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mapmeld / bb.md
Last active Jan 4, 2021
Bangla Benchmark runs
View bb.md

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
mapmeld / twiml-lightning-share.md
Last active Oct 22, 2020
twiml-lightning-share
View twiml-lightning-share.md
@mapmeld
mapmeld / dv-wave.py
Last active Jul 16, 2020
PythonCode
View dv-wave.py
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
mapmeld / add_to_shapefile.py
Created Jul 5, 2020
Add JSON block data to a shapefile with GDAL
View add_to_shapefile.py
# 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
mapmeld / load_acs.py
Last active Jul 8, 2020
Load 5-year ACS race + ethnicity data, ending in 2017
View load_acs.py
# 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
mapmeld / links.md
Last active May 13, 2020
References and links for Spanish counterfactuals
View links.md
View AutoKeras_image_regression.ipynb
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@mapmeld
mapmeld / yolo.py
Created Apr 27, 2020
Adjusting yolo.py to return raw boxes and classes for images
View yolo.py
# -*- 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
View Baby-Hindi-Model.md

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
mapmeld / download_glue_data.py
Last active Mar 25, 2020 — forked from W4ngatang/download_glue_data.py
Script for downloading data of the GLUE benchmark (gluebenchmark.com)
View download_glue_data.py
''' 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