def test_bert_3(self):
vocab = Vocabulary().add_word_lst("this is a test [SEP] .".split())
embed = BertEmbedding(vocab, model_dir_or_name='tests/data_for_tests/embedding/small_bert',
include_cls_sep=False)
model = BertForTokenClassification(embed, 7)
input_ids = torch.LongTensor([[1, 2, 3], [6, 5, 0]])
pred = model(input_ids)
self.assertTrue(isinstance(pred, dict))
self.assertTrue(Const.OUTPUT in pred)
self.assertEqual(tuple(pred[Const.OUTPUT].shape), (2, 3, 7))
def test_bert_1_w(self):
vocab = Vocabulary().add_word_lst("this is a test .".split())
embed = BertEmbedding(vocab, model_dir_or_name='tests/data_for_tests/embedding/small_bert',
include_cls_sep=False)
with self.assertWarns(Warning):
model = BertForSequenceClassification(embed, 2)
input_ids = torch.LongTensor([[1, 2, 3], [5, 6, 0]])
pred = model.predict(input_ids)
self.assertTrue(isinstance(pred, dict))
self.assertTrue(Const.OUTPUT in pred)
self.assertEqual(tuple(pred[Const.OUTPUT].shape), (2,))
def test_bert_4(self):
vocab = Vocabulary().add_word_lst("this is a test [SEP] .".split())
embed = BertEmbedding(vocab, model_dir_or_name='tests/data_for_tests/embedding/small_bert',
include_cls_sep=False)
model = BertForQuestionAnswering(embed)
input_ids = torch.LongTensor([[1, 2, 3], [6, 5, 0]])
pred = model(input_ids)
self.assertTrue(isinstance(pred, dict))
self.assertTrue('pred_start' in pred)
self.assertTrue('pred_end' in pred)
self.assertEqual(tuple(pred['pred_start'].shape), (2, 3))
self.assertEqual(tuple(pred['pred_end'].shape), (2, 3))
def test_bert_2(self):
vocab = Vocabulary().add_word_lst("this is a test [SEP] .".split())
embed = BertEmbedding(vocab, model_dir_or_name='tests/data_for_tests/embedding/small_bert',
include_cls_sep=True)
model = BertForMultipleChoice(embed, 2)
input_ids = torch.LongTensor([[[2, 6, 7], [1, 6, 5]]])
print(input_ids.size())
pred = model(input_ids)
self.assertTrue(isinstance(pred, dict))
self.assertTrue(Const.OUTPUT in pred)
self.assertEqual(tuple(pred[Const.OUTPUT].shape), (1, 2))
def test_bert_for_question_answering_train(self):
from fastNLP import CMRC2018Loss
from fastNLP.io import CMRC2018BertPipe
from fastNLP import Trainer
data_bundle = CMRC2018BertPipe().process_from_file('tests/data_for_tests/io/cmrc')
data_bundle.rename_field('chars', 'words')
train_data = data_bundle.get_dataset('train')
vocab = data_bundle.get_vocab('words')
embed = BertEmbedding(vocab, model_dir_or_name='tests/data_for_tests/embedding/small_bert',
include_cls_sep=False, auto_truncate=True)
model = BertForQuestionAnswering(embed)
loss = CMRC2018Loss()
trainer = Trainer(train_data, model, loss=loss, use_tqdm=False)
trainer.train(load_best_model=False)
vocab.from_dataset(bundle.get_dataset("train"),
field_name='words',
no_create_entry_dataset=[
bundle.get_dataset("test"),
bundle.get_dataset("dev")
])
vocab.index_dataset(bundle.get_dataset("train"), field_name='words')
vocab.index_dataset(bundle.get_dataset("test"), field_name='words')
vocab.index_dataset(bundle.get_dataset("dev"), field_name='words')
# establish the model
from fastNLP import Const
import torch
from fastNLP.models import BertForSentenceMatching
from fastNLP.embeddings.bert_embedding import BertEmbedding
embed = BertEmbedding(vocab, model_dir_or_name='cn-base', requires_grad=False)
#pad the input array
bundle.set_pad_val("words", 0)
bundle.set_input("words")
bundle.set_target("target")
model = BertForSentenceMatching(embed)
from fastNLP import AccuracyMetric
metrics = AccuracyMetric(pred=Const.OUTPUT, target=Const.TARGET)
# train the model
from fastNLP import Trainer
from fastNLP import CrossEntropyLoss
N_EPOCHS = 1
BATCH_SIZE = 16
