seanbenhur

from reg_resampler import resampler

# Initialize the resampler object
rs = resampler()

# You might recieve info about class merger for low sample classes
# Generate classes
Y_classes = rs.fit(train, target=target, bins=num_bins)
# Create the actual target variable
Y = df_train[target]

seanbenhur

from datasets import load_dataset
from transformers import AutoTokenizer

#load the dataset
dataset = load_dataset("imdb")
#create tokenizer
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")

def encode_batch(batch):
  """Encodes a batch of input data using the model tokenizer."""

seanbenhur

import numpy as np
from transformers import AutoConfig, AutoModelWithHeads
from transformers import TrainingArguments, Trainer, EvalPrediction


config = AutoConfig.from_pretrained(
    "distilbert-base-uncased",
    num_labels=2,
)
model = AutoModelWithHeads.from_pretrained(

seanbenhur

import argparse
from transformers import AutoTokenizer
import torch
import numpy as np
from collections import Counter
import pandas as pd
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F

seanbenhur

[verbose]: Creating arXiv submission AutoTeX object
[verbose]: *** Using TeX Live 2020 ***
[verbose]: Calling arXiv submission AutoTeX process
[verbose]: TeX/AutoTeX.pm: admin_timeout = minion
[verbose]: <Copyright-logo.txt> is of type 'unknown'.
[verbose]: <Copyright-lppl.txt> is of type 'unknown'.
[verbose]: <Copyright.txt> is of type 'unknown'.
[verbose]: <Makefile> is of type 'unknown'.
[verbose]: <README.md> is of type 'unknown'.

seanbenhur

def convert_annot_to_yolov5(x_min, y_min, x_max, y_max, img):
    """
    Convert annotations into required yolov5 formamt
    x_center, y_center, width, height
    """
    w = x_max - x_min
    h = y_max - y_min
    imgheight,imgwidth = img.shape[0], img.shape[1]
    #x,y,w,h = a['hbox']   //for each tag in gtboxes object
    """

seanbenhur

for i, data in enumerate(test_dataloader, 0):
    x0, x1 = data
    concat = torch.cat((x0, x1), 0)
    output1, output2 = model(x0.to(device), x1.to(device))
    eucledian_distance = F.pairwise_distance(output1, output2)
    if label == torch.FloatTensor([[0]]):
        label = "Original Pair Of Signature"
    else:
        label = "Forged Pair Of Signature"

seanbenhur

from waitress import serve
import io
from flask import Flask, request,jsonify
from PIL import Image
import base64
from spacymodels.activeorpassive.model import find_passive_or_active
import spacy
import pandas as pd
import torch
import numpy as np

seanbenhur

import re
import wandb
from datasets import load_dataset, concatenate_datasets
from functools import partial
import logging 

logger = logging.getLogger(__name__)


def load_hf_format_dataset(file_path,split):

seanbenhur

import time
import json
import multiprocessing
from multiprocessing import Pool


txt_path = "tamil_dataset.txt"
json_path = "tamil_final_dataset.json"