mapmeld

from simpletransformers.classification import ClassificationModel

# set use_cuda=False on CPU-only platforms
model = ClassificationModel('bert', 'monsoon-nlp/dv-wave', num_labels=8, use_cuda=True, args={
    'reprocess_input_data': True,
    'use_cached_eval_features': False,
    'overwrite_output_dir': True,
    'num_train_epochs': 3,
    'silent': True
})

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# pip install gdal
import json
from osgeo import ogr

# depends on your shapefile
target_shapefile = 'tl_2010_sample_shapefile.shp'
fips_id = 'GEOID10'

saveblocks = json.loads(open('savefile.json', 'r').read())

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# pip install requests
import time, json
import requests

api_key = "API_KEY_STRING"

# look up FIPS for state and county:
# https://www.nrcs.usda.gov/wps/portal/nrcs/detail/national/home/?cid=nrcs143_013697
state = '12'
county_fips = ['086']

mapmeld

Related Research

Towards Debiasing Sentence Representations (Liang et al., 2020) English BERT and ELMO

• https://cs.cmu.edu/~pliang/papers/acl2020_debiasing.pdf
• https://github.com/pliang279/sent_debias

Measuring Bias in Contextualized Word Representations (Kurita et al., 2019) analyzes English BERT with word-association https://aclweb.org/anthology/W19-3823/

mapmeld

mapmeld

# -*- coding: utf-8 -*-
"""
Class definition of YOLO_v3 style detection model on image and video
"""

import colorsys
import os
from timeit import default_timer as timer

import numpy as np

mapmeld

Releasing Hindi ELECTRA model

This is a first attempt at a Hindi language model trained with Google Research's ELECTRA. I don't modify ELECTRA until we get into finetuning, and only then because there's hardcoded train and test files

CoLab: https://colab.research.google.com/drive/1R8TciRSM7BONJRBc9CBZbzOmz39FTLl_

Additional background: https://medium.com/@mapmeld/teaching-hindi-to-electra-b11084baab81

It's available on HuggingFace: https://huggingface.co/monsoon-nlp/hindi-bert - sample usage: https://colab.research.google.com/drive/1mSeeSfVSOT7e-dVhPlmSsQRvpn6xC05w

mapmeld

''' Script for downloading all GLUE data.

Note: for legal reasons, we are unable to host MRPC.
You can either use the version hosted by the SentEval team, which is already tokenized, 
or you can download the original data from (https://download.microsoft.com/download/D/4/6/D46FF87A-F6B9-4252-AA8B-3604ED519838/MSRParaphraseCorpus.msi) and extract the data from it manually.
For Windows users, you can run the .msi file. For Mac and Linux users, consider an external library such as 'cabextract' (see below for an example).
You should then rename and place specific files in a folder (see below for an example).

mkdir MRPC
cabextract MSRParaphraseCorpus.msi -d MRPC

mapmeld

Assuming the final delegate counts and viability number are correct

Unusual

• Carson City 107: extra delegate, Biden's 2nd
• Carson City 407: delegate should have been added to Biden, not Klobuchar
• Clark 1621: needs to add 1 leftover delegate each to Buttigieg and Sanders
• Clark 1642: unclear, assigned too many delegates instead of a +1 to Sanders
• Clark 1643: removed Klobuchar's 1 delegate to match expected delegates, even though viable; all had 1 delegate
• Clark 1645: removed Warren's 1 delegate though viable

mapmeld

# calculate number of plans, by state
import json

plans = open('districtr_full_export.json', 'r').read().strip().split("\n")
places = {}
for raw in plans:
    plan = json.loads(raw)
    if ("plan" in plan) and ("placeId" in plan["plan"]):
        place = plan["plan"]["placeId"]
        if place in places: