Update README.md

README.md

@@ -18,25 +18,11 @@ Citation:
 }
 ```
 
-## Model list
-
-| Model | Dimension | Open Access Case Reports |
-| ---- | --------- | ------------------------- |
-| word2vec | 100 | [Download - 269 MB](w2v_100d_oa_cr.tar.gz)|
-|          | 300 | [Download - 716 MB](w2v_300d_oa_cr.tar.gz) |
-|          | 600 | [Download - 1.4 GB](w2v_600d_oa_cr.tar.gz)| 
-
 ## Quick start
 
 Word embeddings are compatible with the [`gensim` Python package](https://radimrehurek.com/gensim/) format.
 
-First download and extract the files from each archive.
-
-```bash
-tar -xvf w2v_100d_oa_all.tar.gz
-```
-
-Then load the embeddings into Python.
+First download the files from this archive. Then load the embeddings into Python.
 
 
 ```python
@@ -44,7 +30,7 @@ Then load the embeddings into Python.
 from gensim.models import FastText, Word2Vec, KeyedVectors # KeyedVectors are used to load the GloVe models
 
 # Load the model
-model = Word2Vec.load('w2v_oa_all_100d.bin')
+model = Word2Vec.load('w2v_oa_cr_100d.bin')
 
 # Return 100-dimensional vector representations of each word
 model.wv.word_vec('diabetes')