Rani ranihorev

## zip_codes_data.json
{
  "90001": {
    "address_components": [
      {
        "long_name": "90001",
        "short_name": "90001",
        "types": [
          "postal_code"
        ]
      },

## maps_geocoding_api_result
{
   "results" : [
      {
         "address_components" : [
            {
               "long_name" : "1049",
               "short_name" : "1049",
               "types" : [ "street_number" ]
            },
            {

## BPE
import re, collections

def get_stats(vocab):
   pairs = collections.defaultdict(int)
   for word, freq in vocab.items():
       symbols = word.split()
       for i in range(len(symbols)-1):
           pairs[symbols[i],symbols[i+1]] += freq
   return pairs

## BPE
import re, collections

def get_stats(vocab):
   pairs = collections.defaultdict(int)
   for word, freq in vocab.items():
       symbols = word.split()
       for i in range(len(symbols)-1):
           pairs[symbols[i],symbols[i+1]] += freq
   return pairs

## Structured_with_text.py
from fastai.text import *
from fastai.structured import proc_df
import pandas as pd
import numpy as np

class MixedInputModelWithText(nn.Module):
    def __init__(self, emb_szs, n_cont, emb_drop, out_sz, szs, drops,
                 y_range=None, use_bn=False, is_reg=True, is_multi=False, n_text=0):
        super().__init__()
        for i, (c, s) in enumerate(emb_szs): assert c > 1, f"cardinality must be >=2, got emb_szs[{i}]: ({c},{s})"

## test8
0xCe5E7214E74b62F2a1398db5CFb86eF68f8c1EF3

## test7
0x732d75a4000cB2F38914FC1B6440A9E3753e21f2

## test6
0x541ea7e288d7344b28903ca862ff9fe3efc8c6cd

## gist:481e9158afe411ce331f40ab6d31dd62
0xE837758E2f4A21dd0abd4eC3CA252B5ad352bcB2

## test4
0xe837758e2f4a21dd0abd4ec3ca252b5ad352bcb2
	{
	"90001": {
	"address_components": [
	{
	"long_name": "90001",
	"short_name": "90001",
	"types": [
	"postal_code"
	]
	},
	{
	"results" : [
	{
	"address_components" : [
	{
	"long_name" : "1049",
	"short_name" : "1049",
	"types" : [ "street_number" ]
	},
	{
	import re, collections

	def get_stats(vocab):
	pairs = collections.defaultdict(int)
	for word, freq in vocab.items():
	symbols = word.split()
	for i in range(len(symbols)-1):
	pairs[symbols[i],symbols[i+1]] += freq
	return pairs
	from fastai.text import *
	from fastai.structured import proc_df
	import pandas as pd
	import numpy as np

	class MixedInputModelWithText(nn.Module):
	def __init__(self, emb_szs, n_cont, emb_drop, out_sz, szs, drops,
	y_range=None, use_bn=False, is_reg=True, is_multi=False, n_text=0):
	super().__init__()
	for i, (c, s) in enumerate(emb_szs): assert c > 1, f"cardinality must be >=2, got emb_szs[{i}]: ({c},{s})"