Not including how many shares I have or what % of my investments these are
Not including cryptocurrencies
| Ticker | Info | Effective Avg Purchase Price |
|---|---|---|
| VTHRX | Vanguard retirement | ? |
| VDC | Vanguard consumer staples | 172.75 |
| XCEM | Em econ not China | 29.87 |
| HOMZ | Housing | 32.26 |
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.
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.
| 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 | |
| }) |
| # 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()) |
| # 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'] |
Towards Debiasing Sentence Representations (Liang et al., 2020) English BERT and ELMO
Measuring Bias in Contextualized Word Representations (Kurita et al., 2019) analyzes English BERT with word-association https://aclweb.org/anthology/W19-3823/
| # -*- 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 |
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
