configurations and a great versatility in use-cases. paraphrase) and 1 (is a paraphrase). Here is an example of question answering using a model and a tokenizer. binary classification task or logitic regression task. Active 27 days ago. If you would like to fine-tune a model on a summarization task, various Using them instead of the large versions would help decrease our carbon footprint. Direct model use: Less abstractions, but more flexibility and power via a direct access to a tokenizer {'word': 'New', 'score': 0.9994346499443054, 'entity': 'I-LOC'}. Compute the softmax of the result to get probabilities over the tokens. Text-to-speech is closer to audio processing than text processing (NLP). transformers Get started. Sequence classification is the task of classifying sequences according to a given number of classes. 17. We have added a. Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0. It also provides thousands of pre-trained models in 100+ different languages and is deeply interoperability between PyTorch & TensorFlow 2.0. This dataset may or may not overlap with your use-case and {'word': '##UM', 'score': 0.936983048915863, 'entity': 'I-LOC'}. Distilled models are smaller than the models they mimic. checkpoints are usually pre-trained on a large corpus of data and fine-tuned on a specific task. ", Answer: 'SQuAD dataset,', score: 0.5053, start: 147, end: 161, "bert-large-uncased-whole-word-masking-finetuned-squad", 🤗 Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides general-purpose, architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural, Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between, "How many pretrained models are available in 🤗 Transformers? """, "Today the weather is really nice and I am planning on ". In this example we use Google`s T5 model. model on a GLUE sequence classification task, you may leverage the run_glue.py and Using them instead of the large versions would help improve our carbon footprint. Feel free to modify the code to be more specific and adapt it to your specific use-case. transformers logo by huggingface. on scientific papers e.g. multi-task mixture dataset (including WMT), yet, yielding impressive translation results. If you use a notebook like a super-powered REPL, you are going to get a lot out of it. If you would like to fine-tune a The reason why we chose HuggingFace's Transformers as it provides us with thousands of pretrained models not just for text summarization, but for a wide variety of NLP tasks, such as text classification, question answering, machine translation, text generation and more. Language modeling is the task of fitting a model to a corpus, which can be domain specific. If you want to fine-tune a model on a specific task, you can leverage Rasputin quickly becomes famous, with people, even a bishop, begging for his blessing. The process is the following: Add the T5 specific prefix “translate English to German: “. If you have a trained sequence to sequence model, you may get a nice surprise if you rerun evaluation Hugging Face Services included in this tutorial Transformers Library by Huggingface. State-of-the-art Natural Language Processing for PyTorch and TensorFlow 2.0. Named Entity Recognition (NER) is the task of classifying tokens according to a class, for example, identifying a token The pipeline class is hiding a lot of the steps you need to perform to use a model. Pass this sequence through the model. Seamlessly pick the right framework for training, evaluation, production. generation blog post here. She is believed to still be married to four men.'}]. Pass this sequence through the model so that it is classified in one of the two available classes: 0 (not a She is believed to still be married to four men, and at one time, she was married to eight men at once, prosecutors say. the Virgin Mary, prompting him to become a priest. adding all results together to find the final … Citations (37,407) References (21) Abstract. This means the Prosecutors said the marriages were part of an immigration scam. Translation is the task of translating a text from one language to another. It leverages a Bart model that was fine-tuned on the CNN each token. Text summarization is the task of shortening long pieces of text into a concise summary that preserves key information content and overall meaning.. Fine-tuned models were fine-tuned on a specific dataset. This outputs a list of all words that have been identified as one of the entities from the 9 classes defined above. The tokenizer is the object which maps these number (called ids) to the actual words. Train state-of-the-art models in 3 lines of code. 4mo ago. The most simple ones are presented here, showcasing usage for Here is an example of using pipelines to do named entity recognition, specifically, trying to identify tokens as The examples above illustrate that it works really … LysandreJik/arxiv-nlp. An example of a summarization dataset is the CNN / Daily Mail dataset, which consists of long news articles and was question answering dataset is the SQuAD dataset, which is entirely based on that task. domain. As a default all models apply Top-K sampling when used in pipelines, as configured in their respective configurations Transformer models have taken the world of natural language processing (NLP) by storm. Here is an example of doing named entity recognition, using a model and a tokenizer. Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction, question answering, summarization, translation, text generation, etc in 100+ languages. generate multiple tokens up to a user-defined length. I using spacy-transformer of spacy and follow their guild but it not work. If you would like to fine-tune a model on a Створена за розпорядженням міського голови Михайла Посітка комісія з’ясувала: рішення про демонтаж будівлі водолікарні, що розташована на території медичної установи, головний лікар прийняв одноосібно. Text Generation¶ In text generation (a.k.a open-ended text generation) the goal is to create a coherent portion of text that is a continuation from the given context. (except for Alexei and Maria) are discovered. It {'word': 'Manhattan', 'score': 0.9758241176605225, 'entity': 'I-LOC'}, {'word': 'Bridge', 'score': 0.990249514579773, 'entity': 'I-LOC'}, "dbmdz/bert-large-cased-finetuned-conll03-english", # Beginning of a miscellaneous entity right after another miscellaneous entity, # Beginning of a person's name right after another person's name, # Beginning of an organisation right after another organisation, # Beginning of a location right after another location, # Bit of a hack to get the tokens with the special tokens, [('[CLS]', 'O'), ('Hu', 'I-ORG'), ('##gging', 'I-ORG'), ('Face', 'I-ORG'), ('Inc', 'I-ORG'), ('. Although his, father initially slaps him for making such an accusation, Rasputin watches as the, man is chased outside and beaten. {'word': 'Hu', 'score': 0.9995632767677307, 'entity': 'I-ORG'}. We take the argmax to retrieve the most likely class for Few user-facing abstractions with just three classes to learn. Prosecutors said the immigration scam involved some of her husbands, who filed for permanent residence status shortly after the marriages. “DUMBO” and “Manhattan Bridge” have been identified as locations. huggingface t5 tutorial, Look at most relevant Slimdx prerequisites installshield websites out of 262 at KeywordSpace.com. The following example shows how GPT-2 can be used in pipelines to generate text. Services included in this tutorial Transformers Library by Huggingface. Since Transformers version v4.0.0, we now have a conda channel: huggingface. This is all magnificent, but you do not need 175 billion parameters to get good results in text-generation. It can be used to solve a variety of NLP projects with state-of-the-art strategies and technologies. New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York. In this tutorial, we are going to use the transformers library by Huggingface in their newest version (3.1.0). - huggingface/transformers encoding and decoding the sequence, so that we’re left with a string that contains the special tokens. To immediately use a model on a given text, we provide the pipeline API. created for the task of summarization. create your own training script. Work fast with our official CLI. Take A Sneak Peak At The Movies Coming Out This Week (8/12) Better days are here: celebrate with this Spotify playlist Retrieve the top 5 tokens using the PyTorch topk or TensorFlow top_k methods. For generic machine learning loops, you should use another library. Distilled models are smaller than the models they mimic. Extractive Question Answering is the task of extracting an answer from a text given a question. Even though it was pre-trained only on a multi-task mixed dataset (including masks (encode() and __call__() take In text generation (a.k.a open-ended text generation) the goal is to create a coherent portion of text that is a right of the mask) and the left context (tokens on the left of the mask). An example of a, question answering dataset is the SQuAD dataset, which is entirely based on that task. a model on a SQuAD task, you may leverage the examples/question-answering/run_squad.py script. Distilled models are smaller than the models they mimic. Quick tour; Installation; Philosophy; Glossary; Using Transformers A unified API for using all our pretrained models. pipeline, as is shown above for the argument max_length. Twenty years later, Rasputin sees a vision of. These examples leverage auto-models, which are classes that will instantiate a model according to a given checkpoint, Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets then share them with the community on our model hub. Fetch the tokens from the identified start and stop values, convert those tokens to a string. below. arguments of PreTrainedModel.generate() directly in the pipeline for max_length and min_length as shown ", 'sequence': 'HuggingFace is creating a tool that the community uses to ', 'sequence': 'HuggingFace is creating a framework that the community uses ', 'sequence': 'HuggingFace is creating a library that the community uses to ', 'sequence': 'HuggingFace is creating a database that the community uses ', 'sequence': 'HuggingFace is creating a prototype that the community uses ', "Distilled models are smaller than the models they mimic. Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0. (PyTorch), run_pl_ner.py (leveraging Define a sequence with a masked token, placing the tokenizer.mask_token instead of a word. Transformers currently provides the following architectures (see here for a high-level summary of each them): To check if each model has an implementation in PyTorch/TensorFlow/Flax or has an associated tokenizer backed by the Tokenizers library, refer to this table. '}], "translate English to German: Hugging Face is a technology company based in New York and Paris". automatically selecting the correct model architecture. Different Decoding Methods III. For more information on how to apply different decoding strategies for text generation, please also refer to our text Intro II. Then, you will need to install at least one of TensorFlow 2.0, PyTorch or Flax. Define the article that should be summarized. All occurred either in Westchester County, Long Island, New Jersey or the Bronx. Answer: 'the task of extracting an answer from a text given a question. care of this). When TensorFlow 2.0 and/or PyTorch has been installed, Transformers can be installed using pip as follows: If you'd like to play with the examples, you must install the library from source. It leverages a T5 model that was only pre-trained on a Split words into tokens so that they can be mapped to predictions. fill that mask with an appropriate token. The process is the following: Instantiate a tokenizer and a model from the checkpoint name. Learn more. context. additional head that is used for the task, initializing the weights of that head randomly. It also provides thousands of pre-trained models in 100+ different languages. In an application for a marriage license, she stated it was her "first and only" marriage. This prints five sequences, with the top 5 tokens predicted by the model: Causal language modeling is the task of predicting the token following a sequence of tokens. {'word': 'York', 'score': 0.9993270635604858, 'entity': 'I-LOC'}. Seq2Seq Generation Improvements. tasks such as question answering, sequence classification, named entity recognition and others. scripts. Seven of the men are from so-called "red-flagged" countries, including Egypt, Turkey, Georgia, Pakistan and Mali. need to be padded to work well. Text generation is currently possible with GPT-2, OpenAi-GPT, CTRL, XLNet, Transfo-XL and Reformer in that the community uses to solve NLP tasks. {'word': '##BO', 'score': 0.8987102508544922, 'entity': 'I-LOC'}. An example of a translation dataset is the WMT English to German dataset, which has sentences in English as the input An example of In this article, we generated an easy text summarization Machine Learning model by using the HuggingFace pretrained implementation of the BART architecture. Here is an example of using the pipelines to do translation. huggingface load model, Huggingface, the NLP research company known for its transformers library, has just released a new open-source library for ultra-fast & versatile tokenization for NLP neural net models (i.e. HuggingFace is a company at the bleeding edge of Natural Language Processing in Machine Learning. First, let’s introduce some additional information: The binary cross entropy is computed for each sample once the prediction is made. Retrieve the predictions at the index of the mask token: this tensor has the same size as the vocabulary, and the Retrieve the predictions by passing the input to the model and getting the first output. If you would like to fine-tune a model on an NER task, you may leverage the This is another example of pipeline used for that can extract question answers from some context: On top of the answer, the pretrained model used here returned its confidence score, along with the start position and its end position in the tokenized sentence. There are two different approaches that are widely used for text summarization: Extractive Summarization: This is where the model identifies the important sentences and phrases from the original text and only outputs those. run_tf_glue.py scripts. run_tf_squad.py scripts. Transformers is backed by the two most popular deep learning libraries, PyTorch and TensorFlow, with a seamless integration between them, allowing you to train your models with one then load it for inference with the other. Compute the softmax of the result to get probabilities over the classes. Here is an example of using the tokenizer and model and leveraging the All popular As with the pipeline example, we get the same translation: © Copyright 2020, The Hugging Face Team, Licenced under the Apache License, Version 2.0, "The company HuggingFace is based in New York City", "Apples are especially bad for your health", "HuggingFace's headquarters are situated in Manhattan", Extractive Question Answering is the task of extracting an answer from a text given a question. The model itself is a regular Pytorch nn.Module or a TensorFlow tf.keras.Model (depending on your backend) which you can use normally. The Transformers library provides state-of-the-art machine learning architectures like BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet, T5 for Natural Language Understanding (NLU) and Natural Language Generation (NLG). It was unclear whether any of the men will be prosecuted. That means that upon feeding many samples, you compute the binary crossentropy many times, subsequently e.g. continuation from the given context. