Datasets:
itazap
/
transformers_code_embeddings_v3

Dataset card Data Studio Files
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1
identifier
stringlengths
24
102
embedding
listlengths
2.56k
2.56k
tokens
listlengths
4
448
gpt_neo/modeling_gpt_neo.py:GPTNeoPreTrainedModel
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[ "False", "Model", "ModelBlock", "ModelConfig", "ModelSelfAttention", "PreTrainedModel", "True", "_can_compile_fullgraph", "_init_weights", "_no_split_modules", "_skip_keys_device_placement", "_supports_flash_attn", "attention_type", "base_model_prefix", "bias", "bitwise_xor", "bool",...
gpt_neo/modeling_gpt_neo.py:GPTNeoModel
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[ "AttentionMaskConverter", "BaseModelOutputWithPast", "BlockMask", "Dropout", "DynamicCache", "Embedding", "False", "LayerNorm", "Model", "ModelBlock", "ModelPreTrainedModel", "ModuleList", "None", "Setting", "Tensor", "True", "ValueError", "You", "__init__", "_attn_implementati...
gpt_neo/modeling_gpt_neo.py:GPTNeoForCausalLM
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[ "CausalLMOutputWithPast", "GenerationMixin", "Linear", "Model", "ModelModel", "ModelPreTrainedModel", "None", "__init__", "_tied_weights_keys", "attention_mask", "attentions", "auto_docstring", "cache_position", "class", "config", "def", "else", "forward", "hidden_size", "hidde...
gpt_neo/modeling_gpt_neo.py:GPTNeoForSequenceClassification
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gpt_neo/modeling_gpt_neo.py:GPTNeoForTokenClassification
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[ "CrossEntropyLoss", "Dropout", "Linear", "Model", "ModelModel", "ModelPreTrainedModel", "None", "TokenClassifierOutput", "__init__", "attention_mask", "attentions", "auto_docstring", "class", "classifier", "classifier_dropout", "config", "def", "device", "dropout", "else", "f...
gpt_neo/modeling_gpt_neo.py:GPTNeoForQuestionAnswering
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[ "CrossEntropyLoss", "Linear", "Model", "ModelModel", "ModelPreTrainedModel", "None", "QuestionAnsweringModelOutput", "__init__", "and", "attention_mask", "attentions", "auto_docstring", "clamp", "class", "config", "contiguous", "def", "dim", "else", "end_logits", "end_loss", ...
decision_transformer/modeling_decision_transformer.py:eager_attention_forward
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[ "Model_attention_forward", "None", "attention_mask", "attn_dropout", "attn_output", "attn_weights", "bias", "causal_mask", "def", "device", "dim", "dtype", "finfo", "float", "full", "functional", "if", "is", "is_cross_attention", "key", "key_length", "kwargs", "layer_idx"...
decision_transformer/modeling_decision_transformer.py:DecisionTransformerGPT2Attention
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[ "ALL_ATTENTION_FUNCTIONS", "Conv1D", "Dropout", "EncoderDecoderCache", "False", "If", "ModelGPT2Attention", "Module", "None", "Please", "True", "ValueError", "_", "__init__", "_attn_implementation", "_upcast_and_reordered_attn", "alpha", "and", "as", "attention", "attention_i...
decision_transformer/modeling_decision_transformer.py:DecisionTransformerGPT2MLP
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[ "ACT2FN", "Conv1D", "Dropout", "ModelGPT2MLP", "Module", "__init__", "act", "activation_function", "c_fc", "c_proj", "class", "config", "def", "dropout", "embed_dim", "forward", "hidden_size", "hidden_states", "intermediate_size", "nn", "resid_pdrop", "return", "self", ...
decision_transformer/modeling_decision_transformer.py:DecisionTransformerGPT2Block
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[ "False", "GradientCheckpointingLayer", "If", "LayerNorm", "ModelGPT2Attention", "ModelGPT2Block", "ModelGPT2MLP", "None", "True", "ValueError", "__init__", "add_cross_attention", "are", "attention", "attention_mask", "attn", "attn_output", "be", "by", "cache_position", "class...
