mapmeld

Code: https://colab.research.google.com/drive/1vltPI81atzRvlALv4eCvEB0KdFoEaCOb?usp=sharing

Can these scores be improved? YES!

Rerunning with more training data, more epochs of training, or using other libraries to set a learning rate / other hyperparameters before training.

• Experimenting with epochs - when I doubled the number of epochs, MuRIL improves only slightly (69.5->69.7 on one task)

The point of a benchmark is to run these models through a reasonable and identical process; you can tweak hyperparameters on any model to improve results.

mapmeld

Measuring Gender Bias in Spanish Language Models

Presenter

Nick Doiron, Tufts University / Independent Research

GitHub: https://github.com/mapmeld ; LinkedIn: https://www.linkedin.com/in/nickdoiron/

Context

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
})

mapmeld

# 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())

mapmeld

# 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