Update README.md

README.md

@@ -17,7 +17,7 @@ This checkpoint (CodeGen-NL 350M) was pre-trained on [the Pile](https://github.c
 
 CodeGen was trained using cross-entropy loss to maximize the likelihood of sequential inputs.
 The family of models are trained using 4 TPU-v4 chips by Google, leveraging data and model parallelism.
-See Section 2.3 of the [paper](https://arxiv.org/abs/2203.13474)for more details.
+See Section 2.3 of the [paper](https://arxiv.org/abs/2203.13474) for more details.
 
 ## Evaluation results
 
@@ -37,12 +37,11 @@ This model can be easily loaded using the `AutoModelForCausalLM` functionality:
 from transformers import AutoTokenizer, AutoModelForCausalLM
 tokenizer = AutoTokenizer.from_pretrained('Salesforce/codegen-350M-nl')
 model = AutoModelForCausalLM.from_pretrained('Salesforce/codegen-350M-nl')
+
 text = "def hello_world():"
 input_ids = tokenizer(text, return_tensors="pt").input_ids
-# simply generate a single sequence
 generated_ids = model.generate(input_ids, max_length=128)
 print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
-# this prints "{user.username}"
 ```
 
 ## BibTeX entry and citation info