decision_transformer/modeling_decision_transformer.py:DecisionTransformerGPT2PreTrainedModel
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[ "False", "ModelConfig", "ModelGPT2Attention", "ModelGPT2PreTrainedModel", "PreTrainedModel", "True", "_can_compile_fullgraph", "_init_weights", "and", "base_model_prefix", "bias", "bool", "c_proj", "class", "config", "copy_", "def", "dtype", "elif", "for", "if", "in", "in...
decision_transformer/modeling_decision_transformer.py:DecisionTransformerGPT2Model
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[ "BaseModelOutputWithPastAndCrossAttentions", "Dropout", "DynamicCache", "Embedding", "EncoderDecoderCache", "False", "LayerNorm", "ModelGPT2Block", "ModelGPT2Model", "ModelGPT2PreTrainedModel", "ModuleList", "None", "Setting", "True", "ValueError", "_", "__init__", "add_cross_atten...
decision_transformer/modeling_decision_transformer.py:DecisionTransformerOutput
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[ "ModelOutput", "None", "action_preds", "attentions", "class", "hidden_states", "last_hidden_state", "r", "return_preds", "state_preds" ]
decision_transformer/modeling_decision_transformer.py:DecisionTransformerPreTrainedModel
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[ "False", "Model", "ModelConfig", "ModelPreTrainedModel", "PreTrainedModel", "base_model_prefix", "class", "config", "main_input_name", "states", "supports_gradient_checkpointing" ]
decision_transformer/modeling_decision_transformer.py:DecisionTransformerModel
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[ "Embedding", "LayerNorm", "Linear", "ModelGPT2Model", "ModelModel", "ModelOutput", "ModelPreTrainedModel", "None", "Sequential", "Tanh", "__init__", "act_dim", "action_embeddings", "action_preds", "action_tanh", "actions", "attention_mask", "attentions", "auto_docstring", "batc...
qwen2_vl/modeling_qwen2_vl.py:Qwen2VLModelOutputWithPast
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[ "ModelModelOutputWithPast", "ModelOutput", "None", "attentions", "class", "hidden_states", "last_hidden_state", "past_key_values", "r", "rope_deltas" ]
qwen2_vl/modeling_qwen2_vl.py:Qwen2VLCausalLMOutputWithPast
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[ "ModelCausalLMOutputWithPast", "ModelOutput", "None", "attentions", "class", "hidden_states", "logits", "loss", "past_key_values", "r", "rope_deltas" ]
qwen2_vl/modeling_qwen2_vl.py:Qwen2VLRotaryEmbedding
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[ "False", "ModelRotaryEmbedding", "Module", "None", "ROPE_INIT_FUNCTIONS", "Tensor", "__init__", "and", "arange", "attention_factor", "attention_scaling", "base", "cat", "class", "clone", "compute_default_rope_parameters", "config", "cos", "cpu", "def", "default", "device", ...
qwen2_vl/modeling_qwen2_vl.py:rotate_half
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[ "Model_half", "cat", "def", "dim", "return", "shape", "torch", "x", "x1", "x2" ]
qwen2_vl/modeling_qwen2_vl.py:apply_multimodal_rotary_pos_emb
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[ "Model_multimodal_rotary_pos_emb", "cat", "cos", "def", "dim", "enumerate", "for", "i", "in", "k", "k_embed", "m", "mrope_section", "q", "q_embed", "return", "rotate_half", "sin", "split", "torch", "unsqueeze", "unsqueeze_dim" ]
qwen2_vl/modeling_qwen2_vl.py:apply_rotary_pos_emb_vision
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[ "Model_rotary_pos_emb_vision", "cos", "def", "dtype", "float", "k", "k_embed", "orig_q_dtype", "q", "q_embed", "return", "rotate_half", "sin", "unsqueeze" ]
qwen2_vl/modeling_qwen2_vl.py:VisionRotaryEmbedding
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[ "False", "ModelRotaryEmbedding", "Module", "Tensor", "__init__", "arange", "class", "def", "device", "dim", "dtype", "float", "forward", "freqs", "inv_freq", "nn", "outer", "persistent", "register_buffer", "return", "self", "seq", "seqlen", "super", "theta", "torch"...
