T5 - Revision history

BPeat at 16:31, 8 October 2023

2023-10-08T16:31:18Z

BPeat at 15:33, 8 October 2023

2023-10-08T15:33:24Z

BPeat at 20:07, 28 April 2023

2023-04-28T20:07:55Z

BPeat at 12:32, 9 June 2020

2020-06-09T12:32:42Z

BPeat at 21:59, 21 December 2019

2019-12-21T21:59:07Z

BPeat at 21:55, 21 December 2019

2019-12-21T21:55:25Z

BPeat at 21:23, 21 December 2019

2019-12-21T21:23:35Z

BPeat: Created page with "{{#seo: |title=PRIMO.ai |titlemode=append |keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, M..."

2019-12-21T21:20:45Z

Created page with "{{#seo: |title=PRIMO.ai |titlemode=append |keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, M..."

New page

{{#seo:
|title=PRIMO.ai
|titlemode=append
|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
}}
[http://www.youtube.com/results?search_query=T5+natural+language Youtube search...] |
[http://www.google.com/search?q=T5+natural+language ...Google search]

* [[Natural Language Processing (NLP)]]

The T5 (Text-To-Text Transfer Transformer) model. The same model is used for a wide variety of tasks by treating all tasks uniformly as taking some input text and outputting some text where the task type is embedded as descriptors in the input(see bold text in the input on the left above). This approach enables a single model to perform a wide variety of supervised tasks such as translation, classification, Q&A, summarization and even regression (e.g. outputting a similarity score between two sentences in the range 1–5. This in reality quite similar to a 21 class classification problem as explained below). The model is first pretrained unsupervised (masked objective like BERT) on a large corpus before supervised training with input text representing all these tasks and the associated labeled data which is also text (where specific tokens in the input stream “translate English to French” or “stsb sentence 1:… sentence2”, “question”/”context” etc. encode the task type as shown in figure above and the model is trained to output text matching the labeled data). With this approach of specifying input and output for supervised learning, the model shares its loss function, decoder etc. across all the disparate tasks. [http://towardsdatascience.com/t5-a-model-that-explores-the-limits-of-transfer-learning-fb29844890b7 T5 — a model that explores the limits of transfer learning | Ajit Rajasekharan Towards Data Science - Medium]

http://miro.medium.com/max/2766/1*9gRd_Bb3_oUjqqQ5AdiW4w.png

The T5 model treats a wide variety of many-to-many and many-to-one NLP tasks in a unified manner by encoding the different tasks as text directives in the input stream. This enables a single model to be trained supervised on a wide variety of NLP tasks such as translation, classification, Q&A, summarization and even regression (though in reality it is similar to a classification).

<youtube>naOuE9gLbZo</youtube>
<youtube>H5vpBCLo74U</youtube>
<youtube>Ubh03Sh45b0</youtube>

h

@@ Line 10: / Line 10: @@
 * [[Large Language Model (LLM)]] ... [[Natural Language Processing (NLP)]]  ...[[Natural Language Generation (NLG)|Generation]] ... [[Natural Language Classification (NLC)|Classification]] ...  [[Natural Language Processing (NLP)#Natural Language Understanding (NLU)|Understanding]] ... [[Language Translation|Translation]] ... [[Natural Language Tools & Services|Tools & Services]]
 * [http://www.topbots.com/leading-nlp-language-models-2020/ 7 Leading Language Models for NLP in 2020 | Mariya Yao - Topbots]
 The T5 (Text-To-Text Transfer Transformer) model. The same model is used for a wide variety of tasks by treating all tasks uniformly as taking some input text and outputting some text where the task type is embedded as descriptors in the input(see bold text in the input on the left above). This approach enables a single model to perform a wide variety of supervised tasks such as translation, classification, Q&A, summarization and even regression (e.g. outputting a similarity score between two sentences in the range 1–5. This in reality quite similar to a 21 class classification problem as explained below). The model is first pretrained unsupervised (masked objective like BERT) on a large corpus before supervised training with input text representing all these tasks and the associated labeled data which is also text (where specific tokens in the input stream “translate English to French” or “stsb sentence 1:… sentence2”, “question”/”context” etc. encode the task type as shown in figure above and the model is trained to output text matching the labeled data). With this approach of specifying input and output for supervised learning, the model shares its loss function, decoder etc. across all the disparate tasks. [http://towardsdatascience.com/t5-a-model-that-explores-the-limits-of-transfer-learning-fb29844890b7 T5 — a model that explores the limits of transfer learning | Ajit Rajasekharan Towards Data Science - Medium]

@@ Line 8: / Line 8: @@
 [http://www.google.com/search?q=T5+natural+language ...Google search]
-* [[Natural Language Processing (NLP)]]
+* [[Large Language Model (LLM)]] ... [[Natural Language Processing (NLP)]]  ...[[Natural Language Generation (NLG)|Generation]] ... [[Natural Language Classification (NLC)|Classification]] ...  [[Natural Language Processing (NLP)#Natural Language Understanding (NLU)|Understanding]] ... [[Language Translation|Translation]] ... [[Natural Language Tools & Services|Tools & Services]]
 * [http://www.topbots.com/leading-nlp-language-models-2020/ 7 Leading Language Models for NLP in 2020 | Mariya Yao - Topbots]

← Older revision		Revision as of 12:32, 9 June 2020
Line 9:		Line 9:

