updated README

README.md

@@ -15,6 +15,7 @@ description: >-
   Perplexity metric implemented by d-Matrix.
   Perplexity (PPL) is one of the most common metrics for evaluating language models.
   It is defined as the exponentiated average negative log-likelihood of a sequence, calculated with exponent base `e`.
+  Note that this metric is intended for Causual Language Models, the perplexity calculation is only correct if model uses Cross Entropy Loss.
   For more information, see https://huggingface.co/docs/transformers/perplexity
 ---
 
@@ -26,13 +27,14 @@ description: >-
 Perplexity metric implemented by d-Matrix.
 Perplexity (PPL) is one of the most common metrics for evaluating language models.
 It is defined as the exponentiated average negative log-likelihood of a sequence, calculated with exponent base `e`.
+Note that this metric is intended for Causual Language Models, the perplexity calculation is only correct if model uses Cross Entropy Loss.
 For more information, see https://huggingface.co/docs/transformers/perplexity
 
 ## How to Use
 At minimum, this metric requires the model and references as inputs.
 ```python
 >>> import evaluate
->>> perplexity = evaluate.load("dmx_perplexity", module_type="metric")
+>>> perplexity = evaluate.load("d-matrix/dmx_perplexity", module_type="metric")
 >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]
 >>> results = perplexity.compute(model='distilgpt2',references=input_texts)
 >>> print(results)
@@ -59,15 +61,15 @@ This metric outputs a dictionary, containing the loss and perplexity score.
 ```python
 >>> import evaluate
 >>> from datasets import load_dataset
->>> perplexity = evaluate.load("dmx_perplexity", module_type="metric")
+>>> 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)
 >>> print(list(results.keys()))
 ['loss', 'perplexity']
 >>> print(results['loss']) 
-3.8299286365509033
+3.9706921577453613
 >>> print(results['perplexity']) 
-46.05925369262695
+53.021217346191406
 ```
 
 ## Citation(s)