:PeAudioPreTrainedModel
[ -0.00015466495824512094, 0.050167687237262726, -0.003543233498930931, 0.011417086236178875, -0.0005096913082525134, 0.017659924924373627, 0.012598163448274136, -0.028120901435613632, -0.0026293043047189713, -0.007930094376206398, 0.015860188752412796, 0.011585811153054237, -0.005005520302802...
[ "Conv1d", "ConvTranspose1d", "Embedding", "ModelConfig", "ModelEncoderAttention", "ModelEncoderLayer", "ModelEncoderPatchEmbedder", "ModelPreTrainedModel", "PreTrainedModel", "Snake1d", "TimmWrapperForImageClassification", "True", "_can_compile_fullgraph", "_can_record_outputs", "_init_w...
pe_audio/modeling_pe_audio.py:PeAudioEncoderOutput
[ -0.0001516785123385489, 0.02089083194732666, 0.028270745649933815, 0.016689958050847054, -0.0008337883045896888, 0.03519650921225548, 0.048820964992046356, -0.055633194744586945, 0.014419215731322765, 0.006386463530361652, 0.02406987175345421, 0.03451528772711754, -0.0027532754465937614, -...
[ "BaseModelOutputWithPooling", "ModelEncoderOutput", "None", "class", "codec_features", "output_mask", "r" ]
pe_audio/modeling_pe_audio.py:PeAudioEncoderRotaryEmbedding
[ -0.0003340460534673184, 0.04368741437792778, 0.0028902245685458183, -0.009295194409787655, -0.0016339209396392107, 0.035786498337984085, 0.03392745926976204, -0.002425464801490307, -0.005722354166209698, 0.02730463445186615, -0.006535683758556843, 0.007842820137739182, -0.0025561784859746695...
[ "False", "ModelEncoderRotaryEmbedding", "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", "de...
pe_audio/modeling_pe_audio.py:PeAudioEncoder
[ -0.00009142621274804696, 0.027230944484472275, 0.00742662139236927, 0.014515669085085392, -0.0004887785762548447, 0.03038163296878338, 0.020817045122385025, -0.04050884395837784, 0.005766883958131075, -0.01294032484292984, 0.0105773089453578, 0.014065571129322052, -0.00382583518512547, -0....
[ "False", "Linear", "ModelEncoder", "ModelEncoderConfig", "ModelEncoderEmbedder", "ModelEncoderLayer", "ModelEncoderOutput", "ModelEncoderPatchEmbedder", "ModelEncoderRMSNorm", "ModelEncoderRotaryEmbedding", "ModelPreTrainedModel", "ModuleList", "None", "__init__", "arange", "attention_...
pe_audio/modeling_pe_audio.py:PeAudioOutput
[ -0.00012979072926100343, 0.024637281894683838, 0.009380249306559563, -0.004605363588780165, -0.0007063440862111747, 0.026445522904396057, 0.06193224713206291, -0.028592808172106743, 0.012092610821127892, -0.021585874259471893, 0.03164421394467354, 0.02169889025390148, -0.005905036348849535, ...
[ "ModelOutput", "None", "audio_embeds", "audio_outputs", "class", "def", "else", "for", "getattr", "if", "in", "k", "keys", "logits_audio_text", "loss", "not", "return", "self", "text_audio_embeds", "text_outputs", "to_tuple", "tuple" ]
pe_audio/modeling_pe_audio.py:PeAudioModel
[ -0.00007685946911806241, 0.05410906672477722, 0.04181155189871788, 0.00009127062367042527, -0.0002829825971275568, 0.027278125286102295, 0.023141687735915184, -0.01576317846775055, 0.008664158172905445, -0.006540042348206043, 0.03622177243232727, 0.029961219057440758, -0.003912845626473427, ...
[ "AutoModel", "F", "ModelContrastiveHead", "ModelEncoder", "ModelModel", "ModelOutput", "ModelPreTrainedModel", "None", "Parameter", "T", "True", "__init__", "attention_mask", "audio_config", "audio_embeds", "audio_encoder", "audio_head", "audio_outputs", "can_return_tuple", "cl...
pe_audio/modeling_pe_audio.py:PeAudioFrameLevelModel
[ -0.00011172021186212078, 0.02611338533461094, 0.015939338132739067, 0.012321900576353073, -0.0004769084043800831, 0.03798310458660126, 0.03798310458660126, -0.03730483725667, 0.013000169768929482, -0.01458279974758625, 0.0334613062441349, 0.03549611568450928, -0.0023739440366625786, -0.012...
