Datasets:

mrahman2025
/

OpenClassGen

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0	huggingface/pytorch-pretrained-BERT	huggingface_pytorch-pretrained-BERT/benchmark/benchmarks_entrypoint.py	benchmark.benchmarks_entrypoint.ImportModuleException	class ImportModuleException(Exception): pass	class ImportModuleException(Exception): pass	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	10	2	0	2	1	1	0	2	1	1	0	3	0	0
1	huggingface/pytorch-pretrained-BERT	huggingface_pytorch-pretrained-BERT/benchmark/benchmarks_entrypoint.py	benchmark.benchmarks_entrypoint.MetricsRecorder	import pandas as pd import os from datetime import datetime import uuid import logging import json class MetricsRecorder: def __init__(self, connection, logger: logging.Logger, repository: str, branch: str, commit_id: str, commit_msg: str, collect_csv_data: bool=True): self.conn = connection self....	class MetricsRecorder: def __init__(self, connection, logger: logging.Logger, repository: str, branch: str, commit_id: str, commit_msg: str, collect_csv_data: bool=True): pass def initialise_benchmark(self, metadata: dict[str, str]) -> str: ''' Creates a new benchmark, returns the ben...	9	5	10	0	8	2	1	0.23	0	4	0	0	5	5	5	5	54	4	43	15	37	10	24	12	18	1	0	1	5
2	huggingface/pytorch-pretrained-BERT	huggingface_pytorch-pretrained-BERT/examples/modular-transformers/configuration_my_new_model.py	configuration_my_new_model.MyNewModelConfig	from ...modeling_rope_utils import rope_config_validation from ...configuration_utils import PretrainedConfig class MyNewModelConfig(PretrainedConfig): """ This is the configuration class to store the configuration of a [`MyNewModelModel`]. It is used to instantiate an MyNewModel model according to the spe...	class MyNewModelConfig(PretrainedConfig): ''' This is the configuration class to store the configuration of a [`MyNewModelModel`]. It is used to instantiate an MyNewModel model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yi...	2	1	63	2	58	3	4	1.63	1	1	0	0	1	19	1	1	195	11	70	50	42	114	30	24	28	4	1	1	4
3	huggingface/pytorch-pretrained-BERT	huggingface_pytorch-pretrained-BERT/examples/modular-transformers/configuration_my_new_model2.py	configuration_my_new_model2.MyNewModel2Config	from ...modeling_rope_utils import rope_config_validation from ...configuration_utils import PretrainedConfig class MyNewModel2Config(PretrainedConfig): """ This is the configuration class to store the configuration of a [`GemmaModel`]. It is used to instantiate an Gemma model according to the specified ar...	class MyNewModel2Config(PretrainedConfig): ''' This is the configuration class to store the configuration of a [`GemmaModel`]. It is used to instantiate an Gemma model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a sim...	2	1	62	3	56	3	4	0.35	1	1	0	0	1	18	1	1	97	5	68	48	41	24	29	23	27	4	1	1	4
4	huggingface/pytorch-pretrained-BERT	huggingface_pytorch-pretrained-BERT/examples/modular-transformers/configuration_new_model.py	configuration_new_model.NewModelConfig	from ...configuration_utils import PretrainedConfig class NewModelConfig(PretrainedConfig): """ This is the configuration class to store the configuration of a [`NewModelModel`]. It is used to instantiate an NewModel model according to the specified arguments, defining the model architecture. Instantiating...	class NewModelConfig(PretrainedConfig): ''' This is the configuration class to store the configuration of a [`NewModelModel`]. It is used to instantiate an NewModel model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a ...	4	1	25	0	25	0	1	1.25	1	1	0	0	2	16	2	2	122	3	53	45	26	66	23	21	20	1	1	0	2
5	huggingface/pytorch-pretrained-BERT	huggingface_pytorch-pretrained-BERT/conftest.py	conftest.CustomOutputChecker	class CustomOutputChecker(OutputChecker): def check_output(self, want, got, optionflags): if IGNORE_RESULT & optionflags: return True return OutputChecker.check_output(self, want, got, optionflags)	class CustomOutputChecker(OutputChecker): def check_output(self, want, got, optionflags): pass	2	0	4	0	4	0	2	0	1	0	0	0	1	0	1	1	5	0	5	2	3	0	5	2	3	2	1	1	2
