Update README.md

README.md

@@ -6,6 +6,36 @@ https://github.com/martiansideofthemoon/rankgen
 
 RankGen is a suite of encoder models (100M-1.2B parameters) which map prefixes and generations from any pretrained English language model to a shared vector space. RankGen can be used to rerank multiple full-length samples from an LM, and it can also be incorporated as a scoring function into beam search to significantly improve generation quality (0.85 vs 0.77 MAUVE, 75% preference according to humans annotators who are English writers). RankGen can also be used like a dense retriever, and achieves state-of-the-art performance on [literary retrieval](https://relic.cs.umass.edu/leaderboard.html).
 
+## Setup
+
+**Requirements** (`pip` will install these dependencies for you)
+
+Python 3.7+, `torch` (CUDA recommended), `transformers`
+
+**Installation**
+
+```
+python3.7 -m virtualenv rankgen-venv
+source rankgen-venv/bin/activate
+pip install rankgen
+```
+
+Get the data [here](https://drive.google.com/drive/folders/1DRG2ess7fK3apfB-6KoHb_azMuHbsIv4?usp=sharing) and place folder in root directory. Alternatively, use `gdown` as shown below,
+
+```
+gdown --folder https://drive.google.com/drive/folders/1DRG2ess7fK3apfB-6KoHb_azMuHbsIv4
+```
+
+Run the test script to make sure the RankGen checkpoint has loaded correctly,
+
+```
+python -m rankgen.test_rankgen_encoder --model_path kalpeshk2011/rankgen-t5-base-all
+
+### Expected output
+0.0009239262409127233
+0.0011521980725477804
+```
+
 ## Using RankGen
 
 Loading RankGen is simple using the HuggingFace APIs (see Method-2 below), but we suggest using [`RankGenEncoder`](https://github.com/martiansideofthemoon/rankgen/blob/master/rankgen/rankgen_encoder.py), which is a small wrapper around the HuggingFace APIs for correctly preprocessing data and doing tokenization automatically. You can either download [our repository](https://github.com/martiansideofthemoon/rankgen) and install the API, or copy the implementation from [below](#rankgenencoder-implementation).