Fine-Tuning OpenAI
12.4 هزار بار بازدید -
2 سال پیش
-
Fine tuning transformers - Gpt-3
Fine tuning transformers - Gpt-3 and Chatgpt.
In this video, Lucidate discusses the importance of fine-tuning transformer neural networks and how to create bespoke models using simple prompt engineering methods. We highlight that manually populating an excel spreadsheet with prompts and completions will work, but this simple solution (though effective!!) is not scalable. Accordingly we introduce tools to manipulate text and retrieve relevant passages from the Internet.
In the video, we see how to solve the problem of breaking up text into prompts and completions by building a splitter class in Python. We also introduce 'Beautiful Soup', a popular Python library that allows users to extract HTML components of a web page.
We illustrate how to use Beautiful Soup to extract text from websites like "Quotes to Scrape" and the Wikipedia page for Beautiful Soup.
In the next video we will look at more sophisticated ways of building prompts and completions using newsfeed APIs, audio, and video.
Link to Lucidate's video on 'Attention' in transformers:
Attention is all you need explained
For the sample python code shown in the video visit Github:
https://github.com/mrspiggot/prompts_...
For an excellent 'Learn to code in python' site, with an ML/AI focus please see:
https://pythonprogramming.net/
Audio File Title:
Muzyka Do Medytacji
Audio File URL:
https://pixabay.com/music/meditations...
Audio File ID:
113601
Licensor's Username:
https://pixabay.com/users/relaxingtim...
Licensee:
RichardJamesWalker
=========================================================================
Link to introductory series on Neural networks:
Lucidate website: https://www.lucidate.co.uk/blog/categ...
YouTube: https://www.youtube.com/playlist?list...
Link to intro video on 'Backpropagation':
Lucidate website: https://www.lucidate.co.uk/post/intro...
YouTube: How neural networks learn - "Backprop...
'Attention is all you need' paper - https://arxiv.org/pdf/1706.03762.pdf
=========================================================================
Transformers are a type of artificial intelligence (AI) used for natural language processing (NLP) tasks, such as translation and summarisation. They were introduced in 2017 by Google researchers, who sought to address the limitations of recurrent neural networks (RNNs), which had traditionally been used for NLP tasks. RNNs had difficulty parallelizing, and tended to suffer from the vanishing/exploding gradient problem, making it difficult to train them with long input sequences.
Transformers address these limitations by using self-attention, a mechanism which allows the model to selectively choose which parts of the input to pay attention to. This makes the model much easier to parallelize and eliminates the vanishing/exploding gradient problem.
Self-attention works by weighting the importance of different parts of the input, allowing the AI to focus on the most relevant information and better handle input sequences of varying lengths. This is accomplished through three matrices: Query (Q), Key (K) and Value (V). The Query matrix can be interpreted as the word for which attention is being calculated, while the Key matrix can be interpreted as the word to which attention is paid. The eigenvalues and eigenvectors of these matrices tend to be similar, and the product of these two matrices gives the attention score.
=========================================================================
#ai #artificialintelligence #deeplearning #chatgpt #gpt3 #neuralnetworks #attention #attentionisallyouneed
#chatgpt #gpt3 #gpt-3 #artificialintelligence #ai
In this video, Lucidate discusses the importance of fine-tuning transformer neural networks and how to create bespoke models using simple prompt engineering methods. We highlight that manually populating an excel spreadsheet with prompts and completions will work, but this simple solution (though effective!!) is not scalable. Accordingly we introduce tools to manipulate text and retrieve relevant passages from the Internet.
In the video, we see how to solve the problem of breaking up text into prompts and completions by building a splitter class in Python. We also introduce 'Beautiful Soup', a popular Python library that allows users to extract HTML components of a web page.
We illustrate how to use Beautiful Soup to extract text from websites like "Quotes to Scrape" and the Wikipedia page for Beautiful Soup.
In the next video we will look at more sophisticated ways of building prompts and completions using newsfeed APIs, audio, and video.
Link to Lucidate's video on 'Attention' in transformers:
Attention is all you need explained
For the sample python code shown in the video visit Github:
https://github.com/mrspiggot/prompts_...
For an excellent 'Learn to code in python' site, with an ML/AI focus please see:
https://pythonprogramming.net/
Audio File Title:
Muzyka Do Medytacji
Audio File URL:
https://pixabay.com/music/meditations...
Audio File ID:
113601
Licensor's Username:
https://pixabay.com/users/relaxingtim...
Licensee:
RichardJamesWalker
=========================================================================
Link to introductory series on Neural networks:
Lucidate website: https://www.lucidate.co.uk/blog/categ...
YouTube: https://www.youtube.com/playlist?list...
Link to intro video on 'Backpropagation':
Lucidate website: https://www.lucidate.co.uk/post/intro...
YouTube: How neural networks learn - "Backprop...
'Attention is all you need' paper - https://arxiv.org/pdf/1706.03762.pdf
=========================================================================
Transformers are a type of artificial intelligence (AI) used for natural language processing (NLP) tasks, such as translation and summarisation. They were introduced in 2017 by Google researchers, who sought to address the limitations of recurrent neural networks (RNNs), which had traditionally been used for NLP tasks. RNNs had difficulty parallelizing, and tended to suffer from the vanishing/exploding gradient problem, making it difficult to train them with long input sequences.
Transformers address these limitations by using self-attention, a mechanism which allows the model to selectively choose which parts of the input to pay attention to. This makes the model much easier to parallelize and eliminates the vanishing/exploding gradient problem.
Self-attention works by weighting the importance of different parts of the input, allowing the AI to focus on the most relevant information and better handle input sequences of varying lengths. This is accomplished through three matrices: Query (Q), Key (K) and Value (V). The Query matrix can be interpreted as the word for which attention is being calculated, while the Key matrix can be interpreted as the word to which attention is paid. The eigenvalues and eigenvectors of these matrices tend to be similar, and the product of these two matrices gives the attention score.
=========================================================================
#ai #artificialintelligence #deeplearning #chatgpt #gpt3 #neuralnetworks #attention #attentionisallyouneed
#chatgpt #gpt3 #gpt-3 #artificialintelligence #ai
2 سال پیش
در تاریخ 1401/11/30 منتشر شده
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