Adding the prepare_seq2seq_batch function to ProphetNet (#8515)

* Simply insert T5Tokenizer's prepare_seq2seq_batch

* Update/Add some 'import'

* fix RunTimeError caused by '.view'

* Moves .view related error avoidance from seq2seq_trainer to inside prophetnet

* Update test_tokenization_prophetnet.py

* Format the test code with black

* Re-format the test code

* Update test_tokenization_prophetnet.py

* Add importing require_torch in the test code

* Add importing BatchEncoding in the test code

* Re-format the test code on Colab
This commit is contained in:
Yusuke Mori
2020-11-16 22:18:25 +09:00
committed by GitHub
parent 931b10978e
commit 04d8136bde
3 changed files with 71 additions and 2 deletions

View File

@@ -17,7 +17,8 @@
import os
import unittest
from transformers.testing_utils import slow
from transformers import BatchEncoding
from transformers.testing_utils import require_torch, slow
from transformers.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
@@ -150,6 +151,28 @@ class ProphetNetTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
self.assertListEqual(tokenizer.tokenize("unwantedX running"), ["[UNK]", "runn", "##ing"])
@require_torch
def test_prepare_seq2seq_batch(self):
tokenizer = self.tokenizer_class.from_pretrained("microsoft/prophetnet-large-uncased")
src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
tgt_text = [
"Summary of the text.",
"Another summary.",
]
expected_src_tokens = [1037, 2146, 20423, 2005, 7680, 7849, 3989, 1012, 102]
batch = tokenizer.prepare_seq2seq_batch(
src_text,
tgt_texts=tgt_text,
return_tensors="pt",
)
self.assertIsInstance(batch, BatchEncoding)
result = list(batch.input_ids.numpy()[0])
self.assertListEqual(expected_src_tokens, result)
self.assertEqual((2, 9), batch.input_ids.shape)
self.assertEqual((2, 9), batch.attention_mask.shape)
def test_is_whitespace(self):
self.assertTrue(_is_whitespace(" "))
self.assertTrue(_is_whitespace("\t"))