diff --git a/README.md b/README.md
index b83795c..f587ca7 100644
--- a/README.md
+++ b/README.md
@@ -1018,6 +1018,269 @@ Note that using `--submit` option for the main instance at
 repositories are configured there in such a way that an evaluation is
 triggered with each push anyway.
 
+## Reproducibility guidelines
+
+GEval is about evaluation, all you actually need to supply are just
+`out.tsv` files. Remember, GEval (and associated evaluation platform
+Gonito) is not going to _run_ your submission, it just evaluates the
+_output_ of your solution by comparing it against the gold standard,
+i.e. the `expected.tsv` files.
+
+Nevertheless, it would be nice to have some _standards_ for organizing
+your code and models so that it would be easy for other people (and
+you yourself a month later) to reproduce your results. Here I lay out
+some guidelines or standards for this. The conformance to the
+guidelines is not checked by GEval/Gonito (though it may be at some
+time in the future).
+
+### The file structure
+
+Here is the recommended file structure of your submission:
+
+* `dev-?/out.tsv`, `test-?/out.tsv` — files required by GEval/Gonito
+  for the actual evaluation;
+
+* `gonito.yaml` — metadata for Gonito;
+
+* `predict.sh` — this script should read items from standard input in the
+   same format as in `in.tsv` files for a given challenge and
+   print the results on the standard output in the same
+   format as in `out.tsv` files
+  - actually `out.tsv` should be generated with `predict.sh`,
+  - `predict.sh` must print exactly the same number of lines as it read from the input,
+  - `predict.sh` should accept any number of items, including a single item, in other
+     words `echo '...' | ./predict.sh` should work,
+  - `predict.sh` should use models stored in `models/` (generated by `train.sh`),
+  - `predict.sh` can invoke further scripts in `code/`;
+
+* `train.sh` — this script should train a machine-learning model using
+   the data in `train/` (and possibly using development sets in
+   `dev-?/` for fine-tuning, validation, early stopping, etc.), all the models
+   should be saved in the `models/` directory
+   - just as `predict.sh`, `train.sh` can invoke scripts in `code/` (obviously some code
+     in `code/` could be shared between `predict.sh` and `train.sh`)
+   - `train.sh` should generate `out.tsv` files (preferably by running `predict.sh`);
+
+* `code/` — source codes and scripts for training and prediction should be put here;
+
+* `models/` — all the models generated by `train.sh` should be put here;
+
+* `Dockerfile` — recipe for a multi-stage build with `train` and
+   `predict` targets for building containers in which, respectively,
+   `train.sh` and `predict.sh` is guaranteed to run (more details below);
+
+* `.dockerignore` — put at least `models/*` and `train/*` here to
+  speed up building Docker containers;
+
+* `Makefile` (optional) — if you use make, please put your recipe here (not in `code/`).
+
+#### Environment variables
+
+There are some environment variables that should be handled by
+`train.sh` and `predict.sh` (if it is applicable to them):
+
+* `RANDOM_SEED` — the value of the random seed,
+* `THREADS` — number of threads/jobs/cores to be used (usually to be passed
+   to options `-j N`, `--threads N` or similar).
+* `BATCH_SIZE` (only `predict.sh`) — the value of the batch size
+   - by default, `BATCH_SIZE=1` should be assumed
+   - if set to 1, `predict.sh` should immediately return the processed value
+   - if set to N > 1, `predict.sh` can read batches of N items, process the whole
+     batch and return the results for the whole batch
+
+### Example — Classification with fastText
+
+Let's try to reproduce a sample submission conforming to the standards
+laid out above. The challenge is to [guess whether a given tweet
+expresses a positive or a negative
+sentiment](https://gonito.net/challenge/sentiment140). You're given
+the tweet text along with datestamps in two formats.
+
+The [sample solution](https://gonito.net/view-variant/7452) to this challenge, based on
+[fastText](https://fasttext.cc/), can be cloned as a git repo:
+
+```
+git clone --single-branch git://gonito.net/sentiment140 -b submission-07130
+```
+
+(The `--single-branch` is to speed up the download.)
+
+As usual, you could evaluate this solution locally on the dev set:
+
+```
+$ cd sentiment140
+$ geval -t dev-0
+79.88
+```
+
+The accuracy is nearly 80%, so it's pretty good. But now we are not
+interested in evaluating outputs, we'd like to actually _run_ the
+solution, or even reproduce training from scratch.
+
+Let try to run the fastText classifier on the first 5 items from the
+dev-0 set.
+
+```
+$ xzcat dev-0/in.tsv.xz | head -n 5
+2009.4109589041095	20090531	@smaknews I love Santa Barbara! In fact @BCCF's next Black Tie Charity Event is in Santa Barbara on August 15th!
