I've been searching through countless tutorials on fine-tuning for almost a month now, and I finally found yours. Thank you so much! Please keep making more content.
This instructional vid is something I was looking for. I am looking to do more of a rating type of app, but the sentiment analysis is a good start for that. I would like to get a copy of your free data science guide. Thanks.
Glad it was what you were looking for. For rating type of app consider using Text Classification models. And here you can get my free guide: www.maryammiradi.com/free-guide
Hi Maryam, I'm brand new to learning data science. I really enjoy your step by step implementation and explanations. I would love your free data science guide as a reference, as I continue to explore these topics. Many thanks and please keep up the great work!
@@maryammiradi Thank you Maryam! Would love if you could continue to explore Hugging Face - Time Series Analysis and compare setup/performance vs your previous tutorial on the 36 models.
@jdub8204 Do you mean building Time Series with Hugging Face or Do you mean as separate things because Hugging Face is more for LLMs NLP and Computer Vision where ScikitLearn 36!Models are suitable for Machine Learning problems. Then for Time Series you have got so many different options from Statistics, Machine Learning to Deep Learning up to the latest LLMs that can be used for Time series
@@maryammiradi Wow thanks for the quick replies Maryam. There's so many models/technologies - that I'm confusing myself and their application. In particular, I'm interested in time series forecasting - and predicting behavior based on historical data. Since I'm new, would you recommend focusing on one particular model (eg XGBoost?) Thank you
hello I'm from India, Thanks for the clear explanation could you please make more videos on finetuning as many youtuber have not covered it (clear explanation) , try to make it for language translation it would be great if you make it , Thanks in advance I loved the way you explained the things. little suggestion- Try to reduce the size of the face camera
Thank you! Absolutely! Have you seen my other video from my LLM series. I am explaining LLM ladder & Prompt tuning & Prefix tuning 👋 th-cam.com/video/HkZOGGvZzg4/w-d-xo.htmlsi=kxHtTkCfM51eUEGE
Hi Maryam. Really loving your videos. I am seen your last video on time series and really found it helpful. I am exploring the opportunity of Temporal Fusion Transfers on Time Series Forecasting.
@asifurrahman2111 TFTs can forecast multiple time steps into the future, making them great for various applications. In which industry are you applying Time Series?
@@maryammiradi Well I am planning to use TFTs in the field of hydrology. As the data comes from multiple sources and exhibits significant variation, I thought that the TFT model will be ideal.
When I run the trainer.train() syntax at Step #6 (12:20') the following error popped out, what is the problem? All the codes before that line works good. Here is the error: NameError Traceback (most recent call last) in () 1 # Evaluate the model ----> 2 result = trainer.evaluate() 3 print(result) NameError: name 'trainer' is not defined
The Trainer should be applied to your specific model and then you can run the evaluation. trainer = Trainer( model, args, train_dataset=encoded_dataset[“train”], eval_dataset=encoded_dataset[“validation”], tokenizer=tokenizer, compute_metrics=compute_metrics ) trainer.evaluate() trainer.predict(encoded_dataset[“test”])
If you're using Hugging Face's hosted API services, ensure that data is transmitted over secure channels. Hugging Face models are pre-trained and may not be aware of the sensitive nature of your data. Using models locally rather than via API can mitigate this risk.
@@julienduchesneau9747 Would you let me know which part is not clear tp you? To learn NLP and LLMs you need to have some foundation in AI and ML. What is your field of study? Hopefully I can help you with more resources
@@maryammiradi I never used hunging face yet because I don't have a suitable GPU (to try local LLM) so I am lost right at the beggining, I dont even know what do you use to type on. I am just learning for myself, from scratch. With the help of ChatGPT I did a chatbot on my twitch account using Python in VScode. It is super basic because also super cheap. So I watch several youtube video about AI to continu learning and improve my chatBot. So yeah thanks for the reply but you don't have to, I have sub to your channel so I will come back later when I feel I need it.
@julienduchesneau9747 The most important thing that Hugging Face offers is not their local LLMs but their Python library transforms. This library allows you to so any kind of NLP task that you want just with importing this library and then just use their pipeline function. My code is in Google colab. It is notebook with link in the description. What you would learn from this video is not only Hugging face but also what kind of NLP tasks are possible and for which purposes you can use LLMs, like summarization and Sentiment Analysis. Let me know if you have other questions. 👋
@@maryammiradi oh wow really??!!! ok I will definetly watch it again and also search about notebook. Sometime there is so much stuff to learn, especially for a begginer, github ananconda langchain streamlit... AND everthing goes so fast in AI, it is crazy how each month there is new cool stuff. Thanks for the reply also it is really helpfull
@@maryammiradi oh wow really??!!! I will definetly watch it again then and take a look about notebook, another thing that I dont use already but probably a must. So much thing to look into when we begin, Github, anaconda, langchain, streamlit, RAG. AI is so cool tho I am not tired learning about it and it is so crazy to see how things advance month after month. Thanks for the reply I am pretty sure it will help my chatbot a lot
🙋🏻♀️Get Access to my 20+ Years Experience in AI: ⚡️Free guide: www.maryammiradi.com/free-guide
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I've been searching through countless tutorials on fine-tuning for almost a month now, and I finally found yours. Thank you so much! Please keep making more content.
