id int64 0 328k | repository_name stringlengths 7 58 | file_path stringlengths 9 302 | class_name stringlengths 5 256 | human_written_code stringlengths 16 2.16M | class_skeleton stringlengths 18 1.49M ⌀ | total_program_units int64 1 1.76k | total_doc_str int64 0 771 | AvgCountLine float64 0 7.89k | AvgCountLineBlank float64 0 297 | AvgCountLineCode float64 0 7.89k | AvgCountLineComment float64 0 7.89k | AvgCyclomatic float64 0 130 | CommentToCodeRatio float64 0 168 | CountClassBase float64 0 40 | CountClassCoupled float64 0 583 | CountClassCoupledModified float64 0 575 | CountClassDerived float64 0 5.35k | CountDeclInstanceMethod float64 0 529 | CountDeclInstanceVariable float64 0 296 | CountDeclMethod float64 0 599 | CountDeclMethodAll float64 0 1.12k | CountLine float64 1 40.4k | CountLineBlank float64 0 8.16k | CountLineCode float64 1 25.7k | CountLineCodeDecl float64 1 8.15k | CountLineCodeExe float64 0 24.2k | CountLineComment float64 0 16.5k | CountStmt float64 1 9.71k | CountStmtDecl float64 1 8.15k | CountStmtExe float64 0 9.69k | MaxCyclomatic float64 0 759 | MaxInheritanceTree float64 0 16 | MaxNesting float64 0 34 | SumCyclomatic float64 0 2.9k |
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100 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/language-modeling/run_fim.py | run_fim.ModelArguments | from dataclasses import dataclass, field
from typing import Optional
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
"""
model_name_or_path: Optional[str] = field(default=None, metadata={'help': "The model check... | @dataclass
class ModelArguments:
'''
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
'''
def __post_init__(self):
pass | 3 | 1 | 5 | 0 | 5 | 0 | 2 | 0.03 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 100 | 2 | 95 | 16 | 93 | 3 | 18 | 16 | 16 | 2 | 0 | 1 | 2 |
101 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/text-generation/run_generation.py | run_generation._ModelFallbackWrapper | from transformers import AutoTokenizer, BloomForCausalLM, BloomTokenizerFast, CTRLLMHeadModel, CTRLTokenizer, GenerationMixin, GPT2LMHeadModel, GPT2Tokenizer, GPTJForCausalLM, LlamaForCausalLM, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer, OPTForCausalLM, XLMTokenizer, XLMWithLMHeadModel, XLNetLMHeadModel, XLNetTokenizer
i... |
class _ModelFallbackWrapper(GenerationMixin):
def __init__(self, optimized, default):
pass
def __call__(self, *args, **kwargs):
pass
def __getattr__(self, item):
pass
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, use_cache=None,... | 6 | 1 | 8 | 0 | 7 | 1 | 2 | 0.14 | 1 | 4 | 0 | 0 | 5 | 2 | 5 | 43 | 45 | 5 | 35 | 18 | 25 | 5 | 23 | 14 | 17 | 4 | 1 | 2 | 8 |
102 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/text-classification/run_glue.py | run_glue.DataTrainingArguments | from dataclasses import dataclass, field
from typing import Optional
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
Using `HfArgumentParser` we can turn this class
into argparse arguments to be able to specify them o... | @dataclass
class DataTrainingArguments:
'''
Arguments pertaining to what data we are going to input our model for training and eval.
Using `HfArgumentParser` we can turn this class
into argparse arguments to be able to specify them on
the command line.
'''
def __post_init__(self):
p... | 3 | 1 | 16 | 0 | 16 | 0 | 5 | 0.07 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 91 | 3 | 82 | 16 | 80 | 6 | 24 | 16 | 22 | 5 | 0 | 2 | 5 |
103 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/legacy/pytorch-lightning/run_glue.py | run_glue.GLUETransformer | from transformers import glue_compute_metrics as compute_metrics
from lightning_base import BaseTransformer, add_generic_args, generic_train
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
import os
from argpars... |
class GLUETransformer(BaseTransformer):
def __init__(self, hparams):
pass
def forward(self, **inputs):
pass
def training_step(self, batch, batch_idx):
pass
def prepare_data(self):
'''Called to initialize data. Use the call to construct features'''
pass
d... | 12 | 2 | 14 | 2 | 12 | 0 | 2 | 0.03 | 1 | 11 | 0 | 0 | 9 | 2 | 10 | 25 | 152 | 28 | 120 | 49 | 108 | 4 | 81 | 47 | 70 | 4 | 2 | 2 | 23 |
104 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/text-classification/run_glue.py | run_glue.ModelArguments | from typing import Optional
from dataclasses import dataclass, field
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(metadata={'help': 'Path to pretrained model or model identifier from hugging... | @dataclass
class ModelArguments:
'''
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
'''
pass | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0.07 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 49 | 1 | 45 | 10 | 44 | 3 | 10 | 10 | 9 | 0 | 0 | 0 | 0 |
105 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/image-classification/run_image_classification.py | run_image_classification.DataTrainingArguments | from typing import Optional
from dataclasses import dataclass, field
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
Using `HfArgumentParser` we can turn this class into argparse arguments to be able to specify
them on... | @dataclass
class DataTrainingArguments:
'''
Arguments pertaining to what data we are going to input our model for training and eval.
Using `HfArgumentParser` we can turn this class into argparse arguments to be able to specify
them on the command line.
'''
def __post_init__(self):
pass | 3 | 1 | 5 | 0 | 5 | 0 | 2 | 0.11 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 53 | 2 | 46 | 11 | 44 | 5 | 13 | 11 | 11 | 2 | 0 | 1 | 2 |
106 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/image-classification/run_image_classification.py | run_image_classification.ModelArguments | from typing import Optional
from dataclasses import dataclass, field
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(default='google/vit-base-patch16-224-in21k', metadata={'help': 'Path to pret... | @dataclass
class ModelArguments:
'''
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
'''
pass | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0.07 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 47 | 1 | 43 | 10 | 42 | 3 | 10 | 10 | 9 | 0 | 0 | 0 | 0 |
107 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/instance-segmentation/run_instance_segmentation.py | run_instance_segmentation.Evaluator | from torchmetrics.detection.mean_ap import MeanAveragePrecision
from collections.abc import Mapping
from transformers.trainer import EvalPrediction
import torch
from transformers import AutoImageProcessor, AutoModelForUniversalSegmentation, HfArgumentParser, Trainer, TrainingArguments
class Evaluator:
"""
Comp... |
class Evaluator:
'''
Compute metrics for the instance segmentation task.
'''
def __init__(self, image_processor: AutoImageProcessor, id2label: Mapping[int, str], threshold: float=0.0):
'''
Initialize evaluator with image processor, id2label mapping and threshold for filtering predictio... | 9 | 5 | 16 | 2 | 11 | 4 | 2 | 0.36 | 0 | 9 | 1 | 0 | 7 | 4 | 7 | 7 | 127 | 21 | 78 | 41 | 64 | 28 | 53 | 35 | 45 | 4 | 0 | 2 | 14 |
108 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/instance-segmentation/run_instance_segmentation.py | run_instance_segmentation.ModelOutput | from dataclasses import dataclass, field
import torch
@dataclass
class ModelOutput:
class_queries_logits: torch.Tensor
masks_queries_logits: torch.Tensor | @dataclass
class ModelOutput:
pass | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 3 | 1 | 2 | 0 | 3 | 1 | 2 | 0 | 0 | 0 | 0 |
109 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/legacy/run_language_modeling.py | run_language_modeling.DataTrainingArguments | from typing import Optional
from dataclasses import dataclass, field
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
train_data_file: Optional[str] = field(default=None, metadata={'help': 'The input training data f... | @dataclass
class DataTrainingArguments:
'''
Arguments pertaining to what data we are going to input our model for training and eval.
'''
pass | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0.05 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 67 | 3 | 61 | 14 | 60 | 3 | 14 | 14 | 13 | 0 | 0 | 0 | 0 |
110 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/legacy/run_language_modeling.py | run_language_modeling.ModelArguments | from dataclasses import dataclass, field
from typing import Optional
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
"""
model_name_or_path: Optional[str] = field(default=None, metadata={'help': 'The model check... | @dataclass
class ModelArguments:
'''
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
'''
pass | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0.13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 28 | 1 | 24 | 6 | 23 | 3 | 6 | 6 | 5 | 0 | 0 | 0 | 0 |
111 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/image-pretraining/run_mae.py | run_mae.CustomTrainingArguments | from dataclasses import dataclass, field
from transformers import HfArgumentParser, Trainer, TrainingArguments, ViTImageProcessor, ViTMAEConfig, ViTMAEForPreTraining
@dataclass
class CustomTrainingArguments(TrainingArguments):
base_learning_rate: float = field(default=0.001, metadata={'help': 'Base learning rate: ... | @dataclass
class CustomTrainingArguments(TrainingArguments):
pass | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 32 | 4 | 0 | 4 | 2 | 3 | 0 | 2 | 2 | 1 | 0 | 1 | 0 | 0 |
112 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/image-pretraining/run_mae.py | run_mae.DataTrainingArguments | from dataclasses import dataclass, field
from typing import Optional
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
Using `HfArgumentParser` we can turn this class
into argparse arguments to be able to specify them on... | @dataclass
class DataTrainingArguments:
'''
Arguments pertaining to what data we are going to input our model for training and eval.
