manasRK

Installing Homebrew and Anaconda on a Mac

Install Homebrew

Run the following command on your terminal to install Homebrew. Homebrew is a package manager for Macs and is used to install useful development tools and software.

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

Install Anaconda through Homebrew

1. Run brew install --cask anaconda to install Anaconda

manasRK

def _sequence_mask(sequence_length, max_len=None):
    if max_len is None:
        max_len = sequence_length.data.max()
    batch_size = sequence_length.size(0)
    seq_range = torch.range(0, max_len - 1).long()
    seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len)
    seq_range_expand = Variable(seq_range_expand)
    if sequence_length.is_cuda:
        seq_range_expand = seq_range_expand.cuda()
    seq_length_expand = (sequence_length.unsqueeze(1)

manasRK

import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn.utils.rnn import pad_packed_sequence, pack_padded_sequence

x = Variable(torch.randn(10, 20, 30)).cuda()
lens = range(10)

x = pack_padded_sequence(x, lens[::-1], batch_first=True)

manasRK

import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
import torch.nn.functional as F
import numpy as np
import itertools

def flatten(l):
	return list(itertools.chain.from_iterable(l))

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Using Git

Global Settings

Related Setup: https://gist.github.com/hofmannsven/6814278

Related Pro Tips: https://ochronus.com/git-tips-from-the-trenches/

manasRK

"""Information Retrieval metrics

Useful Resources:
http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt
http://www.nii.ac.jp/TechReports/05-014E.pdf
http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf
http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf
Learning to Rank for Information Retrieval (Tie-Yan Liu)
"""
import numpy as np

manasRK

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname

manasRK

import os
import math
import re
import pandas as pd
from collections import Counter
from sklearn.datasets import fetch_20newsgroups

#get a subset of the dataset

categories = [

manasRK

IASNLP-2015 Project list

LTRC, IIIT-Hyderabad

Treebanking

#####1. Shallow Parsers for different languages#####

• Description - POS Tagging and Chunking for Gujarati,Odia, Hindi, Bengali, Marathi, Telugu (individual project for each language)
• We will implement many supervised algorithms including CRF, HMM, MaxEnt, SVM, some semi-supervised classification methods, finally an unsupervised one. Will try to implement Morph Analyzer if time permits. students need to annotate data, understand the challenges, compare results given by multiple
• Mentor: Pruthwik M

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#List unique values in a DataFrame column
pd.unique(df.column_name.ravel())

#Convert Series datatype to numeric, getting rid of any non-numeric values
df['col'] = df['col'].astype(str).convert_objects(convert_numeric=True)

#Grab DataFrame rows where column has certain values
valuelist = ['value1', 'value2', 'value3']
df = df[df.column.isin(value_list)]