update README

README.md

@@ -26,7 +26,11 @@ pinned: false
 
 ### Inputs
 *List all input arguments in the format below*
-- **input_field** *(type): Definition of input, with explanation if necessary. State any default value(s).*
+- **predictions** *(list of strings): list of sentences to test diversity. Each prediction should be a string.*
+- **mode** *(string): 'Expectation-Adjusted-Distinct' or 'Distinct' for diversity calculationg. If the value is 'Expectation-Adjusted-Distinct', the scores of the both modes will be returned. Default value is 'Expectation-Adjusted-Distinct'*
+- **vocab_size** *(int): vocab_size for calculating 'Expectation-Adjusted-Distinct'. When calculating 'Expectation-Adjusted-Distinct', either vocab_size or  dataForVocabCal should not be None. Default value is None*
+- **dataForVocabCal** *(list of string): dataForVocabCal for calculating the vocab_size for 'Expectation-Adjusted-Distinct'. Typically, it should be a list of sentences consisting the task dataset. When calculating 'Expectation-Adjusted-Distinct', either vocab_size or  dataForVocabCal should not be None. Default value is None*
+- **tokenizer** *(string or tokenizer class): tokenizer for splitting sentences into words. Default value is "white_space". NLTK tokenizer is available.*
 
 ### Output Values
 
@@ -35,16 +39,64 @@ pinned: false
 *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."*
 
 #### Values from Popular Papers
-*Give examples, preferrably with links to leaderboards or publications, to papers that have reported this measurement, along with the values they have reported.*
+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.
 
 ### Examples
-*Give code examples of the measurement being used. Try to include examples that clear up any potential ambiguity left from the measurement description above. If possible, provide a range of examples that show both typical and atypical results, as well as examples where a variety of input parameters are passed.*
+Example of calculate Expectation-Adjusted-Distinct byy giving voab_size or data for calculating vocab_size. This will also return Distinct-1,2,and 3.
+```python
+    >>> my_new_module = evaluate.load("lsy641/distinct")
+    >>> results = my_new_module.compute(references=["Hi.", "I'm sorry to hear that", "I don't know"], vocab_size=50257)
+    >>> print(results)
+    
+    
+    >>> 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"]
+    >>> results = my_new_module.compute(references=["Hi.", "I'm sorry to hear that", "I don't know"], dataForVocabCal = dataset)
+    >>> print(results)
+    
+```
+
+
+Example of calculate original Distinct. This will return Distinct-1,2,and 3.
+```python
+    >>> my_new_module = evaluate.load("lsy641/distinct")   
+    >>> results = my_new_module.compute(references=["Hi.", "I'm sorry to hear that", "I don't know"], mode="Distinct")
+    >>> print(results) 
+```
 
 ## Limitations and Bias
-*Note any known limitations or biases that the measurement has, with links and references if possible.*
+
 
 ## Citation
-*Cite the source where this measurement was introduced.*
+```bibtex
+@inproceedings{liu-etal-2022-rethinking,
+    title = "Rethinking and Refining the Distinct Metric",
+    author = "Liu, Siyang  and
+      Sabour, Sahand  and
+      Zheng, Yinhe  and
+      Ke, Pei  and
+      Zhu, Xiaoyan  and
+      Huang, Minlie",
+    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
+    year = "2022",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2022.acl-short.86",
+    doi = "10.18653/v1/2022.acl-short.86",
+    }
+    
+@inproceedings{li-etal-2016-diversity,
+    title = "A Diversity-Promoting Objective Function for Neural Conversation Models",
+    author = "Li, Jiwei  and
+      Galley, Michel  and
+      Brockett, Chris  and
+      Gao, Jianfeng  and
+      Dolan, Bill",
+    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
+    year = "2016",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/N16-1014",
+    doi = "10.18653/v1/N16-1014",
+}
+```
 
 ## Further References
 *Add any useful further references.*