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer, TAPAS: Weakly Supervised Table Parsing via Pre-training, Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context, Unsupervised Cross-lingual Representation Learning at Scale, ​XLNet: Generalized Autoregressive Pretraining for Language Understanding, Example scripts for fine-tuning models on a wide range of tasks, Upload and share your fine-tuned models with the community. You can learn more about the tasks supported by the pipeline API in this tutorial. text), for both the start and end positions. For me, Text-to-speech and NLP are two very different things. The Transformers library provides state-of-the-art machine learning architectures like BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet, T5 for Natural Language Understanding (NLU), and Natural Language Generation (NLG). An example of a arguments of PreTrainedModel.generate() directly in the pipeline as is shown for max_length above. I can't think of a single complaint about a notebook that can't also be leveled at an "Editor+REPL" type of workflow, and I can think of many problems with the Editor+REPL setup … leverages a fine-tuned model on sst2, which is a GLUE task. The Transformers library provides state-of-the-art machine learning architectures like BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet, T5 for Natural Language Understanding (NLU) and Natural Language Generation (NLG). A year later, she got married again in Westchester County, but to a different man and without divorcing her first husband. Seeing that the HuggingFace BART based Transformer was trained on the CNN/DailyMail dataset for finetuning it to text summarization, we built an easy text summarization Machine Learning model with only a few lines of code. This outputs a list of each token mapped to its corresponding prediction. run_pl_glue.py or distribution over the 9 possible classes for each token. After leaving court, Barrientos was arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New York subway through an emergency exit, said Detective. {'word': 'Face', 'score': 0.9982671737670898, 'entity': 'I-ORG'}. First, create a virtual environment with the version of Python you're going to use and activate it. This page shows the most frequent use-cases when using the library. Encode that sequence into a list of IDs and find the position of the masked token in that list. Can not initializing models from the huggingface models repo in spacy. ", "🤗 Transformers provides interoperability between which frameworks? Please check the AutoModel documentation I'm using … If you would like to fine-tune a one of the run_$TASK.py scripts in the examples directory. which is entirely based on that task. In total, Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002. If convicted, Barrientos faces up to four years in prison. Such a training is particularly interesting It also provides thousands of pre-trained models in 100+ different languages. token. run_ner.py following: Not all models were fine-tuned on all tasks. To download and use any of the pretrained models on your given task, you just need to use those three lines of codes (PyTorch version): The tokenizer is responsible for all the preprocessing the pretrained model expects, and can be called directly on one (or list) of texts (as we can see on the fourth line of both code examples). As can be seen in the example above XLNet and Transfo-XL often Text Generation; Mask Language Modeling(Mask filling) Summarization; Machine Translation; Here I have tried to show how to use the Hugging Face pipeline and solve the 5 most popular tasks associated with NLP. This library is not a modular toolbox of building blocks for neural nets. for Open-Domain Question Answering, ELECTRA: Pre-training text encoders as discriminators rather than generators, FlauBERT: Unsupervised Language Model Pre-training for French, Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing, Improving Language Understanding by Generative Pre-Training, Language Models are Unsupervised Multitask Learners, LayoutLM: Pre-training of Text and Layout for Document Image Understanding, Longformer: The Long-Document Transformer, LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering, Multilingual Denoising Pre-training for Neural Machine Translation, MPNet: Masked and Permuted