-
Notifications
You must be signed in to change notification settings - Fork 3k
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Add text similarity task for Taskflow (#1345)
* Add text matching task for Taskflow * Update README.md * Update input check * Update README.md * Update taskname * Update usage * Optimize var name * Update simbert * Update README.md * Update README.md Co-authored-by: Zeyu Chen <chenzeyu01@baidu.com>
- Loading branch information
Showing
3 changed files
with
261 additions
and
5 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,179 @@ | ||
| # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. | ||
| # | ||
| # 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 paddle | ||
| from paddlenlp.transformers import BertModel, BertTokenizer | ||
|
|
||
| from ..data import Pad, Tuple | ||
| from .utils import static_mode_guard | ||
| from .task import Task | ||
|
|
||
| usage = r""" | ||
| from paddlenlp import Taskflow | ||
| similarity = Taskflow("text_similarity") | ||
| similarity([["世界上什么东西最小", "世界上什么东西最小？"]]) | ||
| ''' | ||
| [{'text1': '世界上什么东西最小', 'text2': '世界上什么东西最小？', 'similarity': 0.992725}] | ||
| ''' | ||
| similarity = Taskflow("text_similarity", batch_size=2) | ||
| similarity([["光眼睛大就好看吗", "眼睛好看吗？"], ["小蝌蚪找妈妈怎么样", "小蝌蚪找妈妈是谁画的"]]) | ||
| ''' | ||
| [{'text1': '光眼睛大就好看吗', 'text2': '眼睛好看吗？', 'similarity': 0.74502707}, {'text1': '小蝌蚪找妈妈怎么样', 'text2': '小蝌蚪找妈妈是谁画的', 'similarity': 0.8192149}] | ||
| ''' | ||
| """ | ||
|
|
||
| class TextSimilarityTask(Task): | ||
| """ | ||
| Text similarity task using SimBERT to predict the similarity of sentence pair. | ||
| Args: | ||
| task(string): The name of task. | ||
| model(string): The model name in the task. | ||
| kwargs (dict, optional): Additional keyword arguments passed along to the specific task. | ||
| """ | ||
|
|
||
| def __init__(self, | ||
| task, | ||
| model, | ||
| batch_size=1, | ||
| max_seq_len=128, | ||
| **kwargs): | ||
| super().__init__(task=task, model=model, **kwargs) | ||
| self._static_mode = True | ||
| self._construct_tokenizer(model) | ||
| self._get_inference_model() | ||
| self._batch_size = batch_size | ||
| self._max_seq_len = max_seq_len | ||
| self._usage = usage | ||
|
|
||
| def _construct_input_spec(self): | ||
| """ | ||
| Construct the input spec for the convert dygraph model to static model. | ||
| """ | ||
| self._input_spec = [ | ||
| paddle.static.InputSpec( | ||
| shape=[None, None], dtype="int64", name='input_ids'), | ||
| paddle.static.InputSpec( | ||
| shape=[None], dtype="int64", name='token_type_ids'), | ||
| ] | ||
|
|
||
| def _construct_model(self, model): | ||
| """ | ||
| Construct the inference model for the predictor. | ||
| """ | ||
| self._model = BertModel.from_pretrained(model, pool_act='linear') | ||
| self._model.eval() | ||
|
|
||
| def _construct_tokenizer(self, model): | ||
| """ | ||
| Construct the tokenizer for the predictor. | ||
| """ | ||
| self._tokenizer = BertTokenizer.from_pretrained(model) | ||
|
|
||
| def _check_input_text(self, inputs): | ||
| inputs = inputs[0] | ||
| if not all([isinstance(i, list) and i \ | ||
| and all(i) and len(i) == 2 for i in inputs]): | ||
| raise TypeError( | ||
| "Invalid input format.") | ||
| return inputs | ||
|
|
||
| def _preprocess(self, inputs): | ||
| """ | ||
