def main(args): place = set_device(args.device) fluid.enable_dygraph(place) if args.dynamic else None inputs = [ Input( [None, None], 'int64', name='words'), Input( [None], 'int64', name='length') ] dataset = LacDataset(args) predict_dataset = LacDataLoader(args, place, phase="predict") vocab_size = dataset.vocab_size num_labels = dataset.num_labels model = SeqTagging(args, vocab_size, num_labels, mode="predict") model.mode = "test" model.prepare(inputs=inputs) model.load(args.init_from_checkpoint, skip_mismatch=True) f = open(args.output_file, "wb") for data in predict_dataset.dataloader: if len(data) == 1: input_data = data[0] else: input_data = data results, length = model.test_batch(inputs=flatten(input_data)) for i in range(len(results)): word_len = length[i] word_ids = results[i][:word_len] tags = [dataset.id2label_dict[str(id)] for id in word_ids] f.write("\002".join(tags) + "\n")
def main(args): place = set_device(args.device) fluid.enable_dygraph(place) if args.dynamic else None inputs = [ Input([None, None], 'int64', name='words'), Input([None], 'int64', name='length'), Input([None, None], 'int64', name='target') ] labels = [Input([None, None], 'int64', name='labels')] dataset = LacDataset(args) eval_dataset = LacDataLoader(args, place, phase="test") vocab_size = dataset.vocab_size num_labels = dataset.num_labels model = SeqTagging(args, vocab_size, num_labels, mode="test") model.mode = "test" model.prepare(metrics=ChunkEval(num_labels), inputs=inputs, labels=labels, device=place) model.load(args.init_from_checkpoint, skip_mismatch=True) eval_result = model.evaluate(eval_dataset.dataloader, batch_size=args.batch_size) print("precison: %.5f" % (eval_result["precision"][0])) print("recall: %.5f" % (eval_result["recall"][0])) print("F1: %.5f" % (eval_result["F1"][0]))
def main(args): place = set_device(args.device) fluid.enable_dygraph(place) if args.dynamic else None inputs = [ Input( [None, None], 'int64', name='words'), Input( [None], 'int64', name='length'), Input( [None, None], 'int64', name='target') ] labels = [Input([None, None], 'int64', name='labels')] feed_list = None if args.dynamic else [ x.forward() for x in inputs + labels ] dataset = LacDataset(args) train_dataset = LacDataLoader(args, place, phase="train") vocab_size = dataset.vocab_size num_labels = dataset.num_labels model = SeqTagging(args, vocab_size, num_labels, mode="train") optim = AdamOptimizer( learning_rate=args.base_learning_rate, parameter_list=model.parameters()) model.prepare( optim, LacLoss(), ChunkEval(num_labels), inputs=inputs, labels=labels, device=args.device) if args.init_from_checkpoint: model.load(args.init_from_checkpoint) if args.init_from_pretrain_model: model.load(args.init_from_pretrain_model, reset_optimizer=True) model.fit(train_dataset.dataloader, epochs=args.epoch, batch_size=args.batch_size, eval_freq=args.eval_freq, save_freq=args.save_freq, save_dir=args.save_dir)