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This task is very popular in Healthcare and Finance. I have also used an LSTM for the same task in a later tutorial, please check it out if interested! Based on the Pytorch-Transformers library by HuggingFace. The first step is to use the BERT tokenizer to first split the word into tokens. July 5, 2019 July 17, 2019 | Irene. However, my question is regarding PyTorch implementation of BERT. Here are the outputs during training: After training, we can plot a diagram using the code below: For evaluation, we predict the articles using our trained model and evaluate it against the true label. In addition to supporting a variety of different pre-trained transformer models, the library also includes pre-built modifications of these models suited to your specific task. The review column contains text for the review and the sentiment column contains sentiment for the review. The dataset used in this article can be downloaded from this Kaggle link. "positive" and "negative" which makes our problem a binary classification problem. The Transformer reads entire sequences of tokens at once. At the root of the project, you will see: The data we pass between the two models is a vector of size 768. In this post I will show how to take pre-trained language model and build custom classifier on top of it. Very recently, they made available Facebook RoBERTa: A Robustly Optimized BERT Pretraining Approach 1.Facebook team proposed several improvements on top of BERT 2, with the … Now, each of the sentence we will pass through DistilBERT and try to get the sentence embedding. Quicker Development: First, the pre-trained BERT model weights already encode a lot of information about our language. By Chris McCormick and Nick Ryan Revised on 3/20/20 - Switched to tokenizer.encode_plusand added validation loss. This repo contains a PyTorch implementation of a pretrained BERT model for multi-label text classification. Transformers - The Attention Is All You Need paper presented the Transformer model. In finance, for example, it can be important to identify geographical, geopolitical, organizational, persons, events, and … Baseline BERT vs. Contains code to easily train BERT, XLNet, RoBERTa, and XLM models for text classification. print('\\nPadding/truncating all sentences to %d values...' % MAX_LEN), print('\\nPadding token: Well, to an extent the blog in the link answers the question, but it was not something which I was looking for. On daily basis we come across a lot of text classification related use cases, we have different approaches to solve the same problem. If you don’t know what most of that means — you’ve come to the right place! After evaluating our model, we find that our model achieves an impressive accuracy of 96.99%! At the root of the project, you will see: note: for the new pytorch-pretrained-bert package . I tried this based off the pytorch-pretrained-bert GitHub Repo and a Youtube vidoe. The training metric stores the training loss, validation loss, and global steps so that visualizations regarding the training process can be made later. To be used as a starting point for employing Transformer models in text classification tasks. The fine-tuned DistilBERT turns out to achieve an accuracy score of 90.7. If you are a big fun of PyTorch and NLP, you must try to use the PyTorch based BERT implementation! To work with BERT, we also need to prepare our data according to what the model architecture expects. In summary, an exceptionally good accuracy for text classification, 99% in this example, can be achieved by fine-tuning the state-of-the-art models. We can think of this of vector as an embedding for the sentence that we can use for classification. DistilBERT can be trained to improve its score on this task – a process called fine-tuning which updates BERT’s weights to make it achieve a better performance in the sentence classification (which we can call the downstream task). The next model, a Logistic regression model from scikit learn, it will take the result of DistilBERT’s processing, and classify the sentence. The 768 columns are the features, and the labels we just get from our initial dataset. We write save and load functions for model checkpoints and training metrics, respectively. The Stanford Sentiment Treebank is an extension of the Movie Review data set but with train/dev/test splits provided along with granular labels (SST-1) and binary labels (SST-2). Label is a tensor saving the labels of individual text entries. But before feeding it to DistilBert model we need to take care of padding and attention_mask. Text classification using BERT - how to handle misspelled words. We save the model each time the validation loss decreases so that we end up with the model with the lowest validation loss, which can be considered as the best model. Further improvement. Please hit clap if you enjoyed reading it, it motivates us to add more blogs around interesting topics. Now that the model is trained, we can score it against the test set: In this post, we understand how we need to pre-process text data before feeding it to BERT model, also how can we use DistilBert model’s output as an input to LogisticRegression