Using `HfArgumentParser` we can turn this class
into argparse arguments to be able to specify them on
the command line.
'''
def __post_init__(self):
p... | 3 | 1 | 7 | 0 | 7 | 0 | 4 | 0.12 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 58 | 2 | 50 | 13 | 48 | 6 | 17 | 13 | 15 | 4 | 0 | 1 | 4 |
113 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/image-pretraining/run_mae.py | run_mae.ModelArguments | from typing import Optional
from dataclasses import dataclass, field
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/image processor we are going to pre-train.
"""
model_name_or_path: str = field(default=None, metadata={'help': "The model checkpoint for weights initializ... | @dataclass
class ModelArguments:
'''
Arguments pertaining to which model/config/image processor we are going to pre-train.
'''
pass | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0.07 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 48 | 1 | 44 | 10 | 43 | 3 | 10 | 10 | 9 | 0 | 0 | 0 | 0 |
114 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/image-pretraining/run_mim.py | run_mim.DataTrainingArguments | from typing import Optional
from dataclasses import dataclass, field
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
Using `HfArgumentParser` we can turn this class into argparse arguments to be able to
specify them on... | @dataclass
class DataTrainingArguments:
'''
Arguments pertaining to what data we are going to input our model for training and eval.
Using `HfArgumentParser` we can turn this class into argparse arguments to be able to
specify them on the command line.
'''
def __post_init__(self):
pass | 3 | 1 | 7 | 0 | 7 | 0 | 4 | 0.11 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 53 | 2 | 46 | 14 | 44 | 5 | 18 | 14 | 16 | 4 | 0 | 1 | 4 |
115 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/image-pretraining/run_mim.py | run_mim.MaskGenerator | import torch
import numpy as np
class MaskGenerator:
"""
A class to generate boolean masks for the pretraining task.
A mask is a 1D tensor of shape (model_patch_size**2,) where the value is either 0 or 1,
where 1 indicates "masked".
"""
def __init__(self, input_size=192, mask_patch_size=32, m... |
class MaskGenerator:
'''
A class to generate boolean masks for the pretraining task.
A mask is a 1D tensor of shape (model_patch_size**2,) where the value is either 0 or 1,
where 1 indicates "masked".
'''
def __init__(self, input_size=192, mask_patch_size=32, model_patch_size=4, mask_ratio=0.6... | 3 | 1 | 13 | 3 | 10 | 0 | 2 | 0.24 | 0 | 2 | 0 | 0 | 2 | 8 | 2 | 2 | 34 | 8 | 21 | 13 | 18 | 5 | 21 | 13 | 18 | 3 | 0 | 1 | 4 |
116 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/image-pretraining/run_mim.py | run_mim.ModelArguments | from typing import Optional
from dataclasses import dataclass, field
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/image processor we are going to pre-train.
"""
model_name_or_path: str = field(default=None, metadata={'help': "The model checkpoint for weights initializ... | @dataclass
class ModelArguments:
'''
Arguments pertaining to which model/config/image processor we are going to pre-train.
'''
pass | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0.04 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 79 | 1 | 75 | 13 | 74 | 3 | 13 | 13 | 12 | 0 | 0 | 0 | 0 |
117 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/image-pretraining/run_mim_no_trainer.py | run_mim_no_trainer.MaskGenerator | import numpy as np
import torch
class MaskGenerator:
"""
A class to generate boolean masks for the pretraining task.
A mask is a 1D tensor of shape (model_patch_size**2,) where the value is either 0 or 1,
where 1 indicates "masked".
"""
def __init__(self, input_size=192, mask_patch_size=32, m... |
class MaskGenerator:
'''
A class to generate boolean masks for the pretraining task.
A mask is a 1D tensor of shape (model_patch_size**2,) where the value is either 0 or 1,
where 1 indicates "masked".
'''
def __init__(self, input_size=192, mask_patch_size=32, model_patch_size=4, mask_ratio=0.6... | 3 | 1 | 13 | 3 | 10 | 0 | 2 | 0.24 | 0 | 2 | 0 | 0 | 2 | 8 | 2 | 2 | 34 | 8 | 21 | 13 | 18 | 5 | 21 | 13 | 18 | 3 | 0 | 1 | 4 |
118 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/language-modeling/run_mlm.py | run_mlm.DataTrainingArguments | from dataclasses import dataclass, field
from transformers.utils.versions import require_version
from typing import Optional
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
dataset_name: Optional[str] = field(defau... | @dataclass
class DataTrainingArguments:
'''
Arguments pertaining to what data we are going to input our model for training and eval.
'''
def __post_init__(self):
pass | 3 | 1 | 15 | 1 | 14 | 0 | 7 | 0.04 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 89 | 3 | 83 | 17 | 81 | 3 | 28 | 17 | 26 | 7 | 0 | 3 | 7 |
119 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/language-modeling/run_mlm.py | run_mlm.ModelArguments | from typing import Optional
from dataclasses import dataclass, field
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
"""
model_name_or_path: Optional[str] = field(default=None, metadata={'help': "The model check... | @dataclass
class ModelArguments:
'''
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
'''
def __post_init__(self):
pass | 3 | 1 | 5 | 0 | 5 | 0 | 2 | 0.04 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 88 | 2 | 83 | 14 | 81 | 3 | 16 | 14 | 14 | 2 | 0 | 1 | 2 |
120 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/legacy/multiple_choice/run_multiple_choice.py | run_multiple_choice.DataTrainingArguments | from utils_multiple_choice import MultipleChoiceDataset, Split, processors
from dataclasses import dataclass, field
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
task_name: str = field(metadata={'help': 'The name... | @dataclass
class DataTrainingArguments:
'''
Arguments pertaining to what data we are going to input our model for training and eval.
'''
pass | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0.2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 19 | 1 | 15 | 5 | 14 | 3 | 5 | 5 | 4 | 0 | 0 | 0 | 0 |
121 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/legacy/multiple_choice/run_multiple_choice.py | run_multiple_choice.ModelArguments | from dataclasses import dataclass, field
from typing import Optional
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(metadata={'help': 'Path to pretrained model or model identifier from hugging... | @dataclass
class ModelArguments:
'''
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
'''
pass | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0.21 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 18 | 1 | 14 | 5 | 13 | 3 | 5 | 5 | 4 | 0 | 0 | 0 | 0 |
122 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/legacy/token-classification/run_ner.py | run_ner.DataTrainingArguments | from dataclasses import dataclass, field
from typing import Optional
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
data_dir: str = field(metadata={'help': 'The input data dir. Should contain the .txt files for a ... | @dataclass
class DataTrainingArguments:
'''
Arguments pertaining to what data we are going to input our model for training and eval.
'''
pass | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0.15 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 24 | 1 | 20 | 5 | 19 | 3 | 5 | 5 | 4 | 0 | 0 | 0 | 0 |
123 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/token-classification/run_ner.py | run_ner.DataTrainingArguments | from typing import Optional
from dataclasses import dataclass, field
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
task_name: Optional[str] = field(default='ner', metadata={'help': 'The name of the task (ner, pos... | @dataclass
class DataTrainingArguments:
'''
Arguments pertaining to what data we are going to input our model for training and eval.
'''
def __post_init__(self):
pass | 3 | 1 | 11 | 0 | 11 | 0 | 4 | 0.03 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 107 | 2 | 102 | 20 | 100 | 3 | 28 | 20 | 26 | 4 | 0 | 2 | 4 |
124 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/legacy/token-classification/run_ner.py | run_ner.ModelArguments | from typing import Optional
from dataclasses import dataclass, field
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(metadata={'help': 'Path to pretrained model or model identifier from hugging... | @dataclass
class ModelArguments:
'''
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
'''
pass | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0.28 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 24 | 1 | 18 | 7 | 17 | 5 | 7 | 7 | 6 | 0 | 0 | 0 | 0 |
125 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/token-classification/run_ner.py | run_ner.ModelArguments | from typing import Optional
from dataclasses import dataclass, field
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(metadata={'help': 'Path to pretrained model or model identifier from hugging... | @dataclass
class ModelArguments:
'''
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
'''
pass | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0.07 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 45 | 1 | 41 | 9 | 40 | 3 | 9 | 9 | 8 | 0 | 0 | 0 | 0 |
126 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/legacy/pytorch-lightning/run_ner.py | run_ner.NERTransformer | from utils_ner import TokenClassificationTask
from torch.nn import CrossEntropyLoss
import os
import numpy as np
from argparse import Namespace
from importlib import import_module
import torch
from torch.utils.data import DataLoader, TensorDataset
from lightning_base import BaseTransformer, add_generic_args, generic_tr... |
class NERTransformer(BaseTransformer):
'''
A training module for NER. See BaseTransformer for the core options.
