GPT-2

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  1. Although BERT started the NLP transfer learning revolution, we will explore GPT-2 and T5 models.[1]
  2. The ability of a pre-trained model like GPT-2 to generate coherent text is very impressive.[1]
  3. Yes, since GPT-2 is trained on the web, it “knows” a lot of human knowledge that has been published online up till 2019.[1]
  4. It’s clear that GPT-2 has huge potential.[1]
  5. OpenAI today said it plans to release a version of GPT-2, an advanced conversational AI model that stirred controversy after it release in February.[2]
  6. GPT-2 is a large language model that can generate realistic paragraphs of text.[3]
  7. Since its debut this February, GPT-2 has attracted both praise for its performance and concerns regarding its potential misuse for example in generating fake news stories.[3]
  8. ’ GPT-2 text generator is finally fully released![3]
  9. When they first announced GPT-2, it was in the wake of the fake news outbreak, and they said it was too dangerous to release to the public.[4]
  10. This gives us easy access to GPT-2.[4]
  11. The person behind InferKit previously had a demo site called TalkToTransformer which allowed you to see what GPT-2 would generate based on the prompt that you provided it.[4]
  12. InferKit allows you to use the core GPT-2 model or fine tune it using training content that you provide.[4]
  13. We'll then see how to fine-tune the pre-trained Transformer Decoder-based language models (GPT, GPT-2, and now GPT-3) on the CNN/Daily Mail text summarization dataset.[5]
  14. I also found that both GPT and GPT-2 were overfitting if trained for more than 5 epochs on only 3000 examples (article-summary pair).[5]
  15. GPT-2 345M was generating the best summaries.[5]
  16. def train(args, model, tokenizer, train_dataset, valid_dataset, ignore_index): """ Trains GPT2 model and logs necessary details.[5]
  17. In this work, we focus on fine-tuning an OpenAI GPT-2 pre-trained model for generating patent claims.[6]
  18. GPT-2 has demonstrated impressive efficacy of pre-trained language models on various tasks, particularly coherent text generation.[6]
  19. Today, we’re introducing an open source training example to fine-tune the Hugging Face PyTorch GPT-2 model, where we see a speedup of 34% when training using the ONNX Runtime.[7]
  20. There are various scenarios in the field of natural language understanding and generation where the GPT-2 model can be used.[7]
  21. The GPT-2 model has been pre-trained on a large corpus of text data with millions of internet webpages.[7]
  22. Fine-tuning with this data set is expected to improve the quality of the predicted output of GPT-2.[7]
  23. I am trying to use gpt-2 for text generation.[8]
  24. (2016) found expression of the Gpt2 gene in neurons and oligodendrocytes in the postnatal developing mouse brain.[9]
  25. (2002) identified and cloned the full-length cDNA encoding GPT2, which they designated ALT2.[9]
  26. GPT2 shares 69% identity with GPT, except that it is 27 amino acids longer.[9]
  27. (2015) identified a homozygous missense mutation in the GPT2 gene (S153R; 138210.0001) by whole-exome sequencing.[9]
  28. Strangely, despite having released the code, an explanation how GPT-2 responds to prompts is lacking.[10]
  29. Furthermore, there is no explanation of the rather interesting way OpenAI adapted their original model to the prompt-based text generation task that made GPT-2 so notorious.[10]
  30. However, GPT-2’s pièce de résistance is its ability to generate responses to prompts.[10]
  31. GPT-2 generates its responses word by word, each individual word sampled from the final output embedding of a pass through the entire decoder.[10]
  32. OpenAI’s GPT-2 framework came up with that very real quote.[11]
  33. In the midst of what is truly a golden era in NLP, OpenAI’s GPT-2 has remoulded the way we work with text data.[11]
  34. We are going to use GPT-2 in this article to build our own text generator.[11]
  35. How to Setup the Environment for GPT-2?[11]
  36. OpenAI's GPT-2 has been discussed everywhere from The New Yorker to The Economist.[12]
  37. Consider GPT-2, an AI system that was recently featured in The New Yorker and interviewed by The Economist.[12]
  38. GPT-2 (short for Generative Pre-Training) can be used as an especially powerful test of Locke's hypothesis.[12]