[ "F", "ModelFrameLevelModel", "ModelModel", "ModelOutput", "None", "T", "True", "attention_mask", "audio_embeds", "audio_encoder", "audio_head", "audio_outputs", "can_return_tuple", "class", "def", "device", "eye", "forward", "get_audio_embeds", "hidden_states", "if", "input...
gemma4_unified_assistant/modeling_gemma4_unified_assistant.py:Gemma4UnifiedAssistantOutput
[ -0.00021768773149233311, 0.026602694764733315, 0.00561867281794548, 0.020869355648756027, -0.0010821678442880511, 0.06054406613111496, 0.06558940559625626, -0.004500671289861202, 0.0140466820448637, -0.01651201769709587, 0.02958403155207634, 0.0002275419101351872, -0.0022216690704226494, 0...
[ "BaseModelOutput", "ModelOutput", "None", "class", "logits", "r" ]
gemma4_unified_assistant/modeling_gemma4_unified_assistant.py:Gemma4UnifiedAssistantMaskedEmbedder
[ -0.00048324151430279016, 0.038540370762348175, 0.006720774341374636, -0.013560499995946884, -0.002066786866635084, 0.038064561784267426, 0.01974599063396454, -0.030689552426338196, -0.0052338773384690285, -0.004074097611010075, 0.023790352046489716, 0.029737938195466995, -0.00306300772354006...
[ "Linear", "ModelMaskedEmbedder", "Module", "Tensor", "_", "__init__", "batch", "canonical_positions_per_cluster", "centroid_intermediate_top_k", "centroid_logits", "centroids", "class", "config", "def", "device", "dim", "dtype", "empty", "fill_value", "forward", "full", "ge...
gemma4_unified_assistant/modeling_gemma4_unified_assistant.py:Gemma4UnifiedAssistantPreTrainedModel
[ -0.0003591929853428155, 0.0365041121840477, 0.002700264798477292, -0.002397026401013136, -0.0015378512907773256, 0.033500611782073975, 0.020331405103206635, -0.029572952538728714, -0.006295804399996996, 0.013920081779360771, -0.0008411252638325095, 0.0027580244932323694, -0.00502509158104658...
[ "ModelConfig", "ModelMaskedEmbedder", "ModelPreTrainedModel", "PreTrainedModel", "True", "_can_compile_fullgraph", "_init_weights", "_skip_keys_device_placement", "_supports_attention_backend", "_supports_flash_attn", "_supports_sdpa", "base_model_prefix", "class", "config", "def", "if...
gemma4_unified_assistant/modeling_gemma4_unified_assistant.py:Gemma4UnifiedAssistantForCausalLM
[ -0.00037046981742605567, 0.02787378616631031, 0.011970692314207554, 0.003354107029736042, -0.0016915109008550644, 0.03793611004948616, 0.0012361041735857725, -0.029261693358421326, -0.0004807071527466178, 0.00543596688657999, 0.03192184865474701, 0.0061588347889482975, -0.0003361335839144885...
[ "AutoModel", "False", "GenerationMixin", "Linear", "ModelForCausalLM", "ModelMaskedEmbedder", "ModelOutput", "ModelPreTrainedModel", "None", "Tensor", "__init__", "_pp_plan", "_tied_weights_keys", "_tp_plan", "attention_mask", "attentions", "auto_docstring", "backbone_hidden_size",...
flaubert/modeling_flaubert.py:create_sinusoidal_embeddings
[ -0.00018756451027002186, 0.03406886011362076, 0.029038559645414352, 0.03452616184949875, -0.0008931643678806722, 0.02492285892367363, 0.05624791979789734, -0.011546828784048557, 0.017377406358718872, 0.016577130183577538, 0.017720380797982216, 0.005373276770114899, 0.000539471278898418, -0...
