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        "author": "Remi Cadene",
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        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.6",
            "Topic :: Software Development :: Build Tools"
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        "description": "# Skip-Thoughts.torch for Pytorcb\n\n*Skip-Thoughts.torch* is a lightweight porting of [skip-thought pretrained models from Theano](https://github.com/ryankiros/skip-thoughts) to Pytorch.\n\n## Installation\n\n1. [python3 with anaconda](https://www.continuum.io/downloads)\n2. [pytorch with/out CUDA](http://pytorch.org)\n\n### Install from pip\n\n3. `pip install skipthoughts`\n\n### Install from repo\n\n3. `git clone https://github.com/Cadene/skip-thoughts.torch.git`\n4. `cd skip-thoughts.torch/pytorch`\n5. `python setup.py install`\n\n\n### Available pretrained models\n\n#### UniSkip\n\nIt uses the `nn.GRU` layer from torch with the cudnn backend. It is the fastest implementation, but the dropout is sampled after each time-step in the cudnn implementation... (equals bad regularization)\n\n#### DropUniSkip\n\nIt uses the `nn.GRUCell` layer from torch with the cudnn backend. It is slightly slower than UniSkip, however the dropout is sampled once for all time-steps in a sequence (good regularization).\n\n#### BayesianUniSkip\n\nIt uses a custom GRU layer with a torch backend. It is at least two times slower than UniSkip, however the dropout is sampled once for all time-steps for each Linear (best regularization).\n\n#### BiSkip\n\nEquivalent to UniSkip, but with a bi-sequential GRU.\n\n### Quick example\n\n```python\nimport torch\nfrom torch.autograd import Variable\nimport sys\nsys.path.append('skip-thoughts.torch/pytorch')\nfrom skipthoughts import UniSkip\n\ndir_st = 'data/skip-thoughts'\nvocab = ['robots', 'are', 'very', 'cool', '<eos>', 'BiDiBu']\nuniskip = UniSkip(dir_st, vocab)\n\ninput = Variable(torch.LongTensor([\n    [1,2,3,4,0], # robots are very cool 0\n    [6,2,3,4,5]  # bidibu are very cool <eos>\n])) # <eos> token is optional\nprint(input.size()) # batch_size x seq_len\n\noutput_seq2vec = uniskip(input, lengths=[4,5])\nprint(output_seq2vec.size()) # batch_size x 2400\n\noutput_seq2seq = uniskip(input)\nprint(output_seq2seq.size()) # batch_size x seq_len x 2400\n```\n\n",
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        "keywords": "pytorch pretrained models skipthoughts deep learning",
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