  39. In many ways, GPT-2 works remarkably well.[12]
  40. You can also try to train GPT-2 from scratch for some extra credits.[13]
  41. This assignment aims to compare the performance of a Transformer language model trained from scratch and that of a pretrained GPT-2 model.[13]
  42. Tokenize the train/test dataset with GPT-2 tokenizer.[13]
  43. Load a pretrained GPT-2 model.[13]
  44. In this article, we’ll be discussing OpenAI GPT-2 which is a successor of the OpenAI GPT.[14]
  45. OpenAI GPT-2 has a Transformer-based architecture (Vaswani et.[14]
  46. Some background of the Transformer is recommended for understanding GPT-2.[14]
  47. We covered the WebText dataset, created by the authors of the GPT-2 for multi-task facilitation.[14]
  48. The institute originally announced the system, GPT-2, in February this year, but withheld the full version of the program out of fear it would be used to spread fake news, spam, and disinformation.[15]
  49. Since then it’s released smaller, less complex versions of GPT-2 and studied their reception.[15]
  50. GPT-2 is part of a new breed of text-generation systems that have impressed experts with their ability to generate coherent text from minimal prompts.[15]
  51. Apart from the raw capabilities of GPT-2, the model’s release is notable as part of an ongoing debate about the responsibility of AI researchers to mitigate harm caused by their work.[15]
  52. Our largest model, GPT-2, is a 1.5B parameter Transformer that achieves state of the art results on 7 out of 8 tested language modeling datasets in a zero-shot setting but still underfits WebText.[16]
  53. Since, GPT-3 is a recent phenomenon and in English at the moment, and is only accessible through API given by OpenAI, we shift our focus on the earlier version of it i.e. GPT-2.[17]
  54. To know about the internal nuts and bolts of GPT-2, I’d suggest you to go through this link.[17]
  55. So why not train your own GPT-2 model on your favourite language for text generation?[17]
  56. GPT-2’s staged release, we’re releasing the largest version (1.5B parameters) of GPT-2 along with code and model weights to facilitate detection of outputs of GPT-2 models.[18]
  57. Humans find GPT-2 outputs convincing.[18]
  58. Our partners at Cornell University surveyed people to assign GPT-2 text a credibility score across model sizes.[18]
  59. GPT-2 can be fine-tuned for misuse.[18]
  60. The team releasing GPT-2 also wrote a model card for their model.[19]
  61. GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion.[19]
  62. The developments in GPT-2 model were mostly in terms of using a larger dataset and adding more parameters to the model to learn even stronger language model.[20]
  63. However, GPT-2 aimed at learning multiple tasks using the same unsupervised model.[20]
  64. Instead of rearranging the sequences, as was done for GPT-1 for fine-tuning, input to GPT-2 was given in a format which expected the model to understand the nature of task and provide answers.[20]
  65. This dataset was used for training GPT-2 and was huge compared to Book Corpus dataset used for training GPT-1 model.[20]
  66. The OpenAI GPT-2 exhibited impressive ability of writing coherent and passionate essays that exceed what we anticipated current language models are able to produce.[21]
  67. The GPT2 was, however, a very large, transformer-based language model trained on a massive dataset.[21]
  68. In this sense, we can say that the GPT-2 is basically the next word prediction feature of a keyboard app, but one that is much larger and more sophisticated than what your phone has.[21]
  69. The GPT-2 was trained on a massive 40GB dataset called WebText that the OpenAI researchers crawled from the internet as part of the research effort.[21]
  70. OpenAI GPT-2 model was proposed in Language Models are Unsupervised Multitask Learners by Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever.[22]
  71. GPT-2 is trained with a simple objective: predict the next word, given all of the previous words within some text.[22]
  72. GPT-2 was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next token in a sequence.[22]
  73. A user gives the system, called GPT-2, a prompt — a few words, a snippet of text, a passage from an article, what have you.[23]