[ "False", "FloatTensor", "Model_sinusoidal_embeddings", "array", "cos", "def", "detach_", "dim", "for", "in", "j", "n_pos", "np", "out", "pos", "position_enc", "power", "range", "requires_grad", "return", "sin", "torch" ]
flaubert/modeling_flaubert.py:get_masks
[ -0.000022404396077035926, -0.0036651405971497297, 0.03290233016014099, -0.011638919822871685, 0.00028677628142759204, 0.042079173028469086, -0.0019304938614368439, -0.05729929730296135, 0.018689420074224472, 0.022830188274383545, 0.024285053834319115, 0.018689420074224472, 0.0022802210878580...
[ "False", "Model_masks", "None", "alen", "arange", "assert", "attn_mask", "bs", "causal", "def", "device", "dtype", "else", "if", "is", "item", "lengths", "long", "mask", "not", "or", "padding_mask", "repeat", "return", "size", "slen", "torch" ]
flaubert/modeling_flaubert.py:MultiHeadAttention
[ -0.0001695316459517926, 0.01729222945868969, 0.03842717409133911, -0.005170715507119894, -0.0009182964568026364, 0.015483890660107136, 0.04113968089222908, -0.024525579065084457, 0.0022745495662093163, 0.03074174001812935, -0.014410190284252167, 0.018535461276769638, -0.0015822954010218382, ...
[ "EncoderDecoderCache", "False", "Linear", "ModelHeadAttention", "Module", "None", "True", "__init__", "and", "assert", "attention_dropout", "bs", "cache", "class", "config", "context", "contiguous", "cross_attention_cache", "curr_past_key_values", "current_states", "def", "...
flaubert/modeling_flaubert.py:TransformerFFN
[ -0.0002472423657309264, 0.03760950639843941, 0.02304728887975216, 0.019951384514570236, -0.0006700626690872014, 0.017887448891997337, 0.042425356805324554, -0.03669220209121704, 0.004729853942990303, 0.003755217418074608, 0.014676881022751331, -0.018002111464738846, 0.003927212208509445, -...
[ "Linear", "ModelFFN", "Module", "__init__", "act", "apply_chunking_to_forward", "chunk_size_feed_forward", "class", "config", "def", "dim_hidden", "dropout", "else", "ff_chunk", "forward", "functional", "gelu", "gelu_activation", "if", "in_dim", "input", "lin1", "lin2", ...
flaubert/modeling_flaubert.py:FlaubertPredLayer
[ -0.0004063284723088145, 0.03552471846342087, 0.017646264284849167, 0.029023462906479836, -0.001574522815644741, 0.03482815623283386, 0.03761440888047218, -0.006443208549171686, -0.002481505973264575, -0.01462782546877861, 0.02414752170443535, 0.0007328424253500998, -0.0007654938381165266, ...
[ "AdaptiveLogSoftmaxWithLoss", "False", "Linear", "ModelPredLayer", "Module", "None", "True", "__init__", "asm", "asm_cutoffs", "asm_div_value", "class", "config", "cross_entropy", "cutoffs", "def", "dim", "div_value", "else", "emb_dim", "forward", "functional", "head_bias...
flaubert/modeling_flaubert.py:FlaubertSquadHeadOutput
[ -0.00041627450264059007, 0.013556549325585365, 0.016621509566903114, 0.016621509566903114, -0.0018198194447904825, 0.059648819267749786, 0.07733127474784851, -0.0028586636763066053, 0.008782286196947098, 0.005746798124164343, 0.023458724841475487, 0.03229995444417, -0.003079694462940097, -...
[ "ModelOutput", "ModelSquadHeadOutput", "None", "class", "cls_logits", "end_top_index", "end_top_log_probs", "loss", "r", "start_top_index", "start_top_log_probs" ]
flaubert/modeling_flaubert.py:FlaubertPoolerStartLogits
[ -0.00026520335813984275, 0.014794046990573406, 0.022133728489279747, 0.026721030473709106, -0.0012901785084977746, 0.045184917747974396, 0.039909522980451584, -0.021445633843541145, 0.0017704116180539131, 0.007855753414332867, 0.019725395366549492, 0.0002508680336177349, 0.000142457211040891...
