def test_case1(self):
# 测试能否正确计算
import torch
metric = CMRC2018Metric()
raw_chars = [list("abcsdef"), list("123456s789")]
context_len = torch.LongTensor([3, 6])
answers = [["abc", "abc", "abc"], ["12", "12", "12"]]
pred_start = torch.randn(2, max(map(len, raw_chars)))
pred_end = torch.randn(2, max(map(len, raw_chars)))
pred_start[0, 0] = 1000 # 正好是abc
pred_end[0, 2] = 1000
pred_start[1, 1] = 1000 # 取出234
pred_end[1, 3] = 1000
metric.evaluate(answers, raw_chars, context_len, pred_start, pred_end)
eval_res = metric.get_metric()
self.assertDictEqual(eval_res, {'f1': 70.0, 'em': 50.0})
from fastNLP.core.losses import CMRC2018Loss
from fastNLP.core.metrics import CMRC2018Metric
from fastNLP.io.pipe.qa import CMRC2018BertPipe
from fastNLP import Trainer, BucketSampler
from fastNLP import WarmupCallback, GradientClipCallback
from fastNLP.core.optimizer import AdamW
data_bundle = CMRC2018BertPipe().process_from_file()
data_bundle.rename_field('chars', 'words')
print(data_bundle)
embed = BertEmbedding(data_bundle.get_vocab('words'), model_dir_or_name='cn', requires_grad=True, include_cls_sep=False, auto_truncate=True,
dropout=0.5, word_dropout=0.01)
model = BertForQuestionAnswering(embed)
loss = CMRC2018Loss()
metric = CMRC2018Metric()
wm_callback = WarmupCallback(schedule='linear')
gc_callback = GradientClipCallback(clip_value=1, clip_type='norm')
callbacks = [wm_callback, gc_callback]
optimizer = AdamW(model.parameters(), lr=5e-5)
trainer = Trainer(data_bundle.get_dataset('train'), model, loss=loss, optimizer=optimizer,
sampler=BucketSampler(seq_len_field_name='context_len'),
dev_data=data_bundle.get_dataset('dev'), metrics=metric,
callbacks=callbacks, device=0, batch_size=6, num_workers=2, n_epochs=2, print_every=1,
test_use_tqdm=False, update_every=10)
trainer.train(load_best_model=False)