Datasets:
itazap
/
transformers_code_embeddings_v3

Dataset card Data Studio Files
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1
identifier
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24
117
embedding
listlengths
2.56k
2.56k
tokens
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4
444
segformer/modeling_segformer.py:eager_attention_forward
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segformer/modeling_segformer.py:SegformerAttention
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segformer/modeling_segformer.py:SegformerDepthWiseConv
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segformer/modeling_segformer.py:SegformerMixMLP
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segformer/modeling_segformer.py:SegformerDropPath
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segformer/modeling_segformer.py:SegformerLayer
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segformer/modeling_segformer.py:SegformerStage
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segformer/modeling_segformer.py:SegformerPreTrainedModel
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segformer/modeling_segformer.py:SegformerModel
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segformer/modeling_segformer.py:SegformerForImageClassification
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segformer/modeling_segformer.py:SegformerMLP
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segformer/modeling_segformer.py:SegformerDecodeHead
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segformer/modeling_segformer.py:SegformerForSemanticSegmentation
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mra/modeling_mra.py:load_cuda_kernels
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mra/modeling_mra.py:sparse_max
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mra/modeling_mra.py:sparse_mask
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mra/modeling_mra.py:mm_to_sparse
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mra/modeling_mra.py:sparse_dense_mm
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mra/modeling_mra.py:transpose_indices
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mra/modeling_mra.py:MraSampledDenseMatMul
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mra/modeling_mra.py:MraSparseDenseMatMul
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mra/modeling_mra.py:MraReduceSum
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mra/modeling_mra.py:get_low_resolution_logit
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mra/modeling_mra.py:get_block_idxes
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mra/modeling_mra.py:mra2_attention
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mra/modeling_mra.py:MraEmbeddings
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mra/modeling_mra.py:MraSelfAttention
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mra/modeling_mra.py:MraSelfOutput
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mra/modeling_mra.py:MraAttention
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mra/modeling_mra.py:MraIntermediate
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mra/modeling_mra.py:MraOutput
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mra/modeling_mra.py:MraLayer
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mra/modeling_mra.py:MraEncoder
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mra/modeling_mra.py:MraPredictionHeadTransform
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mra/modeling_mra.py:MraLMPredictionHead
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mra/modeling_mra.py:MraOnlyMLMHead
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mra/modeling_mra.py:MraPreTrainedModel
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mra/modeling_mra.py:MraModel
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mra/modeling_mra.py:MraForMaskedLM
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mra/modeling_mra.py:MraClassificationHead
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mra/modeling_mra.py:MraForSequenceClassification
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mra/modeling_mra.py:MraForMultipleChoice
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mra/modeling_mra.py:MraForTokenClassification
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mra/modeling_mra.py:MraForQuestionAnswering
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minimax_m3_vl/modeling_minimax_m3_vl.py:MiniMaxM3VLSparseCacheLayer
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minimax_m3_vl/modeling_minimax_m3_vl.py:MiniMaxM3VLSparseStaticCacheLayer
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minimax_m3_vl/modeling_minimax_m3_vl.py:MiniMaxM3VLRMSNorm
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minimax_m3_vl/modeling_minimax_m3_vl.py:MiniMaxM3VLDenseMLP
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minimax_m3_vl/modeling_minimax_m3_vl.py:MiniMaxM3VLExperts
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minimax_m3_vl/modeling_minimax_m3_vl.py:MiniMaxM3VLTopKRouter
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minimax_m3_vl/modeling_minimax_m3_vl.py:MiniMaxM3VLSparseMoeBlock
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minimax_m3_vl/modeling_minimax_m3_vl.py:MiniMaxM3VLRotaryEmbedding
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minimax_m3_vl/modeling_minimax_m3_vl.py:repeat_kv
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[ "Model_kv", "None", "batch", "def", "expand", "head_dim", "hidden_states", "if", "n_rep", "num_key_value_heads", "reshape", "return", "shape", "slen" ]
