mirror of
https://github.com/huggingface/transformers.git
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fbd8792195
* beginning of dpr modeling * wip * implement forward * remove biencoder + better init weights * export dpr model to embed model for nlp lib * add new api * remove old code * make style * fix dumb typo * don't load bert weights * docs * docs * style * move the `k` parameter * fix init_weights * add pretrained configs * minor * update config names * style * better config * style * clean code based on PR comments * change Dpr to DPR * fix config * switch encoder config to a dict * style * inheritance -> composition * add messages in assert startements * add dpr reader tokenizer * one tokenizer per model * fix base_model_prefix * fix imports * typo * add convert script * docs * change tokenizers conf names * style * change tokenizers conf names * minor * minor * fix wrong names * minor * remove unused convert functions * rename convert script * use return_tensors in tokenizers * remove n_questions dim * move generate logic to tokenizer * style * add docs * docs * quality * docs * add tests * style * add tokenization tests * DPR full tests * Stay true to the attention mask building * update docs * missing param in bert input docs * docs * style Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
90 lines
3.5 KiB
Python
90 lines
3.5 KiB
Python
# coding=utf-8
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# Copyright 2020 Huggingface
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from transformers.tokenization_dpr import (
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DPRContextEncoderTokenizer,
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DPRContextEncoderTokenizerFast,
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DPRQuestionEncoderTokenizer,
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DPRQuestionEncoderTokenizerFast,
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DPRReaderOutput,
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DPRReaderTokenizer,
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DPRReaderTokenizerFast,
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)
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from transformers.tokenization_utils_base import BatchEncoding
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from .test_tokenization_bert import BertTokenizationTest
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from .utils import slow
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class DPRContextEncoderTokenizationTest(BertTokenizationTest):
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tokenizer_class = DPRContextEncoderTokenizer
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def get_rust_tokenizer(self, **kwargs):
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return DPRContextEncoderTokenizerFast.from_pretrained(self.tmpdirname, **kwargs)
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class DPRQuestionEncoderTokenizationTest(BertTokenizationTest):
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tokenizer_class = DPRQuestionEncoderTokenizer
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def get_rust_tokenizer(self, **kwargs):
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return DPRQuestionEncoderTokenizerFast.from_pretrained(self.tmpdirname, **kwargs)
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class DPRReaderTokenizationTest(BertTokenizationTest):
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tokenizer_class = DPRReaderTokenizer
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def get_rust_tokenizer(self, **kwargs):
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return DPRReaderTokenizerFast.from_pretrained(self.tmpdirname, **kwargs)
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@slow
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def test_decode_best_spans(self):
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tokenizer = self.tokenizer_class.from_pretrained("bert-base-uncased")
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text_1 = tokenizer.encode("question sequence", add_special_tokens=False)
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text_2 = tokenizer.encode("title sequence", add_special_tokens=False)
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text_3 = tokenizer.encode("text sequence " * 4, add_special_tokens=False)
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input_ids = [[101] + text_1 + [102] + text_2 + [102] + text_3]
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reader_input = BatchEncoding({"input_ids": input_ids})
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start_logits = [[0] * len(input_ids[0])]
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end_logits = [[0] * len(input_ids[0])]
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relevance_logits = [0]
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reader_output = DPRReaderOutput(start_logits, end_logits, relevance_logits)
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start_index, end_index = 8, 9
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start_logits[0][start_index] = 10
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end_logits[0][end_index] = 10
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predicted_spans = tokenizer.decode_best_spans(reader_input, reader_output)
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self.assertEqual(predicted_spans[0].start_index, start_index)
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self.assertEqual(predicted_spans[0].end_index, end_index)
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self.assertEqual(predicted_spans[0].doc_id, 0)
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@slow
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def test_call(self):
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tokenizer = self.tokenizer_class.from_pretrained("bert-base-uncased")
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text_1 = tokenizer.encode("question sequence", add_special_tokens=False)
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text_2 = tokenizer.encode("title sequence", add_special_tokens=False)
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text_3 = tokenizer.encode("text sequence", add_special_tokens=False)
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expected_input_ids = [101] + text_1 + [102] + text_2 + [102] + text_3
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encoded_input = tokenizer(questions=["question sequence"], titles=["title sequence"], texts=["text sequence"])
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self.assertIn("input_ids", encoded_input)
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self.assertIn("attention_mask", encoded_input)
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self.assertListEqual(encoded_input["input_ids"][0], expected_input_ids)
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