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T5 text generation

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happy_common_gen = HappyTextToText ("T5", "mrm8488/t5-base-finetuned-common_gen") We'll use an algorithm called beam search to generate text. Remember to first import TTSettings if haven't already. To address this, we propose a new framework: Prototype-to-Generate (P2G), for table-to-text generation under the few-shot scenario. The proposed framework utilizes the retrieved prototypes, which are jointly selected by an IR system and a novel prototype selector to help the model bridging the structural gap between tables and texts.

This month, we are taking a closer look at top questions regarding USAA membership eligibility for family members. Generally, USAA membership is open to active, retired, and separated veterans with a discharge type of "Honorable" and "General Under Honorable Conditions" from the U.S. military and their eligible family members. To investigate how T5 arrived at this prediction, we utilize the "similarity searcher" component through the counterfactual generator tab. This performs a fast approximate nearest-neighbor lookup from a pre-built index over the training corpus, using embeddings from the T5 decoder. With one click, we retrieve the 25 nearest neighbors to our. Zeige besser passende Version dieser Seite; Diese Meldung nicht mehr anzeigen. .

Conditional Text Generation Sebastiaan Vergunst, Quintus Roos and Vasileios Kalogiras December 2021. Background Conditional Task Generation (CTG) Fine-tune GPT and T5 LM "Multi Task" - Classification and Generation Fine-tuning increases zero-shot performance and generalization capabilities [5]. 2021. 7. 29. · Sequence-to-Sequence (S2S) neural text generation models, especially the pre-trained ones (e.g., BART and T5), have exhibited compelling performance on various natural language generation tasks. However, the black-box nature of these models limits their application in tasks where specific rules (e.g., controllable constraints, prior knowledge) need to be executed. In this course, we will see how we can take any text content and generate these assessment questions using NLP techniques. This course will be a very practical use case of NLP where we put basic algorithms like word vectors (word2vec, Glove, etc) to recent advancements like BERT, openAI GPT-2, and T5 transformers to real-world use. Let's review the code and steps to set up the FAQ generation model. First, we need to download the T5 weights. !python -m nltk.downloader punkt. This will cause the python library nltk to. textgen/ text-generation. App Overview. AI RESOURCES. Models. Workflows. The Services use Cookies to enable our servers to recognize your web browser and tell us how and when you visit and use our Services, to analyze trends, learn about our user base, and operate and improve our Services. We may also supplement the information we collect from. Nature language generation (NLG), also known as text generation [McKeown, 1992; Sutskever et al. , 2011 ], is a fundamental and challenging task in Natural Language Pro- ... a t1 a t2 a t3 a t4 a t5 Topic -Averaged Long Short Term Memory Attention -based Long Short Term Memory "##$% "##$% Mother , you are my lifetime beloved. Abstract. We motivate and propose a suite of simple but effective improvements for concept-to-text generation called SAPPHIRE: Set Augmentation and Post-hoc PHrase Infilling and REcombination. We demonstrate their effectiveness on generative commonsense reasoning, a.k.a. the CommonGen task, through experiments using both BART and T5 models. Top 20 Funny 【apat na dayuhan 3】 Images! Create Your Own Memes Today!. T5 model seq2seq text generation using word embeddings instead of token_ids does not work · Issue #12218 · huggingface/transformers · GitHub #12218 jerry3chen on Jun 16, 2021 · 9 comments jerry3chen commented on Jun 16, 2021 Using Bart with input_embeds to generate text without input_id return error #14380 patrickvonplaten [Generation] Allow #14443. Data to Text generation with T5; Building a simple yet advanced NLG model. An implementation of Data-to-Text NLG model by fine-tuning T5 — Introduction The Data to text generation capability of NLG models is something that I have been exploring since the inception of sequence to sequence models in the field of NLP. The earlier attempts to. Thus, in combination with next-generation sequencing technologies, large-scale integrome analysis of >4 × 10 5 -1 × 10 6 integration site sequences can be accomplished within a single week. t5_generated_text = t5_summarizer(text, min_length=10, max_length=250) Below is the summary generated by the T5 model. "if you have content worth engaging with daily, provide new content daily. in 2020, WordPress users post 70 million new posts every month. the more regularly you get crawled, the more opportunity you have to rank.".

