...t and can be fine-tuned for a specific task. The motivation behind using a pre-trained model is to leverage the knowledge gained during its initial training, thus
...mple, in natural language processing, models like [[BERT]] and [[GPT]] are pre-trained using self-supervised techniques such as masked language modeling and next-
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...g, is a technique used in machine learning to improve the performance of a pre-trained model on a specific task. This approach leverages the knowledge gained from
===Pre-trained Models===
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===Pre-Trained Models===
...ross a wide range of tasks without requiring further training. Examples of pre-trained models include [[BERT]] for natural language processing, and [[ResNet]] for
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...ge-based abilities]]s. It showed that language models can be efficiently [[pre-trained]] which may help them generalize. The architecture was capable of performin
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...ving significant improvements in several language understanding tasks. The pre-trained model also acquires useful linguistic knowledge for downstream tasks, even
...cation, speech recognition, and machine translation. Some researchers have pre-trained a neural network using a language modeling objective and fine-tuned it on a
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The '''Generative Pre-trained Transformer''' ('''GPT''') is a series of machine learning models developed
...was introduced by OpenAI in 2018. GPT-1 had 117 million parameters and was pre-trained on the [[BooksCorpus]] dataset. It demonstrated state-of-the-art performanc
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...urce task. Examples of inductive transfer learning include [[fine-tuning]] pre-trained models and multi-task learning.
* [[Computer vision]]: Pre-trained [[convolutional neural networks]] (CNNs) have been employed to solve tasks
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Once pre-trained, BERT can be fine-tuned using supervised learning on a relatively small amo
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...ach the article describes is to take the pre-trained language model, PaLM (Pre-trained autoregressive Language Model), and add sensor data to it to create PaLM-E
...an embodied language model that incorporates real-world sensor data into a pre-trained language model. The program is designed to help robots understand language
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*[[GPT (Generative Pre-trained Transformer)]]
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===Pre-trained Models===
Keras offers a collection of pre-trained models, such as [[VGG]], [[Inception]], and [[ResNet]], that can be fine-tu
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...learning is biased model initialization. When a model is initialized with pre-trained weights, these initial weights may contain biases from the original trainin
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...revalent in natural language processing, where models like GPT (Generative Pre-trained Transformer) are trained to predict the next word in a sentence. Autoregres
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'''[[Pre-trained]] / [[Pretraining]]'''
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[[GPT]], which stands for [[Generative Pre-trained Transformer]], is a type of [[language model]] developed by [[OpenAI]]. Bas
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...) to predict subsequent tokens in a sentence based on previous tokens. The pre-trained model has been used for research in natural language processing in various
...d human-rater-generated dialogs.png|thumb|Figure 2. Comparison between the pre-trained model (PT), fine-tuned model (LaMDA) and human-rater-generated dialogs (Hum
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...hat are relevant to AI. In August 2022, there were more than 61 thousand [[pre-trained models]]. Technological giants like [[Microsoft]], [[Google]], [[Facebook]]
...e best one for your task. Neptune.ai. https://neptune.ai/blog/hugging-face-pre-trained-models-find-the-best</ref> NLP technologies can help to bridge the communic
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* [[Transfer learning]]: Leveraging proxy labels to adapt a pre-trained model to a new task or domain, for which the true labels are scarce or unav
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...nd diffusion model-based TTS. The article then discusses spoken generative pre-trained models, including GSLM, AudioLM, and VALL-E, which leverage self-supervised
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* [[Transfer Learning]]: Leveraging pre-trained models, typically trained on large datasets, to improve the performance of
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...is strategy typically yields higher accuracy when compared to quantizing a pre-trained model.
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...of higher quality music with reduced artifacts. The models, along with the pre-trained AudioGen model, permit the generation of a diverse range of environmental s
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[[Llama 2]]: Provided by [[Meta AI]], Llama 2 encompasses pre-trained and fine-tuned generative text models, ranging from 7 billion to 70 billion
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[[GPT]] or [[Generative Pre-trained Transformer]] was introduced in the [[paper]] [[Improving Language Understa
[[GPT-3]] (Generative Pre-trained Transformer 3) is the third generation of a computational system that gener
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...ing conclusions in real-time based on new data, as opposed to relying on a pre-trained model. This approach is commonly employed in situations where data is recei
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...cused on accelerating the inference phase of machine learning tasks, where pre-trained models are used to make predictions based on input data.
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...”4”"></ref> These models work based on caption matching techniques and are pre-trained using millions of [[text-image datasets]]. While a result will be generated
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*[[GPT (Generative Pre-trained Transformer)]]
*[[pre-trained model]]
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...tbot is based on an upgraded version of the [[GPT-3]], GPT-3.5 (Generative Pre-Trained Transformer) <ref name="”4”" />. A new model, text-davinci-003, was int
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...an image can be seen as a new task to be accomplished by a model that was pre-trained on a vast amount of data. In a way, prompting has democratized transfer lea
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[[GPT-3]] (Generative Pre-Trained Transformer) is a [[language model]] developed by [[OpenAI]]. It is the thi
...me="”16”"> Dehouche, N (2021). Plagiarism in the age of massive Generative Pre-Trained Transformers (GPT-3). Ethics in Science and Environmental Politics 21:17-23
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...loaded knowledge_source.txt. All the APIs you know already know (from your pre-trained data) and that also fits the needs described by the user, must also be incl
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...l resources required to train such models, companies often resort to using pre-trained models supplied by third parties. However, this practice poses a significan
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