mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-06 22:30:09 +06:00
81fe0bf085
* Add init barthez * Add barthez model, tokenizer and docs BARThez is a pre-trained french seq2seq model that uses BART objective. * Apply suggestions from code review docs typos Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Add license * Change URLs scheme * Remove barthez model keep tokenizer * Fix style * Fix quality * Update tokenizer * Add fast tokenizer * Add fast tokenizer test Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
80 lines
2.8 KiB
Python
80 lines
2.8 KiB
Python
# coding=utf-8
|
|
# Copyright 2020 Ecole Polytechnique and HuggingFace Inc. team.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
|
|
import unittest
|
|
|
|
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
|
|
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch
|
|
|
|
from .test_tokenization_common import TokenizerTesterMixin
|
|
|
|
|
|
@require_tokenizers
|
|
@require_sentencepiece
|
|
class BarthezTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
|
|
|
|
tokenizer_class = BarthezTokenizer
|
|
rust_tokenizer_class = BarthezTokenizerFast
|
|
test_rust_tokenizer = True
|
|
|
|
def setUp(self):
|
|
super().setUp()
|
|
|
|
tokenizer = BarthezTokenizer.from_pretrained("moussaKam/mbarthez")
|
|
tokenizer.save_pretrained(self.tmpdirname)
|
|
self.tokenizer = tokenizer
|
|
|
|
@require_torch
|
|
def test_prepare_seq2seq_batch(self):
|
|
src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
|
|
tgt_text = [
|
|
"Summary of the text.",
|
|
"Another summary.",
|
|
]
|
|
expected_src_tokens = [0, 57, 3018, 70307, 91, 2]
|
|
|
|
batch = self.tokenizer.prepare_seq2seq_batch(
|
|
src_text, tgt_texts=tgt_text, max_length=len(expected_src_tokens), return_tensors="pt"
|
|
)
|
|
self.assertIsInstance(batch, BatchEncoding)
|
|
|
|
self.assertEqual((2, 6), batch.input_ids.shape)
|
|
self.assertEqual((2, 6), batch.attention_mask.shape)
|
|
result = batch.input_ids.tolist()[0]
|
|
self.assertListEqual(expected_src_tokens, result)
|
|
|
|
def test_rust_and_python_full_tokenizers(self):
|
|
if not self.test_rust_tokenizer:
|
|
return
|
|
|
|
tokenizer = self.get_tokenizer()
|
|
rust_tokenizer = self.get_rust_tokenizer()
|
|
|
|
sequence = "I was born in 92000, and this is falsé."
|
|
|
|
tokens = tokenizer.tokenize(sequence)
|
|
rust_tokens = rust_tokenizer.tokenize(sequence)
|
|
self.assertListEqual(tokens, rust_tokens)
|
|
|
|
ids = tokenizer.encode(sequence, add_special_tokens=False)
|
|
rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
|
|
self.assertListEqual(ids, rust_ids)
|
|
|
|
rust_tokenizer = self.get_rust_tokenizer()
|
|
ids = tokenizer.encode(sequence)
|
|
rust_ids = rust_tokenizer.encode(sequence)
|
|
self.assertListEqual(ids, rust_ids)
|