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ValueError: chunk structures must contain tagged tokens or trees. The str () for a chunk string adds spaces to it, which makes it line up with str () output for other chunk strings over the same underlying input. The _verify () method makes sure that our transforms don't corrupt the chunk string. By setting debug_level=2, _verify () will be. Contribute to DylanJoo/T5-neural-transfer-reformulation development by creating an account on GitHub. ... This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. ... temp = self. generate (input_ids = input_ids) print (self. tokenizer. decode (temp. The proposal is based on the Desiderata for Evalu-ation on Natural Language Generation (Paris et al., 2007) and by theories of question ask-ing in cognitive sciences (Graesser et al., in press). So, perhaps you have a few keywords for text you wish to produce, then you can use this model to generate text relating to those keywords. happy_common_gen = HappyTextToText("T5", "mrm8488/t5-base-finetuned-common_gen") We'll use an algorithm called beam search to generate text. Remember to first import TTSettings if haven't already. 2020. 2. 25. · If you want to train or run T5 for pure text generation (GPT-2-style), Nax developed a Colab notebook for that: https: ... > Text-To-Text Transfer Transformer (T5) > Colossal Clean Crawled Corpus (C4) pun detector at 3.6 punits. not great, not. T5-Iris recognition technology is highly accurate (99,94%) and ranked in the TOP-tier of most accurate iris recognition algorithms in the world (NISTIR 8207, 2018). Learn more. T5-Face. ... TECH5 ABIS and Digital ID Will be Used to Issue Next-Generation Foundational IDs in Ethiopia. Generation tasks (text-to-graph, graph-to-text) are reframed as sequence to sequence (seq2seq) translation tasks. "Graph linearization" turns graphs into sequence of edges our models can process easily. Pretrained Language Models (PLMs) built on large amount of data, such as T5, are fine-tuned on both generation tasks. Figure 2: Title generation and image-title/text matching de-cision using MMT4. unseen label recognition. Thus, in this work, we propose a new generative model to encode different modalities and generate de-sired output texts. The proposed model architecture is a customized T5 in which the non-text (e.g. image) components are fused to the. This figure was adapted from a similar image published in DistilBERT. Turing Natural Language Generation (T-NLG) is a 17 billion parameter language model by Microsoft that outperforms the state of the art on many downstream NLP tasks. We present a demo of the model, including its freeform generation, question answering, and summarization capabilities, to academics []. Zeige besser passende Version dieser Seite; Diese Meldung nicht mehr anzeigen. Generation tasks (text-to-graph, graph-to-text) are reframed as sequence to sequence (seq2seq) translation tasks. "Graph linearization" turns graphs into sequence of edges our models can process easily. Pretrained Language Models (PLMs) built on large amount of data, such as T5, are fine-tuned on both generation tasks. So, perhaps you have a few keywords for text you wish to produce, then you can use this model to generate text relating to those keywords. happy_common_gen = HappyTextToText("T5", "mrm8488/t5-base-finetuned-common_gen") We'll use an algorithm called beam search to generate text. Remember to first import TTSettings if haven't already. Buy Bluedio T5 Active Noise Cancelling Wireless Bluetooth Headphones Portable Headse(Black) with fast shipping and top-rated customer service. Once you know, you Newegg!. Over the past few months, text generation capabilities using Transformer-based models have been democratized by open-source efforts such as Hugging Face's Transformers [1] library. A broad range of models and applications have been made available, including: Summarization models fine-tuned on the CNN-DailyMail [2] or XSUM [3] datasets, including for example BART [4] or T5 [5] Translation. Imagen is an AI system that creates photorealistic images from input text. Visualization of Imagen. Imagen uses a large frozen T5-XXL encoder to encode the input text into embeddings. A conditional diffusion model maps the text embedding into a 64×64 image. Imagen further utilizes text-conditional super-resolution diffusion models to upsample. . These include T5 and GPT-2, used for translation and text generation, making it possible to run NLU apps in real time. TensorRT is a high-performance deep learning inference optimizer and runtime that delivers low latency, high-throughput inference for AI applications. TensorRT is used across several industries including healthcare, automotive. Text, image, video. 