'''
def __init__(self, hparams):
pass
def forward(self, **inputs):
pass
def training_step(self, batch, batch_num):
'''Compute loss and log.'''
pass
... | 12 | 6 | 16 | 1 | 14 | 2 | 2 | 0.13 | 1 | 12 | 1 | 0 | 9 | 4 | 10 | 25 | 179 | 20 | 143 | 52 | 131 | 18 | 89 | 50 | 78 | 4 | 2 | 3 | 23 |
127 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/object-detection/run_object_detection.py | run_object_detection.ModelArguments | from typing import Any, Optional, Union
from dataclasses import dataclass, field
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(default='facebook/detr-resnet-50', metadata={'help': 'Path to pr... | @dataclass
class ModelArguments:
'''
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
'''
pass | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0.07 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 45 | 1 | 41 | 9 | 40 | 3 | 9 | 9 | 8 | 0 | 0 | 0 | 0 |
128 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/object-detection/run_object_detection.py | run_object_detection.ModelOutput | from dataclasses import dataclass, field
import torch
@dataclass
class ModelOutput:
logits: torch.Tensor
pred_boxes: torch.Tensor | @dataclass
class ModelOutput:
pass | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 3 | 1 | 2 | 0 | 3 | 1 | 2 | 0 | 0 | 0 | 0 |
129 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/language-modeling/run_plm.py | run_plm.DataTrainingArguments | from typing import Optional
from dataclasses import dataclass, field
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
dataset_name: Optional[str] = field(default=None, metadata={'help': 'The name of the dataset to u... | @dataclass
class DataTrainingArguments:
'''
Arguments pertaining to what data we are going to input our model for training and eval.
'''
def __post_init__(self):
pass | 3 | 1 | 10 | 0 | 10 | 0 | 4 | 0.03 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 102 | 2 | 97 | 18 | 95 | 3 | 25 | 18 | 23 | 4 | 0 | 2 | 4 |
130 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/language-modeling/run_plm.py | run_plm.ModelArguments | from dataclasses import dataclass, field
from typing import Optional
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
"""
model_name_or_path: Optional[str] = field(default=None, metadata={'help': "The model check... | @dataclass
class ModelArguments:
'''
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
'''
def __post_init__(self):
pass | 3 | 1 | 5 | 0 | 5 | 0 | 2 | 0.05 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 64 | 2 | 59 | 11 | 57 | 3 | 13 | 11 | 11 | 2 | 0 | 1 | 2 |
131 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/question-answering/run_qa.py | run_qa.DataTrainingArguments | from dataclasses import dataclass, field
from typing import Optional
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
dataset_name: Optional[str] = field(default=None, metadata={'help': 'The name of the dataset to u... | @dataclass
class DataTrainingArguments:
'''
Arguments pertaining to what data we are going to input our model for training and eval.
'''
def __post_init__(self):
pass | 3 | 1 | 18 | 0 | 18 | 0 | 5 | 0.03 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 121 | 2 | 116 | 20 | 114 | 3 | 30 | 20 | 28 | 5 | 0 | 2 | 5 |
132 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/question-answering/run_qa.py | run_qa.ModelArguments | from typing import Optional
from dataclasses import dataclass, field
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(metadata={'help': 'Path to pretrained model or model identifier from hugging... | @dataclass
class ModelArguments:
'''
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
'''
pass | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0.08 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 41 | 1 | 37 | 8 | 36 | 3 | 8 | 8 | 7 | 0 | 0 | 0 | 0 |
133 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/question-answering/run_qa_beam_search.py | run_qa_beam_search.DataTrainingArguments | from dataclasses import dataclass, field
from typing import Optional
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
dataset_name: Optional[str] = field(default=None, metadata={'help': 'The name of the dataset to u... | @dataclass
class DataTrainingArguments:
'''
Arguments pertaining to what data we are going to input our model for training and eval.
'''
def __post_init__(self):
pass | 3 | 1 | 18 | 0 | 18 | 0 | 5 | 0.02 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 131 | 2 | 126 | 21 | 124 | 3 | 31 | 21 | 29 | 5 | 0 | 2 | 5 |
134 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/question-answering/run_qa_beam_search.py | run_qa_beam_search.ModelArguments | from typing import Optional
from dataclasses import dataclass, field
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(metadata={'help': 'Path to pretrained model or model identifier from hugging... | @dataclass
class ModelArguments:
'''
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
'''
pass | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0.11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 31 | 1 | 27 | 7 | 26 | 3 | 7 | 7 | 6 | 0 | 0 | 0 | 0 |
135 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/semantic-segmentation/run_semantic_segmentation.py | run_semantic_segmentation.DataTrainingArguments | import warnings
from typing import Optional
from dataclasses import dataclass, field
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
Using `HfArgumentParser` we can turn this class into argparse arguments to be able to spe... | @dataclass
class DataTrainingArguments:
'''
Arguments pertaining to what data we are going to input our model for training and eval.
Using `HfArgumentParser` we can turn this class into argparse arguments to be able to specify
them on the command line.
'''
def __post_init__(self):
pass | 3 | 1 | 11 | 0 | 11 | 0 | 3 | 0.1 | 0 | 2 | 0 | 0 | 1 | 0 | 1 | 1 | 57 | 2 | 50 | 9 | 48 | 5 | 14 | 9 | 12 | 3 | 0 | 1 | 3 |
136 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/semantic-segmentation/run_semantic_segmentation.py | run_semantic_segmentation.ModelArguments | from typing import Optional
from dataclasses import dataclass, field
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(default='nvidia/mit-b0', metadata={'help': 'Path to pretrained model or mode... | @dataclass
class ModelArguments:
'''
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
'''
pass | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0.09 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 39 | 1 | 35 | 8 | 34 | 3 | 8 | 8 | 7 | 0 | 0 | 0 | 0 |
137 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/question-answering/run_seq2seq_qa.py | run_seq2seq_qa.DataTrainingArguments | from dataclasses import dataclass, field
from typing import Optional
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
dataset_name: Optional[str] = field(default=None, metadata={'help': 'The name of the dataset to u... | @dataclass
class DataTrainingArguments:
'''
Arguments pertaining to what data we are going to input our model for training and eval.
'''
def __post_init__(self):
pass | 3 | 1 | 20 | 0 | 20 | 0 | 6 | 0.02 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 161 | 2 | 156 | 26 | 154 | 3 | 38 | 26 | 36 | 6 | 0 | 2 | 6 |
138 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/question-answering/run_seq2seq_qa.py | run_seq2seq_qa.ModelArguments | from typing import Optional
from dataclasses import dataclass, field
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(metadata={'help': 'Path to pretrained model or model identifier from hugging... | @dataclass
class ModelArguments:
'''
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
'''
pass | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0.07 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 45 | 1 | 41 | 9 | 40 | 3 | 9 | 9 | 8 | 0 | 0 | 0 | 0 |
139 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py | run_speech_recognition_ctc.DataCollatorCTCWithPadding | from transformers import AutoConfig, AutoFeatureExtractor, AutoModelForCTC, AutoProcessor, AutoTokenizer, HfArgumentParser, Trainer, TrainingArguments, Wav2Vec2Processor, set_seed
import torch
from typing import Optional, Union
from dataclasses import dataclass, field
@dataclass
class DataCollatorCTCWithPadding:
"... | @dataclass
class DataCollatorCTCWithPadding:
'''
Data collator that will dynamically pad the inputs received.
Args:
processor (:class:`~transformers.AutoProcessor`)
The processor used for processing the data.
padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_... | 3 | 1 | 30 | 5 | 22 | 3 | 2 | 0.93 | 0 | 3 | 0 | 0 | 1 | 0 | 1 | 1 | 61 | 7 | 28 | 11 | 26 | 26 | 16 | 11 | 14 | 2 | 0 | 1 | 2 |
140 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py | run_speech_recognition_ctc.ModelArguments | from typing import Optional, Union
from dataclasses import dataclass, field
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(metadata={'help': 'Path to pretrained model or model identifier from ... | @dataclass
class ModelArguments:
'''
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
'''
pass | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0.04 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 82 | 1 | 78 | 18 | 77 | 3 | 18 | 18 | 17 | 0 | 0 | 0 | 0 |
141 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/speech-recognition/run_speech_recognition_ctc_adapter.py | run_speech_recognition_ctc_adapter.DataCollatorCTCWithPadding | from transformers import AutoConfig, AutoFeatureExtractor, AutoModelForCTC, AutoProcessor, AutoTokenizer, HfArgumentParser, Trainer, TrainingArguments, Wav2Vec2Processor, set_seed
import torch
from typing import Optional, Union
from dataclasses import dataclass, field
@dataclass
class DataCollatorCTCWithPadding:
"... | @dataclass
class DataCollatorCTCWithPadding:
'''
Data collator that will dynamically pad the inputs received.
Args:
processor (:class:`~transformers.AutoProcessor`)
The processor used for processing the data.
padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_... | 3 | 1 | 28 | 5 | 20 | 3 | 2 | 1.04 | 0 | 3 | 0 | 0 | 1 | 0 | 1 | 1 | 58 | 7 | 25 | 10 | 23 | 26 | 15 | 10 | 13 | 2 | 0 | 1 | 2 |
142 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/speech-recognition/run_speech_recognition_ctc_adapter.py | run_speech_recognition_ctc_adapter.ModelArguments | from dataclasses import dataclass, field
from typing import Optional, Union
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(metadata={'help': 'Path to pretrained model or model identifier from ... | @dataclass
class ModelArguments:
'''
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
'''
pass | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0.06 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 58 | 1 | 54 | 12 | 53 | 3 | 12 | 12 | 11 | 0 | 0 | 0 | 0 |
143 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py | run_speech_recognition_seq2seq.DataCollatorSpeechSeq2SeqWithPadding | import torch
from dataclasses import dataclass, field
from typing import Any, Optional, Union
@dataclass
class DataCollatorSpeechSeq2SeqWithPadding:
"""
Data collator that will dynamically pad the inputs received.
