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- import sys
- sys.path.append('../../..')
-
- from fastNLP.io.pipe.cws import CWSPipe
- from reproduction.seqence_labelling.cws.model.bilstm_crf_cws import BiLSTMCRF
- from fastNLP import Trainer, cache_results
- from fastNLP.embeddings import StaticEmbedding
- from fastNLP import EvaluateCallback, BucketSampler, SpanFPreRecMetric, GradientClipCallback
- from torch.optim import Adagrad
-
- ###########hyper
- dataname = 'pku'
- hidden_size = 400
- num_layers = 1
- lr = 0.05
- ###########hyper
-
-
- @cache_results('{}.pkl'.format(dataname), _refresh=False)
- def get_data():
- data_bundle = CWSPipe(dataset_name=dataname, bigrams=True, trigrams=False).process_from_file()
- char_embed = StaticEmbedding(data_bundle.get_vocab('chars'), dropout=0.33, word_dropout=0.01,
- model_dir_or_name='~/exps/CWS/pretrain/vectors/1grams_t3_m50_corpus.txt')
- bigram_embed = StaticEmbedding(data_bundle.get_vocab('bigrams'), dropout=0.33,min_freq=3, word_dropout=0.01,
- model_dir_or_name='~/exps/CWS/pretrain/vectors/2grams_t3_m50_corpus.txt')
- return data_bundle, char_embed, bigram_embed
-
- data_bundle, char_embed, bigram_embed = get_data()
- print(data_bundle)
-
- model = BiLSTMCRF(char_embed, hidden_size, num_layers, target_vocab=data_bundle.get_vocab('target'), bigram_embed=bigram_embed,
- trigram_embed=None, dropout=0.3)
- model.cuda()
-
- callbacks = []
- callbacks.append(EvaluateCallback(data_bundle.get_dataset('test')))
- callbacks.append(GradientClipCallback(clip_type='value', clip_value=5))
- optimizer = Adagrad(model.parameters(), lr=lr)
-
- metrics = []
- metric1 = SpanFPreRecMetric(tag_vocab=data_bundle.get_vocab('target'), encoding_type='bmes')
- metrics.append(metric1)
-
- trainer = Trainer(data_bundle.get_dataset('train'), model, optimizer=optimizer, loss=None,
- batch_size=128, sampler=BucketSampler(), update_every=1,
- num_workers=1, n_epochs=10, print_every=5,
- dev_data=data_bundle.get_dataset('dev'),
- metrics=metrics,
- metric_key=None,
- validate_every=-1, save_path=None, use_tqdm=True, device=0,
- callbacks=callbacks, check_code_level=0, dev_batch_size=128)
- trainer.train()
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