-
broadly speaking there are three classes of multiple access protocols:
- channel partitioning (including CDMA)
- random access
- taking turns
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This random access protocol is referred to as CSMA with collision avoidance, or more succinctly as CSMA/CA. As with Ethernet’s CSMA/CD, the “CSMA” in CSMA/CA stands for “carrier sense multiple access,” meaning that each station senses the channel before transmitting, and refrains from transmitting when the channel is sensed busy
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CSMA/CA(802.11) vs CDMA(Ethernet???)
- Collision avoidance vs collision detection
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senses before transmitting vs transmits as soon as channel is sensed to be idle
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# Easy editing and sourcing of .bashrc | |
alias vbrc='vim ~/.bashrc ; source ~/.bashrc' | |
# Quick git commands | |
alias gs='git status' | |
alias gd='git diff'; | |
alias gl='git log'; | |
alias glol='git log --oneline' | |
alias gdno='git diff --name-only;' | |
alias gad='git add .' |
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import tensorflow as tf | |
import numpy as np | |
corpus_raw = 'He is the king . The king is royal . She is the royal queen ' | |
# convert to lower case | |
corpus_raw = corpus_raw.lower() | |
words = [] | |
for word in corpus_raw.split(): |
QA-Transfer Model uses:
- SQUAD-T dataset
- BiDAF model (with end layers changed)
BiDAF moedel has 3 open source implementations:
- AllenAI-keras
- Original-BiDAF-tf-0.11 and Original-QA-Transfer-tf-0.11 (QA-Transfer essentially forks the first repo and makes some changes to it.)
- PyTorch
QA-Transfer Model uses:
- SQUAD-T dataset
- BiDAF model (with end layers changed)
BiDAF moedel has 3 open source implementations:
WikiQA test set | w2v 300 dim | MP | FT 300 dim | DRMM_TKS | biMPM |
---|---|---|---|---|---|
map | 0.6277 | 0.6515 | 0.5276 | 0.6259 | 0.3856 |
gm_map | 0.4968 | 0.5147 | 0.3923 | 0.4966 | 0.269 |
Rprec | 0.4667 | 0.5089 | 0.3429 | 0.4613 | 0.1965 |
Current Situation For the task of similarity learning, we are evaluating on the WikiQA Dataset
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import sys | |
import os | |
sys.path.append(os.path.join('..')) | |
import csv | |
import re | |
import gensim.downloader as api | |
from gensim.utils import simple_preprocess | |
import numpy as np |
This document will explain the newly introduced files, how they are to be used and how to reproduce my benchmarks.
Unfortunately, the current state of the code needs the additional dependency of pandas, a module for hadnling .csv, .tsv, etc. I was using it for grouping the datapoints by the document id. There are ways to do it without it and will be pushed soon.
So, you will have to install pandas first by running the command:
pip install pandas
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