Args:
processor ([`WhisperProcessor`])
The processor used for processing ... | @dataclass
class DataCollatorSpeechSeq2SeqWithPadding:
'''
Data collator that will dynamically pad the inputs received.
Args:
processor ([`WhisperProcessor`])
The processor used for processing the data.
decoder_start_token_id (`int`)
The begin-of-sentence of the decod... | 3 | 1 | 25 | 7 | 13 | 5 | 3 | 0.88 | 0 | 3 | 0 | 0 | 1 | 0 | 1 | 1 | 41 | 9 | 17 | 8 | 15 | 15 | 17 | 8 | 15 | 3 | 0 | 1 | 3 |
144 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py | run_speech_recognition_seq2seq.DataTrainingArguments | from typing import Any, Optional, Union
from dataclasses import dataclass, field
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
dataset_name: str = field(default=None, metadata={'help': 'The name of the dataset to... | @dataclass
class DataTrainingArguments:
'''
Arguments pertaining to what data we are going to input our model for training and eval.
'''
pass | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0.03 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 96 | 1 | 92 | 17 | 91 | 3 | 17 | 17 | 16 | 0 | 0 | 0 | 0 |
145 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py | run_speech_recognition_seq2seq.ModelArguments | from typing import Any, Optional, Union
from dataclasses import dataclass, field
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(metadata={'help': 'Path to pretrained model or model identifier ... | @dataclass
class ModelArguments:
'''
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
'''
pass | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0.04 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 73 | 1 | 69 | 15 | 68 | 3 | 15 | 15 | 14 | 0 | 0 | 0 | 0 |
146 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/legacy/question-answering/run_squad_trainer.py | run_squad_trainer.ModelArguments | from dataclasses import dataclass, field
from typing import Optional
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(metadata={'help': 'Path to pretrained model or model identifier from hugging... | @dataclass
class ModelArguments:
'''
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
'''
pass | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0.33 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 21 | 1 | 15 | 6 | 14 | 5 | 6 | 6 | 5 | 0 | 0 | 0 | 0 |
147 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/summarization/run_summarization.py | run_summarization.DataTrainingArguments | from dataclasses import dataclass, field
from typing import Optional
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
lang: Optional[str] = field(default=None, metadata={'help': 'Language id for summarization.'})
... | @dataclass
class DataTrainingArguments:
'''
Arguments pertaining to what data we are going to input our model for training and eval.
'''
def __post_init__(self):
pass | 3 | 1 | 20 | 0 | 20 | 0 | 6 | 0.02 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 161 | 4 | 154 | 24 | 152 | 3 | 36 | 24 | 34 | 6 | 0 | 2 | 6 |
148 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/summarization/run_summarization.py | run_summarization.ModelArguments | from dataclasses import dataclass, field
from typing import Optional
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(metadata={'help': 'Path to pretrained model or model identifier from hugging... | @dataclass
class ModelArguments:
'''
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
'''
pass | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0.06 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 54 | 1 | 50 | 10 | 49 | 3 | 10 | 10 | 9 | 0 | 0 | 0 | 0 |
149 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/multiple-choice/run_swag.py | run_swag.DataTrainingArguments | from dataclasses import dataclass, field
from typing import Optional
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
train_file: Optional[str] = field(default=None, metadata={'help': 'The input training data file (... | @dataclass
class DataTrainingArguments:
'''
Arguments pertaining to what data we are going to input our model for training and eval.
'''
def __post_init__(self):
pass | 3 | 1 | 7 | 0 | 7 | 0 | 3 | 0.05 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 62 | 2 | 57 | 11 | 55 | 3 | 16 | 11 | 14 | 3 | 0 | 1 | 3 |
150 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/legacy/run_swag.py | run_swag.InputFeatures | class InputFeatures:
def __init__(self, example_id, choices_features, label):
self.example_id = example_id
self.choices_features = [{'input_ids': input_ids, 'input_mask': input_mask, 'segment_ids': segment_ids} for _, input_ids, input_mask, segment_ids in choices_features]
self.label = labe... | class InputFeatures:
def __init__(self, example_id, choices_features, label):
pass | 2 | 0 | 7 | 0 | 7 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 3 | 1 | 1 | 8 | 0 | 8 | 6 | 6 | 0 | 5 | 5 | 3 | 1 | 1 | 0 | 1 |
151 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/multiple-choice/run_swag.py | run_swag.ModelArguments | from typing import Optional
from dataclasses import dataclass, field
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(metadata={'help': 'Path to pretrained model or model identifier from hugging... | @dataclass
class ModelArguments:
'''
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
'''
pass | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0.07 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 45 | 1 | 41 | 9 | 40 | 3 | 9 | 9 | 8 | 0 | 0 | 0 | 0 |
152 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/legacy/run_swag.py | run_swag.SwagExample | class SwagExample:
"""A single training/test example for the SWAG dataset."""
def __init__(self, swag_id, context_sentence, start_ending, ending_0, ending_1, ending_2, ending_3, label=None):
self.swag_id = swag_id
self.context_sentence = context_sentence
self.start_ending = start_ending... | class SwagExample:
'''A single training/test example for the SWAG dataset.'''
def __init__(self, swag_id, context_sentence, start_ending, ending_0, ending_1, ending_2, ending_3, label=None):
pass
def __str__(self):
pass
def __repr__(self):
pass | 4 | 1 | 9 | 1 | 9 | 0 | 1 | 0.04 | 1 | 0 | 0 | 0 | 3 | 5 | 3 | 3 | 33 | 5 | 27 | 10 | 23 | 1 | 14 | 10 | 10 | 2 | 1 | 1 | 4 |
153 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/translation/run_translation.py | run_translation.DataTrainingArguments | from typing import Optional
from dataclasses import dataclass, field
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
source_lang: str = field(default=None, metadata={'help': 'Source language id for translation.'})
... | @dataclass
class DataTrainingArguments:
'''
Arguments pertaining to what data we are going to input our model for training and eval.
'''
def __post_init__(self):
pass | 3 | 1 | 18 | 2 | 14 | 2 | 6 | 0.04 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 145 | 5 | 135 | 24 | 133 | 5 | 34 | 24 | 32 | 6 | 0 | 1 | 6 |
154 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/translation/run_translation.py | run_translation.ModelArguments | from dataclasses import dataclass, field
from typing import Optional
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(metadata={'help': 'Path to pretrained model or model identifier from hugging... | @dataclass
class ModelArguments:
'''
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
'''
pass | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0.07 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 45 | 1 | 41 | 9 | 40 | 3 | 9 | 9 | 8 | 0 | 0 | 0 | 0 |
155 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py | run_wav2vec2_pretraining_no_trainer.DataCollatorForWav2Vec2Pretraining | import torch
from typing import Optional, Union
from transformers import SchedulerType, Wav2Vec2Config, Wav2Vec2FeatureExtractor, Wav2Vec2ForPreTraining, get_scheduler, is_wandb_available, set_seed
from dataclasses import dataclass
from transformers.models.wav2vec2.modeling_wav2vec2 import _compute_mask_indices, _sampl... | @dataclass
class DataCollatorForWav2Vec2Pretraining:
'''
Data collator that will dynamically pad the inputs received and prepare masked indices
for self-supervised pretraining.
Args:
model (:class:`~transformers.Wav2Vec2ForPreTraining`):
The Wav2Vec2 model used for pretraining. The d... | 3 | 1 | 43 | 7 | 30 | 6 | 2 | 1.05 | 0 | 3 | 0 | 0 | 1 | 0 | 1 | 1 | 86 | 10 | 37 | 13 | 35 | 39 | 21 | 13 | 19 | 2 | 0 | 1 | 2 |
156 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/text-classification/run_xnli.py | run_xnli.ModelArguments | from typing import Optional
from dataclasses import dataclass, field
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(default=None, metadata={'help': 'Path to pretrained model or model identifie... | @dataclass
class ModelArguments:
'''
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
'''
pass | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0.05 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 59 | 1 | 55 | 13 | 54 | 3 | 13 | 13 | 12 | 0 | 0 | 0 | 0 |
157 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/legacy/seq2seq/finetune_trainer.py | seq2seq.finetune_trainer.DataTrainingArguments | from dataclasses import dataclass, field
from typing import Optional
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
data_dir: str = field(metadata={'help': 'The input data dir. Should contain the .tsv files (or ot... | @dataclass
class DataTrainingArguments:
'''
Arguments pertaining to what data we are going to input our model for training and eval.
