Datasets:
itazap
/
transformers_code_embeddings_v3

Dataset card Data Studio Files
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1
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24
117
embedding
listlengths
2.56k
2.56k
tokens
listlengths
4
444
flaubert/modeling_flaubert.py:FlaubertForQuestionAnswering
[ -0.000231579237151891, 0.030320968478918076, 0.017536381259560585, 0.018667761236429214, -0.0007672166684642434, 0.047065384685993195, 0.05023324862122536, 0.008259070105850697, 0.00763681111857295, 0.01945972628891468, 0.017988933250308037, 0.019233450293540955, 0.0019092027796432376, -0....
[ "ModelForQuestionAnswering", "ModelForQuestionAnsweringOutput", "ModelModel", "ModelPreTrainedModel", "ModelSQuADHead", "None", "Tensor", "__init__", "attention_mask", "attentions", "auto_docstring", "cache", "class", "cls_index", "cls_logits", "config", "def", "else", "end_posit...
flaubert/modeling_flaubert.py:FlaubertForMultipleChoice
[ -0.0002631563111208379, 0.04893285036087036, 0.025945790112018585, 0.03482198342680931, -0.0008143621380440891, 0.04233260452747345, 0.0405118465423584, -0.017979977652430534, -0.0035846156533807516, -0.0005085318116471171, 0.030270088464021683, -0.0002364850661251694, -0.00352771719917655, ...
[ "CrossEntropyLoss", "Linear", "Model", "ModelForMultipleChoice", "ModelModel", "ModelPreTrainedModel", "ModelSequenceSummary", "MultipleChoiceModelOutput", "None", "Please", "Tensor", "The", "__init__", "attention", "attention_mask", "attentions", "auto_docstring", "be", "cache",...
deepseek_v4/modeling_deepseek_v4.py:DeepseekV4RMSNorm
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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", ...
deepseek_v4/modeling_deepseek_v4.py:DeepseekV4UnweightedRMSNorm
[ -0.00013550756557378918, 0.043532468378543854, 0.026300868019461632, 0.04716017469763756, -0.0007687618490308523, 0.04557305574417114, 0.012696970254182816, -0.003684388706460595, 0.011336580850183964, 0.030155304819345474, 0.01779843121767044, 0.010713068768382072, 0.0022956575267016888, ...
[ "ModelUnweightedRMSNorm", "Module", "True", "__init__", "class", "def", "dtype", "eps", "float", "forward", "keepdim", "mean", "nn", "return", "rsqrt", "self", "square", "super", "to", "torch", "x" ]
deepseek_v4/modeling_deepseek_v4.py:DeepseekV4RotaryEmbedding
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[ "False", "ModelRotaryEmbedding", "Module", "None", "ROPE_INIT_FUNCTIONS", "Tensor", "__init__", "_attention_scaling", "_inv_freq", "_original_inv_freq", "and", "arange", "attention_factor", "attention_scaling", "base", "class", "clone", "compute_default_rope_parameters", "config"...
deepseek_v4/modeling_deepseek_v4.py:DeepseekV4HCACache
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[ "DynamicSlidingWindowLayer", "ModelHCACache", "None", "Tensor", "__init__", "_layer_type", "and", "args", "buffer_gate", "buffer_kv", "buffered_gate", "buffered_kv", "cat", "class", "compress_rate", "compress_rates", "compressed", "compressed_kv", "compressor", "config", "cum...
deepseek_v4/modeling_deepseek_v4.py:DeepseekV4CSACache
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[ "ModelCSACache", "ModelHCACache", "None", "Tensor", "__init__", "_layer_type", "buffer_gate", "buffer_kv", "chunk_gate", "chunk_kv", "class", "clone", "compress_rate", "compress_rates", "compressed_kv", "compressed_sparse_attention", "compressor", "config", "def", "entry_count"...
