repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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SimKGC | SimKGC-main/config.py | import os
import random
import torch
import argparse
import warnings
import torch.backends.cudnn as cudnn
parser = argparse.ArgumentParser(description='SimKGC arguments')
parser.add_argument('--pretrained-model', default='bert-base-uncased', type=str, metavar='N',
help='path to pretrained model')
... | 5,436 | 47.981982 | 118 | py |
SimKGC | SimKGC-main/models.py | from abc import ABC
from copy import deepcopy
import torch
import torch.nn as nn
from dataclasses import dataclass
from transformers import AutoModel, AutoConfig
from triplet_mask import construct_mask
def build_model(args) -> nn.Module:
return CustomBertModel(args)
@dataclass
class ModelOutput:
logits: ... | 7,135 | 42.512195 | 108 | py |
SimKGC | SimKGC-main/rerank.py | import torch
from typing import List
from config import args
from triplet import EntityDict
from dict_hub import get_link_graph
from doc import Example
def rerank_by_graph(batch_score: torch.tensor,
examples: List[Example],
entity_dict: EntityDict):
if args.task == 'wiki... | 1,990 | 43.244444 | 98 | py |
SimKGC | SimKGC-main/eval_wiki5m_trans.py | import os
import json
import torch
from config import args
from predict import BertPredictor
from dict_hub import get_entity_dict
from evaluate import eval_single_direction
from logger_config import logger
assert args.task == 'wiki5m_trans', 'This script is only used for wiki5m transductive setting'
entity_dict = ge... | 3,543 | 39.735632 | 104 | py |
SimKGC | SimKGC-main/predict.py | import os
import json
import tqdm
import torch
import torch.utils.data
from typing import List
from collections import OrderedDict
from doc import collate, Example, Dataset
from config import args
from models import build_model
from utils import AttrDict, move_to_cuda
from dict_hub import build_tokenizer
from logger_... | 3,746 | 35.735294 | 123 | py |
SimKGC | SimKGC-main/trainer.py | import glob
import json
import torch
import shutil
import torch.nn as nn
import torch.utils.data
from typing import Dict
from transformers import get_linear_schedule_with_warmup, get_cosine_schedule_with_warmup
from transformers import AdamW
from doc import Dataset, collate
from utils import AverageMeter, ProgressMe... | 8,331 | 38.866029 | 118 | py |
SimKGC | SimKGC-main/metric.py | import torch
from typing import List
def accuracy(output: torch.tensor, target: torch.tensor, topk=(1,)) -> List[torch.tensor]:
"""Computes the accuracy over the k top predictions for the specified values of k"""
with torch.no_grad():
maxk = max(topk)
batch_size = target.size(0)
_, p... | 651 | 30.047619 | 90 | py |
fmsynth | fmsynth-main/synth.py | import torch
from torch import nn
import torch.nn.functional as F
import librosa
import numpy as np
class Synth:
def __init__(self, device):
self.device = device
def fit(
self,
target,
sr,
minimize_num_freqs=False,
carrier_stereo_detune=0.0,
mod_stereo_... | 5,674 | 32.579882 | 140 | py |
fmsynth | fmsynth-main/predict.py | # Prediction interface for Cog ⚙️
# Reference: https://github.com/replicate/cog/blob/main/docs/python.md
import os
import tempfile
import subprocess
from cog import BasePredictor, Path, Input
import torch
from scipy.io import wavfile
import numpy as np
import librosa
from synth import Synth
class Predictor(BasePred... | 2,863 | 32.302326 | 131 | py |
OpenABC | OpenABC-master/models/classification/ClassNetV1/netlistDataset.py | import os.path as osp
import torch
from zipfile import ZipFile
import pandas as pd
from torch_geometric.data import Dataset, download_url
class NetlistGraphDataset(Dataset):
def __init__(self, root, filePath, transform=None, pre_transform=None):
self.filePath = osp.join(root, filePath)
super(Netl... | 932 | 32.321429 | 86 | py |
OpenABC | OpenABC-master/models/classification/ClassNetV1/utils.py | import torch
from sklearn.metrics import mean_squared_error,mean_absolute_error
import matplotlib.pyplot as plt
import os.path as osp
import pandas as pd
import numpy as np
def computeMeanAndVarianceOfNodes(numGatesAndLPStatsDict):
meanAndVarNodesDict = {}
for des in numGatesAndLPStatsDict.keys():
andG... | 7,941 | 39.938144 | 164 | py |
OpenABC | OpenABC-master/models/classification/ClassNetV1/model.py | import torch
