Update README.md

benchs/link_and_code/README.md

@@ -10,12 +10,14 @@ Link & code is an indexing method that combines HNSW indexing with
 compression and exploits the neighborhood structure of the similarity
 graph to improve the reconstruction. It is described in
 
+```
 @inproceedings{link_and_code,
    author = {Matthijs Douze and Alexandre Sablayrolles and Herv\'e J\'egou},
    title = {Link and code: Fast indexing with graphs and compact regression codes},
    booktitle = {CVPR},
    year = {2018}
 }
+```
 
 ArXiV [here](https://arxiv.org/abs/1804.09996)
 
@@ -28,7 +30,7 @@ The test runs with 3 files:
 
 - `datasets.py`: code to load the datasets. The example code runs on the
   deep1b and bigann datasets. See the [toplevel README](../README.md)
-  on how to downlod them. They should be put in a directory, edit
+  on how to download them. They should be put in a directory, edit
   datasets.py to set the path.
 
 - `neighbor_codec.py`: this is where the representation is trained.
@@ -60,7 +62,7 @@ Set `bdir` to a scratch directory.
 
 Explanation of the flags:
 
-- `--db deep1M`: dataset to process
+- `--db deep100M`: dataset to process
 
 - `--M0 6`: number of links on the base level (L6)