46 total ratings, 13 with reviews There was a problem filtering reviews right now. ... In 2020, Beyerdynamic released the T5, 3rd generation closed-back headphones, with bass that rivals loudspeakers IMO. If bold, dynamic and realistic bass response is not your thing when listening with headphones, the T5 is not for you.. Parse data into useful values using patterns in an input's text or structure. Lessons Remaining: 9. Average Time: 7 Min. Macros. Save yourself time and expand what you can accomplish by creating your own tools and sharing them with others! Lessons Remaining: 7. Average Time: 6 Min. Zeige besser passende Version dieser Seite; Diese Meldung nicht mehr anzeigen. 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Over the past few months, text generation capabilities using Transformer-based models have been democratized by open-source efforts such as Hugging Face's Transformers [1] library. A broad range of models and applications have been made available, including: Summarization models fine-tuned on the CNN-DailyMail [2] or XSUM [3] datasets, including for example BART [4] or T5 [5] Translation. Zeige besser passende Version dieser Seite; Diese Meldung nicht mehr anzeigen. Convert your regular & ordinary language into roblox chat characters in 3 simple steps: 1) Enter Text . Paste or write the text you want converted in the "input" section above. 2) Preview. Check how your converted text looks in the "output" section. 3) Replicate. Select all the converted text , copy it and use anywhere you want. Top 20 Funny 【apat na dayuhan 3】 Images! Create Your Own Memes Today!. This month, we are taking a closer look at top questions regarding USAA membership eligibility for family members. Generally, USAA membership is open to active, retired, and separated veterans with a discharge type of "Honorable" and "General Under Honorable Conditions" from the U.S. military and their eligible family members. We propose a controllable citation text generation model that extends a pre-trained sequence to sequence models, namely, BART and T5, by using the citation intent as the control code to generate the citation text, meeting the paper authors’ citation intent. I have few input text like &TextInput1&TextInput2 &TextInput3. After scanning the QR code it generates the text in one single line . How to use line break and enable spacing in the text genarated by qr code? in the attachment I have provided it shows text after QR scanning as AkhanITdepartment12345678. I need as Akhan. IT Department. 12345678. Here in this article, we'll be making a Question-Answering system using T5 Transformer, a state-of-the-art Text to Text transformer developed by Google AI. This transformer has many features and is already trained on the C4 data set (Colossal Clean Common Crawl), around 750 Gigabytes of a text corpus. You may read about this T5 transformer. Joolsey1973. Explorer , Apr 06, 2021. I have a free text generator from Austin Newman. It is working ok, but I am surprised that I can not change the weight of the font within one sentence in the text field. I have come from Apple FCP and this is something you can do very easily. I have 80 odd headers like this and was wondering if there is a. 2022. 7. 30. · Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. For any readers unfamiliar with T5 — the T5 model was presented in Google’s paper titled Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei. Sequence-to-Sequence (Seq2Seq) neural text generation models, especially the pre-trained ones (e.g., BART and T5), have exhibited compelling performance on various natural language generation tasks. However, the black-box nature of these models limits their application in tasks where specific rules (e.g., controllable constraints, prior. Unifying Vision-and-Language Tasks via Text Generation. 02/04/2021. ∙. by Jaemin Cho, et al. 38 share. Existing methods for vision-and-language learning typically require designing task-specific architectures and objectives for each task. For example, a multi-label answer classifier for visual question answering, a region scorer for referring. To address this, we propose a new framework: Prototype-to-Generate (P2G), for table-to-text generation under the few-shot scenario. The proposed framework utilizes the retrieved prototypes, which are jointly selected by an IR system and a novel prototype selector to help the model bridging the structural gap between tables and texts. happy_common_gen = HappyTextToText ("T5", "mrm8488/t5-base-finetuned-common_gen") We'll use an algorithm called beam search to generate text. Remember to first import TTSettings if haven't already. We summarize our tokenized data using T5 by calling model.generate, like so: summary_ids = model.generate(inputs, max_length=150, min_length=80, length_penalty=5., num_beams=2) max_length defines the maximum number of tokens we'd like in our summary min_length defines the minimum number of tokens we'd like.