'''
pass | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0.13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 60 | 1 | 56 | 14 | 55 | 7 | 14 | 14 | 13 | 0 | 0 | 0 | 0 |
158 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/legacy/seq2seq/finetune_trainer.py | seq2seq.finetune_trainer.ModelArguments | from utils import Seq2SeqDataCollator, Seq2SeqDataset, assert_all_frozen, build_compute_metrics_fn, check_output_dir, freeze_embeds, freeze_params, lmap, save_json, use_task_specific_params, write_txt_file
from typing import Optional
from dataclasses import dataclass, field
@dataclass
class ModelArguments:
"""
... | @dataclass
class ModelArguments:
'''
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
'''
pass | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0.19 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 20 | 1 | 16 | 7 | 15 | 3 | 7 | 7 | 6 | 0 | 0 | 0 | 0 |
159 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/legacy/seq2seq/seq2seq_trainer.py | seq2seq.seq2seq_trainer.Seq2SeqTrainer | from transformers import PreTrainedModel, Trainer, logging
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_xla_available
from transformers.optimization import Adafactor, get_constant_schedule, get_constant_schedule_with_warmup, get_cosine_schedule_with_warmup, get_cosine_with... |
class Seq2SeqTrainer(Trainer):
def __init__(self, config=None, data_args=None, *args, **kwargs):
pass
def create_optimizer_and_scheduler(self, num_training_steps: int):
'''
Setup the optimizer and the learning rate scheduler.
We provide a reasonable default that works well. If... | 9 | 2 | 24 | 3 | 18 | 4 | 4 | 0.2 | 1 | 17 | 0 | 0 | 8 | 6 | 8 | 92 | 201 | 29 | 144 | 41 | 128 | 29 | 84 | 34 | 74 | 8 | 1 | 2 | 34 |
160 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/legacy/seq2seq/seq2seq_training_args.py | seq2seq.seq2seq_training_args.Seq2SeqTrainingArguments | from seq2seq_trainer import arg_to_scheduler
from transformers import TrainingArguments
from typing import Optional
from dataclasses import dataclass, field
@dataclass
class Seq2SeqTrainingArguments(TrainingArguments):
"""
Parameters:
label_smoothing (:obj:`float`, `optional`, defaults to 0):
... | @dataclass
class Seq2SeqTrainingArguments(TrainingArguments):
'''
Parameters:
label_smoothing (:obj:`float`, `optional`, defaults to 0):
The label smoothing epsilon to apply (if not zero).
sortish_sampler (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether to Sor... | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0.39 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 32 | 33 | 1 | 23 | 10 | 22 | 9 | 10 | 10 | 9 | 0 | 1 | 0 | 0 |
161 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/legacy/seq2seq/utils.py | seq2seq.utils.AbstractSeq2SeqDataset | from transformers.utils import cached_property
import os
from pathlib import Path
from torch.utils.data import Dataset, Sampler
import numpy as np
from transformers import BartTokenizer, EvalPrediction, PreTrainedTokenizer, T5Tokenizer
class AbstractSeq2SeqDataset(Dataset):
def __init__(self, tokenizer, data_dir,... |
class AbstractSeq2SeqDataset(Dataset):
def __init__(self, tokenizer, data_dir, max_source_length, max_target_length, type_path='train', n_obs=None, prefix='', **dataset_kwargs):
pass
def __len__(self):
pass
@staticmethod
def get_char_lens(data_file):
pass
@cached_property
... | 12 | 1 | 8 | 0 | 8 | 0 | 2 | 0.04 | 1 | 9 | 2 | 2 | 7 | 11 | 8 | 8 | 82 | 10 | 69 | 38 | 47 | 3 | 47 | 26 | 37 | 5 | 1 | 1 | 14 |
162 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/legacy/seq2seq/utils.py | seq2seq.utils.DistributedSortishSampler | import torch
import numpy as np
from torch.utils.data import Dataset, Sampler
import torch.distributed as dist
from collections.abc import Iterable
from transformers.utils import cached_property
import math
class DistributedSortishSampler(Sampler):
"""Copied from torch DistributedSampler"""
def __init__(self,... |
class DistributedSortishSampler(Sampler):
'''Copied from torch DistributedSampler'''
def __init__(self, dataset, batch_size, num_replicas=None, rank=None, add_extra_examples=True, shuffle=True):
pass
def __iter__(self) -> Iterable:
pass
@cached_property
def available_indices(self)... | 7 | 1 | 9 | 0 | 8 | 0 | 2 | 0.07 | 1 | 4 | 0 | 0 | 5 | 9 | 5 | 5 | 51 | 6 | 42 | 22 | 35 | 3 | 40 | 21 | 34 | 6 | 1 | 2 | 10 |
163 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/legacy/seq2seq/utils.py | seq2seq.utils.LegacySeq2SeqDataset | import torch.distributed as dist
import torch
import linecache
class LegacySeq2SeqDataset(AbstractSeq2SeqDataset):
def __getitem__(self, index) -> dict[str, torch.Tensor]:
"""Call tokenizer on src and tgt_lines"""
index = index + 1
source_line = self.prefix + linecache.getline(str(self.src... |
class LegacySeq2SeqDataset(AbstractSeq2SeqDataset):
def __getitem__(self, index) -> dict[str, torch.Tensor]:
'''Call tokenizer on src and tgt_lines'''
pass
def encode_line(self, tokenizer, line, max_length, pad_to_max_length=True, return_tensors='pt'):
'''Only used by LegacyDataset'''... | 4 | 2 | 14 | 0 | 13 | 1 | 1 | 0.08 | 1 | 2 | 0 | 0 | 3 | 0 | 3 | 11 | 44 | 3 | 39 | 17 | 35 | 3 | 24 | 17 | 20 | 2 | 2 | 0 | 4 |
164 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/legacy/seq2seq/utils.py | seq2seq.utils.Seq2SeqDataCollator | import torch.distributed as dist
import torch
from transformers import BartTokenizer, EvalPrediction, PreTrainedTokenizer, T5Tokenizer
from transformers.models.bart.modeling_bart import shift_tokens_right
class Seq2SeqDataCollator:
def __init__(self, tokenizer, data_args, decoder_start_token_id, tpu_num_cores=Non... |
class Seq2SeqDataCollator:
def __init__(self, tokenizer, data_args, decoder_start_token_id, tpu_num_cores=None):
pass
def __call__(self, batch) -> dict[str, torch.Tensor]:
pass
def _shift_right_t5(self, input_ids):
pass
def _encode(self, batch) -> dict[str, torch.Tensor]:
... | 5 | 0 | 15 | 1 | 14 | 1 | 3 | 0.04 | 0 | 4 | 0 | 0 | 4 | 6 | 4 | 4 | 63 | 6 | 56 | 15 | 51 | 2 | 35 | 15 | 30 | 4 | 0 | 1 | 10 |
165 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/legacy/seq2seq/utils.py | seq2seq.utils.Seq2SeqDataset | import torch.distributed as dist
import torch
import linecache
class Seq2SeqDataset(AbstractSeq2SeqDataset):
"""A dataset that calls prepare_seq2seq_batch."""
def __getitem__(self, index) -> dict[str, str]:
index = index + 1
source_line = self.prefix + linecache.getline(str(self.src_file), ind... |
class Seq2SeqDataset(AbstractSeq2SeqDataset):
'''A dataset that calls prepare_seq2seq_batch.'''
def __getitem__(self, index) -> dict[str, str]:
pass
def collate_fn(self, batch) -> dict[str, torch.Tensor]:
'''Call prepare_seq2seq_batch.'''
pass | 3 | 2 | 10 | 0 | 9 | 1 | 1 | 0.16 | 1 | 2 | 0 | 0 | 2 | 2 | 2 | 10 | 23 | 2 | 19 | 8 | 16 | 3 | 12 | 6 | 9 | 1 | 2 | 0 | 2 |
166 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/legacy/seq2seq/utils.py | seq2seq.utils.SortishSampler | from torch.utils.data import Dataset, Sampler
class SortishSampler(Sampler):
"""Go through the text data by order of src length with a bit of randomness. From fastai repo."""
def __init__(self, data, batch_size, shuffle=True):
self.data, self.bs, self.shuffle = (data, batch_size, shuffle)
def __l... |
class SortishSampler(Sampler):
'''Go through the text data by order of src length with a bit of randomness. From fastai repo.'''
def __init__(self, data, batch_size, shuffle=True):
pass
def __len__(self) -> int:
pass
def __iter__(self):
pass | 4 | 1 | 2 | 0 | 2 | 0 | 1 | 0.14 | 1 | 1 | 0 | 0 | 3 | 3 | 3 | 3 | 11 | 3 | 7 | 5 | 3 | 1 | 7 | 5 | 3 | 1 | 1 | 0 | 3 |
167 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/setup.py | setup.DepsTableUpdateCommand | from setuptools import Command, find_packages, setup
class DepsTableUpdateCommand(Command):
"""
A custom distutils command that updates the dependency table.
usage: python setup.py deps_table_update
"""
description = 'build runtime dependency table'
user_options = [('dep-table-update', None, 'u... |
class DepsTableUpdateCommand(Command):
'''
A custom distutils command that updates the dependency table.
