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[N] PyTorch-NLP 0.5.0 Released! Heres to contributing back to open source, hurah! 🤗

Hi There! 🍪

2 years into PyTorch-NLP and another 6 months from the previous release, I am releasing PyTorch-NLP 0.5.0. Also, with your help, we’ll break 50,000 downloads! Thank you 🙂 I love helping the community because I myself benefit from the hard work of other open source contributors!

As always, the theme of PyTorch-NLP is to be small, extensible and intuitive much like PyTorch is! And the goal is to extend PyTorch with basic NLP utilities.

Here are the release notes highlights: 🐕

Python 3.5 Support, Sampler Pipelining, Finer Control of Random State

Major Updates

  • Updated my README emoji game to be more ambiguous while maintaining fun and heartwarming vibe.
  • Support for Python 3.5.
  • Extensive rewrite of README.md to focus on new users and building an NLP pipeline. See here.
  • Support for Pytorch 1.2.
  • Added torchnlp.random for finer grain control of random state building on PyTorch’s fork_rng. This module controls the random state of torch, numpy and random. “`python import random import numpy import torch

from torchnlp.random import fork_rng

with fork_rng(seed=123): # Ensure determinism print(‘Random:’, random.randint(1, 231)) print(‘Numpy:’, numpy.random.randint(1, 231)) print(‘Torch:’, int(torch.randint(1, 2**31, (1,)))) - Refactored `torchnlp.samplers` enabling pipelining. For example: python from torchnlp.samplers import DeterministicSampler from torchnlp.samplers import BalancedSampler

data = [‘a’, ‘b’, ‘c’] + [‘c’] * 100 sampler = BalancedSampler(data, num_samples=3) sampler = DeterministicSampler(sampler, random_seed=12) print([data[i] for i in sampler]) # [‘c’, ‘b’, ‘a’] - Added `torchnlp.samplers.balanced_sampler` for balanced sampling extending Pytorch's `WeightedRandomSampler`. - Added `torchnlp.samplers.deterministic_sampler` for deterministic sampling based on `torchnlp.random`. - Added `torchnlp.samplers.distributed_batch_sampler` for distributed batch sampling that's more extensible and less restrictive than PyTorch's version. - Added `torchnlp.samplers.oom_batch_sampler` to sample large batches first in order to force an out-of-memory error earlier rather than later into training. - Added `torchnlp.utils.get_total_parameters` to measure the number of parameters in a model. - Added `torchnlp.utils.get_tensors` to measure the size of an object in number of tensor elements. This is useful for dynamic batch sizing and for `torchnlp.samplers.oom_batch_sampler`. python from torchnlp.utils import get_tensors

randomobject = tuple([{‘t’: torch.tensor([1, 2])}, torch.tensor([2, 3])]) tensors = gettensors(random_object) assert len(tensors) == 2 “`

submitted by /u/Deepblue129
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.