qwen2_vl/modeling_qwen2_vl.py:PatchEmbed
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[ "Conv3d", "ModelEmbed", "Model_size", "Module", "__init__", "class", "def", "dtype", "embed_dim", "forward", "hidden_states", "in_channels", "kernel_size", "nn", "proj", "return", "self", "stride", "super", "target_dtype", "temporal_Model_size", "view", "weight" ]
qwen2_vl/modeling_qwen2_vl.py:PatchMerger
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[ "GELU", "LayerNorm", "Linear", "ModelMerger", "Module", "Sequential", "__init__", "class", "context_dim", "def", "dim", "eps", "forward", "hidden_size", "ln_q", "mlp", "nn", "return", "self", "spatial_merge_size", "super", "view", "x" ]
qwen2_vl/modeling_qwen2_vl.py:VisionMlp
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[ "ACT2FN", "Linear", "ModelMlp", "Module", "__init__", "act", "class", "def", "dim", "fc1", "fc2", "forward", "hidden_act", "hidden_dim", "nn", "return", "self", "super", "x" ]
qwen2_vl/modeling_qwen2_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" ]
qwen2_vl/modeling_qwen2_vl.py:eager_attention_forward
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[ "Model_attention_forward", "None", "attention_mask", "attn_output", "attn_weights", "causal_mask", "contiguous", "def", "dim", "dropout", "dtype", "float32", "functional", "if", "is", "key", "key_states", "kwargs", "matmul", "module", "nn", "not", "num_key_value_groups", ...
qwen2_vl/modeling_qwen2_vl.py:VisionAttention
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[ "ALL_ATTENTION_FUNCTIONS", "False", "Linear", "ModelAttention", "Module", "None", "Tensor", "_", "__init__", "_attn_implementation", "apply_rotary_pos_emb_Model", "attention_dropout", "attention_interface", "attention_mask", "attn_output", "attn_outputs", "cat", "class", "config"...
qwen2_vl/modeling_qwen2_vl.py:Qwen2VLVisionBlock
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[ "GradientCheckpointingLayer", "LayerNorm", "ModelVisionBlock", "None", "Tensor", "VisionAttention", "VisionMlp", "__init__", "attn", "attn_implementation", "class", "config", "cu_seqlens", "def", "dim", "embed_dim", "eps", "forward", "hidden_act", "hidden_dim", "hidden_states...
qwen2_vl/modeling_qwen2_vl.py:Qwen2MLP
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qwen2_vl/modeling_qwen2_vl.py:Qwen2VLAttention
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qwen2_vl/modeling_qwen2_vl.py:Qwen2VLDecoderLayer
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qwen2_vl/modeling_qwen2_vl.py:Qwen2VLPreTrainedModel
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qwen2_vl/modeling_qwen2_vl.py:Qwen2VisionTransformerPretrainedModel
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qwen2_vl/modeling_qwen2_vl.py:Qwen2VLTextModel
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qwen2_vl/modeling_qwen2_vl.py:Qwen2VLModel
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qwen2_vl/modeling_qwen2_vl.py:Qwen2VLForConditionalGeneration
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xlnet/modeling_xlnet.py:XLNetRelativeAttention
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xlnet/modeling_xlnet.py:XLNetFeedForward
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xlnet/modeling_xlnet.py:XLNetLayer
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xlnet/modeling_xlnet.py:XLNetPoolerStartLogits
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xlnet/modeling_xlnet.py:XLNetPoolerEndLogits
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xlnet/modeling_xlnet.py:XLNetPoolerAnswerClass
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xlnet/modeling_xlnet.py:XLNetSequenceSummary
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xlnet/modeling_xlnet.py:XLNetPreTrainedModel
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xlnet/modeling_xlnet.py:XLNetModelOutput
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xlnet/modeling_xlnet.py:XLNetLMHeadModelOutput
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[ "Model", "ModelOutput", "None", "attentions", "class", "hidden_states", "logits", "loss", "mems", "r" ]
xlnet/modeling_xlnet.py:XLNetForSequenceClassificationOutput
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xlnet/modeling_xlnet.py:XLNetForTokenClassificationOutput
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xlnet/modeling_xlnet.py:XLNetForMultipleChoiceOutput
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xlnet/modeling_xlnet.py:XLNetForQuestionAnsweringSimpleOutput
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xlnet/modeling_xlnet.py:XLNetForQuestionAnsweringOutput
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xlnet/modeling_xlnet.py:XLNetModel
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xlnet/modeling_xlnet.py:XLNetLMHeadModel
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xlnet/modeling_xlnet.py:XLNetForSequenceClassification
