update lvwerra namespace

README.md

@@ -9,8 +9,8 @@ You can load the CodeParrot model and tokenizer directly in `transformers`:
 ```Python
 from transformers import AutoTokenizer, AutoModelWithLMHead
   
-tokenizer = AutoTokenizer.from_pretrained("lvwerra/codeparrot-small")
-model = AutoModelWithLMHead.from_pretrained("lvwerra/codeparrot-small")
+tokenizer = AutoTokenizer.from_pretrained("codeparrot/codeparrot-small")
+model = AutoModelWithLMHead.from_pretrained("codeparrot/codeparrot-small")
 
 inputs = tokenizer("def hello_world():", return_tensors="pt")
 outputs = model(**inputs)
@@ -21,13 +21,13 @@ or with a `pipeline`:
 ```Python
 from transformers import pipeline
 
-pipe = pipeline("text-generation", model="lvwerra/codeparrot-small")
+pipe = pipeline("text-generation", model="codeparrot/codeparrot-small")
 outputs = pipe("def hello_world():")
 ```
 
 ## Training
 
-The model was trained on the cleaned [CodeParrot 🦜 dataset](https://huggingface.co/datasets/lvwerra/codeparrot-clean) with the following settings:
+The model was trained on the cleaned [CodeParrot 🦜 dataset](https://huggingface.co/datasets/codeparrot/codeparrot-clean) with the following settings:
 
 |Config|Value|
 |-------|-----|
@@ -57,6 +57,6 @@ The [pass@k metric](https://huggingface.co/metrics/code_eval) tells the probabil
 
 ## Resources
 
-- Dataset: [full](https://huggingface.co/datasets/lvwerra/codeparrot-clean), [train](https://huggingface.co/datasets/lvwerra/codeparrot-clean-train), [valid](https://huggingface.co/datasets/lvwerra/codeparrot-clean-valid)
+- Dataset: [full](https://huggingface.co/datasets/codeparrot/codeparrot-clean), [train](https://huggingface.co/datasets/codeparrot/codeparrot-clean-train), [valid](https://huggingface.co/datasets/codeparrot/codeparrot-clean-valid)
 - Code: [repository](https://github.com/huggingface/transformers/tree/master/examples/research_projects/codeparrot)
 - Spaces: [generation](), [highlighting]()