Whoever is getting an error while creating the tokenizer in step 1, run this command on your terminal: pip install sentencepiece Great video Nicholas, thanks mate!
Thanks for explaining the video nicely. But, does the pegasus model always generate one line of summary? Is there any way we can increase the number of summary lines?
cant install the torch. searched a lot but can't find the solution tried lots of things but got this error "No matching distribution found for torch" Please help me fix this problem.
6:18 pip install transformers==4.11.3 if you are getting an error ImportError: cannot import name 'PegasusForConditionalGeneration' from 'transformers'
Hi Nicholas, thank you for your content here on youtube :)! I was just wondering if I can also use Preview or Stable, since LTS is not supported on a mac. Thanks!
Really dope! I was looking for the turtorial to guide me through the summarization model and your video has extremely high quality and super practical! I have a question that is abstractive summarization need to be fine-tuned? If so, how can we do it? :D
Hey, would you mind making a video on how the model could be fine tuned for a custom text dataset, because I read the paper and couldn't do it. It would also be a good continuation to this video.
When companies build text summarization models like this one, do they create their own model and launch it for their app or do they generally use pre-existing models?
I have a little question. I would like to create a model to recognize a person, but everything I find online and on youtube uses Face-recognition. However, I would like my model to be able to recognize a person, not necessarily by their face but also by a tattoo or a feature of their body and etc. What do you think would be the best technique to accomplish this task? Would a simple image classifier do the trick?
Aside from the ethical implications, you could look at using a siamese network. Keep in mind it requires a ton of data if you're to do it on more than just faces!
@@NicholasRenotte That's what I understood, I need a lot of data. To practice a little, is it possible to use the landmarks (face) to detect and recognize a person with mediapipe? I'm trying to use your method on the sign languages video, but I get an accuracy of around 27% after 4000 epochs, no good :(
Can fine tune the underlying model on a dataset of your choice! Google did it on a bunch of different text corpuses, e.g. for Journals you could use this model: huggingface.co/google/pegasus-pubmed
hi nicholas, i really appreciate your video. thank you for this very informative video. could you make another one of how to fine tuning a custom text dataset ?
Just found you today...absolutely love your content and wide range of projects. I'm not a programmer but I'm looking to complete some projects very similar to what you've showcased in your videos. Are you available to hire?
Am getting this error when am trying to load the tokkenizer how can i resolve it TypeError Traceback (most recent call last) Cell In[25], line 2 1 # Load tokenize ----> 2 tokenizer = PegasusTokenizer.from_pretrained("google/pegasus-large") TypeError: 'NoneType' object is not callable
Would probably look at converting to english first, summarizing then converting back. One of the other subscribers mentioned the summarization in other languages sucks, would try that approach instead!
Don't even get me started. Honestly I hated every minute of that class and I definitely made it known. What a complete waste of time....if only i knew back then I'd end up coding, would've bailed completely!
I am getting 'NoneType' object is not callable after this code in colab tokens = tokenizer(text, truncation=True, padding="longest", return_tensors="pt") Solution please. Great Videos. Keep it up.
Getting error TypeError Traceback (most recent call last) in () 4 model = PegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum") 5 # Load tokenizer ----> 6 tokenizer = PegasusTokenizer.from_pretrained("google/pegasus-xsum") TypeError: 'NoneType' object is not callable Please tell how should I resolve it
hey iam getting this error of -: tokenizer = PegasusTokenizer.from_pretrained("google/pegasus-xsum") TypeError: 'NoneType' object is not callable any idea whats the mistake ?
I really hope you reply to this. Thanks so much for.this project. It worked before.. now for the autotokenizer.from_pretrained(google/pegasus-xsum) .. it's giving an error that filenotfound
Whoever is getting an error while creating the tokenizer in step 1, run this command on your terminal:
pip install sentencepiece
Great video Nicholas, thanks mate!
Also restart kernel and run all cells if you get "None type error". Sometimes the cell that downloads the pegasus model fails.
@@satoshinakamoto5710 Thanks mate
Thanks
Amazng content as usual!
Samenvattend, wederom een mooie introductie in NLP met de "vliegende paard" 👍
Like always, thank you. This channel is soooo good
Cheers @Alexandre!
@@NicholasRenotte sir how to increase the number of words or how to keep it variable
Very good channel and videos. Thank you Nicholas!
I learned so much from this video. Liked and subscribed. Thank you, Nicholas!
Great video, Nicholas.
Thanks for explaining the video nicely. But, does the pegasus model always generate one line of summary? Is there any way we can increase the number of summary lines?
hey have you found how to generate multiple lines
How to make more than 1 sentence summary? It is possible to configure it to generate a summary of specified sentences like 10 sentence summary?
mark! gonna take a try. many thanks
cant install the torch. searched a lot but can't find the solution tried lots of things but got this error "No matching distribution found for torch" Please help me fix this problem.
6:18 pip install transformers==4.11.3
if you are getting an error ImportError: cannot import name 'PegasusForConditionalGeneration' from 'transformers'
thank u so much.. u have no idea how much u saved me..
love you sir must say your brain is awesome.
Thank You Nicho...
when i import the model i am getting AttributeError: 'Version' object has no attribute 'major'
Pegasus? More like Pega SUS 😳
For real tho, this is actually really helpful, now i don't have to read anymore thanks fam 😂
I think you mean....Mega SUS?!