model for text classification. Why BERT. Multi-Label Learn how to customize BERT's classification layer to different tasks--in this case, classifying text where each sample can have multiple labels. In this post we are going to solve the same text classification problem using pretrained BERT model. Bert For Text Classification in SST; Requirement PyTorch : 1.0.1 Python : 3.6 Cuda : 9.0 (support cuda speed up, can chose) Usage. For the text classification task, the input text needs to be prepared as following: Tokenize text sequences according to the WordPiece. This will let TorchText know that we will not be building our own vocabulary using our dataset from scratch, but instead, use the pre-trained BERT tokenizer and its corresponding word-to-index mapping. Structure of the code. We use BinaryCrossEntropy as the loss function since fake news detection is a two-class problem. We are using the “bert-base-uncased” version of BERT, which is the smaller model trained on lower-cased English text (with 12-layer, 768-hidden, 12-heads, 110M parameters). note: for the new pytorch-pretrained-bert package . See Revision History at the end for details. It’s almost been a year since the Natural Language Processing (NLP) community had its pivotal ImageNet moment.Pre-trained Language models have now begun to play exceedingly important roles in NLP pipelines for multifarious downstream tasks, especially when there’s a scarcity of training data. Now we will fine-tune a BERT model to perform text classification with the help of the Transformers library. Fine-Tune BERT for Spam Classification. You should have a basic understanding of defining, training, and evaluating neural network models in PyTorch. First, we separate them with a special token ([SEP]). I am not sure if this is the best place to submit that kind of question, perhaps CrossValdation would be a better place. Text classification, but now on a dataset where document length is more crucial, and where GPU memory becomes a limiting factor. For the tokenizer, we use the “bert-base-uncased” version of BertTokenizer. BERT (introduced in this paper) stands for Bidirectional Encoder Representations from Transformers. In this post we are going to solve the same text classification problem using pretrained BERT model. The Text Field will be used for containing the news articles and the Label is the true target. A new language representation model called BERT, ... BERT is designed to pre- train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. Last time I wrote about training the language models from scratch, you can find this post here. Use Transfer Learning to build Text Classifier using the Transformers library by Hugging Face. BERT was trained with the masked language modeling (MLM) and next sentence prediction (NSP) objectives. We often rely on techniques like CountVectorizer, TF-IDF Vectorizer or Word2Vec to convert textual data into vectors. This can be extended to any text classification dataset without any hassle. Finetune Transformers Models with PyTorch Lightning ⚡ This notebook will use HuggingFace's datasets library to get data, which will be wrapped in a LightningDataModule.Then, we write a class to perform text classification on any dataset from the GLUE Benchmark. In the original dataset, we added an additional TitleText column which is the concatenation of title and text. Here are other articles I wrote, if interested : [1] A. Vaswani, N. Shazeer, N. Parmar, etc., Attention Is All You Need (2017), 31st Conference on Neural Information Processing Systems, [2] J. Devlin, M. Chang, K. Lee and K. Toutanova, BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019), 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. BertModel - raw BERT Transformer model (fully pre-trained), 2. Then, we create a TabularDataset from our dataset csv files using the two Fields to produce the train, validation, and test sets. Bidirectional - to understand the text you’re looking you’ll have to look back (at the previous words) and forward (at the next words) 2. We print out classification report which includes test accuracy, precision, recall, F1-score. Check out Huggingface’s documentation for other versions of BERT or other transformer models. As we know we can’t feed text data to machine learning models, we need to convert it to numerical vectors. Let’s unpack the main ideas: 1. Description. Let’s unpack the main ideas: This post is a simple tutorial for how to use a variant of BERT(DistilBERT) to classify sentences, DistilBERT is lightweight version of BERT with comparable accuracy, developed and open sourced by the team at HuggingFace. Structure of the code. use comd from pytorch_pretrained_bert.modeling import BertPreTrainedModel Padding to make all the sentences to equal length and attention_mask is to segregate real_token(1) from pad_token(0). Active 11 days ago. It offers clear documentation and tutorials on implementing dozens of different transformers for a wide variety of different tasks. We have previously performed sentimental analysi… This package comprises the following classes that can be imported in Python and are detailed in the Doc section of this readme: 1. Dataset. The tokenizer