Metadata-Version: 2.1
Name: rouge
Version: 1.0.0rc2
Summary: Full Python ROUGE Score Implementation (not a wrapper)
Home-page: http://github.com/pltrdy/rouge
Author: pltrdy
Author-email: pltrdy@gmail.com
License: LICENCE.txt
Download-URL: https://github.com/pltrdy/rouge/archive/1.0.0rc2.tar.gz
Description: # Rouge
        *A full Python librarie for the ROUGE metric [(paper)](http://www.aclweb.org/anthology/W04-1013).*
        
        ### Disclaimer
        This implementation is independant from the "official" ROUGE script (aka. `ROUGE-155`).   
        Results may be *slighlty* different, see [discussions in #2](https://github.com/pltrdy/rouge/issues/2).
        
        ## Quickstart
        #### Clone & Install
        ```shell
        git clone https://github.com/pltrdy/rouge
        cd rouge
        python setup.py install
        # or
        pip install -U .
        ```
        or from pip:
        ```
        pip install rouge
        ```
        #### Use it from the shell (JSON Output)
        ```
        $rouge -h
        usage: rouge [-h] [-f] [-a] hypothesis reference
        
        Rouge Metric Calculator
        
        positional arguments:
          hypothesis  Text of file path
          reference   Text or file path
        
        optional arguments:
          -h, --help  show this help message and exit
          -f, --file  File mode
          -a, --avg   Average mode
        
        ```
        
        e.g. 
        
        
        ```shell
        # Single Sentence
        rouge "transcript is a written version of each day 's cnn student" \
              "this page includes the show transcript use the transcript to help students with"
        
        # Scoring using two files (line by line)
        rouge -f ./tests/hyp.txt ./ref.txt
        
        # Avg scoring - 2 files
        rouge -f ./tests/hyp.txt ./ref.txt --avg
        ```
        
        #### As a library
        
        ###### Score 1 sentence
        
        ```python
        from rouge import Rouge 
        
        hypothesis = "the #### transcript is a written version of each day 's cnn student news program use this transcript to he    lp students with reading comprehension and vocabulary use the weekly newsquiz to test your knowledge of storie s you     saw on cnn student news"
        
        reference = "this page includes the show transcript use the transcript to help students with reading comprehension and     vocabulary at the bottom of the page , comment for a chance to be mentioned on cnn student news . you must be a teac    her or a student age # # or older to request a mention on the cnn student news roll call . the weekly newsquiz tests     students ' knowledge of even ts in the news"
        
        rouge = Rouge()
        scores = rouge.get_scores(hypothesis, reference)
        ```
        
        *Output:*
        
        ```json
        [
          {
            "rouge-1": {
              "f": 0.4786324739396596,
              "p": 0.6363636363636364,
              "r": 0.3835616438356164
            },
            "rouge-2": {
              "f": 0.2608695605353498,
              "p": 0.3488372093023256,
              "r": 0.20833333333333334
            },
            "rouge-l": {
              "f": 0.5128205081276938,
              "p": 0.6818181818181818,
              "r": 0.410958904109589
            }
          }
        ]
        ```
        
        ###### Score multiple sentences
        ```python
        import json
        from rouge import Rouge
        
        # Load some sentences
        with open('./tests/data.json') as f:
          data = json.load(f)
        
        hyps, refs = map(list, zip(*[[d['hyp'], d['ref']] for d in data]))
        rouge = Rouge()
        scores = rouge.get_scores(hyps, refs)
        # or
        scores = rouge.get_scores(hyps, refs, avg=True)
        ```
        
        *Output (`avg=False`)*: a list of `n` dicts:
        
        ```
        {"rouge-1": {"f": _, "p": _, "r": _}, "rouge-2" : { .. }, "rouge-3": { ... }}
        ```
        
        
        *Output (`avg=True`)*: a single dict with average values:
        
        ```
        {"rouge-1": {"f": _, "p": _, "r": _}, "rouge-2" : { ..     }, "rouge-3": { ... }}
        ``` 
        
        ###### Score two files (line by line)
        Given two files `hyp_path`, `ref_path`, with the same number (`n`) of lines, calculate score for each of this lines, or, the average over the whole file. 
        
        ```python
        from rouge import FilesRouge
        
        files_rouge = FilesRouge()
        scores = files_rouge.get_scores(hyp_path, ref_path)
        # or
        scores = files_rouge.get_scores(hyp_path, ref_path, avg=True)
        ```
        
Keywords: NL,CL,natural language processing,computational linguistics,summarization
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Text Processing :: Linguistic
Description-Content-Type: text/markdown
