Update README.md

README.md

@@ -42,7 +42,7 @@ These pairs were obtained from various domains and were carefully selected throu
 The embedding model was trained using 512 sequence length, but extrapolates to 8k sequence length (or even longer) thanks to ALiBi.
 This makes our model useful for a range of use cases, especially when processing long documents is needed, including technical question answering and code search.
 
-This model has 137 million parameters, which enables fast and memory efficient inference, while delivering impressive performance.
+This model has 161 million parameters, which enables fast and memory efficient inference, while delivering impressive performance.
 Additionally, we provide the following embedding models:
 
 - [`jina-embeddings-v2-small-en`](https://huggingface.co/jinaai/jina-embeddings-v2-small-en): 33 million parameters.