README.md

---
title: distinct
datasets:
- None
tags:
- evaluate
- measurement
description: "TODO: add a description here"
sdk: gradio
sdk_version: 3.19.1
app_file: app.py
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.*

## Measurement Description
*Give a brief overview of this measurement, including what task(s) it is usually used for, if any.*

## How to Use
*Give general statement of how to use the measurement*

*Provide simplest possible example for using the measurement*

### Inputs
*List all input arguments in the format below*
- **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

*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."*

#### 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.

### Examples
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


## Citation
```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.*