deepseek_v4/modeling_deepseek_v4.py:DeepseekV4GroupedLinear
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[ "Linear", "ModelGroupedLinear", "__init__", "bmm", "class", "def", "forward", "hidden_dim", "in_features_per_group", "input_shape", "n_groups", "nn", "out_features", "reshape", "return", "self", "shape", "super", "torch", "transpose", "view", "w", "weight", "x", "y" ]
deepseek_v4/modeling_deepseek_v4.py:rotate_half
[ 0.000020890642190352082, 0.007993859238922596, 0.029498467221856117, -0.00021638307953253388, 0.00044683986925520003, 0.031750258058309555, 0.018126919865608215, -0.02184237726032734, 0.016438078135252, 0.026345958933234215, -0.004559877794235945, -0.00911975558847189, 0.00020318898896221071...
[ "Model_half", "def", "dim", "flatten", "return", "stack", "torch", "x", "x1", "x2" ]
deepseek_v4/modeling_deepseek_v4.py:apply_rotary_pos_emb
[ -0.0001931545848492533, 0.0250655896961689, 0.02620493434369564, 0.002449591876938939, -0.0009755641804076731, 0.035319697111845016, 0.033041004091501236, -0.00598156126216054, 0.012361893430352211, 0.018457388505339622, -0.0014455439522862434, 0.009798367507755756, -0.000690727902110666, ...
[ "Model_rotary_pos_emb", "cat", "cos", "def", "dim", "dtype", "float", "nope", "repeat_interleave", "return", "rope", "rope_dim", "rotate_half", "rotated", "shape", "sin", "to", "torch", "unsqueeze", "unsqueeze_dim", "x" ]
deepseek_v4/modeling_deepseek_v4.py:DeepseekV4HCACompressor
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[ "Linear", "ModelHCACompressor", "ModelRMSNorm", "ModelRotaryEmbedding", "Module", "None", "Parameter", "_", "__init__", "apply_rotary_pos_emb", "arange", "batch", "block_bias", "cache_layer", "causal_threshold", "chunk_gate", "chunk_kv", "class", "compress", "compress_rate", ...
deepseek_v4/modeling_deepseek_v4.py:DeepseekV4IndexerScorer
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[ "F", "Linear", "ModelIndexerScorer", "Module", "__init__", "class", "compressed_kv", "config", "def", "dim", "float", "forward", "hidden_size", "hidden_states", "index_head_dim", "index_n_heads", "matmul", "nn", "q", "r", "relu", "return", "scores", "self", "softmax_s...
deepseek_v4/modeling_deepseek_v4.py:DeepseekV4Indexer
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[ "Linear", "ModelIndexer", "ModelIndexerScorer", "ModelRMSNorm", "ModelRotaryEmbedding", "Module", "None", "Parameter", "_", "__init__", "apply_rotary_pos_emb", "arange", "batch", "cache_layer", "causal_threshold", "chunk_gate", "chunk_kv", "class", "compress", "compress_rate", ...
deepseek_v4/modeling_deepseek_v4.py:DeepseekV4CSACompressor
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[ "Linear", "ModelCSACompressor", "ModelIndexer", "ModelRMSNorm", "ModelRotaryEmbedding", "Module", "None", "Parameter", "_", "__init__", "apply_rotary_pos_emb", "arange", "batch", "block_bias", "cache_layer", "chunk_gate", "chunk_kv", "class", "compress", "compress_rate", "com...
deepseek_v4/modeling_deepseek_v4.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" ]
deepseek_v4/modeling_deepseek_v4.py:eager_attention_forward
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[ "F", "Model_attention_forward", "None", "True", "attention_mask", "attn_output", "attn_weights", "cat", "combined_logits", "contiguous", "def", "dim", "dropout", "dtype", "expand", "functional", "if", "is", "keepdim", "key", "key_states", "kwargs", "matmul", "max", "m...