minimax_m3_vl/modeling_minimax_m3_vl.py:eager_attention_forward
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minimax_m3_vl/modeling_minimax_m3_vl.py:apply_rotary_pos_emb
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minimax_m3_vl/modeling_minimax_m3_vl.py:rotate_half
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[ "Model_half", "cat", "def", "dim", "return", "shape", "torch", "x", "x1", "x2" ]
minimax_m3_vl/modeling_minimax_m3_vl.py:MiniMaxM3VLAttention
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minimax_m3_vl/modeling_minimax_m3_vl.py:MiniMaxM3VLIndexer
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minimax_m3_vl/modeling_minimax_m3_vl.py:MiniMaxM3VLDecoderLayer
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minimax_m3_vl/modeling_minimax_m3_vl.py:MiniMaxM3VLPreTrainedModel
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minimax_m3_vl/modeling_minimax_m3_vl.py:MiniMaxM3VLTextModel
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minimax_m3_vl/modeling_minimax_m3_vl.py:load_balancing_loss_func
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minimax_m3_vl/modeling_minimax_m3_vl.py:MiniMaxM3VLForCausalLM
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minimax_m3_vl/modeling_minimax_m3_vl.py:MiniMaxM3VLVisionEmbeddings
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minimax_m3_vl/modeling_minimax_m3_vl.py:MiniMaxM3VL3DRotaryEmbedding
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minimax_m3_vl/modeling_minimax_m3_vl.py:apply_rotary_pos_emb_vision
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minimax_m3_vl/modeling_minimax_m3_vl.py:MiniMaxM3VLVisionAttention
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minimax_m3_vl/modeling_minimax_m3_vl.py:MiniMaxM3VLVisionMLP
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minimax_m3_vl/modeling_minimax_m3_vl.py:MiniMaxM3VLVisionEncoderLayer
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minimax_m3_vl/modeling_minimax_m3_vl.py:MiniMaxM3VLVisionModel
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minimax_m3_vl/modeling_minimax_m3_vl.py:MiniMaxM3VLMultiModalProjector
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minimax_m3_vl/modeling_minimax_m3_vl.py:MiniMaxM3VLModelOutputWithPast
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[ "BaseModelOutputWithPast", "ModelModelOutputWithPast", "None", "class", "image_hidden_states", "r", "video_hidden_states" ]
minimax_m3_vl/modeling_minimax_m3_vl.py:MiniMaxM3VLCausalLMOutputWithPast
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[ "ModelCausalLMOutputWithPast", "ModelOutput", "None", "attentions", "class", "hidden_states", "image_hidden_states", "logits", "loss", "past_key_values", "r", "video_hidden_states" ]
minimax_m3_vl/modeling_minimax_m3_vl.py:MiniMaxM3VLModel
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minimax_m3_vl/modeling_minimax_m3_vl.py:MiniMaxM3SparseForConditionalGeneration
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laguna/modeling_laguna.py:LagunaRMSNorm
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laguna/modeling_laguna.py:LagunaRotaryEmbedding
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laguna/modeling_laguna.py:LagunaMLP
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laguna/modeling_laguna.py:LagunaTopKRouter
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laguna/modeling_laguna.py:LagunaExperts
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laguna/modeling_laguna.py:LagunaSparseMoeBlock
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laguna/modeling_laguna.py:rotate_half
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[ "Model_half", "cat", "def", "dim", "return", "shape", "torch", "x", "x1", "x2" ]
laguna/modeling_laguna.py:apply_rotary_pos_emb
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laguna/modeling_laguna.py:repeat_kv
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laguna/modeling_laguna.py:eager_attention_forward
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laguna/modeling_laguna.py:LagunaAttention
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laguna/modeling_laguna.py:LagunaDecoderLayer
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laguna/modeling_laguna.py:LagunaPreTrainedModel
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laguna/modeling_laguna.py:LagunaModel
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laguna/modeling_laguna.py:load_balancing_loss_func
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laguna/modeling_laguna.py:LagunaForCausalLM
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dinat/modeling_dinat.py:DinatEncoderOutput
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[ "ModelEncoderOutput", "ModelOutput", "None", "attentions", "class", "hidden_states", "last_hidden_state", "r", "reshaped_hidden_states" ]
dinat/modeling_dinat.py:DinatModelOutput
[ -0.0002029311581281945, 0.02472199872136116, 0.026886597275733948, 0.017544643953442574, -0.0010039751650765538, 0.04488694667816162, 0.03303861618041992, -0.02244347333908081, 0.015038266777992249, 0.005297570955008268, -0.000389841414289549, 0.016291456297039986, -0.0014525598380714655, ...
[ "ModelModelOutput", "ModelOutput", "None", "attentions", "class", "hidden_states", "last_hidden_state", "pooler_output", "r", "reshaped_hidden_states" ]
dinat/modeling_dinat.py:DinatImageClassifierOutput
[ -0.00021742016542702913, 0.033938758075237274, 0.02635910175740719, 0.017421895638108253, -0.000883821863681078, 0.03348624333739281, 0.04457290470600128, -0.018326928839087486, 0.013462373986840248, -0.006250387988984585, 0.023304615169763565, 0.017308766022324562, -0.0032807467505335808, ...