6	huggingface/pytorch-pretrained-BERT	huggingface_pytorch-pretrained-BERT/.circleci/create_circleci_config.py	create_circleci_config.CircleCIJob	import copy from typing import Any, Optional from dataclasses import dataclass import os @dataclass class CircleCIJob: name: str additional_env: dict[str, Any] = None docker_image: list[dict[str, str]] = None install_steps: list[str] = None marker: Optional[str] = None parallelism: Optional[int...	@dataclass class CircleCIJob: def __post_init__(self): pass def to_dict(self): pass @property def job_name(self): pass	6	0	32	1	30	1	7	0.05	0	1	0	0	3	0	3	3	113	5	103	28	98	5	57	26	53	10	0	3	20
7	huggingface/pytorch-pretrained-BERT	huggingface_pytorch-pretrained-BERT/.circleci/create_circleci_config.py	create_circleci_config.EmptyJob	import copy class EmptyJob: job_name = 'empty' def to_dict(self): steps = [{'run': 'ls -la'}] if self.job_name == 'collection_job': steps.extend(['checkout', {'run': 'pip install requests \|\| true'}, {'run': 'while [[ $(curl --location --request GET "https://circleci.com/api/v2/work...	class EmptyJob: def to_dict(self): pass	2	0	19	1	18	0	2	0	0	0	0	0	1	0	1	1	22	2	20	4	18	0	7	4	5	2	0	1	2
8	huggingface/pytorch-pretrained-BERT	huggingface_pytorch-pretrained-BERT/examples/modular-transformers/image_processing_new_imgproc_model.py	image_processing_new_imgproc_model.ImgprocModelImageProcessor	from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict import torch from ...image_transforms import convert_to_rgb, resize, to_channel_dimension_format import numpy as np from typing import Optional, Union from ...utils import TensorType, filter_out_non_signature_kwargs, is_vision_availab...	class ImgprocModelImageProcessor(BaseImageProcessor): ''' Constructs a IMGPROC_MODEL image processor. Args: do_resize (`bool`, optional, defaults to `True`): Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the `do_resi...	6	3	53	4	30	18	6	0.85	1	6	0	0	4	9	4	4	251	23	123	53	82	105	54	17	49	17	1	1	24
9	huggingface/pytorch-pretrained-BERT	huggingface_pytorch-pretrained-BERT/examples/legacy/pytorch-lightning/lightning_base.py	lightning_base.BaseTransformer	import os from transformers.optimization import Adafactor, get_cosine_schedule_with_warmup, get_cosine_with_hard_restarts_schedule_with_warmup, get_linear_schedule_with_warmup, get_polynomial_decay_schedule_with_warmup from typing import Any import argparse import pytorch_lightning as pl from pathlib import Path from t...	class BaseTransformer(pl.LightningModule): def __init__(self, hparams: argparse.Namespace, num_labels=None, mode='base', config=None, tokenizer=None, model=None, *config_kwargs): '''Initialize a model, tokenizer and config.''' pass def load_hf_checkpoint(self, args, **kwargs): pass ...	18	3	12	0	12	0	2	0.04	1	17	0	2	14	9	15	15	204	20	178	50	151	7	86	37	70	10	1	2	26
10	huggingface/pytorch-pretrained-BERT	huggingface_pytorch-pretrained-BERT/examples/legacy/pytorch-lightning/lightning_base.py	lightning_base.LoggingCallback	import pytorch_lightning as pl import os from pytorch_lightning.utilities import rank_zero_info class LoggingCallback(pl.Callback): def on_batch_end(self, trainer, pl_module): lr_scheduler = trainer.lr_schedulers[0]['scheduler'] lrs = {f'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr...	class LoggingCallback(pl.Callback): def on_batch_end(self, trainer, pl_module): pass def on_validation_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule): pass def on_test_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule): pass	4	0	7	0	6	1	2	0.1	1	2	0	0	3	0	3	3	24	2	20	12	16	2	20	11	16	3	1	3	7