usage: python setup.py deps_table_update
'''
def initialize_options(self):
pass
def finalize_options(self):
pass
def run(self):
pass | 4 | 1 | 6 | 0 | 6 | 1 | 1 | 0.33 | 1 | 0 | 0 | 0 | 3 | 0 | 3 | 3 | 33 | 4 | 24 | 10 | 20 | 8 | 14 | 9 | 10 | 1 | 1 | 1 | 3 |
168 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/legacy/token-classification/tasks.py | tasks.Chunk | class Chunk(NER):
def __init__(self):
super().__init__(label_idx=-2)
def get_labels(self, path: str) -> list[str]:
if path:
with open(path) as f:
labels = f.read().splitlines()
if 'O' not in labels:
labels = ['O'] + labels
ret... | class Chunk(NER):
def __init__(self):
pass
def get_labels(self, path: str) -> list[str]:
pass | 3 | 0 | 17 | 0 | 17 | 1 | 2 | 0.03 | 1 | 2 | 0 | 0 | 2 | 0 | 2 | 9 | 36 | 1 | 34 | 5 | 31 | 1 | 11 | 4 | 8 | 3 | 2 | 2 | 4 |
169 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/legacy/token-classification/tasks.py | tasks.NER | import os
from typing import TextIO, Union
from utils_ner import InputExample, Split, TokenClassificationTask
class NER(TokenClassificationTask):
def __init__(self, label_idx=-1):
self.label_idx = label_idx
def read_examples_from_file(self, data_dir, mode: Union[Split, str]) -> list[InputExample]:
... |
class NER(TokenClassificationTask):
def __init__(self, label_idx=-1):
pass
def read_examples_from_file(self, data_dir, mode: Union[Split, str]) -> list[InputExample]:
pass
def write_predictions_to_file(self, writer: TextIO, test_input_reader: TextIO, preds_list: list):
pass
... | 5 | 0 | 13 | 0 | 12 | 1 | 4 | 0.04 | 1 | 4 | 2 | 1 | 4 | 1 | 4 | 7 | 55 | 3 | 50 | 19 | 45 | 2 | 45 | 17 | 40 | 7 | 1 | 4 | 16 |
170 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/legacy/token-classification/tasks.py | tasks.POS | from conllu import parse_incr
from typing import TextIO, Union
import os
from utils_ner import InputExample, Split, TokenClassificationTask
class POS(TokenClassificationTask):
def read_examples_from_file(self, data_dir, mode: Union[Split, str]) -> list[InputExample]:
if isinstance(mode, Split):
... |
class POS(TokenClassificationTask):
def read_examples_from_file(self, data_dir, mode: Union[Split, str]) -> list[InputExample]:
pass
def write_predictions_to_file(self, writer: TextIO, test_input_reader: TextIO, preds_list: list):
pass
def get_labels(self, path: str) -> list[str]:
... | 4 | 0 | 18 | 0 | 17 | 0 | 3 | 0 | 1 | 4 | 2 | 0 | 3 | 0 | 3 | 6 | 56 | 3 | 53 | 18 | 49 | 0 | 34 | 16 | 30 | 5 | 1 | 3 | 10 |
171 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/question-answering/trainer_qa.py | trainer_qa.QuestionAnsweringTrainer | import math
import time
from transformers.trainer_utils import PredictionOutput, speed_metrics
from transformers import Trainer, is_torch_xla_available
class QuestionAnsweringTrainer(Trainer):
def __init__(self, *args, eval_examples=None, post_process_function=None, **kwargs):
super().__init__(*args, **kw... |
class QuestionAnsweringTrainer(Trainer):
def __init__(self, *args, eval_examples=None, post_process_function=None, **kwargs):
pass
def evaluate(self, eval_dataset=None, eval_examples=None, ignore_keys=None, metric_key_prefix: str='eval'):
pass
def predict(self, predict_dataset, predict_e... | 4 | 0 | 35 | 3 | 28 | 4 | 6 | 0.13 | 1 | 4 | 0 | 0 | 3 | 4 | 3 | 3 | 107 | 11 | 85 | 26 | 81 | 11 | 56 | 26 | 52 | 11 | 1 | 3 | 19 |
172 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/examples/pytorch/question-answering/trainer_seq2seq_qa.py | trainer_seq2seq_qa.QuestionAnsweringSeq2SeqTrainer | from typing import Optional
from torch.utils.data import Dataset
from transformers.trainer_utils import PredictionOutput, speed_metrics
import math
import time
from transformers import Seq2SeqTrainer, is_torch_xla_available
class QuestionAnsweringSeq2SeqTrainer(Seq2SeqTrainer):
def __init__(self, *args, eval_exam... |
class QuestionAnsweringSeq2SeqTrainer(Seq2SeqTrainer):
def __init__(self, *args, eval_examples=None, post_process_function=None, **kwargs):
pass
def evaluate(self, eval_dataset: Optional[Dataset]=None, eval_examples=None, ignore_keys: Optional[list[str]]=None, metric_key_prefix: str='eval', **gen_kwa... | 4 | 0 | 42 | 5 | 33 | 4 | 7 | 0.14 | 1 | 5 | 0 | 0 | 3 | 5 | 3 | 93 | 131 | 16 | 101 | 36 | 88 | 14 | 63 | 27 | 59 | 13 | 2 | 3 | 21 |
173 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/activations.py | transformers.activations.AccurateGELUActivation | import math
import torch
from torch import Tensor, nn
class AccurateGELUActivation(nn.Module):
"""
Applies GELU approximation that is faster than default and more accurate than QuickGELU. See:
https://github.com/hendrycks/GELUs
Implemented along with MEGA (Moving Average Equipped Gated Attention)
... |
class AccurateGELUActivation(nn.Module):
'''
Applies GELU approximation that is faster than default and more accurate than QuickGELU. See:
https://github.com/hendrycks/GELUs
Implemented along with MEGA (Moving Average Equipped Gated Attention)
'''
def __init__(self):
pass
def forw... | 3 | 1 | 3 | 0 | 3 | 0 | 1 | 0.83 | 1 | 2 | 0 | 0 | 2 | 1 | 2 | 12 | 14 | 3 | 6 | 4 | 3 | 5 | 6 | 4 | 3 | 1 | 1 | 0 | 2 |
174 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/activations.py | transformers.activations.ClassInstantier | from collections import OrderedDict
class ClassInstantier(OrderedDict):
def __getitem__(self, key):
content = super().__getitem__(key)
cls, kwargs = content if isinstance(content, tuple) else (content, {})
return cls(**kwargs) |
class ClassInstantier(OrderedDict):
def __getitem__(self, key):
pass | 2 | 0 | 4 | 0 | 4 | 0 | 2 | 0 | 1 | 2 | 0 | 0 | 1 | 0 | 1 | 51 | 5 | 0 | 5 | 4 | 3 | 0 | 5 | 4 | 3 | 2 | 3 | 0 | 2 |
175 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/activations.py | transformers.activations.ClippedGELUActivation | import torch
from torch import Tensor, nn
class ClippedGELUActivation(nn.Module):
"""
Clip the range of possible GeLU outputs between [min, max]. This is especially useful for quantization purpose, as
it allows mapping negatives values in the GeLU spectrum. For more information on this trick, please refer ... |
class ClippedGELUActivation(nn.Module):
'''
Clip the range of possible GeLU outputs between [min, max]. This is especially useful for quantization purpose, as
it allows mapping negatives values in the GeLU spectrum. For more information on this trick, please refer to
https://huggingface.co/papers/2004.... | 3 | 1 | 5 | 1 | 4 | 0 | 2 | 1 | 1 | 4 | 0 | 0 | 2 | 2 | 2 | 12 | 23 | 5 | 9 | 5 | 6 | 9 | 9 | 5 | 6 | 2 | 1 | 1 | 3 |
176 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/activations.py | transformers.activations.FastGELUActivation | import torch
from .integrations.hub_kernels import use_kernel_forward_from_hub
from torch import Tensor, nn
@use_kernel_forward_from_hub('FastGELU')
class FastGELUActivation(nn.Module):
"""
Applies GELU approximation that is slower than QuickGELU but more accurate. See: https://github.com/hendrycks/GELUs
"... | @use_kernel_forward_from_hub('FastGELU')
class FastGELUActivation(nn.Module):
'''
Applies GELU approximation that is slower than QuickGELU but more accurate. See: https://github.com/hendrycks/GELUs
'''
def forward(self, input: Tensor) -> Tensor:
pass | 3 | 1 | 2 | 0 | 2 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 11 | 7 | 1 | 3 | 2 | 1 | 3 | 3 | 2 | 1 | 1 | 1 | 0 | 1 |
177 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/activations.py | transformers.activations.GELUActivation | import torch
from torch import Tensor, nn
import math
class GELUActivation(nn.Module):
"""
Original Implementation of the GELU activation function in Google BERT repo when initially created. For
information: OpenAI GPT's GELU is slightly different (and gives slightly different results): 0.5 * x * (1 +
... |
class GELUActivation(nn.Module):
'''
Original Implementation of the GELU activation function in Google BERT repo when initially created. For
information: OpenAI GPT's GELU is slightly different (and gives slightly different results): 0.5 * x * (1 +
torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * to... | 4 | 1 | 3 | 0 | 3 | 0 | 1 | 0.55 | 1 | 3 | 0 | 0 | 3 | 1 | 3 | 13 | 20 | 3 | 11 | 5 | 7 | 6 | 10 | 5 | 6 | 2 | 1 | 1 | 4 |
178 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/activations.py | transformers.activations.LaplaceActivation | import torch
from torch import Tensor, nn
import math
class LaplaceActivation(nn.Module):
"""
Applies elementwise activation based on Laplace function, introduced in MEGA as an attention activation. See
https://huggingface.co/papers/2209.10655
Inspired by squared relu, but with bounded range and gradi... |
class LaplaceActivation(nn.Module):
'''
Applies elementwise activation based on Laplace function, introduced in MEGA as an attention activation. See
https://huggingface.co/papers/2209.10655
Inspired by squared relu, but with bounded range and gradient for better stability
'''
def forward(self,... | 2 | 1 | 3 | 0 | 3 | 0 | 1 | 1.25 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 11 | 11 | 2 | 4 | 2 | 2 | 5 | 4 | 2 | 2 | 1 | 1 | 0 | 1 |
179 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/activations.py | transformers.activations.LinearActivation | from torch import Tensor, nn
class LinearActivation(nn.Module):
"""
Applies the linear activation function, i.e. forwarding input directly to output.