from torch_geometric.nn import MessagePassing
from torch_geometric.nn import global_mean_pool, global_add_pool
import torch.nn.functional as F
from torch_geometric.utils import add_self_loops, degree
allowable_features = {
'node_type' : [0,1,2],
'num_inverted_predecessors' : [0,1,2]
}
def get_nod... | 4,333 | 31.103704 | 129 | py |
OpenABC | OpenABC-master/models/classification/ClassNetV1/train.py | import torch
from torch.optim.lr_scheduler import ReduceLROnPlateau
#from NetlistClassification.model import *
from model import *
#from NetlistClassification.utils import *
from utils import *
#from NetlistClassification.netlistDataset import *
from netlistDataset import *
import os
import torch.nn.functional as F
fr... | 7,731 | 38.85567 | 126 | py |
OpenABC | OpenABC-master/models/qor/SynthNetV1/embedding.py | from model import *
from utils import *
from netlistDataset import *
from torch_geometric.data import DataLoader
from torchvision import transforms
from tqdm import tqdm
import os.path as osp
import pickle
import sys,os,argparse
datasetDict = {
'desID': ["train_data_desID.csv", "test_data_desID.csv"], # Test DS ... | 3,615 | 36.666667 | 109 | py |
OpenABC | OpenABC-master/models/qor/SynthNetV1/evaluate.py | import torch
from torch.optim.lr_scheduler import ReduceLROnPlateau
#from NetlistClassification.model import *
from model import *
#from NetlistClassification.utils import *
from utils import *
#from NetlistClassification.netlistDataset import *
from netlistDataset import *
import argparse
import torch.nn.functional as... | 6,399 | 39 | 170 | py |
OpenABC | OpenABC-master/models/qor/SynthNetV1/netlistDataset.py | import os.path as osp
import torch
from zipfile import ZipFile
import pandas as pd
from torch_geometric.data import Dataset, download_url
class NetlistGraphDataset(Dataset):
def __init__(self, root, filePath, transform=None, pre_transform=None):
self.filePath = osp.join(root, filePath)
super(Netl... | 934 | 30.166667 | 86 | py |
OpenABC | OpenABC-master/models/qor/SynthNetV1/utils.py | from statistics import mean
from webbrowser import get
import torch
from sklearn.metrics import mean_squared_error,mean_absolute_error,mean_absolute_percentage_error
import matplotlib.pyplot as plt
import os.path as osp
import pandas as pd
import numpy as np
def getMeanAndVariance(targetList):
return np.mean(np.ar... | 11,642 | 40.881295 | 148 | py |
OpenABC | OpenABC-master/models/qor/SynthNetV1/model.py | import torch
from torch_geometric.nn import MessagePassing
from torch_geometric.nn import global_mean_pool, global_max_pool
import torch.nn.functional as F
from torch_geometric.utils import add_self_loops, degree
allowable_synthesis_features = {
'synth_type' : [0,1,2,3,4,5,6]
}
def get_synth_feature_dims():
r... | 8,508 | 36.157205 | 150 | py |
OpenABC | OpenABC-master/models/qor/SynthNetV1/train.py | import os
import argparse
from torch.optim.lr_scheduler import ReduceLROnPlateau
from model import *
from utils import *
from netlistDataset import *
import torch.nn.functional as F
from torch_geometric.data import DataLoader
import numpy as np
from torchvision import transforms
from tqdm import tqdm
from torch.utils... | 9,629 | 38.62963 | 170 | py |
OpenABC | OpenABC-master/models/qor/SynthNetV3/evaluate.py | import torch
from torch.optim.lr_scheduler import ReduceLROnPlateau
#from NetlistClassification.model import *
from model import *
#from NetlistClassification.utils import *
from utils import *
#from NetlistClassification.netlistDataset import *
from netlistDataset import *
import argparse
import torch.nn.functional as... | 6,399 | 39 | 170 | py |
OpenABC | OpenABC-master/models/qor/SynthNetV3/netlistDataset.py | import os.path as osp
import torch
from zipfile import ZipFile
import pandas as pd
from torch_geometric.data import Dataset, download_url
class NetlistGraphDataset(Dataset):
def __init__(self, root, filePath, transform=None, pre_transform=None):
self.filePath = osp.join(root, filePath)
super(Netl... | 932 | 32.321429 | 86 | py |
OpenABC | OpenABC-master/models/qor/SynthNetV3/utils.py | from statistics import mean
from webbrowser import get
import torch
from sklearn.metrics import mean_squared_error,mean_absolute_error,mean_absolute_percentage_error
import matplotlib.pyplot as plt
import os.path as osp
import pandas as pd
