typo fixes

README.md

@@ -68,7 +68,7 @@ See the [example folder](examples/) and [reference docs](https://ginkgobioworks.
 | ----- | ------------------------------------------- | -------------------------------------------------------------------------------------------- | ---------------------------- | -------- |
 | ESM2  | Large Protein language model from Meta      | [Github](https://github.com/facebookresearch/esm?tab=readme-ov-file#esmfold)                 | Embeddings, masked inference | 3B, 650M |
 | AA0   | Ginkgo's proprietary protein language model | [Announcement](https://www.ginkgobioworks.com/2024/09/17/aa-0-protein-llm-technical-review/) | Embeddings, masked inference | 650M     |
-| 3UTR  | Ginkgo's proprietary 3'UTR language model   | [Preprint](https://www.biorxiv.org/content/10.1101/2024.10.07.616676v1)                      | Embeddings, masked inference | 650M     |
+| 3UTR  | Ginkgo's proprietary 3'UTR language model   | [Preprint](https://www.biorxiv.org/content/10.1101/2024.10.07.616676v1)                      | Embeddings, masked inference | v1       |
 
 ## License
 

examples/aa0_model.py

@@ -1,5 +1,5 @@
 """In this example we compute embedding and run masked inference
- on Ginkgo's AA0 protein language model."""
+ on the aa02 language model."""
 
 from ginkgo_ai_client import (
     GinkgoAIClient,
@@ -10,17 +10,17 @@
 client = GinkgoAIClient()
 
 # Simple query for embedding computation
-prediction = client.query(aa0_mean_embedding_params("ATTGCG"))
+prediction = client.query(aa0_mean_embedding_params("MLYLRRL"))
 # prediction["embedding"] == [1.05, -2.34, ...]
 
 
 # Simple query for masked inference
-prediction = client.query(aa0_masked_inference_params("ATT<mask>TAC"))
+prediction = client.query(aa0_masked_inference_params("MLY<mask>RRL"))
 
 queries = [
-    aa0_mean_embedding_params("AGCGC"),
-    aa0_mean_embedding_params("ATTGCG"),
-    aa0_mean_embedding_params("TACCGCA"),
+    aa0_mean_embedding_params("MLYLRRL"),
+    aa0_mean_embedding_params("MLYRRL"),
+    aa0_mean_embedding_params("MLYLLRRL"),
 ]
 predictions = client.batch_query(queries)