"""
def forward(self, input: Tensor) -> Tensor:
return input |
class LinearActivation(nn.Module):
'''
Applies the linear activation function, i.e. forwarding input directly to output.
'''
def forward(self, input: Tensor) -> Tensor:
pass | 2 | 1 | 2 | 0 | 2 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 11 | 7 | 1 | 3 | 2 | 1 | 3 | 3 | 2 | 1 | 1 | 1 | 0 | 1 |
180 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/activations.py | transformers.activations.MishActivation | from torch import Tensor, nn
import torch
class MishActivation(nn.Module):
"""
See Mish: A Self-Regularized Non-Monotonic Activation Function (Misra., https://huggingface.co/papers/1908.08681). Also
visit the official repository for the paper: https://github.com/digantamisra98/Mish
"""
def __init_... |
class MishActivation(nn.Module):
'''
See Mish: A Self-Regularized Non-Monotonic Activation Function (Misra., https://huggingface.co/papers/1908.08681). Also
visit the official repository for the paper: https://github.com/digantamisra98/Mish
'''
def __init__(self):
pass
def _mish_pytho... | 4 | 1 | 3 | 0 | 3 | 0 | 1 | 0.36 | 1 | 2 | 0 | 0 | 3 | 1 | 3 | 13 | 18 | 3 | 11 | 5 | 7 | 4 | 10 | 5 | 6 | 2 | 1 | 1 | 4 |
181 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/activations.py | transformers.activations.NewGELUActivation | from .integrations.hub_kernels import use_kernel_forward_from_hub
import torch
from torch import Tensor, nn
import math
@use_kernel_forward_from_hub('NewGELU')
class NewGELUActivation(nn.Module):
"""
Implementation of the GELU activation function currently in Google BERT repo (identical to OpenAI GPT). Also se... | @use_kernel_forward_from_hub('NewGELU')
class NewGELUActivation(nn.Module):
'''
Implementation of the GELU activation function currently in Google BERT repo (identical to OpenAI GPT). Also see
the Gaussian Error Linear Units paper: https://huggingface.co/papers/1606.08415
'''
def forward(self, inpu... | 3 | 1 | 2 | 0 | 2 | 0 | 1 | 1.33 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 11 | 8 | 1 | 3 | 2 | 1 | 4 | 3 | 2 | 1 | 1 | 1 | 0 | 1 |
182 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/activations.py | transformers.activations.PytorchGELUTanh | from torch import Tensor, nn
class PytorchGELUTanh(nn.Module):
"""
A fast C implementation of the tanh approximation of the GeLU activation function. See
https://huggingface.co/papers/1606.08415.
This implementation is equivalent to NewGELU and FastGELU but much faster. However, it is not an exact num... |
class PytorchGELUTanh(nn.Module):
'''
A fast C implementation of the tanh approximation of the GeLU activation function. See
https://huggingface.co/papers/1606.08415.
This implementation is equivalent to NewGELU and FastGELU but much faster. However, it is not an exact numerical
match due to roundi... | 2 | 1 | 5 | 0 | 5 | 0 | 2 | 0.6 | 1 | 3 | 0 | 0 | 2 | 0 | 2 | 12 | 19 | 3 | 10 | 3 | 7 | 6 | 7 | 3 | 4 | 2 | 1 | 1 | 3 |
183 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/activations.py | transformers.activations.QuickGELUActivation | from .integrations.hub_kernels import use_kernel_forward_from_hub
from torch import Tensor, nn
import torch
@use_kernel_forward_from_hub('QuickGELU')
class QuickGELUActivation(nn.Module):
"""
Applies GELU approximation that is fast but somewhat inaccurate. See: https://github.com/hendrycks/GELUs
"""
d... | @use_kernel_forward_from_hub('QuickGELU')
class QuickGELUActivation(nn.Module):
'''
Applies GELU approximation that is fast but somewhat inaccurate. See: https://github.com/hendrycks/GELUs
'''
def forward(self, input: Tensor) -> Tensor:
pass | 3 | 1 | 2 | 0 | 2 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 11 | 7 | 1 | 3 | 2 | 1 | 3 | 3 | 2 | 1 | 1 | 1 | 0 | 1 |
184 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/activations.py | transformers.activations.ReLUSquaredActivation | import torch
from torch import Tensor, nn
class ReLUSquaredActivation(nn.Module):
"""
Applies the relu^2 activation introduced in https://huggingface.co/papers/2109.08668v2
"""
def forward(self, input):
relu_applied = nn.functional.relu(input)
squared = torch.square(relu_applied)
... |
class ReLUSquaredActivation(nn.Module):
'''
Applies the relu^2 activation introduced in https://huggingface.co/papers/2109.08668v2
'''
def forward(self, input):
pass | 2 | 1 | 4 | 0 | 4 | 0 | 1 | 0.6 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 11 | 9 | 1 | 5 | 4 | 3 | 3 | 5 | 4 | 3 | 1 | 1 | 0 | 1 |
185 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/cache_utils.py | transformers.cache_utils.Cache | from typing import Any, Optional
import torch
class Cache:
"""
A `Cache` is mostly a list of `CacheLayerMixin` objects, one per model layer. It serves as a container for
the Cache of each layer.
Args:
layers (`Optional`, *optional*):
A list of pre-created `CacheLayerMixin`. If omit... |
class Cache:
'''
A `Cache` is mostly a list of `CacheLayerMixin` objects, one per model layer. It serves as a container for
the Cache of each layer.
Args:
layers (`Optional`, *optional*):
A list of pre-created `CacheLayerMixin`. If omitted (`None`), then `layer_class_to_replicate` w... | 28 | 21 | 9 | 0 | 5 | 3 | 2 | 0.65 | 1 | 7 | 0 | 5 | 7 | 0 | 7 | 17 | 76 | 10 | 40 | 20 | 25 | 26 | 29 | 13 | 21 | 4 | 1 | 2 | 12 |
186 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/cache_utils.py | transformers.cache_utils.DynamicCache | import torch
from collections.abc import Iterable
from typing import Any, Optional
from .configuration_utils import PretrainedConfig
class DynamicCache(Cache):
"""
A cache that grows dynamically as more tokens are generated. This is the default for generative models.
It stores the key and value states as a... |
class DynamicCache(Cache):
'''
A cache that grows dynamically as more tokens are generated. This is the default for generative models.
It stores the key and value states as a list of `CacheLayer`, one for each layer. The expected shape for each tensor
in the `CacheLayer`s is `[batch_size, num_heads, se... | 5 | 3 | 11 | 0 | 7 | 4 | 2 | 0.61 | 1 | 7 | 0 | 5 | 12 | 3 | 14 | 31 | 198 | 26 | 110 | 53 | 79 | 67 | 85 | 39 | 70 | 6 | 2 | 3 | 34 |
187 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/cache_utils.py | transformers.cache_utils.EncoderDecoderCache | from collections.abc import Iterable
from typing import Any, Optional
import torch
class EncoderDecoderCache(Cache):
"""
Base, abstract class for all encoder-decoder caches. Can be used to hold combinations of self-attention and
cross-attention caches.