11	huggingface/pytorch-pretrained-BERT	huggingface_pytorch-pretrained-BERT/examples/modular-transformers/modeling_add_function.py	modeling_add_function.TestAttention	from ...utils.deprecation import deprecate_kwarg import torch from torch import nn from typing import Optional class TestAttention(nn.Module): """ Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer and "Generating Long Sequences with Sparse T...	class TestAttention(nn.Module): ''' Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer and "Generating Long Sequences with Sparse Transformers". Adapted from transformers.models.mistral.modeling_mistral.MistralAttention: The input dim...	4	1	2	0	2	0	1	2.2	1	1	0	0	2	0	2	12	19	3	5	4	2	11	5	4	2	1	1	0	2
12	huggingface/pytorch-pretrained-BERT	huggingface_pytorch-pretrained-BERT/examples/modular-transformers/modeling_dummy_bert.py	modeling_dummy_bert.DummyBertAttention	import torch from torch import nn from typing import Optional, Union from ...utils.deprecation import deprecate_kwarg from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer class DummyBertAttention...	class DummyBertAttention(nn.Module): def __init__(self, config, position_embedding_type=None, layer_idx=None): pass def prune_heads(self, heads): pass @deprecate_kwarg('past_key_value', new_name='past_key_values', version='4.58') def forward(self, hidden_states: torch.Tensor, attentio...	5	0	15	1	14	1	1	0.07	1	5	1	0	3	3	3	13	49	4	43	20	30	3	22	11	18	2	1	1	4
13	huggingface/pytorch-pretrained-BERT	huggingface_pytorch-pretrained-BERT/examples/modular-transformers/modeling_dummy_bert.py	modeling_dummy_bert.DummyBertEmbeddings	import torch from torch import nn from typing import Optional, Union class DummyBertEmbeddings(nn.Module): """Construct the embeddings from word, position and token_type embeddings.""" def __init__(self, config): super().__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config....	class DummyBertEmbeddings(nn.Module): '''Construct the embeddings from word, position and token_type embeddings.''' def __init__(self, config): pass def forward(self, input_ids: Optional[torch.LongTensor]=None, token_type_ids: Optional[torch.LongTensor]=None, position_ids: Optional[torch.LongTens...	3	1	29	3	23	3	4	0.15	1	3	0	0	2	6	2	12	62	8	47	23	37	7	34	16	31	7	1	2	8
14	huggingface/pytorch-pretrained-BERT	huggingface_pytorch-pretrained-BERT/examples/modular-transformers/modeling_dummy_bert.py	modeling_dummy_bert.DummyBertEncoder	from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache from typing import Optional, Union from torch import nn from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions import torch class DummyBertEncoder(nn.Module): def __init__(self, con...	class DummyBertEncoder(nn.Module): def __init__(self, config, layer_idx=None): pass def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor]=None, head_mask: Optional[torch.FloatTensor]=None, encoder_hidden_states: Optional[torch.FloatTensor]=None, encoder_attention_...	3	0	45	4	41	0	9	0	1	7	1	0	2	3	2	12	91	8	83	26	68	0	35	14	32	17	1	3	18
15	huggingface/pytorch-pretrained-BERT	huggingface_pytorch-pretrained-BERT/examples/modular-transformers/modeling_dummy_bert.py	modeling_dummy_bert.DummyBertIntermediate	import torch from torch import nn from ...activations import ACT2FN class DummyBertIntermediate(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermedia...	class DummyBertIntermediate(nn.Module): def __init__(self, config): pass def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: pass	3	0	6	0	6	0	2	0	1	3	0	0	2	2	2	12	13	1	12	5	9	0	11	5	8	2	1	1	3
16	huggingface/pytorch-pretrained-BERT	huggingface_pytorch-pretrained-BERT/examples/modular-transformers/modeling_dummy_bert.py	modeling_dummy_bert.DummyBertLayer	from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer import torch from ...modeling_layers import GradientCheckpointingLayer from typing import Optional, Union from ...utils.deprecation import depr...	class DummyBertLayer(GradientCheckpointingLayer): def __init__(self, config, layer_idx=None): pass @deprecate_kwarg('past_key_value', new_name='past_key_values', version='4.58') def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor]=None, head_mask: Optional[tor...	5	0	27	2	23	2	4	0.1	1	7	3	0	3	8	3	13	84	9	70	32	57	7	41	23	37	7	1	2	11