[ "Linear", "ModelPoolerStartLogits", "Module", "None", "__init__", "class", "config", "def", "dense", "else", "float16", "forward", "hidden_size", "hidden_states", "if", "is", "nn", "not", "p_mask", "return", "self", "squeeze", "super", "torch", "x" ]
flaubert/modeling_flaubert.py:FlaubertPoolerEndLogits
[ -0.0003284755803178996, 0.02171548455953598, 0.03950370103120804, 0.035576432943344116, -0.0014510678593069315, 0.05498175695538521, 0.04158284142613411, -0.0286459568887949, 0.004620315972715616, 0.016864152625203133, 0.030956115573644638, 0.021368959918618202, 0.0020647034980356693, -0.0...
[ "LayerNorm", "Linear", "ModelPoolerEndLogits", "Module", "None", "One", "Tanh", "__init__", "activation", "assert", "be", "cat", "class", "config", "def", "dense_0", "dense_1", "dim", "else", "eps", "expand", "float16", "forward", "gather", "hidden_size", "hidden_st...
flaubert/modeling_flaubert.py:FlaubertPoolerAnswerClass
[ -0.00032379472395405173, 0.017499307170510292, 0.02532794326543808, 0.033617086708545685, -0.0013815241400152445, 0.04720207676291466, 0.031084293499588966, 0, -0.0013959150528535247, 0.01899595744907856, 0.044899534434080124, 0.01795981451869011, 0.0026767030358314514, -0.0178446881473064...
[ "Linear", "ModelPoolerAnswerClass", "Module", "None", "One", "Tanh", "__init__", "activation", "assert", "be", "cat", "class", "cls_index", "cls_token_state", "config", "def", "dense_0", "dense_1", "dim", "else", "expand", "forward", "gather", "hidden_size", "hidden_s...
flaubert/modeling_flaubert.py:FlaubertSQuADHead
[ -0.00048477036762051284, 0.025814948603510857, 0.027235954999923706, 0.025814948603510857, -0.001968685770407319, 0.05162989720702171, 0.04499853402376175, 0.0031084513757377863, -0.002383145969361067, 0.019538836553692818, 0.02759120613336563, 0.02640703320503235, 0.001569027779623866, -0...
[ "BCEWithLogitsLoss", "CrossEntropyLoss", "False", "ModelPoolerAnswerClass", "ModelPoolerEndLogits", "ModelPoolerStartLogits", "ModelSQuADHead", "ModelSquadHeadOutput", "Module", "None", "__init__", "and", "answer_class", "auto_docstring", "bh", "bl", "blh", "class", "cls_index", ...
flaubert/modeling_flaubert.py:FlaubertSequenceSummary
[ -0.0003412900259718299, 0.022655848413705826, 0.02079690806567669, 0.046241167932748795, -0.0015684819081798196, 0.025328077375888824, 0.02056453935801983, -0.012722130864858627, -0.0034419463481754065, 0.0074938577599823475, 0.029045959934592247, 0.0210292749106884, 0.0016120508080348372, ...
[ "Dropout", "Identity", "Linear", "ModelSequenceSummary", "Module", "None", "NotImplementedError", "__init__", "activation", "activation_string", "and", "attn", "class", "cls_index", "config", "def", "dim", "dtype", "elif", "else", "expand", "first", "first_dropout", "fo...
flaubert/modeling_flaubert.py:FlaubertPreTrainedModel
[ -0.00026991215418092906, 0.05353913456201553, 0.005834392737597227, 0.023108771070837975, -0.001294148387387395, 0.026426367461681366, 0.030659161508083344, -0.010238787159323692, -0.0062919920310378075, -0.00571999279782176, 0.00863718893378973, -0.001108248601667583, -0.0025024968199431896...
[ "Embedding", "False", "ModelConfig", "ModelModel", "ModelPreTrainedModel", "None", "PreTrainedModel", "_init_weights", "_is_hf_initialized", "and", "arange", "attention_mask", "attns_list", "base_model_prefix", "class", "config", "copy_", "create_sinusoidal_embeddings", "def", ...
flaubert/modeling_flaubert.py:FlaubertModel
[ -0.00006675723125226796, 0.05554201453924179, 0.017089851200580597, 0.007533026393502951, -0.00019060945487581193, 0.053967949002981186, 0.020237980410456657, -0.021812045946717262, 0.006689777132123709, -0.003316780086606741, 0.017427150160074234, 0.007533026393502951, 0.0003302725963294506...
