Dmitry Abulkhanov mponty

## EDA.ipynb

      
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                mponty
                / EDA.ipynb
            
            
              Created
              January 18, 2018 21:33
            
          
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## EDA u xaB4uK
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import os.path as osp\n",

## cascade.py
cfg['model']['mask_head']['num_classes'] = N_CLASSES + 1
if isinstance(cfg['model']['bbox_head'], list):
    for i in range(len(cfg['model']['bbox_head'])):
        cfg['model']['bbox_head'][i]['num_classes'] = N_CLASSES + 1
else:
    cfg['model']['bbox_head']['num_classes'] = N_CLASSES + 1

## BPE_tokenizer_analysis.ipynb

      
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                mponty
                / BPE_tokenizer_analysis.ipynb
            
            
              Created
              November 15, 2022 16:36
            
          
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## EDA of labeled-python-data-pii-detection.ipynb

      
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                mponty
                / EDA of labeled-python-data-pii-detection.ipynb
            
            
              Created
              March 1, 2023 16:34
            
              
                EDA of labeled-python-data-pii-detection
              
          
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## merge_fragmented_entities.py
from collections import defaultdict, Counter
from typing import Tuple, Dict, Any, List
from nltk import RegexpTokenizer
import re


def is_overlap(span: Tuple[int, int], reference_span: Tuple[int, int]) -> bool:
    l1, r1 = min(*span), max(*span)
    l2, r2 = min(*reference_span), max(*reference_span)
    return l1 <= l2 < r1 or l1 < r2 <= r1 or l2 <= l1 < r2 or l2 < r1 <= r2
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {},
	"outputs": [],
	"source": [
	"import os\n",
	"import os.path as osp\n",
	cfg['model']['mask_head']['num_classes'] = N_CLASSES + 1
	if isinstance(cfg['model']['bbox_head'], list):
	for i in range(len(cfg['model']['bbox_head'])):
	cfg['model']['bbox_head'][i]['num_classes'] = N_CLASSES + 1
	else:
	cfg['model']['bbox_head']['num_classes'] = N_CLASSES + 1
	from collections import defaultdict, Counter
	from typing import Tuple, Dict, Any, List
	from nltk import RegexpTokenizer
	import re


	def is_overlap(span: Tuple[int, int], reference_span: Tuple[int, int]) -> bool:
	l1, r1 = min(span), max(span)
	l2, r2 = min(reference_span), max(reference_span)
	return l1 <= l2 < r1 or l1 < r2 <= r1 or l2 <= l1 < r2 or l2 < r1 <= r2