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+# Datasets, Kernels, Models, and Problems
+
+As we start publishing more datasets and models, it is important to keep in mind why we're doing this.
+
+> We publish datasets because we want to contribute back to the Open Source and Machine Learning communities.
+
+We consider datasets and models to be good when they are:
+- discoverable,
+- reproducible, and
+- reusable.
+
+Keeping all of this in mind, let me propose a way to write documentation for these.
+
+## A Common Vocabulary
+
+It seems to be quite established that the relationship between datasets, models, and other concepts is somehow expressed in the following graph.
+
+![dataset graph](graph.png)
+<!--
+To rebuild the graph above, run:
+
+$ dot -Tpng -o graph.png
+
+And give the following as input:
+
+digraph G {
+	dataset -> kernel [ label = "feeds" ];
+        {kernel dataset} -> model [ label = "generates" ];
+        model -> problem [ label = "solves" ];
+        predictor -> model [ label = "uses" ];
+        predictor -> problem [ label = "solves" ];
+}
+-->
+
+The following sections get into more detail on each concept,
+but let me give a quick intro of all of these concepts.
+
+### Problems
+
+Everything we do at source{d} is around solving problems and
+making predictions. Problems are the starting motivation
+and ending point of most of our Machine Learning processes.
+
+Problems have a clear objective, and a measure of success that
+let us rank different solutions to any problem in an objective
+way. Think about accuracy, recall, etc.
+
+An example problem could be predicting what is the next key
+a developer will press given what they've written so far.
+
+### Models
+
+Problems are solved using Models. Models are trained
+to solve a specific problem by feeding Dataset to a
+Kernel that optimizes a set of parameters.
+These parameters, once optimized, are what models are made of.
+
+Models can be considered as a black box, where the only thing
+we care about is the input and output formats. This provides
+the possibility of reusing a model, to solve the same problem,
+or to somehow feed into a different model (by knowledge
+transfer or other techniques).
+
+Given the previous problem of predicting the next key pressed,
+a model could get as an input the sequence of all keys pressed
+so far, as ASCII codes, and the output could be a single ASCII
+code with the prediction.
+
+A secondary goal of models is to be reproducible, meaning that
+someone could try to repeat the same process we went through and
+expect to obtain a similar result. If the kernel that generated
+the dataset requires metaparameters (such as learning rate),
+these values should also be documented.
+
+This is normally documented in research papers, with references
+to what datasets and kernels were used, as well as how much
+training time it took to obtain the resulting model.
+
+### Kernels
+
+Kernels are algorithms that feed from datasets and
+generate models. These algorithms are responsible for describing
+the model architecture chosen to solve a problem, e.g. RNN,
+CNN, etc, and what metaparamaters were used
+
+### Datasets
+
+Datasets contain information retrieved from one or more
+data sources, then pre-processed so it can easily be used to
+answer questions, solve problems, train models, or even as
+the data source to another dataset.
+
+The most important aspects of a dataset are its format, how to
+download it, reproduce it, and what version contains what
+exactly.
+
+Datasets evolve over time, and it's important to have versions
+that can be explicitly referred to from trained models.
+
+### Predictor
+
+The last piece of the puzzle is what I call a predictor.
+A predictor uses a model (sometimes more, sometimes no model
+at all) to predict the answer to a question given some input.
+
+For instance, given a model trained with a large dataset of
+the keystrokes of thousands of developers, we could write a
+predictor that uses that trained model to create predictions.
+That would be a pretty decent predictor.
+
+But we could also use a simple function that outputs random
+ASCII codes, ignoring any other information available. This
+predictor would probably have a lower accuracy for the given
+problem.
+
+## Documenting these Artifacts
+
+So far we've documented models and some datasets to a certain
+extent, but I think it's time to provide a framework for all
+of these elements to be uniformly documented to improve the
+discoverability, reproducibility, and reusability of our
+results.
+
+We will evolve our documentation over time, into something that
+hopefully will delight every one of our engineers and users.