import numpy as np
def getMeanAndVariance(targetList):
return np.mean(np.ar... | 11,642 | 40.881295 | 148 | py |
OpenABC | OpenABC-master/models/qor/SynthNetV3/model.py | import torch
from torch_geometric.nn import MessagePassing
from torch_geometric.nn import global_mean_pool, global_max_pool
import torch.nn.functional as F
from torch_geometric.utils import add_self_loops, degree
allowable_synthesis_features = {
'synth_type' : [0,1,2,3,4,5,6]
}
def get_synth_feature_dims():
r... | 8,503 | 38.37037 | 178 | py |
OpenABC | OpenABC-master/models/qor/SynthNetV3/train.py | import os
import argparse
from torch.optim.lr_scheduler import ReduceLROnPlateau
from model import *
from utils import *
from netlistDataset import *
import torch.nn.functional as F
from torch_geometric.data import DataLoader
import numpy as np
from torchvision import transforms
from tqdm import tqdm
from torch.utils... | 9,733 | 38.893443 | 170 | py |
OpenABC | OpenABC-master/models/qor/SynthNetV2/evaluate.py | import torch
from torch.optim.lr_scheduler import ReduceLROnPlateau
#from NetlistClassification.model import *
from model import *
#from NetlistClassification.utils import *
from utils import *
#from NetlistClassification.netlistDataset import *
from netlistDataset import *
import argparse
import torch.nn.functional as... | 6,399 | 39 | 170 | py |
OpenABC | OpenABC-master/models/qor/SynthNetV2/netlistDataset.py | import os.path as osp
import torch
from zipfile import ZipFile
import pandas as pd
from torch_geometric.data import Dataset, download_url
class NetlistGraphDataset(Dataset):
def __init__(self, root, filePath, transform=None, pre_transform=None):
self.filePath = osp.join(root, filePath)
super(Netl... | 932 | 32.321429 | 86 | py |
OpenABC | OpenABC-master/models/qor/SynthNetV2/utils.py | from statistics import mean
from webbrowser import get
import torch
from sklearn.metrics import mean_squared_error,mean_absolute_error,mean_absolute_percentage_error
import matplotlib.pyplot as plt
import os.path as osp
import pandas as pd
import numpy as np
def getMeanAndVariance(targetList):
return np.mean(np.ar... | 11,642 | 40.881295 | 148 | py |
OpenABC | OpenABC-master/models/qor/SynthNetV2/model.py | import torch
from torch_geometric.nn import MessagePassing,GCNConv
from torch_geometric.nn import global_mean_pool, global_max_pool
import torch.nn.functional as F
from torch_geometric.utils import add_self_loops, degree
allowable_synthesis_features = {
'synth_type' : [0,1,2,3,4,5,6]
}
def get_synth_feature_dims(... | 7,483 | 38.597884 | 178 | py |
OpenABC | OpenABC-master/models/qor/SynthNetV2/train.py | import os
import argparse
from torch.optim.lr_scheduler import ReduceLROnPlateau
from model import *
from utils import *
from netlistDataset import *
import torch.nn.functional as F
from torch_geometric.data import DataLoader
import numpy as np
from torchvision import transforms
from tqdm import tqdm
from torch.utils... | 9,698 | 38.91358 | 170 | py |
OpenABC | OpenABC-master/datagen/utilities/PyGDataAIG.py | import os
import shutil
import pandas as pd
import networkx as nx
import glob
import pickle
import copy
from typing import Optional, Tuple
import torch
from torch import Tensor
from torch.utils.dlpack import to_dlpack, from_dlpack
import scipy.sparse
import zipfile
import argparse
import torch_geometric
import torc... | 6,119 | 33.576271 | 140 | py |
LISA | LISA-main/domain_shifts/main.py | import argparse
import datetime
import time
import json
import os
import pdb
import sys
import csv
import tqdm
from collections import defaultdict
from transformers import get_cosine_schedule_with_warmup
from tempfile import mkdtemp
import ipdb
import numpy as np
import pandas as pd
import torch
import torch.optim as ... | 16,444 | 39.306373 | 120 | py |
LISA | LISA-main/domain_shifts/utils.py | import pdb
import numpy as np
import os
import random
import shutil
import sys
import operator
from numbers import Number
from collections import OrderedDict
import torch
from torch import nn
from torch.utils.data import Dataset
# https://stackoverflow.com/questions/14906764/how-to-redirect-stdout-to-both-file-and-... | 5,494 | 27.769634 | 109 | py |
LISA | LISA-main/domain_shifts/mixup.py | import pdb
import torch
import numpy as np
def rand_bbox(size, lam):
W = size[2]
H = size[3]
