update README

README.md

@@ -54,18 +54,18 @@ def query(payload):
 query({"inputs": "<insert-query-here"})
 ```
 
+
 ## Usage (Gensim)
 
 ```python
-from huggingface_hub import hf_hub_url, cached_download
+from huggingface_hub import hf_hub_download
 from gensim.models.fasttext import load_facebook_model
 
 # download model from huggingface hub
-url = hf_hub_url(repo_id="simonschoe/call2vec", filename="model.bin")
-cached_download(url)
+model_path = hf_hub_download(repo_id="simonschoe/call2vec", filename="model.bin")
 
 # load model via gensim
-model = load_facebook_model(<PATH-MODEL>)
+model = load_facebook_model(model_path)
 
 # extract word embeddings
 model.wv['transformation']
@@ -77,6 +77,23 @@ model.wv.most_similar(negative='transformation', topn=5, restrict_vocab=None)
 model.wv.similarity('transformation', 'continuity')
 ```
 
+## Usage (fasttext)
+
+```python
+import fasttext
+from huggingface_hub import hf_hub_download
+
+# download model from huggingface hub
+model_path = hf_hub_download(repo_id="simonschoe/call2vec", filename="model.bin")
+
+# load model via fasttext
+model = fasttext.load_model(model_path)
+
+# get similar phrases
+model.get_nearest_neighbors("transformation", k=5)
+```
+
+
 If model size is crucial, the final model could be additionally compressed using the [`compress-fasttext`](https://github.com/avidale/compress-fasttext) library (e.g., via pruning, conversion to `float16`, or product quantization).