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60 lines
2.1 KiB
Markdown
60 lines
2.1 KiB
Markdown
### Get the CNN Data
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To be able to reproduce the authors' results on the CNN/Daily Mail dataset you first need to download both CNN and Daily Mail datasets [from Kyunghyun Cho's website](https://cs.nyu.edu/~kcho/DMQA/) (the links next to "Stories") in the same folder. Then uncompress the archives by running:
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```bash
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tar -xvf cnn_stories.tgz && tar -xvf dailymail_stories.tgz
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```
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this should make a directory called cnn_dm/ with files like `test.source`.
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To use your own data, copy that files format. Each article to be summarized is on its own line.
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### Usage
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To create summaries for each article in dataset, run:
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```bash
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python evaluate_cnn.py <path_to_test.source> cnn_test_summaries.txt
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```
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the default batch size, 8, fits in 16GB GPU memory, but may need to be adjusted to fit your system.
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### Training
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After downloading the CNN and Daily Mail datasets, preprocess the dataset:
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```commandline
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git clone https://github.com/artmatsak/cnn-dailymail
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cd cnn-dailymail && python make_datafiles.py ../cnn/stories/ ../dailymail/stories/
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```
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Run the training script: `run_train.sh`
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### Where is the code?
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The core model is in `src/transformers/modeling_bart.py`. This directory only contains examples.
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### (WIP) Rouge Scores
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### Stanford CoreNLP Setup
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```
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ptb_tokenize () {
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cat $1 | java edu.stanford.nlp.process.PTBTokenizer -ioFileList -preserveLines > $2
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}
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sudo apt install openjdk-8-jre-headless
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sudo apt-get install ant
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wget http://nlp.stanford.edu/software/stanford-corenlp-full-2018-10-05.zip
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unzip stanford-corenlp-full-2018-10-05.zip
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cd stanford-corenlp-full-2018-10-05
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export CLASSPATH=stanford-corenlp-3.9.2.jar:stanford-corenlp-3.9.2-models.jar
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```
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Then run `ptb_tokenize` on `test.target` and your generated hypotheses.
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### Rouge Setup
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Install `files2rouge` following the instructions at [here](https://github.com/pltrdy/files2rouge).
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I also needed to run `sudo apt-get install libxml-parser-perl`
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```python
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from files2rouge import files2rouge
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from files2rouge import settings
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files2rouge.run(<path_to_tokenized_hypo>,
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<path_to_tokenized_target>,
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saveto='rouge_output.txt')
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```
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