text stringlengths 1 93.6k |
|---|
elif author == response:
|
if author == prev_author:
|
prev = result_dict[count]['completion'][:-4]
|
result_dict[count]['completion'] = f"{prev}. {message} ###"
|
else:
|
result_dict[count].update({'completion': message + " ###"})
|
prev_author = author
|
return result_dict
|
def parse_whatsapp_text_into_dataframe(raw_text, prompter, responder):
|
result_dict = text_to_dictionary(raw_text, prompter, responder)
|
df = pd.DataFrame.from_dict(result_dict).T[['prompt', 'completion']]
|
return df
|
def converter(
|
filepath: str,
|
prompter: str,
|
responder: str,
|
) -> pd.DataFrame:
|
"""
|
Turn whatsapp chat data into a format
|
that can be trained by openai's fine tuning api.
|
:param file: Path to file we want to convert
|
:param prompter: The person to be labelled as the prompter
|
:param responder: The person to be labelled as the responder
|
:return: a parsed pandas dataframe
|
"""
|
with open(filepath, 'r') as fp:
|
text = fp.read()
|
df = parse_whatsapp_text_into_dataframe(text, prompter, responder)
|
df = df.dropna()
|
return df
|
if __name__ == "__main__":
|
parser = argparse.ArgumentParser(description='Process your whatsapp chat data.')
|
parser.add_argument('path', type=str, help='Path to file')
|
parser.add_argument('prompter', type=str, help='Name of Prompter')
|
parser.add_argument('responder', type=str, help='Name of Responder')
|
parser.add_argument('-filename', type=str, help='Destination filename')
|
args = parser.parse_args()
|
path = args.path
|
prompter = args.prompter
|
responder = args.responder
|
filename = args.filename
|
save_file = filename if filename else datetime.datetime.now()
|
converter(path, prompter, responder).to_json(f'output_{save_file}.json',lines=True,orient='records', force_ascii=False)
|
# <FILESEP>
|
import argparse
|
import os
|
import random
|
import shutil
|
import time
|
import warnings
|
import torch
|
import torch.backends.cudnn as cudnn
|
import torch.distributed as dist
|
import torch.multiprocessing as mp
|
import torch.nn as nn
|
import torch.nn.functional as F
|
import torch.nn.parallel
|
import torch.optim
|
import torch.utils.data
|
import torch.utils.data.distributed
|
import torchvision.datasets as datasets
|
import torchvision.transforms as transforms
|
from kornia.filters.gaussian import gaussian_blur2d
|
from cam import GroupCAM
|
# import torchvision.models as models
|
import backbones as models
|
# Function that blurs input image
|
blur = lambda x: gaussian_blur2d(x, kernel_size=(51, 51), sigma=(50., 50.))
|
model_names = sorted(name for name in models.__dict__
|
if name.islower() and not name.startswith("__")
|
and callable(models.__dict__[name]))
|
parser = argparse.ArgumentParser(description='An example of adopting group-cam to fine-tune classification models')
|
parser.add_argument('data', metavar='DIR',
|
help='path to dataset')
|
parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet18',
|
choices=model_names,
|
help='model architecture: ' +
|
' | '.join(model_names) +
|
' (default: resnet18)')
|
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
|
help='number of data loading workers (default: 4)')
|
parser.add_argument('--epochs', default=90, type=int, metavar='N',
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.