deepseek_v4/modeling_deepseek_v4.py:DeepseekV4Attention
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[ "ALL_ATTENTION_FUNCTIONS", "COMPRESSOR_CLASSES", "F", "Linear", "ModelAttention", "ModelGroupedLinear", "ModelRMSNorm", "ModelUnweightedRMSNorm", "Module", "None", "Parameter", "Tensor", "True", "__init__", "_attn_implementation", "and", "apply_rotary_pos_emb", "attention_dropout",...
deepseek_v4/modeling_deepseek_v4.py:DeepseekV4HyperConnection
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[ "F", "ModelHyperConnection", "ModelUnweightedRMSNorm", "Module", "Parameter", "True", "_", "__init__", "base", "class", "collapsed", "comb", "comb_b", "comb_logits", "comb_scale", "comb_w", "config", "def", "dim", "dtype", "empty", "eps", "flat", "flatten", "float", ...
deepseek_v4/modeling_deepseek_v4.py:DeepseekV4HyperHead
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[ "F", "ModelHyperHead", "ModelUnweightedRMSNorm", "Module", "Parameter", "__init__", "class", "config", "def", "dim", "dtype", "empty", "eps", "flat", "flatten", "float", "forward", "hc_base", "hc_eps", "hc_fn", "hc_mult", "hc_scale", "hidden_size", "input_norm", "line...
deepseek_v4/modeling_deepseek_v4.py:DeepseekV4MLP
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[ "ACT2FN", "Linear", "ModelMLP", "Module", "__init__", "act_fn", "clamp", "class", "config", "def", "down_proj", "forward", "gate", "gate_proj", "hidden_act", "hidden_size", "intermediate_size", "limit", "max", "min", "nn", "return", "self", "super", "swiglu_limit", ...
deepseek_v4/modeling_deepseek_v4.py:DeepseekV4Experts
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[ "ACT2FN", "F", "ModelExperts", "Module", "None", "Parameter", "__init__", "_apply_gate", "act_fn", "chunk", "clamp", "class", "config", "continue", "current", "def", "dim", "down_proj", "dtype", "empty", "expert_idx", "final", "for", "forward", "gate", "gate_up", ...
deepseek_v4/modeling_deepseek_v4.py:DeepseekV4TopKRouter
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[ "ACT2FN", "F", "False", "ModelTopKRouter", "Module", "Parameter", "True", "__init__", "class", "config", "def", "dim", "e_score_correction_bias", "empty", "flat", "forward", "gather", "hidden_dim", "hidden_size", "hidden_states", "indices", "keepdim", "linear", "logits"...
deepseek_v4/modeling_deepseek_v4.py:DeepseekV4HashRouter
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[ "ACT2FN", "F", "ModelHashRouter", "Module", "Parameter", "True", "__init__", "class", "config", "def", "dim", "dtype", "empty", "flat", "forward", "gather", "hidden_dim", "hidden_size", "hidden_states", "indices", "input_ids", "keepdim", "linear", "logits", "long", ...
deepseek_v4/modeling_deepseek_v4.py:DeepseekV4SparseMoeBlock
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[ "ModelExperts", "ModelHashRouter", "ModelMLP", "ModelSparseMoeBlock", "ModelTopKRouter", "Module", "None", "__init__", "batch", "class", "config", "def", "else", "experts", "flat", "forward", "gate", "hash_moe", "hidden_dim", "hidden_states", "if", "indices", "input_ids",...
deepseek_v4/modeling_deepseek_v4.py:DeepseekV4DecoderLayer
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[ "GradientCheckpointingLayer", "ModelAttention", "ModelDecoderLayer", "ModelHyperConnection", "ModelRMSNorm", "ModelSparseMoeBlock", "None", "_", "__init__", "attn_hc", "attn_output", "class", "collapsed", "comb", "config", "def", "dtype", "eps", "ffn_hc", "forward", "hidden_s...