Super glad to hear that this was helpful! Let me know if there are specific subjects that you want to have content about 👋
The quality of explanation reflects your doctorate .Just classic.
@gudapativenkateswararao706 Thanks alot!!
Wow. Awesome tutorial, Doc. Many thanks.
Thanks alot!! Glad it was useful 👋😄
Great educational video! Lots of insights in just 15 minutes, thank you for making this!
Really glad to hear that it was useful for you 👋
What a brilliant video and resourceful channel! Thanks so much, doc!
Thank you very much and very glad to hear it is useful to you 😄👋
What a discovery! Thank you Dr. Maryam.
Glad it is useful to you 👋😄
This instructional vid is something I was looking for. I am looking to do more of a rating type of app, but the sentiment analysis is a good start for that. I would like to get a copy of your free data science guide. Thanks.
Glad it was what you were looking for. For rating type of app consider using Text Classification models. And here you can get my free guide: www.maryammiradi.com/free-guide
Thank you for making such an insightful video!
Glad to hear it was insightful for you! Have you recently started working with LLMs?
Oh wow! Fabulous content, Dr. Hugging Face truly rocks. Could I get the Data Science Guide, please? Many thanks. 💯🙌
Thank you very much!! Glad to hear!! The link to guide is here: www.maryammiradi.com/free-guide
Thanks a million for the Clear Explanation...
Thank you for listening and glad it was helpful 👋
Hi Maryam, I'm brand new to learning data science. I really enjoy your step by step implementation and explanations. I would love your free data science guide as a reference, as I continue to explore these topics. Many thanks and please keep up the great work!
Super glad to hear it is helpful for you. Here is the link to grab your free guide: www.maryammiradi.com/free-guide
@@maryammiradi Thank you Maryam! Would love if you could continue to explore Hugging Face - Time Series Analysis and compare setup/performance vs your previous tutorial on the 36 models.
@jdub8204 Do you mean building Time Series with Hugging Face or Do you mean as separate things because Hugging Face is more for LLMs NLP and Computer Vision where ScikitLearn 36!Models are suitable for Machine Learning problems. Then for Time Series you have got so many different options from Statistics, Machine Learning to Deep Learning up to the latest LLMs that can be used for Time series
@@maryammiradi Wow thanks for the quick replies Maryam. There's so many models/technologies - that I'm confusing myself and their application. In particular, I'm interested in time series forecasting - and predicting behavior based on historical data. Since I'm new, would you recommend focusing on one particular model (eg XGBoost?) Thank you
@jdub8204, yes absolutely one model, specially XGBoost is great!! So idd please focus on that one! I will make also Time Series Videos in the future 👋
Great video! I enjoyed and learned a lot. Thank you. I would love to receive the Data Science Guide
Really glad to hear!! Here you can find the Free Data Science Guide: www.maryammiradi.com/free-guide
Free data science guide
Of course! Please grab it hear: www.maryammiradi.com/free-guide
Excellent Video once again 😍 can you make a video on how to evaluate LLM Apps like RAGs using various metrics
Glad it was useful! Absolutely! LLM evaluation is absolutely on my list. Thank for the suggestion
@@maryammiradi Yes! A RAG and LLM OS architecture video would be amazing.
RAG is also important part of the LLM ladder do I will definitely make a video about it
Excellent. please make more videos.
Thanks alot!! I will do 👋
thnaks for producing such excellent contents😍
Glad to hear it is useful 👋
ممنون مریم جان🌻
Glad it is helpful 👋
Great content! subscribing right awayy
Glad to hear!!
Excellent explanation!
Glad you liked the explanation 👋
Keep up the good work 👍
Thanks
hello I'm from India, Thanks for the clear explanation could you please make more videos on finetuning as many youtuber have not covered it (clear explanation) , try to make it for language translation it would be great if you make it , Thanks in advance
I loved the way you explained the things.
little suggestion- Try to reduce the size of the face camera
Glad to hear that my explanation was clear. Thanks alot for the suggestion! Will definitely talk about Fine Tuning in the coming weeks!!
Great job. Maryam.
Thanks alot!! 👋
Super useful. Thanks Maryam. Could you make a video on langchain and langsmith?
