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Update docs to describe models (#31)
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‎.travis.yml

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before_install:
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- sudo add-apt-repository -y ppa:ubuntu-toolchain-r/test
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- sudo apt-get update
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- sudo apt-get install pandoc
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- pip install -r requirements.txt
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- pip install -r requirements.dev.txt
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- pip install coveralls

‎docs/requirements.txt

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sphinxcontrib-bibtex
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sphinx-rtd-theme
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recommonmark
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nbsphinx
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pandoc

‎docs/source/README.md

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../../README.md

‎docs/source/README_files

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../../README_files/

‎docs/source/advanced.rst

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============================
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Advanced Usage: Semirings
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============================
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All of the distributional code is implemented through a series of
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semiring objects. These are passed through dynamic programming
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backends to compute the distributions.
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Standard Semirings
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===================
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.. autoclass:: torch_struct.LogSemiring
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.. autoclass:: torch_struct.StdSemiring
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.. autoclass:: torch_struct.MaxSemiring
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Higher-Order Semirings
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=========================
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.. autoclass:: torch_struct.EntropySemiring
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Sampling Semirings
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===================
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.. autoclass:: torch_struct.SampledSemiring
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.. autoclass:: torch_struct.MultiSampledSemiring
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Dynamic Programming
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===================
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.. autoclass:: torch_struct.LinearChain
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.. autoclass:: torch_struct.SemiMarkov
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.. autoclass:: torch_struct.DepTree
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.. autoclass:: torch_struct.CKY

‎docs/source/conf.py

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'sphinx.ext.napoleon',
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'sphinxcontrib.jinja',
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'sphinxcontrib.bibtex',
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'sphinx.ext.intersphinx'
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'sphinx.ext.intersphinx',
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'recommonmark',
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'nbsphinx'
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]
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‎docs/source/index.rst

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=================
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PyTorch-Struct
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==========================================
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=================
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.. toctree::
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:maxdepth: 2
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:maxdepth: 3
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:caption: Contents:
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Introduction
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============
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A library for structured prediction.
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Distributional Interface
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========================
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The main interface is through a structured distribution objects. Each
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of these implement a conditional random field over a class of
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structures. Roughly, these represent specialized softmax's over
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exponentially sized spaces. Each distribution object takes in
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log_potentials (generalized logits) and can return properties of the
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distribution. The properties of interest are,
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* Partition (e.g. logsumexp)
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* Marginals (e.g. softmax)
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* Argmax
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* Entropy
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* Samples
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* to_event / from_event (adapters)
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.. autoclass:: torch_struct.StructDistribution
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:members:
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Linear Chain
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--------------
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.. autoclass:: torch_struct.LinearChainCRF
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Semi-Markov
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--------------
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.. autoclass:: torch_struct.SemiMarkovCRF
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Dependency Tree
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----------------
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.. autoclass:: torch_struct.DependencyCRF
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Binary Tree
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--------------
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.. autoclass:: torch_struct.TreeCRF
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Context-Free Grammar
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---------------------
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.. autoclass:: torch_struct.SentCFG
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Networks
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===========
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Common structured networks.
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.. autoclass:: torch_struct.networks.TreeLSTM
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.. autoclass:: torch_struct.networks.NeuralCFG
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.. autoclass:: torch_struct.networks.SpanLSTM
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Data
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====
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Datasets for common structured prediction tasks.
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.. autoclass:: torch_struct.data.ConllXDataset
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.. autoclass:: torch_struct.data.ListOpsDataset
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Advanced Usage: Semirings
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=========================
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All of the distributional code is implemented through a series of
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semiring objects. These are passed through dynamic programming
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backends to compute the distributions.
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Standard Semirings
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------------------
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.. autoclass:: torch_struct.LogSemiring
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.. autoclass:: torch_struct.StdSemiring
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.. autoclass:: torch_struct.MaxSemiring
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Higher-Order Semirings
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----------------------
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.. autoclass:: torch_struct.EntropySemiring
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Sampling Semirings
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----------------------
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.. autoclass:: torch_struct.SampledSemiring
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.. autoclass:: torch_struct.MultiSampledSemiring
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Dynamic Programming
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-------------------
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.. autoclass:: torch_struct.LinearChain
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.. autoclass:: torch_struct.SemiMarkov
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.. autoclass:: torch_struct.DepTree
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.. autoclass:: torch_struct.CKY
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README
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model
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networks
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advanced
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refs
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References
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==========
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.. bibliography:: refs.bib
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Indices and tables

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