deepseek_v4/modeling_deepseek_v4.py:DeepseekV4PreTrainedModel
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[ "False", "ModelAttention", "ModelCSACompressor", "ModelConfig", "ModelDecoderLayer", "ModelExperts", "ModelHCACompressor", "ModelHashRouter", "ModelHyperConnection", "ModelHyperHead", "ModelIndexer", "ModelPreTrainedModel", "ModelRotaryEmbedding", "ModelTopKRouter", "OutputRecorder", "...
deepseek_v4/modeling_deepseek_v4.py:DeepseekV4Model
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[ "DynamicCache", "Embedding", "False", "ModelDecoderLayer", "ModelHyperHead", "ModelModel", "ModelPreTrainedModel", "ModelRMSNorm", "ModelRotaryEmbedding", "ModuleList", "MoeModelOutputWithPast", "None", "ValueError", "You", "__init__", "and", "arange", "attention_mask", "auto_doc...
deepseek_v4/modeling_deepseek_v4.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", ...
deepseek_v4/modeling_deepseek_v4.py:DeepseekV4ForCausalLM
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[ "GenerationMixin", "Linear", "ModelForCausalLM", "ModelModel", "ModelPreTrainedModel", "MoeCausalLMOutputWithPast", "None", "__init__", "_fsdp_plan", "_pp_plan", "_tied_weights_keys", "_tp_plan", "attention_mask", "attentions", "auto_docstring", "aux_loss", "can_return_tuple", "cla...
cohere2_vision/modeling_cohere2_vision.py:Cohere2VisionMultiModalProjector
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cohere2_vision/modeling_cohere2_vision.py:Cohere2VisionModelOutputWithPast
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cohere2_vision/modeling_cohere2_vision.py:Cohere2VisionCausalLMOutputWithPast
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cohere2_vision/modeling_cohere2_vision.py:Cohere2VisionPreTrainedModel
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cohere2_vision/modeling_cohere2_vision.py:Cohere2VisionModel
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cohere2_vision/modeling_cohere2_vision.py:Cohere2VisionForConditionalGeneration
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qwen3_5/modeling_qwen3_5.py:Qwen3_5VisionRotaryEmbedding
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qwen3_5/modeling_qwen3_5.py:Qwen3_5TextRotaryEmbedding
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qwen3_5/modeling_qwen3_5.py:Qwen3_5RMSNormGated
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qwen3_5/modeling_qwen3_5.py:apply_mask_to_padding_states
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qwen3_5/modeling_qwen3_5.py:causal_conv1d_update
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qwen3_5/modeling_qwen3_5.py:causal_conv1d_fn
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qwen3_5/modeling_qwen3_5.py:l2norm
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qwen3_5/modeling_qwen3_5.py:torch_chunk_gated_delta_rule
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qwen3_5/modeling_qwen3_5.py:torch_recurrent_gated_delta_rule
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qwen3_5/modeling_qwen3_5.py:Qwen3_5GatedDeltaNet
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qwen3_5/modeling_qwen3_5.py:rotate_half
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qwen3_5/modeling_qwen3_5.py:apply_rotary_pos_emb
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qwen3_5/modeling_qwen3_5.py:repeat_kv
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qwen3_5/modeling_qwen3_5.py:eager_attention_forward
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qwen3_5/modeling_qwen3_5.py:Qwen3_5Attention
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qwen3_5/modeling_qwen3_5.py:Qwen3_5MLP
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qwen3_5/modeling_qwen3_5.py:Qwen3_5RMSNorm
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qwen3_5/modeling_qwen3_5.py:Qwen3_5DecoderLayer
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qwen3_5/modeling_qwen3_5.py:Qwen3_5PreTrainedModel
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qwen3_5/modeling_qwen3_5.py:Qwen3_5VisionMLP
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qwen3_5/modeling_qwen3_5.py:Qwen3_5VisionPatchEmbed
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qwen3_5/modeling_qwen3_5.py:Qwen3_5VisionPatchMerger
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qwen3_5/modeling_qwen3_5.py:apply_rotary_pos_emb_vision
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qwen3_5/modeling_qwen3_5.py:Qwen3_5VisionAttention