17	huggingface/pytorch-pretrained-BERT	huggingface_pytorch-pretrained-BERT/examples/modular-transformers/modeling_dummy_bert.py	modeling_dummy_bert.DummyBertModel	from ...utils import auto_docstring, logging import torch from typing import Optional, Union from ...modeling_attn_mask_utils import _prepare_4d_attention_mask_for_sdpa, _prepare_4d_causal_attention_mask_for_sdpa from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCro...	null	8	2	37	4	25	8	5	0.35	1	7	3	0	5	6	5	6	211	29	135	45	108	47	65	29	59	21	2	2	27
18	huggingface/pytorch-pretrained-BERT	huggingface_pytorch-pretrained-BERT/examples/modular-transformers/modeling_dummy_bert.py	modeling_dummy_bert.DummyBertOutput	import torch from torch import nn class DummyBertOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dro...	class DummyBertOutput(nn.Module): def __init__(self, config): pass def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: pass	3	0	5	0	5	0	1	0	1	2	0	0	2	3	2	12	12	1	11	6	8	0	11	6	8	1	1	0	2
19	huggingface/pytorch-pretrained-BERT	huggingface_pytorch-pretrained-BERT/examples/modular-transformers/modeling_dummy_bert.py	modeling_dummy_bert.DummyBertPooler	from torch import nn import torch class DummyBertPooler(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: first_t...	class DummyBertPooler(nn.Module): def __init__(self, config): pass def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: pass	3	0	6	0	5	1	1	0.2	1	2	0	0	2	2	2	12	13	1	10	7	7	2	10	7	7	1	1	0	2
20	huggingface/pytorch-pretrained-BERT	huggingface_pytorch-pretrained-BERT/examples/modular-transformers/modeling_dummy_bert.py	modeling_dummy_bert.DummyBertPreTrainedModel	from ...modeling_utils import PreTrainedModel from .configuration_dummy_bert import DummyBertConfig from ...utils import auto_docstring, logging from torch import nn @auto_docstring class DummyBertPreTrainedModel(PreTrainedModel): config: DummyBertConfig base_model_prefix = 'dummy_bert' supports_gradient_c...	@auto_docstring class DummyBertPreTrainedModel(PreTrainedModel): def _init_weights(self, module): '''Initialize the weights''' pass	3	1	15	0	12	3	6	0.39	1	0	0	1	1	0	1	1	27	2	18	7	16	7	16	7	14	6	1	2	6
21	huggingface/pytorch-pretrained-BERT	huggingface_pytorch-pretrained-BERT/examples/modular-transformers/modeling_dummy_bert.py	modeling_dummy_bert.DummyBertSdpaSelfAttention	from ...utils.deprecation import deprecate_kwarg from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache from typing import Optional, Union import torch class DummyBertSdpaSelfAttention(DummyBertSelfAttention): def __init__(self, config, position_embedding_type=None, layer_idx=None): super()._...	class DummyBertSdpaSelfAttention(DummyBertSelfAttention): def __init__(self, config, position_embedding_type=None, layer_idx=None): pass @deprecate_kwarg('past_key_value', new_name='past_key_values', version='4.58') def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tens...	4	0	48	6	34	9	6	0.28	1	3	0	0	2	2	2	15	99	12	68	22	56	19	35	13	32	11	2	2	12
22	huggingface/pytorch-pretrained-BERT	huggingface_pytorch-pretrained-BERT/examples/modular-transformers/modeling_dummy_bert.py	modeling_dummy_bert.DummyBertSelfAttention	import math import torch from ...utils.deprecation import deprecate_kwarg from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache from torch import nn from typing import Optional, Union class DummyBertSelfAttention(nn.Module): def __init__(self, config, position_embedding_type=None, layer_idx=None): ...	class DummyBertSelfAttention(nn.Module): def __init__(self, config, position_embedding_type=None, layer_idx=None): pass @deprecate_kwarg('past_key_value', new_name='past_key_values', version='4.58') def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor]=None, he...	4	0	43	7	31	6	6	0.19	1	5	0	1	3	11	3	13	132	22	93	44	80	18	72	35	68	13	1	2	17
