mirror of
https://github.com/marian-nmt/marian.git
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49 lines
1.4 KiB
Python
Executable File
49 lines
1.4 KiB
Python
Executable File
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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from __future__ import print_function
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import argparse
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import yaml
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import numpy as np
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def main():
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desc = """Export word embeddings from model"""
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parser = argparse.ArgumentParser(
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formatter_class=argparse.RawDescriptionHelpFormatter, description=desc)
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parser.add_argument("-m", "--model", help="path to model.npz file", required=True)
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parser.add_argument("-o", "--output-prefix", help="prefix for output files", required=True)
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args = parser.parse_args()
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print("Loading model")
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model = np.load(args.model)
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special = yaml.safe_load(model["special:model.yml"][:-1].tobytes())
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if special["tied-embeddings-all"] or special["tied-embeddings-src"]:
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all_emb = model["Wemb"]
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export_emb(args.output_prefix + ".all", all_emb)
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exit()
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if special["type"] == "amun":
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enc_emb = model["Wemb"]
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dec_emb = model["Wemb_dec"]
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else:
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enc_emb = model["encoder_Wemb"]
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dec_emb = model["decoder_Wemb"]
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export_emb(args.output_prefix + ".src", enc_emb)
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export_emb(args.output_prefix + ".trg", dec_emb)
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def export_emb(filename, emb):
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with open(filename, "w") as out:
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out.write("{0} {1}\n".format(*emb.shape))
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for i in range(emb.shape[0]):
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vec = " ".join("{0:.8f}".format(v) for v in emb[i])
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out.write("{0} {1}\n".format(i, vec))
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if __name__ == '__main__':
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main()
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