See `Cache` for details on common methods that ar... | null | 23 | 13 | 9 | 0 | 8 | 2 | 2 | 0.35 | 1 | 10 | 1 | 0 | 12 | 4 | 14 | 31 | 176 | 23 | 113 | 44 | 88 | 40 | 79 | 35 | 64 | 5 | 2 | 3 | 28 |
188 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/cache_utils.py | transformers.cache_utils.HQQQuantizedCache | from .configuration_utils import PretrainedConfig
class HQQQuantizedCache(QuantizedCache):
def __init__(self, config: PretrainedConfig, nbits: int=4, axis_key: int=0, axis_value: int=0, q_group_size: int=64, residual_length: int=128):
logger.warning_once("`HQQQuantizedCache` is deprecated and will be remo... |
class HQQQuantizedCache(QuantizedCache):
def __init__(self, config: PretrainedConfig, nbits: int=4, axis_key: int=0, axis_value: int=0, q_group_size: int=64, residual_length: int=128):
pass | 2 | 0 | 10 | 1 | 9 | 0 | 2 | 0.75 | 1 | 3 | 1 | 0 | 3 | 4 | 3 | 39 | 60 | 12 | 28 | 11 | 24 | 21 | 19 | 8 | 15 | 4 | 4 | 1 | 6 |
189 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/cache_utils.py | transformers.cache_utils.HybridCache | from .configuration_utils import PretrainedConfig
class HybridCache(StaticCache):
def __init__(self, config: PretrainedConfig, max_cache_len: int, *args, **kwargs):
logger.warning_once('`HybridCache` is deprecated and will be removed in version v4.59 Use `StaticCache(...)` instead which will correctly inf... |
class HybridCache(StaticCache):
def __init__(self, config: PretrainedConfig, max_cache_len: int, *args, **kwargs):
pass | 2 | 0 | 20 | 1 | 17 | 2 | 3 | 0.36 | 1 | 8 | 0 | 0 | 8 | 9 | 8 | 25 | 213 | 26 | 138 | 52 | 112 | 50 | 87 | 35 | 78 | 10 | 2 | 2 | 24 |
190 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/cache_utils.py | transformers.cache_utils.OffloadedCache | class OffloadedCache(DynamicCache):
def __init__(self) -> None:
logger.warning_once('`OffloadedCache` is deprecated and will be removed in version v4.59 Use `DynamicCache(offloading=True)` instead')
super().__init__(offloading=True) | class OffloadedCache(DynamicCache):
def __init__(self) -> None:
pass | 2 | 0 | 15 | 0 | 10 | 5 | 2 | 0.67 | 1 | 9 | 0 | 0 | 6 | 3 | 6 | 37 | 112 | 11 | 61 | 24 | 48 | 41 | 52 | 18 | 45 | 4 | 3 | 2 | 14 |
191 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/cache_utils.py | transformers.cache_utils.OffloadedStaticCache | from .configuration_utils import PretrainedConfig
class OffloadedStaticCache(StaticCache):
def __init__(self, config: PretrainedConfig, max_cache_len: int, *args, **kwargs):
logger.warning_once('`OffloadedStaticCache` is deprecated and will be removed in version v4.59 Use `StaticCache(..., offloading=True... |
class OffloadedStaticCache(StaticCache):
def __init__(self, config: PretrainedConfig, max_cache_len: int, *args, **kwargs):
pass | 2 | 0 | 23 | 4 | 12 | 7 | 3 | 0.97 | 1 | 8 | 0 | 0 | 9 | 11 | 9 | 32 | 280 | 53 | 115 | 53 | 86 | 112 | 89 | 34 | 79 | 10 | 3 | 3 | 29 |
192 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/cache_utils.py | transformers.cache_utils.QuantizedCache | from .configuration_utils import PretrainedConfig
class QuantizedCache(Cache):
"""
A quantizer cache similar to what is described in the
[KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache paper](https://huggingface.co/papers/2402.02750).
It allows the model to generate longer sequence lengt... |
class QuantizedCache(Cache):
'''
A quantizer cache similar to what is described in the
[KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache paper](https://huggingface.co/papers/2402.02750).
It allows the model to generate longer sequence length without allocating too much memory for keys and ... | 2 | 1 | 14 | 1 | 12 | 1 | 2 | 0.29 | 1 | 8 | 1 | 2 | 5 | 9 | 5 | 36 | 88 | 12 | 59 | 24 | 47 | 17 | 45 | 18 | 39 | 5 | 3 | 2 | 11 |
193 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/cache_utils.py | transformers.cache_utils.QuantoQuantizedCache | from .configuration_utils import PretrainedConfig
class QuantoQuantizedCache(QuantizedCache):
def __init__(self, config: PretrainedConfig, nbits: int=4, axis_key: int=0, axis_value: int=0, q_group_size: int=64, residual_length: int=128):
logger.warning_once("`QuantoQuantizedCache` is deprecated and will b... |
class QuantoQuantizedCache(QuantizedCache):
def __init__(self, config: PretrainedConfig, nbits: int=4, axis_key: int=0, axis_value: int=0, q_group_size: int=64, residual_length: int=128):
pass | 2 | 0 | 11 | 2 | 9 | 1 | 3 | 0.79 | 1 | 4 | 1 | 0 | 3 | 2 | 3 | 39 | 64 | 15 | 28 | 11 | 22 | 22 | 24 | 11 | 18 | 7 | 4 | 2 | 10 |
194 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/cache_utils.py | transformers.cache_utils.SinkCache | class SinkCache(Cache):
"""
It is now a `custom_generate` repository on the Hub: https://huggingface.co/transformers-community/sink_cache.
See [these docs](https://huggingface.co/docs/transformers/generation_strategies#custom-decoding-methods) for
general `custom_generate`usage.
"""
def __init_... | class SinkCache(Cache):
'''
It is now a `custom_generate` repository on the Hub: https://huggingface.co/transformers-community/sink_cache.
See [these docs](https://huggingface.co/docs/transformers/generation_strategies#custom-decoding-methods) for
general `custom_generate`usage.
'''
def __init_... | 2 | 1 | 20 | 2 | 14 | 5 | 3 | 0.6 | 1 | 5 | 0 | 0 | 6 | 8 | 7 | 24 | 185 | 28 | 99 | 47 | 80 | 59 | 72 | 36 | 64 | 11 | 2 | 3 | 19 |
195 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/cache_utils.py | transformers.cache_utils.SlidingWindowCache | from .configuration_utils import PretrainedConfig
class SlidingWindowCache(StaticCache):
def __init__(self, config: PretrainedConfig, max_cache_len: int, *args, **kwargs):
logger.warning_once('`SlidingWindowCache` is deprecated and will be removed in version v4.59 Use `StaticCache(...)` instead which will... |
class SlidingWindowCache(StaticCache):
def __init__(self, config: PretrainedConfig, max_cache_len: int, *args, **kwargs):
pass | 2 | 0 | 22 | 2 | 18 | 2 | 3 | 0.7 | 1 | 8 | 0 | 0 | 4 | 1 | 4 | 27 | 149 | 23 | 74 | 29 | 54 | 52 | 47 | 14 | 42 | 4 | 3 | 2 | 10 |
196 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/cache_utils.py | transformers.cache_utils.StaticCache | from .configuration_utils import PretrainedConfig
class StaticCache(Cache):
"""
Static Cache class to be used with `torch.compile(model)` and `torch.export()`. It will check the `config`
for potential hybrid cache structure, and initialize each layer accordingly.
See `Cache` for details on common meth... |
class StaticCache(Cache):
'''
Static Cache class to be used with `torch.compile(model)` and `torch.export()`. It will check the `config`
for potential hybrid cache structure, and initialize each layer accordingly.
See `Cache` for details on common methods that are implemented by all cache classes.
... | 2 | 1 | 22 | 2 | 15 | 6 | 3 | 0.72 | 1 | 7 | 0 | 2 | 6 | 8 | 6 | 23 | 186 | 24 | 94 | 42 | 70 | 68 | 63 | 25 | 56 | 9 | 2 | 2 | 20 |
197 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/commands/__init__.py | transformers.commands.BaseTransformersCLICommand | from abc import ABC, abstractmethod
from argparse import ArgumentParser
class BaseTransformersCLICommand(ABC):
@staticmethod
@abstractmethod
def register_subcommand(parser: ArgumentParser):
raise NotImplementedError()
@abstractmethod
def run(self):
raise NotImplementedError() |
class BaseTransformersCLICommand(ABC):
@staticmethod
@abstractmethod
def register_subcommand(parser: ArgumentParser):
pass
@abstractmethod
def run(self):
pass | 6 | 0 | 2 | 0 | 2 | 0 | 1 | 0 | 1 | 2 | 0 | 11 | 1 | 0 | 2 | 22 | 9 | 1 | 8 | 5 | 2 | 0 | 5 | 3 | 2 | 1 | 4 | 0 | 2 |
198 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/commands/add_fast_image_processor.py | transformers.commands.add_fast_image_processor.AddFastImageProcessorCommand | from argparse import ArgumentParser, Namespace
from . import BaseTransformersCLICommand
class AddFastImageProcessorCommand(BaseTransformersCLICommand):
@staticmethod
def register_subcommand(parser: ArgumentParser):
add_fast_image_processor_parser = parser.add_parser('add-fast-image-processor')
... |
class AddFastImageProcessorCommand(BaseTransformersCLICommand):
@staticmethod
def register_subcommand(parser: ArgumentParser):
pass
def __init__(self, model_name: str, *args):
pass
def run(self):
pass | 5 | 0 | 4 | 0 | 4 | 0 | 1 | 0 | 1 | 2 | 0 | 0 | 2 | 1 | 3 | 25 | 17 | 2 | 15 | 7 | 10 | 0 | 9 | 6 | 5 | 1 | 5 | 0 | 3 |
199 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/commands/add_new_model_like.py | transformers.commands.add_new_model_like.AddNewModelLikeCommand | from . import BaseTransformersCLICommand
from pathlib import Path
from argparse import ArgumentParser, Namespace
class AddNewModelLikeCommand(BaseTransformersCLICommand):
@staticmethod
def register_subcommand(parser: ArgumentParser):
add_new_model_like_parser = parser.add_parser('add-new-model-like')
... |
class AddNewModelLikeCommand(BaseTransformersCLICommand):
@staticmethod
def register_subcommand(parser: ArgumentParser):
pass
def __init__(self, path_to_repo=None, *args):
pass
def run(self):
pass | 5 | 0 | 15 | 1 | 13 | 0 | 2 | 0.02 | 1 | 4 | 1 | 0 | 2 | 6 | 3 | 25 | 48 | 5 | 42 | 16 | 35 | 1 | 24 | 14 | 18 | 2 | 5 | 2 | 5 |
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