23	huggingface/pytorch-pretrained-BERT	huggingface_pytorch-pretrained-BERT/examples/modular-transformers/modeling_dummy_bert.py	modeling_dummy_bert.DummyBertSelfOutput	import torch from torch import nn class DummyBertSelfOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropo...	class DummyBertSelfOutput(nn.Module): def __init__(self, config): pass def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: pass	3	0	5	0	5	0	1	0	1	2	0	0	2	3	2	12	12	1	11	6	8	0	11	6	8	1	1	0	2
24	huggingface/pytorch-pretrained-BERT	huggingface_pytorch-pretrained-BERT/examples/modular-transformers/modeling_from_uppercase_model.py	modeling_from_uppercase_model.FromUppercaseModelAttention	import torch from .configuration_from_uppercase_model import FromUppercaseModelTextConfig, FromUppercaseModelVisionConfig from torch import nn from ...modeling_utils import ALL_ATTENTION_FUNCTIONS from typing import Callable, Optional, Union class FromUppercaseModelAttention(nn.Module): """Multi-headed attention f...	class FromUppercaseModelAttention(nn.Module): '''Multi-headed attention from 'Attention Is All You Need' paper''' def __init__(self, config: Union[FromUppercaseModelVisionConfig, FromUppercaseModelTextConfig]): pass def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Ten...	3	2	32	5	25	2	4	0.11	1	5	0	2	3	10	3	13	102	19	75	30	65	8	54	24	50	8	1	2	11
25	huggingface/pytorch-pretrained-BERT	huggingface_pytorch-pretrained-BERT/examples/modular-transformers/modeling_from_uppercase_model.py	modeling_from_uppercase_model.FromUppercaseModelEncoderLayer	from ...modeling_layers import GradientCheckpointingLayer from .configuration_from_uppercase_model import FromUppercaseModelTextConfig, FromUppercaseModelVisionConfig from torch import nn from typing import Callable, Optional, Union import torch class FromUppercaseModelEncoderLayer(GradientCheckpointingLayer): de...	class FromUppercaseModelEncoderLayer(GradientCheckpointingLayer): def __init__(self, config: Union[FromUppercaseModelVisionConfig, FromUppercaseModelTextConfig]): pass def forward(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, causal_attention_mask: torch.Tensor, output_attentions: ...	3	1	23	3	16	5	2	0.31	1	4	1	0	2	5	2	12	48	6	32	17	23	10	21	11	18	2	1	1	3
26	huggingface/pytorch-pretrained-BERT	huggingface_pytorch-pretrained-BERT/examples/modular-transformers/modeling_from_uppercase_model.py	modeling_from_uppercase_model.FromUppercaseModelMLP	import torch from torch import nn from ...activations import ACT2FN class FromUppercaseModelMLP(nn.Module): def __init__(self, config): super().__init__() self.config = config self.activation_fn = ACT2FN[config.hidden_act] self.fc1 = nn.Linear(config.hidden_size, config.intermediat...	class FromUppercaseModelMLP(nn.Module): def __init__(self, config): pass def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: pass	3	0	6	0	6	0	1	0	1	2	0	0	2	4	2	12	13	1	12	7	9	0	12	7	9	1	1	0	2
27	huggingface/pytorch-pretrained-BERT	huggingface_pytorch-pretrained-BERT/examples/modular-transformers/modeling_multimodal2.py	modeling_multimodal2.Multimodal2VisionAttention	from torch import nn from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel import torch from typing import Callable, Optional, Union from .configuration_multimodal2 import Multimodal2Config, Multimodal2TextConfig, Multimodal2VisionConfig class Multimodal2VisionAttention(nn.Module): """Multi-headed...	class Multimodal2VisionAttention(nn.Module): '''Multi-headed attention from 'Attention Is All You Need' paper''' def __init__(self, config: Union[Multimodal2VisionConfig, Multimodal2TextConfig]): pass def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor]=None, caus...	3	2	32	5	25	2	4	0.11	1	5	0	2	3	10	3	13	102	19	75	30	65	8	54	24	50	8	1	2	11