+But for now, let's keep it realistic and propose a reduced set
+of measure we can start applying today to evolve towards that
+perfect solution.
+
+## Current status
+
+Currently we document only datasets and models in two different
+repositories: github.com/src-d/datasets and
+github.com/src-d/models.
+
+We also have a modelforge tool that is intended to provide a way
+to discover and download existing models.
+
+### Datasets
+
+We currently have only one public dataset: Public Git Archive.
+For this dataset we document:
+
+- how to download the current version of the dataset with the `pga` CLI tool
+- how to reproduce the dataset with borges and GHTorrent
+
+What are we missing?
+
+- versioning of the resulting dataset, how to download this an previous versions?
+- format of the dataset
+- what other datasets (and versions) were used to generate this?
+- what models have been trained with this dataset
+- LICENSE (the tools and scripts are licensed, but not the datasets?)
+
+### Models
+
+Models are already documented following some structure, following the
+efforts put in place for [modelforge](https://github.com/src-d/modelforge).
+
+Currently models have an ID, which looks like a long random string like
+`f64bacd4-67fb-4c64-8382-399a8e7db52a`.
+
+Models are accompanied by an example on how to use them, unfortunately the
+examples are a bit simpler than expected. They mostly look like this:
+
+```python
+from ast2vec import DocumentFrequencies
+df = DocumentFrequencies().load()
+print("Number of tokens:", len(df))
+```
+
+What are we missing?
+- Versioned models, corresponding to versioned datasets.
+- Reference to the code (kernel) that was used to generate the model.
+- Technical sheet with accuracy, recall, etc for the given model and dataset
+- Format of input and output of the model
+- At least one example using the model to make a prediction
+
+## My Proposal
+
+Since we care about individual versioning of datasets and models,
+it seems like it's an obvious choice to use a git repository per dataset,
+and model.
+
+Problems, predictors, and kernels can, for now, be documented directly with
+a model. If we see that we start to have too much repetition because we have
+many models for a single problem we will reassess this decision.
+
+### Dataset Repository
+
+A dataset repository should contain the following information:
+
+- short description
+- long description and links to papers and blog posts
+- technical sheet
+    - size of dataset
+    - schema(s) of the dataset
+    - download link
+- using the dataset:
+    - downloading the dataset
+    - related tools
+- reproducing the dataset:
+    - link to the original data sources
+    - related tools
+
+### Model Repository
+
+A dataset repository should contain the following information:
+
+- short description
+- long description and links to papers and blog posts
+- technical sheet
+    - size of model
+    - input/output schemas
+    - download link
+    - datasets used to train the model (including versions)
+- using the model:
+    - downloading the model
+    - loading the model
+        - prerequisites (tensorflow? keras?)
+    - quick guide: making a prediction
+- reproducing the model:
+    - link to the original dataset
+    - kernel used to train the model
+    - training process
+        - hardware and time spent
+        - metaparameters if any
+        - any other relevant details
+
+### General
+
+As any other source{d} repository, we need to follow the guidelines in
+[Documentation at source{d}](https://github.com/src-d/guide/blob/master/engineering/documentation.md).
+This includes having a CONTRIBUTING.md, Code of Conduct, etc.
+
+Every time a new version of a dataset or model is released a new tag and
+associated release should be created in the repository.
+The release should include links to anything that has changed since the
+previous relaease: such as a new version of the datasets or changes in
+the kernel.
+
+### github.com/src-d/datasest and github.com/src-d/models
+
+These two repositories should simply contain what is common to all datasets,
+or to all models. They will also provide all the tooling build on top of
+the documentation for datasets and models.
+
+Since we imagine these tools extracting information from the repositories
+automatically, it is important to keep formatting in mind.
+
+I'm currently considering whether a `toml` file should be defined containing
+the data common to all the datasets and models.
+For instance, we could have the download size for each dataset and model,
+as well as the associated schemas. A simple tool could then generate
+documentation based on these values.
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