| Transform the raw text to the model inputs, two steps involved: | ||
| 1) Transform the raw text to token ids. | ||
| 2) Generate the other model inputs from the raw text and token ids. | ||
| """ | ||
| inputs = self._check_input_text(inputs) | ||
| num_workers = self.kwargs[ | ||
| 'num_workers'] if 'num_workers' in self.kwargs else 0 | ||
| lazy_load = self.kwargs[ | ||
| 'lazy_load'] if 'lazy_load' in self.kwargs else False | ||
|
|
||
| examples = [] | ||
|
|
||
| for data in inputs: | ||
| text1, text2 = data[0], data[1] | ||
|
|
||
| text1_encoded_inputs = self._tokenizer( | ||
| text=text1, max_seq_len=self._max_seq_len) | ||
| text1_input_ids = text1_encoded_inputs["input_ids"] | ||
| text1_token_type_ids = text1_encoded_inputs["token_type_ids"] | ||
|
|
||
| text2_encoded_inputs = self._tokenizer( | ||
| text=text2, max_seq_len=self._max_seq_len) | ||
| text2_input_ids = text2_encoded_inputs["input_ids"] | ||
| text2_token_type_ids = text2_encoded_inputs["token_type_ids"] | ||
|
|
||
| examples.append((text1_input_ids, text1_token_type_ids, | ||
| text2_input_ids, text2_token_type_ids)) | ||
|
|
||
| batches = [ | ||
| examples[idx:idx + self._batch_size] | ||
| for idx in range(0, len(examples), self._batch_size) | ||
| ] | ||
|
|
||
| batchify_fn = lambda samples, fn=Tuple( | ||
| Pad(axis=0, pad_val=self._tokenizer.pad_token_id), # text1_input_ids | ||
| Pad(axis=0, pad_val=self._tokenizer.pad_token_type_id), # text1_token_type_ids | ||
| Pad(axis=0, pad_val=self._tokenizer.pad_token_id), # text2_input_ids | ||
| Pad(axis=0, pad_val=self._tokenizer.pad_token_type_id), # text2_token_type_ids | ||
| ): [data for data in fn(samples)] | ||
|
|
||
| outputs = {} | ||
| outputs['data_loader'] = batches | ||
| outputs['text'] = inputs | ||
| self._batchify_fn = batchify_fn | ||
| return outputs | ||
|
|
||
| def _run_model(self, inputs): | ||
| """ | ||
| Run the task model from the outputs of the `_tokenize` function. | ||
| """ | ||
| results = [] | ||
| with static_mode_guard(): | ||
| for batch in inputs['data_loader']: | ||
| text1_ids, text1_segment_ids, text2_ids, text2_segment_ids = self._batchify_fn(batch) | ||
| self.input_handles[0].copy_from_cpu(text1_ids) | ||
| self.input_handles[1].copy_from_cpu(text1_segment_ids) | ||
| self.predictor.run() | ||
| vecs_text1 = self.output_handle[1].copy_to_cpu() | ||
|
|
||
| self.input_handles[0].copy_from_cpu(text2_ids) | ||
| self.input_handles[1].copy_from_cpu(text2_segment_ids) | ||
| self.predictor.run() | ||
| vecs_text2 = self.output_handle[1].copy_to_cpu() | ||
|
|
||
| vecs_text1 = vecs_text1 / (vecs_text1**2).sum(axis=1, | ||
| keepdims=True)**0.5 | ||
| vecs_text2 = vecs_text2 / (vecs_text2**2).sum(axis=1, | ||
| keepdims=True)**0.5 | ||
| similarity = (vecs_text1 * vecs_text2).sum(axis=1) | ||
| results.extend(similarity) | ||
| inputs['result'] = results | ||
| return inputs | ||
|
|
||
| def _postprocess(self, inputs): | ||
| """ | ||
| The model output is tag ids, this function will convert the model output to raw text. | ||
| """ | ||
| final_results = [] | ||
| for text, similarity in zip(inputs['text'], inputs['result']): | ||
| result = {} | ||
| result['text1'] = text[0] | ||
| result['text2'] = text[1] | ||
| result['similarity'] = similarity | ||
| final_results.append(result) | ||
| return final_results |