49 lines
1.6 KiB
Python
49 lines
1.6 KiB
Python
from dataManager.gFunDataset import SimpleGfunDataset
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from gfun.generalizedFunnelling import GeneralizedFunnelling
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from main import save_preds
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DATASET_NAME = "inference-dataset" # can be anything
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def main(args):
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dataset = SimpleGfunDataset(
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dataset_name=DATASET_NAME,
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datadir=args.datadir,
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multilabel=False,
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only_inference=True,
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labels=args.nlabels,
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)
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lX, _ = dataset.test()
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print("Ok")
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gfun = GeneralizedFunnelling(
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dataset_name=dataset.get_name(),
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langs=dataset.langs(),
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num_labels=dataset.num_labels(),
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classification_type="singlelabel",
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embed_dir=args.muse_dir,
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posterior=True,
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multilingual=True,
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textual_transformer=True,
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load_trained=args.trained_gfun,
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load_meta=True,
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)
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predictions = gfun.transform(lX)
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save_preds(
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preds=predictions,
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targets=None,
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config=f"Inference dataset: {dataset.get_name()}"
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)
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if __name__ == "__main__":
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from argparse import ArgumentParser
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parser = ArgumentParser()
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parser.add_argument("--datadir", type=str, default="~/datasets/rai/csv", help="directory should contain train.csv and test.csv. Train.csv is not required at inferece time!")
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parser.add_argument("--nlabels", type=int, default=28)
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parser.add_argument("--muse_dir", type=str, default="~/resources/muse_embeddings", help="Path to muse embeddings")
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parser.add_argument("--trained_gfun", type=str, default="rai_pmt_mean_231029", help="name of the trained gfun instance")
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args = parser.parse_args()
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main(args)
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