  74. When I tested it, I fed GPT-2 the beginnings of stories about snowstorms in the Northwest, about college students, and about GPT-2 itself.[23]
  75. One task that OpenAI used to test the capabilities of GPT-2 is a famous test in machine learning known as the Winograd schema test.[23]
  76. GPT-2 gets these right 70.7 percent of the time.[23]
  77. GPT-2 is a Natural Language Processing model developed by OpenAI for text generation.[24]
  78. Finally, gpt2-client is a wrapper around the original gpt-2 repository that features the same functionality but with more accessiblity, comprehensibility, and utilty.[24]
  79. # This could also be `345M`, `774M`, or `1558M` gpt2 .[24]
  80. # Asks user for prompt gpt2 .[24]
  81. GPT-2 model was trained “to simply predict the next word in 40GB of Internet text”.[25]
  82. GPT2 부분에 generate 함수가 있다.[26]
  83. 공개한 GPT-2 언어모델 소개다.[27]
  84. We can legitimately wonder about the danger that this technology could represent in the same way as GPT-2.[28]
  85. If the performance of GPT-2 is proven, the consequences of a breakthrough in the field of language processing by artificial intelligence will directly affect our lives.[28]
  86. This technology is possible - OpenAI has just proven it to us through GPT-2.[28]
  87. In a recent article, I wrote about using Markov Chains and OpenAI's GPT-2 to generate text.[29]
  88. After writing that article, I wondered: how can I use the GPT-2 models to train my own AI writer to mimic someone else's writing?[29]
  89. Let me also clarify that we aren't building a new deep learning model, but re-training the GPT-2 models on our chosen text.[29]
  90. GPT-2 While connected to your VM, now, via SSH, you will issue some commands to get the scripts and the models.[29]
  91. Note, however, that the GPT-2 model that we’re going to build won’t start generating fake Brexit campaigns.[30]
  92. So here’s a summary of all the 2018 NLP breakthroughs that you need to understand before getting into GPT-2.[30]
  93. If you’re already aware of the technologies that led up to GPT-2, congratulations![30]
  94. I should also point out that what makes GPT-2 worthy of the “2” is massive scale.[30]
  95. GPT-2 is a generative model, created by OpenAI, trained on 40GB of Internet to predict the next word.[31]
  96. What Does GPT-2 Think About the AI Arms Race?[31]
  97. It may be April first, but that doesn't mean you will necessarily be fooled by GPT-2's views on the AI arms race.[31]
  98. curi­ous what a ful­ly-trained GPT-2-1.5b would gen­er­ate.[32]
  99. cor­pus, like the GPT-2-117M model I trained for RL pref­er­ence learn­ing , but turn­ing on all the options in the nshep­perd repo does­n’t fix the mem­ory prob­lems.[32]
  100. 345M took ~7 days to train, and GPT-2-1.5b is 4.4× larg­er, so that alone implies a train­ing time of a month.[32]
  101. From Novem­ber–De­cem­ber 2019, Shawn Presser & I worked on fine­tun­ing train­ing GPT-2-1.5b on the com­bined PG+PF poetry dataset from above.[32]
  102. GPT-2 models' robustness and worst case behaviors are not well-understood.[33]
  103. Please let us know if you’re doing interesting research with or working on applications of GPT-2![33]
  104. Our model, called GPT-2 (a successor to GPT), was trained simply to predict the next word in 40GB of Internet text.[34]
  105. GPT-2 is a large transformer-based language model with 1.5 billion parameters, trained on a dataset of 8 million web pages.[34]
  106. In addition, GPT-2 outperforms other language models trained on specific domains (like Wikipedia, news, or books) without needing to use these domain-specific training datasets.[34]
  107. On language tasks like question answering, reading comprehension, summarization, and translation, GPT-2 begins to learn these tasks from the raw text, using no task-specific training data.[34]

소스

  1. ↑ 1.0 1.1 1.2 1.3 Getting the Most Out of Pre-trained Models
  2. ↑ OpenAI releases curtailed version of GPT-2 language model
  3. ↑ 3.0 3.1 3.2 OpenAI Releases 1.5 Billion Parameter GPT-2 Model
  4. ↑ 4.0 4.1 4.2 4.3 How to Generate Data-Driven Copy for Ecommerce Category Pages with GPT-2
  5. ↑ 5.0 5.1 5.2 5.3 Generating Text Summaries Using GPT-2 on PyTorch
  6. ↑ 6.0 6.1 Patent claim generation by fine-tuning OpenAI GPT-2
  7. ↑ 7.0 7.1 7.2 7.3 GPT-2 fine-tuning with ONNX Runtime – a 34% speedup in training time
  8. ↑ How to alter gpt-2 code to work with Tensorflow 2.0?