	* [[Natural Language Processing (NLP)]]		* [[Natural Language Processing (NLP)]]
		+	* [http://www.topbots.com/leading-nlp-language-models-2020/ 7 Leading Language Models for NLP in 2020 \| Mariya Yao - Topbots]

	The T5 (Text-To-Text Transfer Transformer) model. The same model is used for a wide variety of tasks by treating all tasks uniformly as taking some input text and outputting some text where the task type is embedded as descriptors in the input(see bold text in the input on the left above). This approach enables a single model to perform a wide variety of supervised tasks such as translation, classification, Q&A, summarization and even regression (e.g. outputting a similarity score between two sentences in the range 1–5. This in reality quite similar to a 21 class classification problem as explained below). The model is first pretrained unsupervised (masked objective like BERT) on a large corpus before supervised training with input text representing all these tasks and the associated labeled data which is also text (where specific tokens in the input stream “translate English to French” or “stsb sentence 1:… sentence2”, “question”/”context” etc. encode the task type as shown in figure above and the model is trained to output text matching the labeled data). With this approach of specifying input and output for supervised learning, the model shares its loss function, decoder etc. across all the disparate tasks. [http://towardsdatascience.com/t5-a-model-that-explores-the-limits-of-transfer-learning-fb29844890b7 T5 — a model that explores the limits of transfer learning \| Ajit Rajasekharan Towards Data Science - Medium]		The T5 (Text-To-Text Transfer Transformer) model. The same model is used for a wide variety of tasks by treating all tasks uniformly as taking some input text and outputting some text where the task type is embedded as descriptors in the input(see bold text in the input on the left above). This approach enables a single model to perform a wide variety of supervised tasks such as translation, classification, Q&A, summarization and even regression (e.g. outputting a similarity score between two sentences in the range 1–5. This in reality quite similar to a 21 class classification problem as explained below). The model is first pretrained unsupervised (masked objective like BERT) on a large corpus before supervised training with input text representing all these tasks and the associated labeled data which is also text (where specific tokens in the input stream “translate English to French” or “stsb sentence 1:… sentence2”, “question”/”context” etc. encode the task type as shown in figure above and the model is trained to output text matching the labeled data). With this approach of specifying input and output for supervised learning, the model shares its loss function, decoder etc. across all the disparate tasks. [http://towardsdatascience.com/t5-a-model-that-explores-the-limits-of-transfer-learning-fb29844890b7 T5 — a model that explores the limits of transfer learning \| Ajit Rajasekharan Towards Data Science - Medium]

@@ Line 12: / Line 12: @@
 The T5 (Text-To-Text Transfer Transformer) model. The same model is used for a wide variety of tasks by treating all tasks uniformly as taking some input text and outputting some text where the task type is embedded as descriptors in the input(see bold text in the input on the left above). This approach enables a single model to perform a wide variety of supervised tasks such as translation, classification, Q&A, summarization and even regression (e.g. outputting a similarity score between two sentences in the range 1–5. This in reality quite similar to a 21 class classification problem as explained below). The model is first pretrained unsupervised (masked objective like BERT) on a large corpus before supervised training with input text representing all these tasks and the associated labeled data which is also text (where specific tokens in the input stream “translate English to French” or “stsb sentence 1:… sentence2”, “question”/”context” etc. encode the task type as shown in figure above and the model is trained to output text matching the labeled data). With this approach of specifying input and output for supervised learning, the model shares its loss function, decoder etc. across all the disparate tasks. [http://towardsdatascience.com/t5-a-model-that-explores-the-limits-of-transfer-learning-fb29844890b7 T5 — a model that explores the limits of transfer learning | Ajit Rajasekharan Towards Data Science - Medium]
-<img src="http://miro.medium.com/max/2766/1*9gRd_Bb3_oUjqqQ5AdiW4w.png" width="900" height="900">
+<img src="http://miro.medium.com/max/2766/1*9gRd_Bb3_oUjqqQ5AdiW4w.png" width="800" height="750">
 The T5 model treats a wide variety of many-to-many and many-to-one NLP tasks in a unified manner by encoding the different tasks as text directives in the input stream. This enables a single model to be trained supervised on a wide variety of NLP tasks such as translation, classification, Q&A, summarization and even regression (though in reality it is similar to a classification).
 <youtube>uz_eYqutEG4</youtube>

← Older revision		Revision as of 21:23, 21 December 2019
Line 16:		Line 16:
	The T5 model treats a wide variety of many-to-many and many-to-one NLP tasks in a unified manner by encoding the different tasks as text directives in the input stream. This enables a single model to be trained supervised on a wide variety of NLP tasks such as translation, classification, Q&A, summarization and even regression (though in reality it is similar to a classification).		The T5 model treats a wide variety of many-to-many and many-to-one NLP tasks in a unified manner by encoding the different tasks as text directives in the input stream. This enables a single model to be trained supervised on a wide variety of NLP tasks such as translation, classification, Q&A, summarization and even regression (though in reality it is similar to a classification).

−	<youtube>~~naOuE9gLbZo~~</youtube>	+	<youtube>uz_eYqutEG4</youtube>
−	~~<youtube>H5vpBCLo74U</youtube>~~
−	~~<youtube>Ubh03Sh45b0</youtube>~~
−
−
−	h

@@ Line 2: / Line 2: @@
 |title=PRIMO.ai
 |titlemode=append
-|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
+|keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
 [http://www.youtube.com/results?search_query=T5+natural+language Youtube search...] |