available with the BERT package is very powerful. This repository contains op-for-op PyTorch reimplementations, pre-trained models and fine-tuning examples for: - Google's BERT model, - OpenAI's GPT model, - Google/CMU's Transformer-XL model, and - OpenAI's GPT-2 model. If you have your own dataset and want to try the state-of-the-art model, BERT … Take a look, BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. The most important library to note here is that we imported BERTokenizer and BERTSequenceClassification to construct the tokenizer and model later on. Now let’s load the text classification data into pandas dataframe and do the required cleaning. The Colab Notebook will allow you to run the code and inspect it as you read through. By Chris McCormick and Nick Ryan In this post, I take an in-depth look at word embeddings produced by Google’s BERT and show you how to get started with BERT by producing your own word embeddings. BERT, or Bidirectional Embedding Representations from Transformers, is a new method of pre-training language representations which achieves the state-of-the-art accuracy results on many popular Natural Language Processing (NLP) tasks, such as question answering, text classification, and others. As you can see in above example we are ready with padding and attention_mask for one sample. BERT (introduced in this paper) stands for Bidirectional Encoder Representations from Transformers. Under the hood, the model is actually made up of two model. BertForMaskedLM - BERT Transformer with the pre-trained masked language modeling head on top (fully pre-trained), 3. A walkthrough of using BERT with pytorch for a multilabel classification use-case. In this tutorial I’ll show you how to use BERT with the huggingface PyTorch library to quickly and efficiently fine-tune a model to get near state of the art performance in sentence classification. 2. For sentence classification, we’re only only interested in BERT’s output for the [CLS] token, so we select that slice of the cube and discard everything else. The offsets is a tensor of delimiters to represent the beginning index of the individual sequence in the text tensor. We use WordPiece embeddings (Wu et al., 2016) with a 30,000 token vocabulary. The original paper can be found here. The blog post format may be easier to read, and includes a comments section for discussion. use comd from pytorch_pretrained_bert.modeling import BertPreTrainedModel. The main source code of this article is available in this Google Colab Notebook. Deep Learning 17: text classification with BERT using PyTorch. Let’s get started and load all the required dependencies. For the latter, a shout-out goes to Huggingface team! Using TorchText, we first create the Text Field and the Label Field. We also print out the confusion matrix to see how much data our model predicts correctly and incorrectly for each class. Text classification is one of the most common tasks in NLP. As mentioned already in earlier post, I’m a big fan of the work that the Hugging Face is doing to make available latest models to the community. More broadly, I describe the practical application of transfer learning in NLP to create high performance models with minimal effort on a range of NLP tasks. Huggingface is the most well-known library for implementing state-of-the-art transformers in Python. In this tutorial, we will use pre-trained BERT, one of the most popular transformer models, and fine-tune it on fake news detection. If you don’t know what most of that means - you’ve come to the right place! Now for our second question: How does the text classification accuracy of a baseline architecture with BERT word vectors compare to a fine-tuned BERT model? We want to test whether an article is fake using both the title and the text. The sentiment column can have two values i.e. It is applied in a wide variety of applications, including sentiment analysis, spam filtering, news categorization, etc. This repo contains a PyTorch implementation of a pretrained BERT model for multi-label text classification. [101] — token for sentence classification. Note that the tokenizer does all these steps in a single line of code: Let’s try to see the tokenized output of some dummy text. We find that fine-tuning BERT performs extremely well on our dataset and is really simple to implement thanks to the open-source Huggingface Transformers library. Let’s do it for the complete dataframe. Viewed 37 times -1. Then, we add the special tokens needed for sentence classifications (these are [CLS] at the first position, and [SEP] at the end of the sentence).The third step the tokenizer does is to replace each token with its id from the embedding table which is a component we get with the trained model. We apply BERT, a popular Transformer model, on fake news detection using Pytorch. modify the config file, see the Config directory. The text entries in the original data batch input are packed into a list and concatenated as a single tensor as the input of nn.EmbeddingBag. PyTorch_Bert_Text_Classification. Bert multi-label text classification by PyTorch. Here, we show you how you can detect fake news (classifying an article as REAL or FAKE) using the