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xlnet/modeling_xlnet.py:XLNetForTokenClassification
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xlnet/modeling_xlnet.py:XLNetForMultipleChoice
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xlnet/modeling_xlnet.py:XLNetForQuestionAnsweringSimple
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xlnet/modeling_xlnet.py:XLNetForQuestionAnswering
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vitpose_backbone/modeling_vitpose_backbone.py:VitPoseBackbonePatchEmbeddings
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vitpose_backbone/modeling_vitpose_backbone.py:VitPoseBackboneEmbeddings
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vitpose_backbone/modeling_vitpose_backbone.py:eager_attention_forward
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vitpose_backbone/modeling_vitpose_backbone.py:VitPoseBackboneSelfAttention
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vitpose_backbone/modeling_vitpose_backbone.py:VitPoseBackboneSelfOutput
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vitpose_backbone/modeling_vitpose_backbone.py:VitPoseBackboneAttention
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vitpose_backbone/modeling_vitpose_backbone.py:VitPoseNaiveMoe
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vitpose_backbone/modeling_vitpose_backbone.py:VitPoseBackboneMoeMLP
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vitpose_backbone/modeling_vitpose_backbone.py:VitPoseBackboneMLP
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vitpose_backbone/modeling_vitpose_backbone.py:VitPoseBackboneLayer
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vitpose_backbone/modeling_vitpose_backbone.py:VitPoseBackboneEncoder
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vitpose_backbone/modeling_vitpose_backbone.py:VitPoseBackbonePreTrainedModel
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vitpose_backbone/modeling_vitpose_backbone.py:VitPoseBackbone
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mpt/modeling_mpt.py:build_mpt_alibi_tensor
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mpt/modeling_mpt.py:MptAttention
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mpt/modeling_mpt.py:MptMLP
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mpt/modeling_mpt.py:MptBlock
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mpt/modeling_mpt.py:MptPreTrainedModel
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[ "ModelBlock", "ModelConfig", "ModelPreTrainedModel", "PreTrainedModel", "True", "_no_split_modules", "base_model_prefix", "class", "config", "supports_gradient_checkpointing", "transformer" ]
mpt/modeling_mpt.py:MptModel
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mpt/modeling_mpt.py:MptForCausalLM
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mpt/modeling_mpt.py:MptForSequenceClassification
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mpt/modeling_mpt.py:MptForTokenClassification
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mpt/modeling_mpt.py:MptForQuestionAnswering
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mamba/modeling_mamba.py:MambaCache
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mamba/modeling_mamba.py:MambaMixer
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mamba/modeling_mamba.py:MambaRMSNorm
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mamba/modeling_mamba.py:MambaBlock
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mamba/modeling_mamba.py:MambaPreTrainedModel
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mamba/modeling_mamba.py:MambaOutput
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[ "ModelOutput", "None", "cache_params", "class", "hidden_states", "last_hidden_state", "r" ]
mamba/modeling_mamba.py:MambaCausalLMOutput
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[ "ModelCausalLMOutput", "ModelOutput", "None", "cache_params", "class", "hidden_states", "logits", "loss", "r" ]
mamba/modeling_mamba.py:MambaModel
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mamba/modeling_mamba.py:MambaForCausalLM
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internvl/modeling_internvl.py:InternVLVisionRMSNorm
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internvl/modeling_internvl.py:eager_attention_forward
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internvl/modeling_internvl.py:InternVLVisionAttention
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[ "ALL_ATTENTION_FUNCTIONS", "Dropout", "False", "Identity", "Linear", "ModelVisionAttention", "ModelVisionRMSNorm", "Module", "None", "ValueError", "_", "__init__", "_attn_implementation", "and", "attention_dropout", "attention_interface", "attention_mask", "attn_output", "attn_we...
