Split documentation into multiple markdown files.

README.md

 # COMBO
 <p align="center">
-    A GPL-3.0 system, build on top of PyTorch and AllenNLP, for morphosyntactic analysis.
+    A language-independent NLP system for dependency parsing, part-of-speech tagging, lemmatisation and more built on top of PyTorch and AllenNLP.
 </p>
 <hr/>
 <p align="center">
@@ -9,118 +9,26 @@
     </a>
 </p>
 
-[Pre-trained models!](http://mozart.ipipan.waw.pl/~mklimaszewski/models/)
-```python
-import combo.predict as predict
-nlp = predict.SemanticMultitaskPredictor.from_pretrained("polish-herbert-base")
-sentence = nlp("Moje zdanie.")
-print(sentence.tokens)
-```
-
-## Installation
-
-Clone this repository and run:
+## Quick start
+Clone this repository and install COMBO (we suggest using virtualenv/conda with Python 3.6+):
 ```bash
+git clone https://github.com/ipipan/combo.git
+cd combo
 python setup.py develop
 ```
-
-### Problems & solutions
-* **jsonnet** installation error
-
-use `conda install -c conda-forge jsonnet=0.15.0`
-
-## Training
-
-Command:
-```bash
-combo --mode train \
-      --training_data_path your_training_path \
-      --validation_data_path your_validation_path
-```
-
-Options:
-```bash
-combo --helpfull
-```
-
-Examples (for clarity without training/validation data paths):
-
-* train on gpu 0
-
-    ```bash
-    combo --mode train --cuda_device 0
-    ```
-
-* use pretrained embeddings:
-
-    ```bash
-    combo --mode train --pretrained_tokens your_pretrained_embeddings_path --embedding_dim your_embeddings_dim
-    ```
-
-* use pretrained transformer embeddings:
-
-    ```bash
-    combo --mode train --pretrained_transformer_name your_choosen_pretrained_transformer
-    ```
-
-* predict only dependency tree:
-
-    ```bash
-    combo --mode train --targets head,deprel
-    ```
-
-* use part-of-speech tags for predicting only dependency tree
-
-    ```bash
-    combo --mode train --targets head,deprel --features token,char,upostag
-    ```
-
-Advanced configuration: [Configuration](#configuration)
-
-## Prediction
-
-### ConLLU file prediction:
-Input and output are both in `*.conllu` format.
-```bash
-combo --mode predict --model_path your_model_tar_gz --input_file your_conllu_file --output_file your_output_file --silent
-```
-
-### Console
-Works for models where input was text-based only.
-
-Interactive testing in console (load model and just type sentence in console).
-
-```bash
-combo --mode predict --model_path your_model_tar_gz --input_file "-" --nosilent
-```
-### Raw text
-Works for models where input was text-based only. 
-
-Input: one sentence per line.
-
-Output: List of token jsons.
-
-```bash
-combo --mode predict --model_path your_model_tar_gz --input_file your_text_file --output_file your_output_file --silent
-```
-#### Advanced
-
-There are 2 tokenizers: whitespace and spacy-based (`en_core_web_sm` model).
-
-Use either `--predictor_name semantic-multitask-predictor` or `--predictor_name semantic-multitask-predictor-spacy`.
-
-### Python
+Run the following lines in your Python console to make predictions with a pre-trained model:
 ```python
 import combo.predict as predict
 
-model_path = "your_model.tar.gz"
-nlp = predict.SemanticMultitaskPredictor.from_pretrained(model_path)
-sentence = nlp("Sentence to parse.")
+nlp = predict.SemanticMultitaskPredictor.from_pretrained("polish-herbert-base")
+sentence = nlp("Moje zdanie.")
+print(sentence.tokens)
 ```
 
-## Configuration
+## Details
+
+- [**Installation**](docs/installation.md)
+- [**Pre-trained models**](docs/models.md)
+- [**Training**](docs/training.md)
+- [**Prediction**](docs/prediction.md)
 
