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fast-bleu-0.0.90
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مشاهده بیشترتوضیحات
A fast multithreaded C++ implementation of nltk BLEU with python wrapper.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | fast-bleu-0.0.90 |
| نام | fast-bleu |
| نسخه کتابخانه | 0.0.90 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Danial Alihosseini |
| ایمیل نویسنده | danial.alihosseini@gmail.com |
| آدرس صفحه اصلی | https://github.com/Danial-Alh/fast-bleu |
| آدرس اینترنتی | https://pypi.org/project/fast-bleu/ |
| مجوز | OSI Approved :: MIT License |
# fast-bleu Package
This is a fast multithreaded C++ implementation of NLTK BLEU with Python wrapper; computing BLEU and SelfBLEU scores for a fixed reference set.
It can return (Self)BLEU for different (max) n-grams simultaneously and efficiently (e.g. BLEU-2, BLEU-3, etc.).
## Installation
The installation requires `c++11`.
The `requirements.txt` file is the required python packages to run the `test_cases.py` file.
### Linux and WSL
Installing [PyPI latest stable release](https://pypi.org/project/fast-bleu/):
``` bash
pip install --user fast-bleu
```
### MacOS
As the macOS uses clang and it does not support OpenMP, one workaround is to first install gcc with `brew install gcc`. After that, gcc specific binaries will be added (for example, it will be maybe `gcc-10`
and `g++-10`).
To change the default compiler, an option to the installation command is added, so you can install the [PyPI latest stable release](https://pypi.org/project/fast-bleu/) with the following command:
``` bash
pip install --user fast-bleu --install-option="--CC=<path-to-gcc>" --install-option="--CXX=<path-to-g++>"
```
### Windows
Not tested yet!
## Sample Usage
Here is an example to compute BLEU-2, BLEU-3, SelfBLEU-2 and SelfBLEU-3:
``` python
>>> from fast_bleu import BLEU, SelfBLEU
>>> ref1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
... 'ensures', 'that', 'the', 'military', 'will', 'forever',
... 'heed', 'Party', 'commands']
>>> ref2 = ['It', 'is', 'the', 'guiding', 'principle', 'which',
... 'guarantees', 'the', 'military', 'forces', 'always',
... 'being', 'under', 'the', 'command', 'of', 'the', 'Party']
>>> ref3 = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
... 'army', 'always', 'to', 'heed', 'the', 'directions',
... 'of', 'the', 'party']
>>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
... 'ensures', 'that', 'the', 'military', 'always',
... 'obeys', 'the', 'commands', 'of', 'the', 'party']
>>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
... 'interested', 'in', 'world', 'history']
>>> list_of_references = [ref1, ref2, ref3]
>>> hypotheses = [hyp1, hyp2]
>>> weights = {'bigram': (1/2., 1/2.), 'trigram': (1/3., 1/3., 1/3.)}
>>> bleu = BLEU(list_of_references, weights)
>>> bleu.get_score(hypotheses)
{'bigram': [0.7453559924999299, 0.0191380231127159], 'trigram': [0.6240726901657495, 0.013720869575946234]}
```
which means:
* BLEU-2 for hyp1 is 0.7453559924999299
* BLEU-2 for hyp2 is 0.0191380231127159
* BLEU-3 for hyp1 is 0.6240726901657495
* BLEU-3 for hyp2 is 0.013720869575946234
```python
>>> self_bleu = SelfBLEU(list_of_references, weights)
>>> self_bleu.get_score()
{'bigram': [0.25819888974716115, 0.3615507630310936, 0.37080992435478316],
'trigram': [0.07808966062765045, 0.20140620205719248, 0.21415334758254043]}
```
which means:
* SelfBLEU-2 for ref1 is 0.25819888974716115
* SelfBLEU-2 for ref2 is 0.3615507630310936
* SelfBLEU-2 for ref3 is 0.37080992435478316
* SelfBLEU-3 for ref1 is 0.07808966062765045
* SelfBLEU-3 for ref2 is 0.20140620205719248
* SelfBLEU-3 for ref3 is 0.21415334758254043
**Caution** Each token of reference set is converted to string format during computation.
For further details, refer to the documentation provided in the source codes.
## Citation
Please cite our paper if it helps with your research.
* ACL Anthology: <https://www.aclweb.org/anthology/W19-2311>
* Arxiv link: <https://arxiv.org/abs/1904.03971>
```latex
@inproceedings{alihosseini-etal-2019-jointly,
title = {Jointly Measuring Diversity and Quality in Text Generation Models},
author = {Alihosseini, Danial and
Montahaei, Ehsan and
Soleymani Baghshah, Mahdieh},
booktitle = {Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation},
month = {jun},
year = {2019},
address = {Minneapolis, Minnesota},
publisher = {Association for Computational Linguistics},
url = {https://www.aclweb.org/anthology/W19-2311},
doi = {10.18653/v1/W19-2311},
pages = {90--98},
}
```
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.3 | Python |
نحوه نصب
نصب پکیج whl fast-bleu-0.0.90:
pip install fast-bleu-0.0.90.whl
نصب پکیج tar.gz fast-bleu-0.0.90:
pip install fast-bleu-0.0.90.tar.gz