cut_rat = np.sqrt(1. - lam)
cut_w = np.int(W * cut_rat)
cut_h = np.int(H * cut_rat)
# uniform
cx = np.random.randint(W)
cy = np.random.randint(H)
bbx1 = np.clip(cx - cut_w // 2, 0, W)
... | 3,672 | 26.410448 | 133 | py |
LISA | LISA-main/domain_shifts/models/amazon.py | import os
from copy import deepcopy
import torch
from torch import nn
from torch.utils.data import DataLoader
from transformers import BertForSequenceClassification
from transformers import BertTokenizerFast
from transformers import DistilBertTokenizerFast
from wilds.common.data_loaders import get_eval_loader
from wil... | 4,069 | 34.701754 | 98 | py |
LISA | LISA-main/domain_shifts/models/fmow.py | import os
from copy import deepcopy
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torchvision.models import densenet121
from wilds.common.data_loaders import get_eval_loader
from wilds.datasets.fmow_dataset im... | 2,158 | 34.983333 | 98 | py |
LISA | LISA-main/domain_shifts/models/rxrx.py | import numpy as np
import os
from copy import deepcopy
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision.transforms.functional as TF
from torch.utils.data import DataLoader
from torchvision.models import resnet50
from wilds.common.data_loaders import get_eval_loader
from... | 3,474 | 34.10101 | 98 | py |
LISA | LISA-main/domain_shifts/models/camelyon.py | import os
from copy import deepcopy
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torchvision.models import densenet121
from wilds.common.data_loaders import get_eval_loader
from wilds.datasets.camelyon17_data... | 2,497 | 36.848485 | 98 | py |
LISA | LISA-main/domain_shifts/models/datasets.py | import copy
import glob
import os
import pdb
import numpy as np
import pandas as pd
import torch
from PIL import Image
from torch.utils.data import Dataset
from torch.nn import functional
class FMoW_Batched_Dataset(Dataset):
"""
Batched dataset for FMoW. Allows for getting a batch of data given
a specific... | 14,673 | 43.874618 | 117 | py |
LISA | LISA-main/domain_shifts/models/civil.py | import os
from copy import deepcopy
import torch
import numpy as np
from torch import nn
from torch.utils.data import DataLoader
from transformers import BertForSequenceClassification
from transformers import BertTokenizerFast
from transformers import logging
from .bert.bert import BertFeaturizer
from .bert.distilbert... | 5,154 | 37.470149 | 113 | py |
LISA | LISA-main/domain_shifts/models/bert/bert.py | from transformers import BertForSequenceClassification, BertModel
import torch
class BertClassifier(BertForSequenceClassification):
def __init__(self, config):
super().__init__(config)
self.d_out = config.num_labels
def __call__(self, x):
input_ids = x[:, :, 0]
attention_mask ... | 1,041 | 27.162162 | 65 | py |
LISA | LISA-main/subpopulation_shifts/loss.py | import os
import torch
import torch.nn.functional as F
import itertools
import numpy as np
class LossComputer:
def __init__(self, args, criterion, is_robust, dataset, alpha=None, gamma=0.1, adj=None, min_var_weight=0, step_size=0.01, normalize_loss=False, btl=False, is_val=False):
self.criterion = criterio... | 10,049 | 43.666667 | 174 | py |
LISA | LISA-main/subpopulation_shifts/utils.py | import sys
import os
import torch
import numpy as np
import csv
import argparse
import torch.nn as nn
class Identity(nn.Module):
"""An identity layer"""
def __init__(self, d):
super().__init__()
self.in_features = d
self.out_features = d
def forward(self, x):
return x
d... | 4,836 | 28.138554 | 104 | py |
LISA | LISA-main/subpopulation_shifts/train.py | import os
import numpy as np
import torch
from pytorch_transformers import AdamW, WarmupLinearSchedule
from torch.optim.lr_scheduler import StepLR
from tqdm import tqdm
from transformers import (get_linear_schedule_with_warmup,
get_cosine_schedule_with_warmup)
from loss import LossComputer
... | 18,666 | 36.483936 | 142 | py |
LISA | LISA-main/subpopulation_shifts/run_expt.py | import argparse
import os
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torchvision
from data import folds
from data.data import dataset_attributes, shift_types, prepare_data, log_data, log_meta_data
from data.dro_dataset import DRODataset
from data.folds import Subset
from models i... | 12,907 | 40.909091 | 130 | py |
LISA | LISA-main/subpopulation_shifts/bert/bert.py | from transformers import BertForSequenceClassification, BertModel