-### Advanced
-Config template [config.template.jsonnet](config.template.jsonnet) is formed in `allennlp` format so you can freely modify it.
-There is configuration for all the training/model parameters (learning rates, epochs number etc.).
-Some of them use `jsonnet` syntax to get values from configuration flags, however most of them can be modified directly there.

docs/installation.md

+# Installation
+Clone this repository and install COMBO (we suggest using virtualenv/conda with Python 3.6+):
+```bash
+git clone https://github.com/ipipan/combo.git
+cd combo
+python setup.py develop
+combo --helpfull
+```
+
+## Problems & solutions
+* **jsonnet** installation error
+
+use `conda install -c conda-forge jsonnet=0.15.0`

docs/models.md

+# Models
+
+Pre-trained models are available [here](http://mozart.ipipan.waw.pl/~mklimaszewski/models/).
+
+## Automatic download
+Python `from_pretrained` method will download the pre-trained model if the provided name (without the extension .tar.gz) matches one of the names in [here](http://mozart.ipipan.waw.pl/~mklimaszewski/models/).
+```python
+import combo.predict as predict
+
+nlp = predict.SemanticMultitaskPredictor.from_pretrained("polish-herbert-base")
+```
+Otherwise it looks for a model in local env.
+
+## Console prediction/Local model
+If you want to use the console version of COMBO, you need to download a pre-trained model manually
+```bash
+wget http://mozart.ipipan.waw.pl/~mklimaszewski/models/polish-herbert-base.tar.gz
+```
+and pass it as a parameter (see [prediction doc](prediction.md)).

docs/prediction.md

+# Prediction
+
+## ConLLU file prediction:
+Input and output are both in `*.conllu` format.
+```bash
+combo --mode predict --model_path your_model_tar_gz --input_file your_conllu_file --output_file your_output_file --silent
+```
+
+## Console
+Works for models where input was text-based only.
+
+Interactive testing in console (load model and just type sentence in console).
+
+```bash
+combo --mode predict --model_path your_model_tar_gz --input_file "-" --nosilent
+```
+## Raw text
+Works for models where input was text-based only. 
+
+Input: one sentence per line.
+
+Output: List of token jsons.
+
+```bash
+combo --mode predict --model_path your_model_tar_gz --input_file your_text_file --output_file your_output_file --silent --noconllu_format
+```
+### Advanced
+
+There are 2 tokenizers: whitespace and spacy-based (`en_core_web_sm` model).
+
+Use either `--predictor_name semantic-multitask-predictor` or `--predictor_name semantic-multitask-predictor-spacy`.
+
+## Python
+```python
+import combo.predict as predict
+
+model_path = "your_model.tar.gz"
+nlp = predict.SemanticMultitaskPredictor.from_pretrained(model_path)
+sentence = nlp("Sentence to parse.")
+```

docs/training.md

+# Training
+
+Command:
+```bash
+combo --mode train \
+      --training_data_path your_training_path \
+      --validation_data_path your_validation_path
+```
+
+Options:
+```bash
+combo --helpfull
+```
+
+Examples (for clarity without training/validation data paths):
+
+* train on gpu 0
+
+    ```bash
+    combo --mode train --cuda_device 0
+    ```
+
+* use pretrained embeddings:
+
+    ```bash
+    combo --mode train --pretrained_tokens your_pretrained_embeddings_path --embedding_dim your_embeddings_dim
+    ```
+
+* use pretrained transformer embeddings:
+
+    ```bash
+    combo --mode train --pretrained_transformer_name your_choosen_pretrained_transformer
+    ```
+
+* predict only dependency tree:
+
+    ```bash
+    combo --mode train --targets head,deprel
+    ```
+
+* use part-of-speech tags for predicting only dependency tree
+
+    ```bash
+    combo --mode train --targets head,deprel --features token,char,upostag
+    ```
+  
+## Configuration
+
+### Advanced
+Config template [config.template.jsonnet](config.template.jsonnet) is formed in `allennlp` format so you can freely modify it.
+There is configuration for all the training/model parameters (learning rates, epochs number etc.).
+Some of them use `jsonnet` syntax to get values from configuration flags, however most of them can be modified directly there.
\ No newline at end of file