Update README.md

README.md

@@ -3,14 +3,14 @@ title: Perplexity
 emoji: 🤗
 colorFrom: blue
 colorTo: red
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
+sdk: static
 pinned: false
 tags:
 - evaluate
 - metric
 description: >-
+  This is a fork of the huggingface evaluate library's implementation of perplexity. 
+  
   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`.
@@ -21,8 +21,10 @@ description: >-
 
 # Metric Card for Perplexity
 
-## Metric Description
-Given a model and an input text sequence, perplexity measures how likely the model is to generate the input text sequence.
+> This is a fork of the huggingface evaluate library's implementation of perplexity. 
+
+
+## Metric DescriptionGiven a model and an input text sequence, perplexity measures how likely the model is to generate the input text sequence.
 
 As a metric, it can be used to evaluate how well the model has learned the distribution of the text it was trained on.
 
@@ -39,7 +41,7 @@ The metric takes a list of text as input, as well as the name of the model used
 
 ```python
 from evaluate import load
-perplexity = load("perplexity", module_type="metric")
+perplexity = load("pico-lm/perplexity")
 results = perplexity.compute(predictions=predictions, model_id='gpt2')
 ```
 
@@ -50,6 +52,7 @@ results = perplexity.compute(predictions=predictions, model_id='gpt2')
 - **batch_size** (int): the batch size to run texts through the model. Defaults to 16.
 - **add_start_token** (bool): whether to add the start token to the texts, so the perplexity can include the probability of the first word. Defaults to True.
 - **device** (str): device to run on, defaults to `cuda` when available
+- **trust_remote_code** (bool): enables running metric on custom models 
 
 ### Output Values
 This metric outputs a dictionary with the perplexity scores for the text input in the list, and the average perplexity.