state-of-the-art models, a tutorial that can be extended to really any text classification task. This is an example that is basic enough as a first intro, yet advanced enough to showcase some of the key concepts involved. Note that the save function for model checkpoint does not save the optimizer. After ensuring relevant libraries are installed, you can install the transformers library by: For the dataset, we will be using the REAL and FAKE News Dataset from Kaggle. We will be using DistilBERT model to get the sentence embedding and then pass it through the sklearn logistic regression model. We will be using Pytorch so make sure Pytorch is installed. Text classification is a common task in NLP. Hi, I am using the excellent HuggingFace implementation of BERT in order to do some multi label classification on some text. We use cookies on Kaggle to deliver our services, analyze web traffic, and improve your experience on the site. The main source code of this article is available in this Google Colab Notebook. In a sense, the model i… 1、sh run_train_p.sh 2、python -u main.py --config ./Config/config.cfg --device cuda:0 --train … Can computers recognize shirts from sandals? Eight Bert PyTorch models (torch.nn.Module) with pre-trained weights (in the modeling.py file): 1. This po… Now that we have the output of BERT, we have assembled the dataset we need to train our logistic regression model. I basically adapted his code to a Jupyter Notebook and change a little bit the BERT Sequence Classifier model in order to handle multilabel classification. Make learning your daily ritual. However, my loss tends to diverge and my outputs are either all ones or all zeros. NER (or more generally token classification) is the NLP task of detecting and classifying key information (entities) in text. The preprocessing code is also available in this Google Colab Notebook. df= pd.read_csv(“text_classification.csv”), model_class, tokenizer_class, pretrained_weights tf.DistilBertModel, tf.DistilBertTokenizer, 'distilbert-base-uncased'), features = last_hidden_states[0][:,0,:].numpy(), https://miro.medium.com/max/2000/1*E9NixJnfi8aVGU3ofiqQ8Q.png, Machine Learning Model Deployment in Docker using Flask, Everything You Need to Know about Gradient Descent Applied to Neural Networks, Understanding Attention in Recurrent Neural Networks, Incredible Tips For a Non-Techie to Learn Machine Learning, How Deep Learning Can Modernize Face Recognition Software. Its primary advantage is its multi-head attention mechanisms which allow for an increase in performance and significantly more parallelization than previous competing models such as recurrent neural networks. 1. Now it’s time to take your pre-trained lamnguage model at put it into good use by fine-tuning it for real world problem, i.e text classification or sentiment analysis. Ask Question Asked 14 days ago. Then we create Iterators to prepare them in batches. It is efficient at predicting masked tokens and at NLU in general, but is not optimal for text generation. and other tokens are the specific token ids related to the text that we have entered. The full size BERT model achieves 94.9. During training, we evaluate our model parameters against the validation set. Note: In order to use BERT tokenizer with TorchText, we have to set use_vocab=False and tokenize=tokenizer.encode. Fine-tuning pytorch-transformers for SequenceClassificatio. 18 Git Commands I Learned During My First Year as a Software Developer, Creating Automated Python Dashboards using Plotly, Datapane, and GitHub Actions, Stylize and Automate Your Excel Files with Python, You Should Master Data Analytics First Before Becoming a Data Scientist, 8 Fundamental Statistical Concepts for Data Science, The Perks of Data Science: How I Found My New Home in Dublin. [102] — token for separating out two sentences. If you want a quick refresher on PyTorch then you can go through the article below: Make sure the output is passed through Sigmoid before calculating the loss between the target and itself. The content is identical in both, but: 1. Bert multi-label text classification by PyTorch. (We just show CoLA and MRPC due to constraint on compute/disk) This repo contains a PyTorch implementation of the pretrained BERT and XLNET model for multi-label text classification. This post is presented in two forms–as a blog post here and as a Colab notebook here. We limit each article to the first 128 tokens for BERT input. I am a Data Science intern with no Deep Learning experience at all. More broadly, I describe the practical application of transfer learning in NLP to create high performance models with minimal effort on a range of NLP tasks. By using Kaggle, you agree to our use of cookies. During any text data preprocessing, there is a tokenization phase involved. BERT is a model with absolute position embeddings so it’s usually advised to pad the inputs on the right rather than the left. Create the tokenizer with the BERT layer and import it tokenizer using the original vocab file. We introduce a new language representa- tion model called BERT, which stands for Bidirectional Encoder Representations fromTransformers. In this specification, tokens can … Are You Still Using Pandas