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qwen3_5/modeling_qwen3_5.py:Qwen3_5VisionBlock
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qwen3_5/modeling_qwen3_5.py:Qwen3_5VisionModel
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qwen3_5/modeling_qwen3_5.py:Qwen3_5ModelOutputWithPast
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qwen3_5/modeling_qwen3_5.py:Qwen3_5TextModel
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qwen3_5/modeling_qwen3_5.py:Qwen3_5Model
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qwen3_5/modeling_qwen3_5.py:Qwen3_5ForCausalLM
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qwen3_5/modeling_qwen3_5.py:Qwen3_5ForTokenClassification
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qwen3_5/modeling_qwen3_5.py:Qwen3_5CausalLMOutputWithPast
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[ "CausalLMOutputWithPast", "ModelCausalLMOutputWithPast", "None", "class", "r", "rope_deltas" ]
qwen3_5/modeling_qwen3_5.py:Qwen3_5ForConditionalGeneration
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qwen3_5/modeling_qwen3_5.py:Qwen3_5TextForSequenceClassification
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[ "GenericForSequenceClassification", "ModelPreTrainedModel", "ModelTextConfig", "ModelTextForSequenceClassification", "class", "config", "input_modalities", "text" ]
qwen3_5/modeling_qwen3_5.py:Qwen3_5ForSequenceClassification
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pp_ocrv6_tiny_rec/modeling_pp_ocrv6_tiny_rec.py:PPOCRV6TinyRecPreTrainedModel
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pp_ocrv6_tiny_rec/modeling_pp_ocrv6_tiny_rec.py:PPOCRV6TinyRecHead
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pp_ocrv6_tiny_rec/modeling_pp_ocrv6_tiny_rec.py:PPOCRV6TinyRecModel
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pp_ocrv6_tiny_rec/modeling_pp_ocrv6_tiny_rec.py:PPOCRV6TinyRecForTextRecognition
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trocr/modeling_trocr.py:TrOCRLearnedPositionalEmbedding
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trocr/modeling_trocr.py:TrOCRScaledWordEmbedding
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trocr/modeling_trocr.py:TrOCRSinusoidalPositionalEmbedding
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trocr/modeling_trocr.py:TrOCRAttention
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trocr/modeling_trocr.py:TrOCRDecoderLayer
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trocr/modeling_trocr.py:TrOCRPreTrainedModel
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trocr/modeling_trocr.py:TrOCRDecoder
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trocr/modeling_trocr.py:TrOCRDecoderWrapper
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trocr/modeling_trocr.py:TrOCRForCausalLM
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lfm2_vl/modeling_lfm2_vl.py:Lfm2VlMultiModalProjector
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lfm2_vl/modeling_lfm2_vl.py:Lfm2VlPreTrainedModel
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lfm2_vl/modeling_lfm2_vl.py:Lfm2VlCausalLMOutputWithPast
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[ "ModelCausalLMOutputWithPast", "ModelOutput", "None", "attentions", "class", "hidden_states", "image_hidden_states", "logits", "loss", "past_key_values", "r" ]
lfm2_vl/modeling_lfm2_vl.py:Lfm2VlModelOutputWithPast
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lfm2_vl/modeling_lfm2_vl.py:Lfm2VlModel
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lfm2_vl/modeling_lfm2_vl.py:Lfm2VlForConditionalGeneration
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canine/modeling_canine.py:CanineModelOutputWithPooling
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[ "ModelModelOutputWithPooling", "ModelOutput", "None", "attentions", "class", "hidden_states", "last_hidden_state", "pooler_output", "r" ]
canine/modeling_canine.py:CanineEmbeddings
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canine/modeling_canine.py:CharactersToMolecules
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canine/modeling_canine.py:ConvProjection
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[ "ACT2FN", "ConstantPad1d", "Dropout", "LayerNorm", "Model", "Model1d", "ModelProjection", "Module", "None", "__init__", "activation", "class", "config", "def", "dropout", "else", "eps", "final_char_seq", "final_seq_char_positions", "forward", "hidden_act", "hidden_dropout_p...
