update README

README.md

@@ -14,15 +14,31 @@ pinned: false
 
 # Measurement Card for distinct
 
-***Module Card Instructions:*** *Fill out the following subsections. Feel free to take a look at existing measurement cards if you'd like examples.*
+***Module Card Instructions:*** 
 
 ## Measurement Description
-*Give a brief overview of this measurement, including what task(s) it is usually used for, if any.*
+This metric is used to calculate the diversity of a group of sentences. It can be used to evaluate the diversity of generated responses on the testset (i.e., corpus level diversity). The original paper only used it as corpus-level while some may use it to calculate diversity of several sampled responses given on context (i.e., utterence level diversity).  However, we don't recommend to calculate Distinct on a small group as it is sensitive to sentence length and number.
 
 ## How to Use
-*Give general statement of how to use the measurement*
 
-*Provide simplest possible example for using the measurement*
+```python
+>>> import evaluate
+>>> results = my_new_module.compute(predictions=["Hi.", "I am sorry to hear that", "I don't know", "Do you know who that person is?"], vocab
+_size=50257)
+>>> my_new_module = evaluate.load("lsy641/distinct")
+Downloading builder script: 100%|██████████████████████████████████████████████████████████████████████| 8.62k/8.62k [00:00<00:00, 4.19MB/s]
+>>> results = my_new_module.compute(predictions=["Hi.", "I am sorry to hear that", "I don't know", "Do you know who that person is?"], vocab_size=50257)
+>>> print(results)
+{'Expectation-Adjusted-Distinct': 0.8236605104867569, 'Distinct-1': 0.8235294117647058, 'Distinct-2': 0.9411764705882353, 'Distinct-3': 0.9411764705882353}
+
+
+>>> dataset = ["This is my friend jack", "I'm sorry to hear that", "But you know I am the one who always support you", "Welcome to our family","Hi.", "I am sorry to hear that", "I don't know", "Do you know who that person is?"]
+>>> results = my_new_module.compute(predictions=["But you know I am the one who always support you", "Hi.", "I am sorry to hear that", "I don't know", "I'm sorry to hear that"], dataForVocabCal=dataset)
+>>> print(results)
+{'Expectation-Adjusted-Distinct': 0.9928137111900845, 'Distinct-1': 0.6538461538461539, 'Distinct-2': 0.8076923076923077, 'Distinct-3': 0.8846153846153846}
+
+
+```
 
 ### Inputs
 *List all input arguments in the format below*
@@ -34,9 +50,10 @@ pinned: false
 
 ### Output Values
 
-*Explain what this measurement outputs and provide an example of what the measurement output looks like. Modules should return a dictionary with one or multiple key-value pairs, e.g. {"bleu" : 6.02}*
-
-*State the range of possible values that the measurement's output can take, as well as what in that range is considered good. For example: "This measurement can take on any value between 0 and 100, inclusive. Higher scores are better."*
+- Expectation-Adjusted-Distinct: Normally it should stay in range 0-1. But it can be more than 1. See the formula property in the [Expectation-Adjusted-Distinct paper](https://aclanthology.org/2022.acl-short.86) (Liu and Sabour et al. 2022)
+- Distinct-1: Range 0-1
+- Distinct-2: Range 0-1
+- Distinct-3: Range 0-1
 
 #### Values from Popular Papers
 The [Expectation-Adjusted-Distinct paper](https://aclanthology.org/2022.acl-short.86) (Liu and Sabour et al. 2022) compares Expectation-Adjusted-Distinct scores of ten different methods with the original Distinct. These scores get higher human correlation from 0.56 to 0.65.
@@ -64,7 +81,7 @@ Example of calculate original Distinct. This will return Distinct-1,2,and 3.
 ```
 
 ## Limitations and Bias
-
+TODO
 
 ## Citation
 ```bibtex
@@ -100,4 +117,4 @@ Example of calculate original Distinct. This will return Distinct-1,2,and 3.
 ```
 
 ## Further References
-*Add any useful further references.*
+TODO