Update README

updated example in README

README.md

@@ -61,9 +61,11 @@ This metric outputs a dictionary, containing the loss and perplexity score.
 ```python
 >>> import evaluate
 >>> from datasets import load_dataset
+>>> from transformers import AutoTokenizer, AutoModelForCausalLM
 >>> perplexity = evaluate.load("d-matrix/dmx_perplexity", module_type="metric")
->>> input_texts = load_dataset("wikitext", "wikitext-2-raw-v1", split="test")["text"][:10] 
->>> results = perplexity.compute(model='distilgpt2',references=input_texts)
+>>> input_texts = load_dataset("wikitext", "wikitext-2-raw-v1", split="test")["text"][:10]
+>>> model = AutoModelForCausalLM.from_pretrained("distilgpt2")
+>>> results = perplexity.compute(model=model,references=input_texts)
 >>> print(list(results.keys()))
 ['loss', 'perplexity']
 >>> print(results['loss'])