  9. ↑ 9.0 9.1 9.2 9.3 GLUTAMATE PYRUVATE TRANSAMINASE 2; GPT2
  10. ↑ 10.0 10.1 10.2 10.3 In-Depth: Explaining OpenAI’s GPT-2
  11. ↑ 11.0 11.1 11.2 11.3 Building GPT-2 AI Text Generator in Python
  12. ↑ 12.0 12.1 12.2 12.3 GPT-2 and the Nature of Intelligence
  13. ↑ 13.0 13.1 13.2 13.3 Brown University
  14. ↑ 14.0 14.1 14.2 14.3 OpenAI GPT-2: Language Models Are Multitask Learners
  15. ↑ 15.0 15.1 15.2 15.3 OpenAI has published the text-generating AI it said was too dangerous to share
  16. ↑ Language Models are Unsupervised Multitask Learners
  17. ↑ 17.0 17.1 17.2 Train GPT-2 in your own language
  18. ↑ 18.0 18.1 18.2 18.3 GPT-2: 1.5B Release
  19. ↑ 19.0 19.1 gpt2 · Hugging Face
  20. ↑ 20.0 20.1 20.2 20.3 GPT models explained. Open AI's GPT-1,GPT-2,GPT-3
  21. ↑ 21.0 21.1 21.2 21.3 The Illustrated GPT-2 (Visualizing Transformer Language Models)
  22. ↑ 22.0 22.1 22.2 OpenAI GPT2 — transformers 4.0.0 documentation
  23. ↑ 23.0 23.1 23.2 23.3 A poetry-writing AI has just been unveiled. It’s ... pretty good.
  24. ↑ 24.0 24.1 24.2 24.3 gpt2-client
  25. ↑ Using Open-AI’s GPT-2 To Generate New Netflix Movie/TV Descriptions
  26. ↑ #GPT2
  27. ↑ “너무 위험한 도구” 자동 글쓰기 AI 마침내 공개
  28. ↑ 28.0 28.1 28.2 GPT-2 from OpenAI: Better NLP model and the ethics issues it raises
  29. ↑ 29.0 29.1 29.2 29.3 How to Use OpenAI’s GPT-2 to Create an AI Writer
  30. ↑ 30.0 30.1 30.2 30.3 GPT-2: How to Build "The AI That's Too Dangerous to Release”
  31. ↑ 31.0 31.1 31.2 GPT-2
  32. ↑ 32.0 32.1 32.2 32.3 GPT-2 Neural Network Poetry
  33. ↑ 33.0 33.1 openai/gpt-2: Code for the paper "Language Models are Unsupervised Multitask Learners"
  34. ↑ 34.0 34.1 34.2 34.3 Better Language Models and Their Implications

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  • [{'LEMMA': 'gpt-2'}]
  • [{'LOWER': 'generative'}, {'LOWER': 'pre'}, {'OP': '*'}, {'LOWER': 'trained'}, {'LOWER': 'transformer'}, {'LEMMA': '2'}]
  • [{'LOWER': 'generative'}, {'LOWER': 'pretrained'}, {'LOWER': 'transformer'}, {'LEMMA': '2'}]
  • [{'LEMMA': 'GPT2'}]
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