import torch
class BertClassifier(BertForSequenceClassification):
def __init__(self, config):
super().__init__(config)
self.d_out = config.num_labels
def __call__(self, x):
input_ids = x[:, :, 0]
attention_mask ... | 1,041 | 27.162162 | 65 | py |
LISA | LISA-main/subpopulation_shifts/data/confounder_utils.py | import os
import torch
import pandas as pd
from PIL import Image
import numpy as np
import torchvision.transforms as transforms
from models import model_attributes
from torch.utils.data import Dataset
from data.celebA_dataset import CelebADataset
from data.cub_dataset import CUBDataset
from data.dro_dataset import DROD... | 2,943 | 32.83908 | 102 | py |
LISA | LISA-main/subpopulation_shifts/data/meta_dataset_cat_dog.py | import glob
from enum import unique
import os
import torch
import pandas as pd
from tqdm import tqdm
from PIL import Image
import numpy as np
import torchvision.transforms as transforms
from models import model_attributes
from torch.utils.data import Dataset, Subset
from data.confounder_dataset import ConfounderDataset... | 10,189 | 36.740741 | 117 | py |
LISA | LISA-main/subpopulation_shifts/data/cmnist_dataset.py | import os
import numpy as np
import torch
from PIL import Image
from torchvision import datasets
from torchvision import transforms
from data.confounder_dataset import ConfounderDataset
def color_grayscale_arr(arr, red=True):
"""Converts grayscale image to either red or green"""
assert arr.ndim == 2
dtype = a... | 8,548 | 38.396313 | 132 | py |
LISA | LISA-main/subpopulation_shifts/data/confounder_dataset.py | import os
import pdb
import torch
import pandas as pd
from PIL import Image
import numpy as np
import torchvision.transforms as transforms
from models import model_attributes
from torch.utils.data import Dataset
from data.folds import Subset
class ConfounderDataset(Dataset):
def __init__(self, root_dir,
... | 3,969 | 35.090909 | 123 | py |
LISA | LISA-main/subpopulation_shifts/data/utils.py | import torch
import numpy as np
from torch.utils.data import Subset
# Train val split
def train_val_split(dataset, val_frac):
# split into train and val
indices = np.arange(len(dataset))
np.random.shuffle(indices)
val_size = int(np.round(len(dataset)*val_frac))
train_indices, val_indices = indices[... | 742 | 34.380952 | 87 | py |
LISA | LISA-main/subpopulation_shifts/data/data.py | import os
import torch
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from torch.utils.data import Subset
from data.confounder_utils import prepare_confounder_data, prepare_group_confounder_data
from data.label_shift_utils import prepare_label_shift_data
root_dir = './data/'
dat... | 5,570 | 43.568 | 143 | py |
LISA | LISA-main/subpopulation_shifts/data/cub_dataset.py | from enum import unique
import os
import torch
import pandas as pd
from tqdm import tqdm
from PIL import Image
import numpy as np
import torchvision.transforms as transforms
from models import model_attributes
from torch.utils.data import Dataset, Subset
from data.confounder_dataset import ConfounderDataset
class CUBD... | 6,208 | 37.092025 | 115 | py |
LISA | LISA-main/subpopulation_shifts/data/label_shift_utils.py | import os
import torch
import pandas as pd
from PIL import Image
import numpy as np
import torchvision
import torchvision.transforms as transforms
from models import model_attributes
from torch.utils.data import Dataset, Subset
from data.dro_dataset import DRODataset
from data.utils import *
from data.torchvision_datas... | 3,083 | 30.469388 | 124 | py |
LISA | LISA-main/subpopulation_shifts/data/torchvision_datasets.py | import torch
from torch.utils.data import Subset
import torchvision
import torchvision.transforms as transforms
from models import model_attributes
from data.utils import *
### CIFAR10 ###
def load_CIFAR10(args, train):
transform = get_transform_CIFAR10(args, train)
dataset = torchvision.datasets.CIFAR10(args.... | 1,008 | 35.035714 | 100 | py |
LISA | LISA-main/subpopulation_shifts/data/folds.py | import numpy as np
import torch
from data import dro_dataset
import pdb
import bisect
import warnings
from torch._utils import _accumulate
from torch import randperm, default_generator
class Subset(torch.utils.data.Dataset):
"""
Subsets a dataset while preserving original indexing.