internvl/modeling_internvl.py:InternVLVisionModelOutputWithPooling
[ -0.00007458389882231131, 0.017858019098639488, 0.011736873537302017, 0.04245491325855255, -0.00007984865078469738, 0.040657877922058105, 0.04200565442442894, -0.004296032711863518, 0.01561172679066658, 0.009041323326528072, 0.03391900286078453, 0.017633389681577682, 0.0006317695369943976, ...
[ "BaseModelOutputWithPooling", "ModelVisionModelOutputWithPooling", "class", "r" ]
internvl/modeling_internvl.py:InternVLVisionPatchEmbeddings
[ -0.00002250772013212554, 0.005509011447429657, 0.013884957879781723, 0.016976749524474144, -0.00009617933392291889, -0.0035977219231426716, 0.02259819023311138, -0.01618974842131138, 0.011130452156066895, 0.007982444949448109, 0.013547671027481556, 0.003133953083306551, -0.002796666463837027...
[ "Conv2d", "Make", "ModelVisionPatchEmbeddings", "Module", "ValueError", "__init__", "batch_size", "channel", "class", "config", "configuration", "def", "dimension", "dtype", "embeddings", "flatten", "forward", "height", "hidden_size", "if", "image_size", "in", "kernel_siz...
internvl/modeling_internvl.py:InternVLVisionEmbeddings
[ -0.00023984175641089678, 0.027970878407359123, 0.014781358651816845, 0.01108601875603199, -0.0012294111074879766, 0.019215766340494156, 0.026833850890398026, -0.031381960958242416, 0.0031268259044736624, 0.00943732913583517, 0.028198285028338432, 0.037294503301382065, -0.0005720669869333506,...
[ "Dropout", "False", "Iterable", "ModelVisionEmbeddings", "ModelVisionPatchEmbeddings", "Module", "None", "Parameter", "_", "__init__", "abc", "align_corners", "and", "batch_size", "bicubic", "bool_masked_pos", "cat", "class", "class_pos_embed", "cls_token", "cls_tokens", "c...
internvl/modeling_internvl.py:InternVLVisionMLP
[ -0.0001662031572777778, 0.03708730265498161, 0.032536715269088745, 0.034129418432712555, -0.00047283468302339315, 0.04709859937429428, 0.03162659704685211, -0.03481200709939003, 0.003341838950291276, 0, 0.049373894929885864, -0.020136358216404915, 0.0019482210045680404, -0.0018913387320935...
[ "ACT2FN", "Linear", "ModelVisionMLP", "Module", "__init__", "activation_fn", "class", "config", "def", "fc1", "fc2", "forward", "hidden_act", "hidden_size", "hidden_states", "intermediate_size", "nn", "return", "self", "super" ]
internvl/modeling_internvl.py:InternVLVisionLayer
[ -0.00010581125388853252, 0.0405195914208889, 0.01723761484026909, 0.03313203901052475, 0.0001010016567306593, 0.0405195914208889, 0.02540869265794754, -0.01925240270793438, 0.0019028537208214402, 0.034027501940727234, 0.029102468863129616, 0.01981206424534321, 0.004645201843231916, -0.0154...
[ "Dropout", "GradientCheckpointingLayer", "ModelVisionAttention", "ModelVisionLayer", "ModelVisionMLP", "NORM2FN", "None", "Parameter", "True", "_", "__init__", "attention", "attention_output", "chunk_size_feed_forward", "class", "config", "def", "dropout", "eps", "forward", "...
internvl/modeling_internvl.py:InternVLVisionEncoder
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[ "BaseModelOutput", "False", "ModelVisionEncoder", "ModelVisionLayer", "Module", "ModuleList", "__init__", "class", "config", "def", "for", "forward", "gradient_checkpointing", "hidden_states", "i", "in", "last_hidden_state", "layer", "layer_module", "nn", "num_hidden_layers",...