Add conda installation instructions

1 files changed, 16 insertions, 4 deletions

diff --git a/README.md b/README.md
index cc4bc441..4eb05e51 100644
--- a/README.md
+++ b/README.md
@@ -40,20 +40,32 @@ If you use the CoreNLP software through Stanza, please cite the CoreNLP software
 
 To ask questions, report issues or request features, please use the [GitHub Issue Tracker](https://github.com/stanfordnlp/stanza/issues).
 
-## Setup
+## Installation
+
+### pip
 
 Stanza supports Python 3.6 or later. We strongly recommend that you install Stanza from PyPI. If you already have [pip](https://pip.pypa.io/en/stable/installing/), the Python package manager, installed on your system, simply run:
 ```bash
 pip install stanza
 ```
-this should also help resolve all of the dependencies of Stanza, for instance [PyTorch](https://pytorch.org/) 1.0.0 or above.
+This should also help resolve all of the dependencies of Stanza, for instance [PyTorch](https://pytorch.org/) 1.2.0 or above.
 
 If you currently have a previous version of `stanza` installed, use:
 ```bash
 pip install stanza -U
 ```
 
-Alternatively, you can also install from source of this git repository, which will give you more flexibility in developing on top of Stanza and training your own models. For this option, run
+### Anaconda
+
+To install Stanza via Anaconda, use the following conda command:
+
+```bash
+conda install -c stanfordnlp stanza
+```
+
+### From Source
+
+Alternatively, you can also install from source of this git repository, which will give you more flexibility in developing on top of Stanza. For this option, run
 ```bash
 git clone https://github.com/stanfordnlp/stanza.git
 cd stanza
@@ -121,7 +133,7 @@ To maximize speed performance, it is essential to run the pipeline on batches of
 
 ## Training your own neural pipelines
 
-All neural modules in this library can be trained with your own data. The tokenizer, the multi-word token (MWT) expander, the POS/morphological features tagger, the lemmatizer and the dependency parser require [CoNLL-U](https://universaldependencies.org/format.html) formatted data, while the NER model requires the BIOES format. Currently, we do not support model training via the `Pipeline` interface. Therefore, to train your own models, you need to clone this git repository and set up from source.
+All neural modules in this library can be trained with your own data. The tokenizer, the multi-word token (MWT) expander, the POS/morphological features tagger, the lemmatizer and the dependency parser require [CoNLL-U](https://universaldependencies.org/format.html) formatted data, while the NER model requires the BIOES format. Currently, we do not support model training via the `Pipeline` interface. Therefore, to train your own models, you need to clone this git repository and run training from the source.
 
 For detailed step-by-step guidance on how to train and evaluate your own models, please visit our [training documentation](https://stanfordnlp.github.io/stanza/training.html).