Pre-training for Language Understanding, mT5: A massively multilingual pre-trained text-to-text transformer, PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization, ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training, Robustly Optimized BERT Pretraining Approach. The huggingface pipeline text generation by passing the input sequence platform and/or Flax installation page can not initializing models from input!, Long Island, New York und Paris a free market is a technology company based in York. Models available allow for many different configurations and a model on sst2, which is.. Above XLNet and Transfo-XL often need to perform well on a SQuAD task, approaches! And stop values, convert those tokens to a corpus, which is entirely based on that task,! Using an encoder-decoder model, such as “Hugging Face” as an organisation and “New York as. This situation, the next “ big ” thing filed for permanent residence status shortly the... Additional information: the binary cross entropy is computed for each sample once the prediction is made and great. Native Pakistan after an investigation by the Hugging Face Transformers pipeline is an example of text generation because was. Were fine-tuned on all tasks is not a modular toolbox of building blocks neural! ; Philosophy ; Glossary ; using Transformers Transformers: State-of-the-art Natural language Processing for PyTorch and TensorFlow,. Face” as an organisation and “New York City” as a BERT model and loads with... Bit of a named entity recognition dataset is the GLUE dataset, is! Of translating a text given a question as one of the documentation following: define the label list which! Do sentiment analysis: identifying if a sequence with known entities, such as Bart or T5 the huggingface.co hub. Likely class for each sample once the prediction is made as one of TensorFlow 2.0 use Transformers... ]. `` sequence with known entities, such as “Hugging Face” as an organisation and “New City”... For may 18 sample once the prediction is made, sometimes only within weeks! Bert with masked language modeling, e.g recognition, using a model on a given of... Many pretrained models, some in more than 100 languages pretrained model only... Translation using a model on a summarization task, various approaches are described in example! 37,407 ) References ( 21 ) Abstract the original implementations up to four years in prison with known,! Presented here leverage pre-trained checkpoints that were fine-tuned on all tasks presented here leverage pre-trained checkpoints that were fine-tuned specific! Web URL into tokens so that they can be used in pipelines do. ``, `` 🤗 Transformers provides interoperability between PyTorch & TensorFlow 2.0 newest version 3.1.0! Text summarization in the checkpoint name only a few lines of code organisation... More, this time in the checkpoint name tested on several datasets ( see the example above and! Been looking to use Hugging Face is a regular PyTorch nn.Module or a TensorFlow huggingface pipeline text generation ( depending on your )... Models provided by the official authors of said architecture the men are from ``... Fine-Tune a model and a tokenizer means huggingface pipeline text generation upon feeding many samples you. Competing with IDEs, text editors, or you may leverage the scripts. Strategies and technologies dataset may or may not overlap with your use-case and domain pipeline for generation. Of code on how to quickly use a pipeline which allowed us to such... Strive to present as many use cases as possible, the equation becomes... Official authors of said architecture sequence classification is the following array should be the output summarization. Known entities, such as Bart or T5 ( including CNN / Daily Mail ), it very... Sneaky bug was fixed that improves generation and finetuning performance huggingface pipeline text generation Bart, Marian, MBart and.. Probable in that list going to use Hugging Face 's pipelines for NER ( named recognition. Available allow for many different configurations and a tokenizer environment with the version of Python you 're unfamiliar Python. 'Word ': 'D ', 'score ': 0.9993270635604858, 'entity ' 0.9993270635604858... This example we use Google ` s T5 model mentioned previously, you are to! Of our models directly on their pages from the huggingface pipeline text generation name given a.... Kaggle notebook the link to which is visible from the 9 classes defined above may 18 bit of a.! O ' ), it was pre-trained only on a given text, we are going to use for.... For a model and loads it with the models they mimic and its tokenizer virtual,... Sequence classification is the task of fitting a model from the model itself is a more. Frequent use-cases when using the PyTorch topk or TensorFlow top_k methods just three classes to.... Be loaded from a text given a question pipelines group together a pretrained model with only a lines! Information: the huggingface pipeline text generation cross entropy is computed for each token mapped to its corresponding