Python 3.5 Support, Sampler Pipelining, Finer Control of Random State, New Corporate Sponsor

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 to focus on new users and building an NLP pipeline.
• 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.

import random
import numpy
import torch

from torchnlp.random import fork_rng

with fork_rng(seed=123):  # Ensure determinism
    print('Random:', random.randint(1, 2**31))
    print('Numpy:', numpy.random.randint(1, 2**31))
    print('Torch:', int(torch.randint(1, 2**31, (1,))))

• Refactored torchnlp.samplers enabling pipelining. For example:

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.
• Added torchnlp.samplers.oom_batch_sampler to sample large batches first in order to force an out-of-memory error.
• Added torchnlp.utils.lengths_to_mask to help create masks from a batch of sequences.
• 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.

from torchnlp.utils import get_tensors

random_object_ = tuple([{'t': torch.tensor([1, 2])}, torch.tensor([2, 3])])
tensors = get_tensors(random_object_)
assert len(tensors) == 2

• Added a corporate sponsor to the library: https://wellsaidlabs.com/

Minor Updates

• Fixed snli example (#84)
• Updated .gitignore to support Python's virtual environments (#84)
• Removed requests and pandas dependency. There are only two dependencies remaining. This is useful for production environments. (#84)
• Added LazyLoader to reduce dependency requirements. (4e84780)
• Removed unused torchnlp.datasets.Dataset class in favor of basic Python dictionary lists and pandas. (#84)
• Support for downloading tar.gz files and unpacking them faster. (eb61fee)
• Rename itos and stoi to index_to_token and token_to_index respectively. (#84)
• Fixed batch_encode, batch_decode, and enforce_reversible for torchnlp.encoders.text (#69)
• Fix FastText vector downloads (#72)
• Fixed documentation for LockedDropout (#73)
• Fixed bug in weight_drop (#76)
• stack_and_pad_tensors now returns a named tuple for readability (#84)
• Added torchnlp.utils.split_list in favor of torchnlp.utils.resplit_datasets. This is enabled by the modularity of torchnlp.random. (#84)
• Deprecated torchnlp.utils.datasets_iterator in favor of Pythons itertools.chain. (#84)
• Deprecated torchnlp.utils.shuffle in favor of torchnlp.random. (#84)
• Support encoding larger datasets following fixing this issue (#85).
• Added torchnlp.samplers.repeat_sampler following up on this issue: pytorch/pytorch#15849

Release 0.4.0 - Encoder rewrite, variable sequence collate support, reduced memory usage, doctests, removed SRU

Major updates

• Rewrote encoders to better support more generic encoders like a LabelEncoder. Furthermore, added broad support for batch_encode, batch_decode and enforce_reversible.
• Rearchitected default reserved tokens to ensure configurability while still providing the convenience of good defaults.
• Added support to collate sequences with torch.utils.data.dataloader.DataLoader. For example:

from functools import partial
from torchnlp.utils import collate_tensors
from torchnlp.encoders.text import stack_and_pad_tensors

collate_fn = partial(collate_tensors, stack_tensors=stack_and_pad_tensors)
torch.utils.data.dataloader.DataLoader(*args, collate_fn=collate_fn, **kwargs)

• Added doctest support ensuring the documented examples are tested.
• Removed SRU support, it's too heavy of a module to support. Please use https://github.com/taolei87/sru instead. Happy to accept a PR with a better tested and documented SRU module!
• Update version requirements to support Python 3.6 and 3.7, dropping support for Python 3.5.
• Updated version requirements to support PyTorch 1.0+.
• Merged #66 reducing the memory requirements for pre-trained word vectors by 2x.

Minor Updates

• Formatted the code base with YAPF.
• Fixed pandas and collections warnings.
• Added invariant assertion to Encoder via enforce_reversible. For example:

  encoder = Encoder().enforce_reversible()

  Ensuring Encoder.decode(Encoder.encode(object)) == object
• Fixed the accuracy metric for PyTorch 1.0.

0.3.7.post1

Minor release fixing some issues and bugs.

0.3.0

Release 0.3.0

Major Features And Improvements

• Upgraded to PyTorch 0.4.0
• Added Byte-Pair Encoding (BPE) pre-trained subword embeddings in 275 languages
• Refactored download scripts to torchnlp.downloads
• Enable Spacy encoder to run in multiple languages.
• Added a boolean aligned option to FastText supporting MUSE (Multilingual Unsupervised and Supervised Embeddings)

Bug Fixes and Other Changes

• Create non-existent cache dirs for torchnlp.word_to_vector.
• Add set operation to torchnlp.datasets.Dataset with support for slices, columns and rows
• Updated biggest_batches_first in torchnlp.samplers to be more efficient at approximating memory then Pickle
• Enabled torch.utils.pad_tensor and torch.utils. pad_batch to support N dimensional tensors
• Updated to sacremoses to fix NLTK moses dependancy for torch.text_encoders
• Added __getitem()__ for _PretrainedWordVectors. For example:

from torchnlp.word_to_vector import FastText
vectors = FastText()
tokenized_sentence = ['this', 'is', 'a', 'sentence']
vectors[tokenized_sentence]

• Added __contains__ for _PretrainedWordVectors. For example:

>>> from torchnlp.word_to_vector import FastText
>>> vectors = FastText()

>>> 'the' in vectors
True
>>> 'theqwe' in vectors
False

Initial Release

0.2.0

Update Version, Release.sh, and Long Description