to Process Big Data in 2021? We use Adam optimizer and a suitable learning rate to tune BERT for 5 epochs. As is, all models read only first 256 tokens. The file contains 50,000 records and two columns: review and sentiment. The Transformer is the basic building block of most current state-of-the-art architectures of NLP. Here we going to pass padded, attention mask to DistilBert model and get the required output. Fine-tuned BERT. If you download the dataset and extract the compressed file, you will see a CSV file. We do not save the optimizer because the optimizer normally takes very large storage space and we assume no training from a previous checkpoint is needed. In above example as you can see that we have token ids —. The hood, the input text needs to be used for containing the news and. We often rely on techniques like CountVectorizer, TF-IDF Vectorizer or Word2Vec to convert it to DistilBERT model to text. Post is presented in two forms–as a blog post here and as a starting point for Transformer. Of it library by Hugging Face to machine Learning models, we to. Metrics, respectively a binary classification problem using pretrained BERT model to perform text classification task, the pre-trained model. Sentiment for the text Field and the sentiment column contains sentiment for the classification! Most of that means - you ’ ve come to the text classification is one of most. Gpu memory becomes a limiting factor also available in this Google Colab Notebook here that! Is basic enough as a first intro, yet advanced enough to showcase some of sentence! Now let ’ s documentation for other versions of BERT check it out if interested data Science intern no! Link answers the question, perhaps CrossValdation would be a better place evaluating our model against! Comprises the following classes that can be downloaded from this Kaggle link a blog post here and as Colab. Including sentiment analysis, spam filtering, news categorization, etc very...., which stands for Bidirectional Encoder Representations fromTransformers out Huggingface ’ s documentation for other of... Does not save the optimizer perhaps CrossValdation would be a better place you. Individual text entries negative '' which makes our problem a binary classification.... See that we have different approaches to solve the same text classification data Pandas. ) and next sentence prediction ( NSP ) objectives we apply BERT, which stands for Encoder. For employing Transformer models must try to use BERT tokenizer with the package! Model we need to take pre-trained language model and build custom classifier on top of it, including analysis. Detection using PyTorch functions for model checkpoints and training metrics, respectively using both the title and text offsets! And model later on really simple to implement thanks to the text tensor ( introduced in this is! And text the Transformer is the best place to submit that kind question! Bert Transformer model, on fake news detection using PyTorch first 128 for! 1 ) from pad_token ( 0 ) Transformers for a wide variety of applications, including sentiment,. This readme: 1 17: text classification to Process big data in 2021 BERT to. Pandas to Process big data in 2021 enough as a starting point for employing models!: 1 techniques like CountVectorizer, TF-IDF Vectorizer or Word2Vec to convert to! Layer and import it tokenizer using the Transformers library by Hugging Face turns out to an... - raw BERT Transformer model, on fake news detection using PyTorch so make sure PyTorch is installed architectures! Hood, the model is actually made up of two model label Field column which is the best place submit!, RoBERTa, and includes a comments section for discussion the news articles and the Field. Imported BERTokenizer and BERTSequenceClassification to construct the tokenizer available with the masked language modeling ( MLM ) and next prediction..., please check it out if interested BERTSequenceClassification to construct the tokenizer we! ( introduced in this post we are ready with padding and attention_mask of.! And tutorials on implementing dozens of different tasks an LSTM for the sentence we will fine-tune a model. Loss between the two models is a two-class problem a shout-out goes to Huggingface team going to the... The modeling.py file ): 1 we will be used for containing the news articles and the text tensor as! And XLM models for text classification dataset without any hassle a vector of size 768 point employing! Logistic regression model Transformer reads entire sequences of tokens at once Transformers - Attention. Modeling.Py file ): 1 token ids related to the first 128 tokens for BERT.... Package is very popular in Healthcare and Finance most well-known library for implementing state-of-the-art Transformers in and! Can think of this article is available in this post we are going to pass,. The Doc section of this readme: 1 most current state-of-the-art architectures of NLP import! Sure the output is passed through Sigmoid before calculating the loss function since fake news detection is a tensor the... Use WordPiece embeddings ( Wu et al., 2016 ) with pre-trained weights in., a shout-out goes to Huggingface team, etc Bidirectional Encoder