canine/modeling_canine.py:CanineSelfAttention
[ -0.000033958949643420056, 0.04038266837596893, 0.0446452833712101, -0.015255673788487911, -0.00038559839595109224, 0.019406115636229515, 0.0318574383854866, -0.019966986030340195, 0.004543050192296505, 0.02041568234562874, 0.004430876113474369, 0.01817220076918602, -0.0006379900733008981, ...
[ "Dropout", "False", "Linear", "ModelSelfAttention", "Module", "None", "The", "ValueError", "_", "__init__", "a", "all_head_size", "and", "attention", "attention_head_size", "attention_mask", "attention_probs", "attention_probs_dropout_prob", "attention_scores", "batch_size", ...
canine/modeling_canine.py:CanineSelfOutput
[ -0.00013246347953099757, 0.04747490957379341, 0.03797992691397667, 0.02125067450106144, -0.0007523925160057843, 0.05629167705774307, 0.023850489407777786, -0.019555142149329185, 0.0037866891361773014, 0.018198715522885323, 0.013451224192976952, 0.002840016968548298, 0.00370191247202456, -0...
[ "Dropout", "LayerNorm", "Linear", "ModelSelfOutput", "Module", "__init__", "class", "config", "def", "dense", "dropout", "eps", "forward", "hidden_dropout_prob", "hidden_size", "hidden_states", "input_tensor", "layer_norm_eps", "nn", "return", "self", "super" ]
canine/modeling_canine.py:CanineAttention
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[ "Check", "Expected", "False", "ModelAttention", "ModelSelfAttention", "ModelSelfOutput", "Module", "None", "ValueError", "__init__", "always_attend_to_first_position", "and", "append", "attend_from_chunk_stride", "attend_from_chunk_width", "attend_to_chunk_stride", "attend_to_chunk_w...
canine/modeling_canine.py:CanineIntermediate
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[ "ACT2FN", "Linear", "ModelIntermediate", "Module", "__init__", "class", "config", "def", "dense", "else", "forward", "hidden_act", "hidden_size", "hidden_states", "if", "intermediate_act_fn", "intermediate_size", "isinstance", "nn", "return", "self", "str", "super" ]
canine/modeling_canine.py:CanineOutput
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[ "Dropout", "LayerNorm", "Linear", "ModelOutput", "Module", "__init__", "class", "config", "def", "dense", "dropout", "eps", "forward", "hidden_dropout_prob", "hidden_size", "hidden_states", "input_tensor", "intermediate_size", "layer_norm_eps", "nn", "return", "self", "su...
canine/modeling_canine.py:CanineLayer
[ -0.00016472685092594475, 0.014996309764683247, 0.020521266385912895, 0.00029245621408335865, -0.0005003468249924481, 0.04104253277182579, 0.02503143437206745, -0.00845656543970108, 0.003777266014367342, -0.0020154814701527357, 0.007554532028734684, 0.011839191429316998, 0.002508781151846051,...
[ "False", "GradientCheckpointingLayer", "ModelAttention", "ModelIntermediate", "ModelLayer", "ModelOutput", "None", "__init__", "always_attend_to_first_position", "apply_chunking_to_forward", "attend_from_chunk_stride", "attend_from_chunk_width", "attend_to_chunk_stride", "attend_to_chunk_w...
canine/modeling_canine.py:CanineEncoder
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[ "BaseModelOutput", "False", "ModelEncoder", "ModelLayer", "Module", "ModuleList", "None", "True", "_", "__init__", "all_hidden_states", "all_self_attentions", "always_attend_to_first_position", "attend_from_chunk_stride", "attend_from_chunk_width", "attend_to_chunk_stride", "attend_t...