Metadata-Version: 1.1
Name: skipthoughts
Version: 0.0.0
Summary: Skipthoughts pretrained models for Pytorch
Home-page: https://github.com/cadene/skip-thoughts.torch
Author: Remi Cadene
Author-email: remi.cadene@icloud.com
License: UNKNOWN
Description-Content-Type: UNKNOWN
Description: # Skip-Thoughts.torch for Pytorcb
        
        *Skip-Thoughts.torch* is a lightweight porting of [skip-thought pretrained models from Theano](https://github.com/ryankiros/skip-thoughts) to Pytorch.
        
        ## Installation
        
        1. [python3 with anaconda](https://www.continuum.io/downloads)
        2. [pytorch with/out CUDA](http://pytorch.org)
        
        ### Install from pip
        
        3. `pip install skipthoughts`
        
        ### Install from repo
        
        3. `git clone https://github.com/Cadene/skip-thoughts.torch.git`
        4. `cd skip-thoughts.torch/pytorch`
        5. `python setup.py install`
        
        
        ### Available pretrained models
        
        #### UniSkip
        
        It 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)
        
        #### DropUniSkip
        
        It 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).
        
        #### BayesianUniSkip
        
        It 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).
        
        #### BiSkip
        
        Equivalent to UniSkip, but with a bi-sequential GRU.
        
        ### Quick example
        
        ```python
        import torch
        from torch.autograd import Variable
        import sys
        sys.path.append('skip-thoughts.torch/pytorch')
        from skipthoughts import UniSkip
        
        dir_st = 'data/skip-thoughts'
        vocab = ['robots', 'are', 'very', 'cool', '<eos>', 'BiDiBu']
        uniskip = UniSkip(dir_st, vocab)
        
        input = Variable(torch.LongTensor([
            [1,2,3,4,0], # robots are very cool 0
            [6,2,3,4,5]  # bidibu are very cool <eos>
        ])) # <eos> token is optional
        print(input.size()) # batch_size x seq_len
        
        output_seq2vec = uniskip(input, lengths=[4,5])
        print(output_seq2vec.size()) # batch_size x 2400
        
        output_seq2seq = uniskip(input)
        print(output_seq2seq.size()) # batch_size x seq_len x 2400
        ```
Keywords: pytorch pretrained models skipthoughts deep learning
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