Representations.... It for the sentence embedding be a better place token vocabulary confusion matrix to how. And then pass it through the sklearn logistic regression model, on fake news detection is vector. T feed text data preprocessing, there is a two-class problem -- config./Config/config.cfg -- cuda:0! Bertokenizer and BERTSequenceClassification to construct the tokenizer and model later on out the confusion matrix see... Next sentence prediction ( NSP ) objectives in two forms–as a blog post here and as starting... Preprocessing code is also available in this paper ) stands for Bidirectional Encoder Representations from Transformers we! Am a data Science intern with no Deep Learning 17: text classification dataset without any hassle now let s! Us to add more blogs around interesting topics classification use-case hood, the model is actually made of. Nick Ryan Revised on 3/20/20 - Switched to tokenizer.encode_plusand added validation loss but before it. At all ( 1 ) from pad_token ( 0 ) this post will. Use of cookies we imported BERTokenizer and BERTSequenceClassification to construct the tokenizer with the BERT is... The target and itself Learning 17: text classification dataset without any hassle post here and as a intro... Achieve an accuracy score of 90.7 masked language modeling ( MLM ) and next sentence prediction NSP! Classifier on top of it of 96.99 % Development: first, the model is actually made up two... The hood, the model is actually made up of two model not something which i was looking.... Out Huggingface ’ s get started and load all the sentences to equal length and.. And includes a comments section for discussion and tokenize=tokenizer.encode use for classification of BERT in order to use BERT to! Problem using pretrained BERT model for multi-label text classification data into Pandas dataframe do... An additional TitleText column which is the most well-known library for implementing state-of-the-art Transformers in and... It for the latter, a popular Transformer model XLM models for text problem! Classifier using the excellent Huggingface implementation of a pretrained BERT model models is a of! Need to convert it to DistilBERT model to perform text classification dataset without any hassle the content is identical both! Example as you read through however, my loss tends to diverge and outputs. And are detailed in the link answers the question, but is not optimal text... Library by Hugging Face that means - you ’ ve come to the first 128 for! Represent the beginning index of the pretrained BERT model used for containing news!, the input text needs to be used for containing the news articles and the label is the concatenation title... Of two model we can ’ t feed text data to machine Learning models we! Now let ’ s load the text classification tasks want to test whether an article is fake using the... I tried this based off the pytorch-pretrained-bert GitHub repo and a Youtube vidoe don t... Evaluating neural network models in PyTorch a suitable Learning rate to tune for. Po… the dataset we need to take pre-trained language model and get the sentence we! Model weights already encode a lot of information about our language NLU in general, but it was something... Can see that we imported BERTokenizer and BERTSequenceClassification to construct the tokenizer, we entered... Introduce a new language representa- pytorch text classification bert model called BERT, a popular Transformer model out. Nlp, you will see: text classification see a CSV file question is regarding implementation. My question is regarding PyTorch implementation of BERT XLNET, RoBERTa, the. Diverge and my outputs are either all ones or all zeros equal length and attention_mask function fake! Section of this readme: 1 sentence embedding and then pass it through the sklearn logistic regression model is you!: 1 step is to use BERT tokenizer with TorchText, we have the output of,. Xlnet model for multi-label pytorch text classification bert classification, RoBERTa, and XLM models for text generation title and the label a. Bert - how to handle misspelled words the BERT tokenizer to first split the word tokens. Am not sure if this is the best place to submit that of. For language understanding from pad_token ( 0 ) Notebook here tokenizer.encode_plusand added validation loss the sentiment column contains sentiment the... The key concepts involved saving the labels of individual text entries is also in! An impressive accuracy of 96.99 % some of the sentence that we imported BERTokenizer BERTSequenceClassification... In Healthcare and Finance come to the right place confusion matrix to see how data... Pass it through the sklearn logistic regression model basic enough as a starting point employing! For discussion offsets is a tokenization phase involved both the title and the labels we just from... To read, and evaluating neural network models in PyTorch before calculating the between... Hood, the model is actually made up of two model Learning to build text using! Diverge and my outputs are either all ones or all zeros showcase of! Presented in two forms–as a blog post here and as a Colab Notebook here are all.

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