28	huggingface/pytorch-pretrained-BERT	huggingface_pytorch-pretrained-BERT/examples/modular-transformers/modeling_multimodal2.py	modeling_multimodal2.Multimodal2VisionEmbeddings	from torch import nn import torch from .configuration_multimodal2 import Multimodal2Config, Multimodal2TextConfig, Multimodal2VisionConfig from ...utils import auto_docstring, can_return_tuple, torch_int class Multimodal2VisionEmbeddings(nn.Module): def __init__(self, config: Multimodal2VisionConfig): sup...	class Multimodal2VisionEmbeddings(nn.Module): def __init__(self, config: Multimodal2VisionConfig): pass def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor: ''' This method allows to interpolate the pre-trained position encodings, to b...	4	1	26	5	19	3	2	0.16	1	4	0	0	3	9	3	13	81	16	57	27	53	9	43	27	39	3	1	1	6
29	huggingface/pytorch-pretrained-BERT	huggingface_pytorch-pretrained-BERT/examples/modular-transformers/modeling_multimodal2.py	modeling_multimodal2.Multimodal2VisionEncoder	from typing import Callable, Optional, Union from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling from torch import nn import torch class Multimodal2VisionEncoder(nn.Module): """ Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a [`Mul...	class Multimodal2VisionEncoder(nn.Module): ''' Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a [`Multimodal2VisionEncoderLayer`]. Args: config: Multimodal2VisionConfig ''' def __init__(self, config): pass def forward(self...	3	2	43	5	25	13	7	0.61	1	7	1	0	2	3	2	12	95	13	51	19	40	31	27	11	24	12	1	2	13
30	huggingface/pytorch-pretrained-BERT	huggingface_pytorch-pretrained-BERT/examples/modular-transformers/modeling_multimodal2.py	modeling_multimodal2.Multimodal2VisionEncoderLayer	import torch from ...modeling_layers import GradientCheckpointingLayer from typing import Callable, Optional, Union from torch import nn class Multimodal2VisionEncoderLayer(GradientCheckpointingLayer): def __init__(self, config): super().__init__() self.embed_dim = config.hidden_size self....	class Multimodal2VisionEncoderLayer(GradientCheckpointingLayer): def __init__(self, config): pass def forward(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, causal_attention_mask: torch.Tensor, output_attentions: Optional[bool]=False) -> tuple[torch.FloatTensor]: ''' ...	3	1	23	3	16	5	2	0.31	1	4	1	0	2	5	2	12	48	6	32	17	23	10	21	11	18	2	1	1	3
31	huggingface/pytorch-pretrained-BERT	huggingface_pytorch-pretrained-BERT/examples/modular-transformers/modeling_multimodal2.py	modeling_multimodal2.Multimodal2VisionMLP	from torch import nn from ...activations import ACT2FN import torch class Multimodal2VisionMLP(nn.Module): def __init__(self, config): super().__init__() self.config = config self.activation_fn = ACT2FN[config.hidden_act] self.fc1 = nn.Linear(config.hidden_size, config.intermediate...	class Multimodal2VisionMLP(nn.Module): def __init__(self, config): pass def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: pass	3	0	6	0	6	0	1	0	1	2	0	0	2	4	2	12	13	1	12	7	9	0	12	7	9	1	1	0	2
32	huggingface/pytorch-pretrained-BERT	huggingface_pytorch-pretrained-BERT/examples/modular-transformers/modeling_multimodal2.py	modeling_multimodal2.Multimodal2VisionModel	from typing import Callable, Optional, Union from torch import nn import torch from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling from .configuration_multimodal2 import Multimodal2Config, Multimodal2TextConfig, Multimodal2VisionConfig from ...utils import auto_docstring, can_return_tuple, torch...	@add_start_docstrings('New doc', MULTIMODAL2_VISION_START_DOCSTRING) class Multimodal2VisionModel(Multimodal2VisionPreTrainedModel): def __init__(self, config: Multimodal2VisionConfig): pass def get_input_embeddings(self) -> nn.Module: pass @can_return_tuple @auto_docstring def for...	7	1	15	2	7	6	1	0.61	1	3	1	0	3	1	3	4	55	10	28	16	15	17	13	8	9	2	2	0	4