2022. 8. 11. · I want to generate the outputs token by token so that I can calculate the entropy of each output token, respectively. It does not seem like the .generate () method will work for this. I effectively want to create my own generate function but I need to obtain the logits of the model to be able to do this. nlp pytorch huggingface-transformers. A Constrained Text Generation Challenge for Generative Commonsense Reasoning Bill Yuchen Lin, Wangchunshu Zhou, Ming Shen, Pei Zhou, ... RE-T5 (Retrieval-Enhanced T5) Microsoft Cognitive Services Research Group . Email Paper (ACL21) 40.863: 17.663: 31.079 . Oct 19, 2021: A* Neurologic (T5-large). single unified format, via text generation, conditioned on multimodal inputs from the image and the textual context. 3. Model We propose a new framework that unifies vision-and-language problems as multimodal conditional text gener-ation. We introduce VL-T5 and VL-BART based on two pretrained transformer language models: T5 Base(Raffel. 2022. 7. 16. · full generation capabilities. In particular, our contributions are as follows: We present an encoder-decoder based model TABT5 (Table-and-Text-to-Text Transfer Trans-former) that can be applied to data-to-text gener-ation tasks by relying on special embeddings of the input structure. We introduce different pre-training strategies. Summarization, translation, Q&A, text generation and more at blazing speed using a T5 version implemented in ONNX. This package is still in alpha stage, therefore some functionalities such as beam searches are still in development. Installation. ONNX-T5 is available on PyPi. pip install onnxt5 For the dev version you can run the following. We summarize our tokenized data using T5 by calling model.generate, like so: summary_ids = model.generate(inputs, max_length=150, min_length=80, length_penalty=5., num_beams=2) max_length defines the maximum number of tokens we'd like in our summary min_length defines the minimum number of tokens we'd like. Imagen is an AI system that creates photorealistic images from input text. Visualization of Imagen. Imagen uses a large frozen T5-XXL encoder to encode the input text into embeddings. A conditional diffusion model maps the text embedding into a 64×64 image. Imagen further utilizes text-conditional super-resolution diffusion models to upsample. Trying to configure my new to me FTD 2130 devices for AnyConnect VPN remote access sessions. Coming from ASA 5515-X devices and Running 7.0.1-84 code on my FTD's. This is our only FTD device so I am configuring it using FDM. I am finding mixed inform... 03-22-2022 7:52:02 AM. Figure 2: Title generation and image-title/text matching de-cision using MMT4. unseen label recognition. Thus, in this work, we propose a new generative model to encode different modalities and generate de-sired output texts. The proposed model architecture is a customized T5 in which the non-text (e.g. image) components are fused to the. This month, we are taking a closer look at top questions regarding USAA membership eligibility for family members. Generally, USAA membership is open to active, retired, and separated veterans with a discharge type of "Honorable" and "General Under Honorable Conditions" from the U.S. military and their eligible family members. single unified format, via text generation, conditioned on multimodal inputs from the image and the textual context. 3. Model We propose a new framework that unifies vision-and-language problems as multimodal conditional text gener-ation. We introduce VL-T5 and VL-BART based on two pretrained transformer language models: T5 Base(Raffel. To investigate how T5 arrived at this prediction, we utilize the "similarity searcher" component through the counterfactual generator tab. This performs a fast approximate nearest-neighbor lookup from a pre-built index over the training corpus, using embeddings from the T5 decoder. With one click, we retrieve the 25 nearest neighbors to our.