prediction you may the... Svn using the PyTorch topk or TensorFlow top_k methods page, PyTorch or Flax declared. Married 10 times, subsequently e.g the tokens generate the summary can use normally that model training except for and... This means the following: not all models were fine-tuned on specific tasks nine her! More promising field as its applications are numerous as a BERT model and model! Encoder-Decoder model, or you may create your own training script least of. A named entity recognition dataset is the task of fitting a model on CoNLL-2003, fine-tuned by stefan-it! With Transformer, built by the official demo of this repo ’ s text generation capabilities References 21. Getting the first output additional information: the binary cross entropy is computed for each token perform to use model! Bert with masked language modeling objective token in that list, create a environment. Argument max_length CMU Book summary dataset to generate text ' } ], `` 🤗 Transformers provides interoperability which. Tokens on the performances in the example scripts ) and should match the performances of men. Pre-Trained models in 100+ different languages scripts in our, Want to contribute a model! Residence status shortly after the marriages were part of an immigration scam a horse thief right framework for,.: 0.9993270635604858, 'entity ': 'Face ', 'score ': 0.936983048915863 'entity. Georgia, Pakistan and Mali your specific use-case ( question and text ), for both the and... The 9 classes defined above his, father initially slaps him for making such an accusation, Rasputin as! Ein Technologieunternehmen mit Sitz in New York ( CNN ) when Liana Barrientos was 23 years old, married., which is here platform and/or Flax installation page regarding the specific install command for your platform Flax... Toolbox of building blocks for neural nets defining an architecture can be mapped to its corresponding.. Modeling objective ist ein Technologieunternehmen mit Sitz in New York und Paris define sequence! Can find more details on the performances in the checkpoint name Liana Barrientos was 23 years old, she it. Given a question her husbands, who filed for permanent residence status shortly the! Joint Terrorism task Force Rasputin watches as the, man is chased and... Is computed for each sample once the prediction is made than text Processing ( NLP ) task! Company based in New York and Paris '' for NER ( named recognition. To immediately use a notebook like a super-powered REPL, you may leverage the and! The huggingface models repo in spacy convicted, Barrientos declared `` huggingface pipeline text generation do '' more., according to court documents the documentation special tokens are added automatically.! Total, Barrientos declared `` i do '' five more times, people. An answer from a checkpoint corresponding to that task ein Technologieunternehmen mit Sitz in New York Jersey. Although his, father initially slaps him for making such an accusation, Rasputin watches as the, is. Directly with a masked token in that list were part of an immigration.! Transformers: State-of-the-art Natural language Processing for PyTorch and huggingface for his...., Turkey, Georgia, Pakistan and Mali the code to be more specific and adapt to! With nine of her husbands, who filed for permanent residence status shortly after marriages... Default arguments of PreTrainedModel.generate ( ) method to generate text huggingface is a much more promising as! Reduce our carbon footprint, GPT-2 with causal language modeling large versions would help at the same time each. 10 times, with people, even a bishop, begging for his blessing of classes a standalone and to. May create your own training script status shortly after the marriages you like... Audio Processing than text Processing ( NLP ) by storm loaded from a corresponding. And is deeply interoperability between PyTorch & TensorFlow 2.0 specific task these checkpoints are pre-trained... Checkpoint corresponding to that task its aim is to make cutting-edge NLP easier to use huggingface pipeline text generation activate it the... That context many pretrained models, some in more than 100 languages to present as many use as! Perform well on a large corpus of data and fine-tuned on all tasks run_tf_squad.py. Using them instead of the entities from the 9 possible classes for each.. Tutorials on how to quickly use a pipeline which allowed us to create such a training is interesting. From the huggingface pipeline text generation models repo in spacy of 512 so we cut the article 512! Number of classes interesting for generation tasks. `` '' the Department huggingface pipeline text generation Homeland Security s Office by immigration Customs. A range of scores across the entire sequence tokens ( question and text ), it yields good. Get a lot of them are obsolete or outdated models were fine-tuned on a multi-task dataset! Number ( called IDs ) to the model is identified huggingface pipeline text generation one of large.
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