Thank you! Absolutely! Have you seen my other video from my LLM series. I am explaining LLM ladder & Prompt tuning & Prefix tuning 👋 th-cam.com/video/HkZOGGvZzg4/w-d-xo.htmlsi=kxHtTkCfM51eUEGE
Free Data Science Guide
Sure! Here you go: www.maryammiradi.com/free-guide
Free data science guide
Of course! Here you go: www.maryammiradi.com/free-guide
french accent ..very nice and good video
I am not French 😅
@@maryammiradi means french speaking
@@majukanumi9639 I am not French Speaking either
@@maryammiradi ok sorry than you have a beautiful accent musical words coming from your mouth
Thank you very much! 🙏
Hi Maryam.
Really loving your videos. I am seen your last video on time series and really found it helpful. I am exploring the opportunity of Temporal Fusion Transfers on Time Series Forecasting.
@asifurrahman2111 TFTs can forecast multiple time steps into the future, making them great for various applications. In which industry are you applying Time Series?
@@maryammiradi Well I am planning to use TFTs in the field of hydrology. As the data comes from multiple sources and exhibits significant variation, I thought that the TFT model will be ideal.
@@asifurrahman2111 Great Idea!! It is idd a very good choice
Dear Everyone, Sorry I forgot to mention in Video Intro 😅but Sentiment Analysis is in Step 2, NLP Tasks!! Time: 2:15
Free DataScience Guide!
Here you go: www.maryammiradi.com/free-guide
Free Datascience guide
Of course! Here you go: www.maryammiradi.com/free-guide
Free Data science guide
Sure! Here it is: www.maryammiradi.com/free-guide
Good work doctor, can you tell in some video how to do quantization for large models
Sure! I will put it on Video Ideas 💡 if you have specific questions please let me know.
When I run the trainer.train() syntax at Step #6 (12:20') the following error popped out, what is the problem? All the codes before that line works good. Here is the error:
NameError Traceback (most recent call last)
in ()
1 # Evaluate the model
----> 2 result = trainer.evaluate()
3 print(result)
NameError: name 'trainer' is not defined
The Trainer should be applied to your specific model and then you can run the evaluation.
trainer = Trainer(
model,
args,
train_dataset=encoded_dataset[“train”],
eval_dataset=encoded_dataset[“validation”],
tokenizer=tokenizer,
compute_metrics=compute_metrics
)
trainer.evaluate()
trainer.predict(encoded_dataset[“test”])
Cool!
Science guide
Of course! Go ahead and grab it here: www.maryammiradi.com/free-guide
If we are dealing with sensitive/confidential data is it safe to use embeddings from huggingfaceembeddings?How secure is it?
If you're using Hugging Face's hosted API services, ensure that data is transmitted over secure channels.
Hugging Face models are pre-trained and may not be aware of the sensitive nature of your data. Using models locally rather than via API can mitigate this risk.
your explanations and vedios is good can ypu tell which software using for doing vedio.
Do you mean for video editing?
Yes Correct pls
@@raom2127 Camtasia
Thanks for you reply my namaste🙏
looks like a valuable video but I dont get it, I guess I am not there yet.
@@julienduchesneau9747 Would you let me know which part is not clear tp you? To learn NLP and LLMs you need to have some foundation in AI and ML. What is your field of study? Hopefully I can help you with more resources
@@maryammiradi I never used hunging face yet because I don't have a suitable GPU (to try local LLM) so I am lost right at the beggining, I dont even know what do you use to type on. I am just learning for myself, from scratch. With the help of ChatGPT I did a chatbot on my twitch account using Python in VScode. It is super basic because also super cheap. So I watch several youtube video about AI to continu learning and improve my chatBot. So yeah thanks for the reply but you don't have to, I have sub to your channel so I will come back later when I feel I need it.
@julienduchesneau9747 The most important thing that Hugging Face offers is not their local LLMs but their Python library transforms. This library allows you to so any kind of NLP task that you want just with importing this library and then just use their pipeline function. My code is in Google colab. It is notebook with link in the description. What you would learn from this video is not only Hugging face but also what kind of NLP tasks are possible and for which purposes you can use LLMs, like summarization and Sentiment Analysis. Let me know if you have other questions. 👋
@@maryammiradi oh wow really??!!! ok I will definetly watch it again and also search about notebook. Sometime there is so much stuff to learn, especially for a begginer, github ananconda langchain streamlit... AND everthing goes so fast in AI, it is crazy how each month there is new cool stuff. Thanks for the reply also it is really helpfull
@@maryammiradi oh wow really??!!! I will definetly watch it again then and take a look about notebook, another thing that I dont use already but probably a must. So much thing to look into when we begin, Github, anaconda, langchain, streamlit, RAG. AI is so cool tho I am not tired learning about it and it is so crazy to see how things advance month after month. Thanks for the reply I am pretty sure it will help my chatbot a lot
Free Data Science Guide
Of course! Please go ahead and pick up guide here: www.maryammiradi.com/free-guide