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@ -37,7 +37,7 @@ depth = 8
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mix_hidden_lr = 1e-3
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IS_SPARSE = True
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PASS_NUM = 10
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PASS_NUM = 100
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BATCH_SIZE = 10
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embedding_name = 'emb'
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@ -234,7 +234,7 @@ def train(use_cuda, save_dirname=None, is_local=True):
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print("second per batch: " + str((time.time(
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) - start_time) / batch_id))
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# Set the threshold low to speed up the CI test
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if float(pass_precision) > 0.05:
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if float(pass_precision) > 0.01:
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if save_dirname is not None:
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# TODO(liuyiqun): Change the target to crf_decode
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fluid.io.save_inference_model(save_dirname, [
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