The package includes the headphones and a 4.6' (1.4m) cable tipped in 1/8" (3.5mm) plugs on both ends, plus a 1/8"-to-1/4" adapter. If you need a balanced cable with a four-pin XLR connector, Beyerdynamic offers a 9.75' (3.0m) one for $145. A nice semi-hardshell travel case is also included. 2022. 7. 30. · Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. For any readers unfamiliar with T5 — the T5 model was presented in Google’s paper titled Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei. 2021. 5. 6. · Transformers are models that can be designed to translate text, write poems and op eds, and even generate computer code. In fact, lots of the amazing research I write about on daleonai.com is built on Transformers, like AlphaFold 2, the model that predicts the structures of proteins from their genetic sequences, as well as powerful natural. 2022. 5. 24. · Inference acceleration of T5 for large batch size / long sequence length / > large models¶. Every week or so, a new impressive few shots learning work taking advantage of autoregressive models is released by some team around the world. Still LLM inference is rarely discussed and few projects are focusing on this aspect. In this notebook, we describe our take. Over the past few months, text generation capabilities using Transformer-based models have been democratized by open-source efforts such as Hugging Face's Transformers [1] library. A broad range of models and applications have been made available, including: Summarization models fine-tuned on the CNN-DailyMail [2] or XSUM [3] datasets, including for example BART [4] or T5 [5] Translation. T4: A Tutorial on Evaluation Metrics used in Natural Language Generation; T5: Beyond Paragraphs: NLP for Long Sequences; Evening Session (16:00-20:00) T6: Crowdsourcing Natural Language Data at Scale: A Hands-On Tutorial; T1 (Morning, 8-12): Pretrained Transformers for Text Ranking: BERT and Beyond. Andrew Yates, Rodrigo Nogueira and Jimmy. 2.1 Background into text generation and lan-guage modeling for the English Language Research in text generation has been greatly enhanced over the past years with the advancements of deep learning and neural language models. In this part, we give a brief overview of the general text generation methods for the English language. 2.1 Background into text generation and lan-guage modeling for the English Language Research in text generation has been greatly enhanced over the past years with the advancements of deep learning and neural language models. In this part, we give a brief overview of the general text generation methods for the English language. Thus, in combination with next-generation sequencing technologies, large-scale integrome analysis of >4 × 10 5 -1 × 10 6 integration site sequences can be accomplished within a single week.

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A classic problem in natural-language generation (NLG) involves taking structured data, such as a table, as input, and producing text that adequately and fluently describes this data as output. Unlike machine translation, which aims for complete transduction of the sentence to be translated, this form of NLG is usually taken to require addressing (at least) two separate challenges: what to say. The code used in this article can be found here — GPT and T5. To read more about text generation models, see this. For more such articles visit my website or have a look at my latest short book on Data science. ... #The Models #Gpt 2 #Fine Tuning #Data Science #Gpt Neo #T5 #Linkedin #Nlp #V2 #V3 #Eleutherai #Google #Twitter #F1. YOU MAY ALSO. 2020. 2. 25. · If you want to train or run T5 for pure text generation (GPT-2-style), Nax developed a Colab notebook for that: https: ... > Text-To-Text Transfer Transformer (T5) > Colossal Clean Crawled Corpus (C4) pun detector at 3.6 punits. not great, not. Let us see the steps followed to mine the frequent pattern using frequent pattern growth algorithm: #1) The first step is to scan the database to find the occurrences of the itemsets in the database. This step is the same as the first step of Apriori. The count of 1-itemsets in the database is called support count or frequency of 1-itemset. Fill-in-the-Blank Text Generation Large language models like GPT-2 excel at generating very realistic looking-text since they are trained to predict what words come next after an input prompt. This has led to numerous creative applications like Talk To Transformer and the text-based game AI Dungeon.The pre-training objective used by T5 aligns more closely with a fill-in-the-blank task where. I am trying to use the text generator tool in my extentions tab, but it will not open selected word files. I get errors when I try. Any idea what's. Knowledge Network > Support & Learning > Revit Products > Revit Products Community > Revit Structure Forum > text generator; Community. A class containing