Updated README.md to include more information

README.md

@@ -1,31 +1,59 @@
 # INTREPPPID
 
-This is the documentation for the amino acid sequence-based Protein-Protein Interaction inference method INTREPPPID (**In**corporating **Tr**iplet **E**rror for **P**redicting **PPI**s using **D**eep Learning). INTREPPPID is particularly well-suited to the cross-species use case.
+***IN**corporating **TR**iplet **E**rror for **P**redicting **P**rotein-**P**protein **I**nteractions using **D**eep Learning*
 
-By far, the easiest way to use INTREPPPID is through the `https://PPI.bio <https://ppi.bio>`_ website.
+---
 
 
-## Documentation
+INTREPPPID is a deep learning model for predicting protein interactions. 
+It's especially good at making prediction on species other than those it was trained on (cross-species prediction).
 
-You can read the documentation at [https://emad-combine-lab.github.io/intrepppid](https://emad-combine-lab.github.io/intrepppid/)
+## How to Use INTREPPPID
+
+Here are some quick highlights, but be sure to [read the documentation](https://emad-combine-lab.github.io/intrepppid/) for more details!
+
+### Installing
+
+To install INTREPPPID, clone the repository and install the dependencies using `pip` and the `requirements.txt` file at
+the root of the repository
+
+```bash
+git clone https://github.com/jszym/intrepppid
+cd intreppid
+python -m virtualenv venv
+source venv/bin/activate
+pip install -r requirements.txt
+```
+
+### Training Models
+
+To train INTREPPPID, simply use the `train e2e_rnn_triplet` command like so:
+
+```bash
+python -m intrepppid train e2e_rnn_triplet DATASET.h5 spm.model 3 100 80 --seed 3927704 --vocab_size 250 --trunc_len 1500 --embedding_size 64 --rnn_num_layers 2 --rnn_dropout_rate 0.3 --variational_dropout false --bi_reduce last --workers 4 --embedding_droprate 0.3 --do_rate 0.3 --log_path logs/e2e_rnn_triplet --beta_classifier 2 --use_projection false --optimizer_type ranger21_xx --lr 1e-2
+```
+
+### Documentation
+
+Be sure to [read the documentation]((https://emad-combine-lab.github.io/intrepppid/)) for more details.
 
 ## License
 
->INTREPPPID
->
->***IN**corporating **TR**iplet **E**rror for **P**redicting **P**rotein-**P**protein **I**nteractions using **D**eep Learning*
->
->Copyright (C) 2023  Joseph Szymborski
->
->This program is free software: you can redistribute it and/or modify
->it under the terms of the GNU Affero General Public License as published by
->the Free Software Foundation, either version 3 of the License, or
->(at your option) any later version.
->
->This program is distributed in the hope that it will be useful,
->but WITHOUT ANY WARRANTY; without even the implied warranty of
->MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
->GNU Affero General Public License for more details.
->
->You should have received a copy of the GNU Affero General Public License
->along with this program.  If not, see <https://www.gnu.org/licenses/>.
+INTREPPPID
+
+***IN**corporating **TR**iplet **E**rror for **P**redicting **P**rotein-**P**protein **I**nteractions using **D**eep Learning*
+
+Copyright (C) 2023  Joseph Szymborski
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU Affero General Public License as published by
+the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU Affero General Public License for more details.
+
+You should have received a copy of the GNU Affero General Public License
+along with this program.  If not, see <https://www.gnu.org/licenses/>.