fixed imports + readme.md
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main.py
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main.py
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@ -109,7 +109,7 @@ if __name__ == '__main__':
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parser.add_argument('dataset', help='Path to the dataset')
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parser.add_argument('-o', '--output', dest='csv_dir',
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help='Result file (default ../csv_log/gfun_results.csv)', type=str,
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help='Result file (default ../csv_logs/gfun/gfun_results.csv)', type=str,
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default='../csv_logs/gfun/gfun_results.csv')
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parser.add_argument('-x', '--post_embedder', dest='post_embedder', action='store_true',
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@ -0,0 +1,40 @@
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```
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usage: main.py [-h] [-o CSV_DIR] [-x] [-w] [-m] [-b] [-g] [-c] [-n NEPOCHS]
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[-j N_JOBS] [--muse_dir MUSE_DIR] [--gru_wce]
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[--gru_dir GRU_DIR] [--bert_dir BERT_DIR] [--gpus GPUS]
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dataset
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Run generalized funnelling, A. Moreo, A. Pedrotti and F. Sebastiani (2020).
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positional arguments:
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dataset Path to the dataset
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optional arguments:
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-h, --help show this help message and exit
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-o CSV_DIR, --output CSV_DIR
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Result file (default ../csv_logs/gfun/gfun_results.csv)
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-x, --post_embedder deploy posterior probabilities embedder to compute
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document embeddings
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-w, --wce_embedder deploy (supervised) Word-Class embedder to the compute
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document embeddings
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-m, --muse_embedder deploy (pretrained) MUSE embedder to compute document
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embeddings
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-b, --bert_embedder deploy multilingual Bert to compute document
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embeddings
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-g, --gru_embedder deploy a GRU in order to compute document embeddings
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-c, --c_optimize Optimize SVMs C hyperparameter
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-n NEPOCHS, --nepochs NEPOCHS
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Number of max epochs to train Recurrent embedder
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(i.e., -g)
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-j N_JOBS, --n_jobs N_JOBS
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Number of parallel jobs (default is -1, all)
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--muse_dir MUSE_DIR Path to the MUSE polylingual word embeddings (default
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../embeddings)
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--gru_wce Deploy WCE embedding as embedding layer of the GRU
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View Generator
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--gru_dir GRU_DIR Set the path to a pretrained GRU model (i.e., -g view
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generator)
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--bert_dir BERT_DIR Set the path to a pretrained mBERT model (i.e., -b
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view generator)
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--gpus GPUS specifies how many GPUs to use per node
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```
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