+2009.4054794520548	20090529	@GreenMommaSmith yeah man, I really need an exercise bike. Tris laughs when I mention it
+2009.2630136986302	20090407	Anticipating a slow empty boring summer
+2009.4164383561645	20090602	just crossed the kankakee river    i need to go back soon & see my family. *tori*
+2009.4301369863015	20090607	is o tired because of my HillBilly Family and my histerical sister! Stress is not good for me, lol. Stuck at work
+
+$ xzcat dev-0/in.tsv.xz | head -n 5 | ./predict.sh
+terminate called after throwing an instance of 'std::invalid_argument'
+  what():  models/sentiment140.fasttext.bin cannot be opened for loading!
+```
+
+What went wrong!? The fastText model is pretty large (420 MB), so it
+would not be a good idea to commit it to the git repository directly.
+It was stored using git-annex instead.
+[Git-annex](https://git-annex.branchable.com/) is a neat git extension
+with which you commit only metadata and keep the actual contents
+wherever you want (directory, rsync host, S3 bucket, DropBox etc.).
+
+I put the model on my server, you can download it using the bash script supplied:
+
+```
+./get-annexed-files.sh models/sentiment140.fasttext.bin
+```
+
+Now should be OK:
+
+```
+$  xzcat dev-0/in.tsv.xz | head -n 5 | ./predict.sh
+positive
+positive
+negative
+positive
+negative
+```
+
+Well… provided that you have fastText installed. So it's not exactly a
+perfect reproducibility. Don't worry, we solve this issue with
+Docker in a moment.
+
+What if you want retrain the model from scratch, then you should run
+the `train.sh` script, let's set the random seed to some other value:
+
+```
+./get-annexed-files.sh train/in.tsv.xz
+rm models/*
+RANDOM_SEED=42 ./train.sh
+```
+
+Note that we need to download the input part of the train set first. As it
+is pretty large, I decided to store it in a git-annex storage too.
+
+The evaluation results are slightly different:
+
+```
+$ geval -t dev-0
+79.86
+```
+
+It's not surprising as a different seed was chosen (and fastText might
+not be deterministic itself).
+
+#### How did I actually uploaded this solution?
+
+I ran the `train.sh` script. All files except the model were added
+using the regular `git add` command:
+
+```
+git add code dev-0/out.tsv .dockerignore Dockerfile gonito.yaml predict.sh test-A/out.tsv train.sh
+```
+
+The model was added with `git annex`:
+
+```
+git annex add models/sentiment140.fasttext.bin
+```
+
+Then I committed the changes and pushed the files to the repo. Still,
+the model file had to be uploaded to the git-annex storage.
+I was using a directory on a server to which I have access via SSH:
+
+```
+git annex initremote gonito type=rsync rsyncurl=gonito.vm.wmi.amu.edu.pl:/srv/http/annex encryption=none
+```
+
+I uploaded the file there:
+
+```
+git annex copy models/* --to gonito
+```
+
+The problem is that only I could download the files from this
+git-annex remote. In order to make it available to the whole world, I
+set up an HTTP server and served the files from there. The trick is to
+add
+[httpalso](https://git-annex.branchable.com/special_remotes/httpalso/)
+special remote:
+
+```
+git annex initremote --sameas=gonito gonito-https type=httpalso url=https://gonito.vm.wmi.amu.edu.pl/annex
+```
+
+Finally, you need to synchronize the information about special remotes:
+
+```
+git annex -a --no-content
+```
+
+### Docker
+
+Still, the problem with reproducibility of the sample solution
+remains, as you must install requirements: fastText (plus some Python
+modules for training). It's a quite a big hassle, if you consider that
+there might a lot of different solutions each with a different set of
+requirements.
+
+Docker containers might come in handy here. The idea is that a
+submitter should supply a Dockerfile meeting the following conditions:
+
+* defined as a multi-stage build;
+* there are at least 2 images defined: `train` and `predict`;
+* `train` defines an environment required for training
+  - but training scripts and the data set should _not_ be included in the image,
+  - the image should be run on the directory with the solution mounted to `/workspace`
+  - i.e. the following commands should run training
+
+```
+docker build . --target train -t foo-train
+
+docker run -v $(pwd):/workspace -it foo-train /workspace/train.sh
+```
+
+* `predict` defines a self-contained predictor
+  - contrary to the `train` image all the scripts and binaries needed
+    for the actual prediction should be there,
+  - … except for the models that needs to be supplied in the directory mounted
+    at `/workspace/models`, i.e. the following commands should just work:
+
+```
+docker build . --target predict -t foo-predict
+
+docker run -v $(pwd)/models:/workspace/models -i foo-predict
+```
+  - this way you can easily switch to another model without changing the base code
+
+#### Back to the example
+
+And it works for the example given above. With one caveat: due an
+unfortunate interaction of git-annex and Docker, you need to _unlock_ model files
+before running the Docker container:
+
+```
+$ docker build . --target predict -t sentiment140-predict
+
+$ git annex unlock models/*
+
+$ echo -e '2021.99999\t20211231\tGEval is awesome!' | docker run -v $(pwd)/models:/workspace/models -i sentiment140-predict
+positive
+```
+
 ## `geval` options
 
 ```