NOTE: torch.utils.d... | 5,460 | 32.709877 | 118 | py |
LISA | LISA-main/subpopulation_shifts/data/dro_dataset.py | import pdb
import torch
import numpy as np
from torch.utils.data import Dataset, DataLoader
from torch.utils.data.sampler import WeightedRandomSampler
class DRODataset(Dataset):
def __init__(self, dataset, process_item_fn, n_groups, n_classes, group_str_fn):
self.dataset = dataset
self.process_ite... | 4,188 | 36.070796 | 105 | py |
LISA | LISA-main/subpopulation_shifts/data/celebA_dataset.py | import os
import torch
import pandas as pd
from PIL import Image
import numpy as np
from tqdm import tqdm
import torchvision.transforms as transforms
from models import model_attributes
from torch.utils.data import Dataset, Subset
from data.confounder_dataset import ConfounderDataset
class CelebADataset(ConfounderData... | 5,141 | 38.251908 | 113 | py |
Match-Prompt | Match-Prompt-main/mixed_training.py | import json
import os
import torch
import argparse
import numpy as np
from torch.utils.data import DataLoader
from datetime import datetime
from tqdm import tqdm
from transformers import AutoTokenizer
import random
from os.path import join, abspath, dirname
from data_utils.vocab import init_vocab
from p_tuning.model... | 24,478 | 43.833333 | 160 | py |
Match-Prompt | Match-Prompt-main/train_continuous_prompt_hp/cli_all_layer_pi_6.py | import json
import os
import torch
import argparse
import numpy as np
from torch.utils.data import DataLoader
from datetime import datetime
from tqdm import tqdm
from transformers import AutoTokenizer
import random
from os.path import join, abspath, dirname
from data_utils.vocab import init_vocab
from p_tuning.model... | 13,081 | 41.064309 | 204 | py |
Match-Prompt | Match-Prompt-main/train_continuous_prompt_hp/cli_all_layer_qa_6.py | import json
import os
import torch
import argparse
import numpy as np
from torch.utils.data import DataLoader
from datetime import datetime
from tqdm import tqdm
from transformers import AutoTokenizer
import random
from os.path import join, abspath, dirname
from data_utils.vocab import init_vocab
from p_tuning.model... | 12,219 | 39.598007 | 119 | py |
Match-Prompt | Match-Prompt-main/train_continuous_prompt_hp/cli_all_layer_daily_6.py | import json
import os
import torch
import argparse
import numpy as np
from torch.utils.data import DataLoader
from datetime import datetime
from tqdm import tqdm
from transformers import AutoTokenizer
import random
from os.path import join, abspath, dirname
from data_utils.vocab import init_vocab
from p_tuning.model... | 12,840 | 40.964052 | 119 | py |
Match-Prompt | Match-Prompt-main/train_continuous_prompt_hp/cli_all_layer_nli_6.py | import json
import os
import torch
import argparse
import numpy as np
from torch.utils.data import DataLoader
from datetime import datetime
from tqdm import tqdm
from transformers import AutoTokenizer
import random
from os.path import join, abspath, dirname
from data_utils.vocab import init_vocab
from p_tuning.model... | 12,520 | 40.323432 | 206 | py |
Match-Prompt | Match-Prompt-main/train_continuous_prompt_hp/cli_all_layer_adhoc_6.py | import json
import os
import torch
import argparse
import numpy as np
from torch.utils.data import DataLoader
from datetime import datetime
from tqdm import tqdm
from transformers import AutoTokenizer
import random
from os.path import join, abspath, dirname
from data_utils.vocab import init_vocab
from p_tuning.model... | 13,464 | 42.435484 | 211 | py |
Match-Prompt | Match-Prompt-main/p_tuning/prompt_encoder_init.py | import torch
import torch.nn as nn
class PromptEncoder(torch.nn.Module):
def __init__(self, template, hidden_size, tokenizer, device, args):
super().__init__()
self.device = device
self.spell_length = sum(template)
self.hidden_size = hidden_size
self.tokenizer = tokenizer
... | 1,842 | 41.860465 | 102 | py |
Match-Prompt | Match-Prompt-main/p_tuning/prompt_encoder.py | import torch
import torch.nn as nn
class PromptEncoder(torch.nn.Module):
def __init__(self, template, hidden_size, tokenizer, device, args):
super().__init__()
self.device = device
self.spell_length = sum(template)
self.hidden_size = hidden_size
self.tokenizer = tokenizer
... | 14,267 | 51.263736 | 112 | py |
Match-Prompt | Match-Prompt-main/p_tuning/modeling_all_layer.py | import torch
from torch.nn.utils.rnn import pad_sequence
from os.path import join
import re
from transformers import AutoTokenizer
from p_tuning.models import get_embedding_layer, create_model
from data_utils.vocab import get_vocab_by_strategy, token_wrapper
from data_utils.dataset import load_file
from p_tuning.pr... | 9,518 | 45.208738 | 163 | py |
Match-Prompt | Match-Prompt-main/p_tuning/modeling_p_tunning_all_layer.py | import torch
from torch.nn.utils.rnn import pad_sequence
from os.path import join
import torch.nn.functional as F
import re
import numpy as np
from transformers import AutoTokenizer
from p_tuning.models import get_embedding_layer, create_model
from data_utils.vocab import get_vocab_by_strategy, token_wrapper
from data... | 95,475 | 43.782364 | 280 | py |
Match-Prompt | Match-Prompt-main/mytransformers_ptunning/setup.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | 12,664 | 33.137466 | 259 | py |
Match-Prompt | Match-Prompt-main/mytransformers_ptunning/hubconf.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | 8,496 | 51.450617 | 189 | py |
Match-Prompt | Match-Prompt-main/mytransformers_ptunning/examples/research_projects/longform-qa/eli5_app.py | import datasets
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
import faiss
import transformers
from eli5_utils import (
embed_questions_for_retrieval,
make_qa_s2s_model,
qa_s2s_generate,
query_es_index,
query_qa_dense_index,
)
from transformers impor... | 13,474 | 37.28125 | 159 | py |
Match-Prompt | Match-Prompt-main/mytransformers_ptunning/examples/research_projects/longform-qa/eli5_utils.py | import functools
import math
import os # noqa: F401
from random import choice, randint
from time import time
import datasets # noqa: F401
import numpy as np
import pandas as pd
import torch
import torch.utils.checkpoint as checkpoint
from elasticsearch import Elasticsearch # noqa: F401
from elasticsearch.helpers im... | 28,299 | 40.07402 | 119 | py |
Match-Prompt | Match-Prompt-main/mytransformers_ptunning/examples/research_projects/bertology/run_prune_gpt.py | #!/usr/bin/env python3
""" This script is adapted from the Bertology pruning code (https://github.com/huggingface/transformers/blob/783d7d2629e97c5f0c5f9ef01b8c66410275c204/examples/research_projects/bertology/run_bertology.py)
to prune GPT-like models. The author is @altsoph.