[ "ModelImageClassifierOutput", "ModelOutput", "None", "attentions", "class", "hidden_states", "logits", "loss", "r", "reshaped_hidden_states" ]
dinat/modeling_dinat.py:DinatEmbeddings
[ 0.00005406180935096927, 0.01991584338247776, 0.022953853011131287, 0.0384814590215683, 0.00018460130377206951, 0.014402418397367, 0.02149110846221447, -0.006357317324727774, 0.011758224107325077, 0.014177380129694939, 0.025654306635260582, 0.012208299711346626, 0.0007700511487200856, -0.03...
[ "Dropout", "LayerNorm", "ModelEmbeddings", "ModelPatchEmbeddings", "Module", "__init__", "class", "config", "def", "dropout", "embed_dim", "embeddings", "forward", "hidden_dropout_prob", "nn", "norm", "patch_embeddings", "pixel_values", "return", "self", "super" ]
dinat/modeling_dinat.py:DinatPatchEmbeddings
[ -0.00010337297135265544, 0.012049329467117786, 0.01970684714615345, 0.004110285546630621, 0.00009809435141505674, -0.0019284557783976197, 0.009740812703967094, -0.01734202541410923, 0.006221733056008816, -0.0030123323667794466, 0.015540257096290588, 0.003195324447005987, -0.00202699005603790...
[ "Conv2d", "Make", "ModelPatchEmbeddings", "Module", "Sequential", "ValueError", "_", "__init__", "channel", "class", "config", "configuration", "def", "dimension", "else", "embed_dim", "embeddings", "forward", "height", "hidden_size", "if", "in", "kernel_size", "match",...
dinat/modeling_dinat.py:DinatDownsampler
[ -0.00007646272570127621, 0.05861152336001396, 0.03691183775663376, 0.026733117178082466, 0.0001870059932116419, 0.02639755606651306, 0.011800602078437805, -0.012192090973258018, 0.0100668640807271, 0.02930576168000698, -0.004558052401989698, -0.0028103329241275787, 0.003579329699277878, 0....
[ "Conv2d", "LayerNorm", "ModelDownsampler", "Module", "__init__", "class", "def", "dim", "forward", "input_feature", "kernel_size", "nn", "norm", "norm_layer", "padding", "permute", "reduction", "return", "self", "stride", "super" ]
dinat/modeling_dinat.py:NeighborhoodAttention
[ -0.00007154783816076815, 0.02056363970041275, 0.03011505678296089, -0.0194399431347847, -0.0001018349576042965, 0.031912971287965775, 0.023934727534651756, -0.02685633674263954, 0.008371535688638687, 0.018990464508533478, 0.026743967086076736, 0.014608049765229225, 0.001629359321668744, -0...
[ "Dropout", "False", "Linear", "ModelAttention", "Module", "Parameter", "The", "ValueError", "__init__", "a", "all_head_size", "attention", "attention_head_size", "attention_probs", "attention_probs_dropout_prob", "attention_scores", "class", "config", "context_layer", "contiguo...
dinat/modeling_dinat.py:NeighborhoodAttentionOutput
[ -0.0000909648442757316, 0.0423196442425251, 0.07030882686376572, -0.0053459336049854755, -0.0004828133969567716, 0.030004402622580528, 0.03560223802924156, -0.03940876945853233, 0.00045482421410270035, 0.00722120888531208, 0.011195672675967216, 0.030004402622580528, 0.0021551670506596565, ...
[ "Dropout", "Linear", "ModelAttentionOutput", "Module", "__init__", "attention_probs_dropout_prob", "class", "config", "def", "dense", "dim", "dropout", "forward", "hidden_states", "input_tensor", "nn", "return", "self", "super" ]
dinat/modeling_dinat.py:NeighborhoodAttentionModule
[ 0.00003225555701646954, 0.03325438126921654, 0.03235561400651932, -0.01595311425626278, 0.0000921587270568125, 0.03887167572975159, 0.0314568467438221, -0.03797290846705437, 0.011234587989747524, -0.0006881185108795762, 0.024266710504889488, 0.013481505215168, 0.0024435229133814573, -0.010...
[ "False", "ModelAttention", "ModelAttentionModule", "ModelAttentionOutput", "Module", "__init__", "attention_output", "class", "config", "def", "dilation", "dim", "forward", "hidden_states", "kernel_size", "nn", "num_heads", "output", "output_attentions", "outputs", "return", ...