| import torch | |
| from torch import LongTensor | |
| from torch.nn import Embedding, LSTM | |
| from torch.autograd import Variable | |
| from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence | |
| ## We want to run LSTM on a batch of 3 character sequences ['long_str', 'tiny', 'medium'] | |
| # | |
| # Step 1: Construct Vocabulary | |
| # Step 2: Load indexed data (list of instances, where each instance is list of character indices) |
| from __future__ import print_function, absolute_import | |
| __all__ = ['accuracy'] | |
| def accuracy(output, target, topk=(1,)): | |
| """Computes the precision@k for the specified values of k""" | |
| maxk = max(topk) | |
| batch_size = target.size(0) | |
| _, pred = output.topk(maxk, 1, True, True) |
| ''' 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 |
I actually found very few indications on the net to install Armadillo with OpenBLAS on Ubuntu, and I ran into problems when I tried to install the Ubuntu pre-build packages (using apt-get). So I decided to share my experience building the whole thing from scratch, hopefully this can serve as a step-by-step through the install.
As of writing this, I'm using Ubuntu desktop 14.04 with an Intel i7 4820k Ivy-Bridge E CPU.