"""
import argparse
import logging
import... | 15,469 | 38.666667 | 204 | py |
Match-Prompt | Match-Prompt-main/mytransformers_ptunning/examples/research_projects/bertology/run_bertology.py | #!/usr/bin/env python3
# Copyright 2018 CMU and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requir... | 18,572 | 40.181818 | 118 | py |
Match-Prompt | Match-Prompt-main/mytransformers_ptunning/examples/research_projects/rag/use_own_knowledge_dataset.py | import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import torch
from datasets import Features, Sequence, Value, load_dataset
import faiss
from transformers import (
DPRCo... | 8,174 | 38.878049 | 152 | py |
Match-Prompt | Match-Prompt-main/mytransformers_ptunning/examples/research_projects/rag/utils_rag.py | import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transfo... | 8,114 | 32.122449 | 118 | py |
Match-Prompt | Match-Prompt-main/mytransformers_ptunning/examples/research_projects/rag/finetune_rag.py | """Finetuning script for RAG models. Adapted from examples.seq2seq.finetune.py"""
import argparse
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Any, Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
import... | 25,597 | 40.420712 | 197 | py |
Match-Prompt | Match-Prompt-main/mytransformers_ptunning/examples/research_projects/rag/distributed_pytorch_retriever.py | import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
logger = logging.getLogger(__name__)
class RagPyTorchDistributedRetriever(RagRetriever):
"""
A distributed retriever built on top of ... | 6,539 | 46.05036 | 155 | py |
Match-Prompt | Match-Prompt-main/mytransformers_ptunning/examples/research_projects/rag/test_distributed_retriever.py | import json
import os
import shutil
import sys
import tempfile
import unittest
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
import faiss
from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transform... | 13,794 | 39.693215 | 118 | py |
Match-Prompt | Match-Prompt-main/mytransformers_ptunning/examples/research_projects/rag/eval_rag.py | """ Evaluation script for RAG models."""
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as trans... | 11,101 | 34.469649 | 132 | py |
Match-Prompt | Match-Prompt-main/mytransformers_ptunning/examples/research_projects/rag/lightning_base.py | import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
... | 15,005 | 37.280612 | 119 | py |
Match-Prompt | Match-Prompt-main/mytransformers_ptunning/examples/research_projects/rag/callbacks_rag.py | import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def count_trainable_parameters(model):
model_parame... | 4,420 | 36.786325 | 126 | py |
Match-Prompt | Match-Prompt-main/mytransformers_ptunning/examples/research_projects/rag/_test_finetune_rag.py | import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
logging.ba... | 3,969 | 34.765766 | 85 | py |
Match-Prompt | Match-Prompt-main/mytransformers_ptunning/examples/research_projects/pplm/run_pplm.py | #! /usr/bin/env python3
# coding=utf-8
# Copyright (c) 2019 Uber Technologies, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless ... | 28,735 | 34.001218 | 182 | py |
Match-Prompt | Match-Prompt-main/mytransformers_ptunning/examples/research_projects/pplm/run_pplm_discrim_train.py | #! /usr/bin/env python3
# coding=utf-8
# Copyright (c) 2019 Uber Technologies, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless ... | 18,788 | 34.92543 | 117 | py |
Match-Prompt | Match-Prompt-main/mytransformers_ptunning/examples/research_projects/pplm/pplm_classification_head.py | from torch import nn
class ClassificationHead(nn.Module):
"""Classification Head for transformer encoders"""
def __init__(self, class_size, embed_size):
super().__init__()
self.class_size = class_size
self.embed_size = embed_size
# self.mlp1 = nn.Linear(embed_size, embed_size... | 651 | 31.6 | 68 | py |
Match-Prompt | Match-Prompt-main/mytransformers_ptunning/examples/research_projects/deebert/test_glue_deebert.py | import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger()
def get_setup_file():
parser = argparse.... | 3,690 | 34.152381 | 109 | py |
Match-Prompt | Match-Prompt-main/mytransformers_ptunning/examples/research_projects/deebert/run_glue_deebert.py | from __future__ import absolute_import, division, print_function
