diff --git a/README.md b/README.md
index cba225d..f9c548a 100644
--- a/README.md
+++ b/README.md
@@ -3,31 +3,45 @@ A T5 model trained on 370,000 research papers, to generate one line summary base
 
 ## Usage
 ```python
-model_name="snrspeaks/t5-one-line-summary"
+model_name = "snrspeaks/t5-one-line-summary"
 
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
 model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 
-abstract="""We describe a system called Overton, whose main design goal is to support engineers 
-in building, monitoring, and improving production machine learning systems. Key challenges engineers 
-face are monitoring fine-grained quality, diagnosing errors in sophisticated applications, and 
-handling contradictory or incomplete supervision data. Overton automates the life cycle of model 
-construction, deployment, and monitoring by providing a set of novel high-level, declarative abstractions. 
-Overton's vision is to shift developers to these higher-level tasks instead of lower-level machine learning tasks. 
-In fact, using Overton, engineers can build deep-learning-based applications without writing any code 
+abstract = """We describe a system called Overton, whose main design goal is to 
+support engineers in building, monitoring, and improving production machine learning systems. 
+Key challenges engineers face are monitoring fine-grained quality, diagnosing errors in 
+sophisticated applications, and handling contradictory or incomplete supervision data. 
+Overton automates the life cycle of model construction, deployment, and monitoring by providing a 
+set of novel high-level, declarative abstractions. Overton's vision is to shift developers to 
+these higher-level tasks instead of lower-level machine learning tasks. In fact, using Overton, 
+engineers can build deep-learning-based applications without writing any code 
 in frameworks like TensorFlow. For over a year, Overton has been used in production to support multiple 
 applications in both near-real-time applications and back-of-house processing. 
 In that time, Overton-based applications have answered billions of queries in multiple 
 languages and processed trillions of records reducing errors 1.7-2.9 times versus production systems.
 """
 
-input_ids = tokenizer.encode("summarize: " + abstract, return_tensors="pt", add_special_tokens=True)
+input_ids = tokenizer.encode(
+    "summarize: " + abstract, return_tensors="pt", add_special_tokens=True
+)
 
-generated_ids = model.generate(input_ids=input_ids, num_beams=5, max_length=50, repetition_penalty=2.5, length_penalty=1, early_stopping=True, num_return_sequences=3)
+generated_ids = model.generate(
+    input_ids=input_ids,
+    num_beams=5,
+    max_length=50,
+    repetition_penalty=2.5,
+    length_penalty=1,
+    early_stopping=True,
+    num_return_sequences=3,
+)
 
-preds = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in generated_ids]
+preds = [
+    tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True)
+    for g in generated_ids
+]
 
 print(preds)