[ "BaseModelOutput", "DynamicCache", "Embedding", "EncoderDecoderCache", "False", "FloatTensor", "LayerNorm", "ModelModel", "ModelPreTrainedModel", "ModuleList", "MultiHeadAttention", "None", "TransformerFFN", "__init__", "_slen", "a", "and", "append", "arange", "assert", "atte...
flaubert/modeling_flaubert.py:FlaubertWithLMHeadModel
[ -0.00019964968669228256, 0.022515559569001198, 0.00624806759878993, -0.017562136054039, -0.0007071293075568974, 0.04458080977201462, 0.02386649325489998, -0.013790780678391457, -0.0033351173624396324, -0.007767868228256702, 0.014241091907024384, 0.01632378064095974, -0.0020545448642224073, ...
[ "GenerationMixin", "MaskedLMOutput", "ModelModel", "ModelPreTrainedModel", "ModelPredLayer", "ModelWithLMHeadModel", "None", "Tensor", "__init__", "_tied_weights_keys", "attention_mask", "attentions", "auto_docstring", "cache", "cat", "class", "config", "def", "device", "dim", ...
flaubert/modeling_flaubert.py:FlaubertForSequenceClassification
[ -0.00040191964944824576, 0.02826054021716118, 0.015110737644135952, 0.021339591592550278, -0.001326515106484294, 0.031836364418268204, 0.014187944121658802, -0.0021772149484604597, -0.006459551863372326, 0.0036190792452543974, 0.04683174937963486, 0.006373039912432432, -0.0000784013682277873...
[ "BCEWithLogitsLoss", "CrossEntropyLoss", "MSELoss", "ModelForSequenceClassification", "ModelModel", "ModelPreTrainedModel", "ModelSequenceSummary", "None", "SequenceClassifierOutput", "Tensor", "__init__", "and", "attention_mask", "attentions", "auto_docstring", "cache", "class", "...
flaubert/modeling_flaubert.py:FlaubertForTokenClassification
[ -0.00028920083423145115, 0.037703219801187515, 0.010682578198611736, -0.027306269854307175, -0.001092536491341889, 0.04204479977488518, 0.04204479977488518, 0.0006962242187000811, -0.0017137826653197408, 0.006055365316569805, 0.05347001925110817, 0.0225076787173748, -0.0013853076379746199, ...
[ "CrossEntropyLoss", "Dropout", "Linear", "ModelForTokenClassification", "ModelModel", "ModelPreTrainedModel", "None", "Tensor", "TokenClassifierOutput", "__init__", "attention_mask", "attentions", "auto_docstring", "cache", "class", "classifier", "config", "def", "dropout", "el...
flaubert/modeling_flaubert.py:FlaubertForQuestionAnsweringSimple
[ -0.00020756368758156896, 0.030751222744584084, 0.026002872735261917, 0.02487231232225895, -0.000748995749745518, 0.04997073858976364, 0.03843902796506882, 0.017862843349575996, 0.0008797167101874948, 0.028829270973801613, 0.015940891578793526, 0.01921951398253441, 0.0011729556135833263, 0....
[ "CrossEntropyLoss", "Linear", "ModelForQuestionAnsweringSimple", "ModelModel", "ModelPreTrainedModel", "None", "QuestionAnsweringModelOutput", "Tensor", "__init__", "and", "attention_mask", "attentions", "auto_docstring", "cache", "clamp", "class", "config", "contiguous", "def", ...
flaubert/modeling_flaubert.py:FlaubertForQuestionAnsweringOutput
[ -0.00022325769532471895, 0.02400466613471508, 0.030405910685658455, 0.02491912990808487, -0.0011716564185917377, 0.06218351796269417, 0.0722426176071167, 0.012631027027964592, 0.012230949476361275, 0.005858281627297401, 0.01634603552520275, 0.029720064252614975, -0.0011573678348213434, -0....
[ "ModelForQuestionAnsweringOutput", "ModelOutput", "None", "attentions", "class", "cls_logits", "end_top_index", "end_top_log_probs", "hidden_states", "loss", "r", "start_top_index", "start_top_log_probs" ]