import argparse
import glob
import logging
import os
import random
import time
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from torch.utils.data.distribute... | 31,693 | 42.297814 | 150 | py |
Match-Prompt | Match-Prompt-main/mytransformers_ptunning/examples/research_projects/deebert/src/modeling_highway_bert.py | import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
BertLayer,
... | 17,668 | 43.506297 | 172 | py |
Match-Prompt | Match-Prompt-main/mytransformers_ptunning/examples/research_projects/deebert/src/modeling_highway_roberta.py | from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta... | 6,791 | 42.261146 | 172 | py |
Match-Prompt | Match-Prompt-main/mytransformers_ptunning/examples/research_projects/lxmert/modeling_frcnn.py | """
coding=utf-8
Copyright 2018, Antonio Mendoza Hao Tan, Mohit Bansal
Adapted From Facebook Inc, Detectron2 && Huggingface Co.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www... | 73,730 | 37.361602 | 152 | py |
Match-Prompt | Match-Prompt-main/mytransformers_ptunning/examples/research_projects/lxmert/extracting_data.py | import getopt
import json
import os
# import numpy as np
import sys
from collections import OrderedDict
import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess
from utils import Config
"""
USAGE:
``python extracting_data.py -i <img_dir> -o ... | 5,254 | 34.033333 | 109 | py |
Match-Prompt | Match-Prompt-main/mytransformers_ptunning/examples/research_projects/lxmert/utils.py | """
coding=utf-8
Copyright 2018, Antonio Mendoza Hao Tan, Mohit Bansal, Huggingface team :)
Adapted From Facebook Inc, Detectron2
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://w... | 18,199 | 31.5 | 143 | py |
Match-Prompt | Match-Prompt-main/mytransformers_ptunning/examples/research_projects/lxmert/visualizing_image.py | """
coding=utf-8
Copyright 2018, Antonio Mendoza Hao Tan, Mohit Bansal
Adapted From Facebook Inc, Detectron2
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/license... | 13,420 | 25.842 | 100 | py |
Match-Prompt | Match-Prompt-main/mytransformers_ptunning/examples/research_projects/lxmert/processing_image.py | """
coding=utf-8
Copyright 2018, Antonio Mendoza Hao Tan, Mohit Bansal
Adapted From Facebook Inc, Detectron2
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/license... | 5,678 | 36.86 | 114 | py |
Match-Prompt | Match-Prompt-main/mytransformers_ptunning/examples/research_projects/bertabs/modeling_bertabs.py | # MIT License
# Copyright (c) 2019 Yang Liu and the HuggingFace team
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, c... | 38,263 | 35.1322 | 114 | py |
Match-Prompt | Match-Prompt-main/mytransformers_ptunning/examples/research_projects/bertabs/convert_bertabs_original_pytorch_checkpoint.py | # coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | 6,523 | 34.075269 | 117 | py |
Match-Prompt | Match-Prompt-main/mytransformers_ptunning/examples/research_projects/bertabs/utils_summarization.py | import os
from collections import deque
import torch
from torch.utils.data import Dataset
# ------------
# Data loading
# ------------
class CNNDMDataset(Dataset):
"""Abstracts the dataset used to train seq2seq models.
The class will process the documents that are located in the specified
folder. The ... | 5,753 | 33.25 | 106 | py |
Match-Prompt | Match-Prompt-main/mytransformers_ptunning/examples/research_projects/bertabs/test_utils_summarization.py | # coding=utf-8
# Copyright 2019 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | 4,419 | 43.646465 | 99 | py |
Match-Prompt | Match-Prompt-main/mytransformers_ptunning/examples/research_projects/bertabs/run_summarization.py | #! /usr/bin/python3
import argparse
import logging
import os
import sys
from collections import namedtuple
import torch
from torch.utils.data import DataLoader, SequentialSampler
from tqdm import tqdm
from modeling_bertabs import BertAbs, build_predictor
from transformers import BertTokenizer
from .utils_summarizati... | 10,188 | 28.278736 | 137 | py |
Match-Prompt | Match-Prompt-main/mytransformers_ptunning/examples/research_projects/adversarial/run_hans.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a cop... | 8,213 | 33.225 | 133 | py |
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