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--- |
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title: distinct |
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datasets: |
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- None |
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tags: |
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- evaluate |
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- measurement |
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description: "TODO: add a description here" |
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sdk: gradio |
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sdk_version: 3.19.1 |
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app_file: app.py |
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pinned: false |
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--- |
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# Measurement Card for distinct |
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***Module Card Instructions:*** |
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## Measurement Description |
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This metric is used to calculate the diversity of a group of sentences. It can be used to either evaluate the diversity of generated responses of the testset (i.e., corpus-level diversity), or calculate diversity of a group of sampled responses given one context (i.e., utterence-level diversity). The [original paper](https://aclanthology.org/N16-1014) (Li et al. 2022) used it as corpus-level while some may use it as utterance-level. However, we don't recommend to calculate Distinct on a small group as it is sensitive to the sentence length and number. |
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## How to Use |
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```python |
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>>> import evaluate |
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>>> 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 |
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_size=50257) |
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>>> my_new_module = evaluate.load("lsy641/distinct") |
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Downloading builder script: 100%|██████████████████████████████████████████████████████████████████████| 8.62k/8.62k [00:00<00:00, 4.19MB/s] |
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>>> 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) |
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>>> print(results) |
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{'Expectation-Adjusted-Distinct': 0.8236605104867569, 'Distinct-1': 0.8235294117647058, 'Distinct-2': 0.9411764705882353, 'Distinct-3': 0.9411764705882353} |
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>>> 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?"] |
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>>> 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) |
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>>> print(results) |
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{'Expectation-Adjusted-Distinct': 0.9928137111900845, 'Distinct-1': 0.6538461538461539, 'Distinct-2': 0.8076923076923077, 'Distinct-3': 0.8846153846153846} |
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``` |
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### Inputs |
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*List all input arguments in the format below* |
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- **predictions** *(list of strings): list of sentences to test diversity. Each prediction should be a string.* |
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- **mode** *(string): 'Expectation-Adjusted-Distinct' or 'Distinct' for diversity calculation. If 'Expectation-Adjusted-Distinct', the scores for both modes will be returned. The default value is 'Expectation-Adjusted-Distinct'* |
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- **vocab_size** *(int): For calculating 'Expectation-Adjusted-Distinct', either vocab_size or dataForVocabCal should not be None. Default value is None* |
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- **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. For calculating 'Expectation-Adjusted-Distinct', either vocab_size or dataForVocabCal should not be None. Default value is None* |
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- **tokenizer** *(string or tokenizer class): tokenizer for splitting sentences into words. Default value is "white_space". NLTK tokenizer is available.* |
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### Output Values |
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- 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) |
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- Distinct-1: Range 0-1 |
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- Distinct-2: Range 0-1 |
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- Distinct-3: Range 0-1 |
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#### Values from Popular Papers |
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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. |
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### Examples |
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Example of calculating Expectation-Adjusted-Distinct, given either voab_size or data for vocab_size calculation. Besides returning Expectation-Adjusted-Distinct, this mode will also return Distinct-1, 2, and 3. |
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```python |
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>>> my_new_module = evaluate.load("lsy641/distinct") |
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>>> results = my_new_module.compute(references=["Hi.", "I'm sorry to hear that", "I don't know"], vocab_size=50257) |
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>>> print(results) |
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>>> 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"] |
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>>> results = my_new_module.compute(references=["Hi.", "I'm sorry to hear that", "I don't know"], dataForVocabCal = dataset) |
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>>> print(results) |
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``` |
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Example of calculating original Distinct. This will return Distinct-1,2,and 3. |
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```python |
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>>> my_new_module = evaluate.load("lsy641/distinct") |
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>>> results = my_new_module.compute(references=["Hi.", "I'm sorry to hear that", "I don't know"], mode="Distinct") |
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>>> print(results) |
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``` |
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## Limitations and Bias |
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TODO |
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## Citation |
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```bibtex |
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@inproceedings{liu-etal-2022-rethinking, |
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title = "Rethinking and Refining the Distinct Metric", |
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author = "Liu, Siyang and |
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Sabour, Sahand and |
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Zheng, Yinhe and |
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Ke, Pei and |
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Zhu, Xiaoyan and |
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Huang, Minlie", |
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booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)", |
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year = "2022", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2022.acl-short.86", |
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doi = "10.18653/v1/2022.acl-short.86", |
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} |
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``` |
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```bibtex |
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@inproceedings{li-etal-2016-diversity, |
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title = "A Diversity-Promoting Objective Function for Neural Conversation Models", |
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author = "Li, Jiwei and |
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Galley, Michel and |
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Brockett, Chris and |
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Gao, Jianfeng and |
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Dolan, Bill", |
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booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies", |
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year = "2016", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/N16-1014", |
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doi = "10.18653/v1/N16-1014", |
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} |
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``` |
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## Further References |
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TODO |
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