33	huggingface/pytorch-pretrained-BERT	huggingface_pytorch-pretrained-BERT/examples/modular-transformers/modeling_multimodal2.py	modeling_multimodal2.Multimodal2VisionPreTrainedModel	from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel from .configuration_multimodal2 import Multimodal2Config, Multimodal2TextConfig, Multimodal2VisionConfig from ...utils import auto_docstring, can_return_tuple, torch_int @auto_docstring class Multimodal2VisionPreTrainedModel(PreTrainedModel): c...	@auto_docstring class Multimodal2VisionPreTrainedModel(PreTrainedModel): def _init_weights(self, module): '''Initialize the weights''' pass	3	1	4	0	3	1	2	0.56	1	1	1	1	1	0	1	1	16	2	9	7	7	5	9	7	7	2	1	1	2

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📘 Data Dictionary for the Curated Class-level Dataset

Field	Description
`id`	A unique identifier for each data point, starting from 0.
`repository_name`	Name of the GitHub repository from which the class was extracted.
`file_path`	Full path to the file containing the class within the repository.
`class_name`	Name of the class defined in the corresponding file.
`human_written_code`	Full source code of the human-written class, including all docstrings.
`class_skeleton`	Extracted skeleton of the class, including class and method signatures along with associated docstrings (if present).
`total_program_units`	Total number of program units (i.e., classes and methods) within the class skeleton.
`total_doc_str`	Number of program units in the class skeleton that contain associated docstrings.
`AvgCountLine`	Average number of lines per class.
`AvgCountLineBlank`	Average number of blank lines per class.
`AvgCountLineCode`	Average number of code lines per class (excluding comments and blanks).
`AvgCountLineComment`	Average number of comment lines per class.
`AvgCyclomatic`	Average cyclomatic complexity across methods in the class.
`CommentToCodeRatio`	Ratio of comment lines to code lines in the class.
`CountClassBase`	Number of base classes (i.e., direct superclasses).
`CountClassCoupled`	Number of other classes referenced (coupled) by this class.
`CountClassCoupledModified`	Number of coupled classes after removing standard library dependencies.
`CountClassDerived`	Number of classes that inherit from this class.
`CountDeclInstanceMethod`	Number of instance methods declared in the class.
`CountDeclInstanceVariable`	Number of instance variables declared in the class.
`CountDeclMethod`	Number of methods declared in the class (excluding inherited ones).
`CountDeclMethodAll`	Total number of declared methods, including inherited ones.
`CountLine`	Total number of lines in the class.
`CountLineBlank`	Number of blank lines in the class.
`CountLineCode`	Number of executable code lines in the class.
`CountLineCodeDecl`	Number of declaration lines in the class.
`CountLineCodeExe`	Number of executable statement lines in the class.
`CountLineComment`	Number of comment lines in the class.
`CountStmt`	Total number of statements in the class.
`CountStmtDecl`	Number of declaration statements in the class.
`CountStmtExe`	Number of executable statements in the class.
`MaxCyclomatic`	Maximum cyclomatic complexity among all methods in the class.
`MaxInheritanceTree`	Maximum depth of the class in the inheritance hierarchy.
`MaxNesting`	Maximum level of nested control structures in the class.
`SumCyclomatic`	Sum of cyclomatic complexity across all methods in the class.

If you use this dataset, please cite:

@article{rahman2025large,
  title={A Large-scale Class-level Benchmark Dataset for Code Generation with LLMs},
  author={Rahman, Musfiqur and Khatoonabadi, SayedHassan and Shihab, Emad},
  journal={arXiv preprint arXiv:2504.15564},
  year={2025}
}

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Paper for mrahman2025/OpenClassGen

A Large-scale Class-level Benchmark Dataset for Code Generation with LLMs

Paper • 2504.15564 • Published Apr 22, 2025