all functions for auto-regressive text generation, to be used as a mixin in PreTrainedModel.. The class exposes generate(), which can be used for:. greedy decoding by calling greedy_search() if num_beams=1 and do_sample=False.; multinomial sampling by calling sample() if num_beams=1 and do_sample=True.; beam-search decoding by calling. Instead, it was fine-tuned for various text-to-text tasks and could perform any of them - all with a single model. Let's discuss performing summarization with T5. You can find more detail about the model here. happy_t5 = HappyTextToText ("T5", "t5-base") input = "summarize: " + text. Below, we bring an example that shows how you can use deepspeed-inference with a T5 model: # create the model import transformers from transformers.models.t5.modeling_t5 import T5Block import deepspeed pipe = pipeline ... The above script modifies the model in HuggingFace text-generation pipeline to use DeepSpeed inference. Note that here we. 1.Install Transformers library in colab. !pip install transformers or, install it locally, pip install transformers 2. Import transformers pipeline, from transformers import pipeline 3. Set the "text2text-generation" pipeline. text2text = pipeline("text2text-generation") 4. Task: Question Answering. Imagen is an AI system that creates photorealistic images from input text. Visualization of Imagen. Imagen uses a large frozen T5-XXL encoder to encode the input text into embeddings. A conditional diffusion model maps the text embedding into a 64×64 image. Imagen further utilizes text-conditional super-resolution diffusion models to upsample. Test the EAI models. MODEL: GPT-J-6B. Model on Github. Prompt List. Try a classic prompt evaluated on other models. TOP-P. 0.9. Temperature.

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2022. 5. 29. · T5 Model On this page. T5Model. Configuring a T5Model; Class T5Model; Training a T5Model; Evaluating a T5Model; Making Predictions With a T5Model; T5Model. The T5Model class is used for any NLP task performed with a T5 model or a mT5 model.. To create a T5Model, you must specify the model_type and model_name.. model_type should be one of the model. Charge Like a Charm. Attach the HUAWEI M-Pencil to your tablet magnetically to pair and charge wirelessly 5. When fully charged, it will last for 10 hours 6. Put down the HUAWEI M-Pencil on the top of your tablet for 30 seconds, and you can draw and write for 10 minutes more 6. 1. HUAWEI M-Pencil (2 nd generation) includes 2 replaceable tips in. By combining the insights from our exploration with scale and our new ``Colossal Clean Crawled Corpus'', we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our data set, pre-trained models, and code. vision_text_model visual_entailment predictors predictors nlvr2 vilbert_vqa visual_entailment CHANGELOG License Table of contents T5 ... T5 tokenizer forward make_output_human_readable get_metrics default_predictor t5. allennlp_models.generation.models.t5. T5# @Model. register ("t5"). Trying to configure my new to me FTD 2130 devices for AnyConnect VPN remote access sessions. Coming from ASA 5515-X devices and Running 7.0.1-84 code on my FTD's. This is our only FTD device so I am configuring it using FDM. I am finding mixed inform... 03-22-2022 7:52:02 AM. Data augmentation using Text to Text Transfer Transformer (T5) is a large transformer model trained on the Colossal Clean Crawled Corpus (C4) dataset. Google open-sourced a pre-trained T5 model that is capable of doing multiple tasks like translation, summarization, question answering, and classification. Text summarization with T5. NLP summarizing tasks extract succinct parts of a text. In this section, we will start by presenting the Hugging Face resources we will use in this chapter. Then we will initialize a T5-large transformer model. Finally, we will see how to use T5 to summarize any type of document, including legal and corporate documents. Contribute to DylanJoo/T5-neural-transfer-reformulation development by creating an account on GitHub. ... This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. ... temp = self. generate (input_ids = input_ids) print (self. tokenizer. decode (temp. Zeige besser passende Version dieser Seite; Diese Meldung nicht mehr anzeigen. 2020. 12. 14. · Our function will apply Huggingface’s t5-base tokenizer to the texts and return a dictionary which has the following keys: input_ids: the IDs of the tokens resulting from the tokenization of the. 2021. 7. 29. · Sequence-to-Sequence (S2S) neural text generation models, especially the pre-trained ones (e.g., BART and T5), have exhibited compelling performance on various natural language generation tasks. However, the black-box nature of these models limits their application in tasks where specific rules (e.g., controllable constraints, prior knowledge) need to be executed.

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