Hahaha, hell yeah, bail on all that reading rubbish!
@@NicholasRenotte sussy baka 😳😳😳
Bro please make a video on creating custom dataset for pose estimation and which architecture will be best to train
Hi Nicholas, thank you for your content here on youtube :)! I was just wondering if I can also use Preview or Stable, since LTS is not supported on a mac. Thanks!
Yup, suggest using stable!
can we set the length of summary?
Really dope! I was looking for the turtorial to guide me through the summarization model and your video has extremely high quality and super practical!
I have a question that is abstractive summarization need to be fine-tuned? If so, how can we do it? :D
great video, thanks - are there any summarization models that accept more than 1k tokens as input?
Hey, would you mind making a video on how the model could be fine tuned for a custom text dataset, because I read the paper and couldn't do it. It would also be a good continuation to this video.
Hey, Did you find the way to do that?
sir how to increase the number of words or how to keep it variable
yooooo this is sick!
When companies build text summarization models like this one, do they create their own model and launch it for their app or do they generally use pre-existing models?
thanks, it was so helpful can you do a video on how we can fine-tune the pegasus model on a different dataset?
Hey, Did you find the way to do that?
@@rudreshmehta6510 did you find it ?
I have a little question. I would like to create a model to recognize a person, but everything I find online and on youtube uses Face-recognition.
However, I would like my model to be able to recognize a person, not necessarily by their face but also by a tattoo or a feature of their body and etc. What do you think would be the best technique to accomplish this task? Would a simple image classifier do the trick?
Aside from the ethical implications, you could look at using a siamese network. Keep in mind it requires a ton of data if you're to do it on more than just faces!
@@NicholasRenotte That's what I understood, I need a lot of data. To practice a little, is it possible to use the landmarks (face) to detect and recognize a person with mediapipe? I'm trying to use your method on the sign languages video, but I get an accuracy of around 27% after 4000 epochs, no good :(
@@alexandregagne4151 might be a bit late to this, have you checked out the facial rec tutorial?
Is there any way to specify the length of summary
hey have you found it?
First I thought that you are talking the Pegasus virus then ooh ok summarize Pegasus 😂
😂
Can you also show how do you fine tune the Pegasus model with a custom dataset for text summarization?
Can you make a text summarizer using gpt 3 or 2?
Could try!
Great video! thanks 🎉
I was unable to understand the last part, what fine tuning can be done exactly for the model to perform better?
Can fine tune the underlying model on a dataset of your choice! Google did it on a bunch of different text corpuses, e.g. for Journals you could use this model: huggingface.co/google/pegasus-pubmed
@@NicholasRenotte Thanks!
hi nicholas, i really appreciate your video. thank you for this very informative video.
could you make another one of how to fine tuning a custom text dataset ?
Thanks Nick! Also how do I add a dataset to it instead of a piece of text?
For summarization? This is very much focused on NLP. For structured data I would be focused on using Pandas, got a crash course on the channel!
Great video
Is Pegasus the best model for text summarization? if not which model is the best?
Just found you today...absolutely love your content and wide range of projects. I'm not a programmer but I'm looking to complete some projects very similar to what you've showcased in your videos. Are you available to hire?
Hey thanks man! I'm wondering is there is way we can create book summaries as well with one of these transformers?
Am getting this error when am trying to load the tokkenizer how can i resolve it
TypeError Traceback (most recent call last)
Cell In[25], line 2
1 # Load tokenize
----> 2 tokenizer = PegasusTokenizer.from_pretrained("google/pegasus-large")
TypeError: 'NoneType' object is not callable
Nicholas why don't you start reinforcement learning for gaming, everybody wants it.
by the way love your videos
Ik ik, just gotta get back to it.
He is doing his best, we should have some patience. Quality over quantity!
thank you so much
great video :D. How can we implement this for another language ?
Would probably look at converting to english first, summarizing then converting back. One of the other subscribers mentioned the summarization in other languages sucks, would try that approach instead!
@@NicholasRenotte Thanks. I'll give a try :D
I remember that nightmarish novel.
Don't even get me started. Honestly I hated every minute of that class and I definitely made it known. What a complete waste of time....if only i knew back then I'd end up coding, would've bailed completely!
love it
🙏🙏🙏
hi there. why do we bother ourselves to summarize just 512 tokens :)
I am getting 'NoneType' object is not callable after this code in colab
tokens = tokenizer(text, truncation=True, padding="longest", return_tensors="pt")
Solution please.
Great Videos. Keep it up.
What's in the text?
@@NicholasRenotte
TypeError Traceback (most recent call last)
in ()
----> 1 tokens = tokenizer(text, truncation=True, padding="longest", return_tensors="pt")
TypeError: 'NoneType' object is not callable
running into same problem
reloading jupyter fixed my issue :)
@@qwertl99 Didn't work for me in colab
reading research papers can really be hard, wish there was some trick
thank's
Bro pls make a python chat bot with deep learning and actions like: join school class answer ok sir and join the class
Alrighty, will build it into the plan!
You are insane bro
Don't lie. Bet you love Jane Austin.
M8 😂
Getting error
TypeError Traceback (most recent call last)
in ()
4 model = PegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum")
5 # Load tokenizer
----> 6 tokenizer = PegasusTokenizer.from_pretrained("google/pegasus-xsum")
TypeError: 'NoneType' object is not callable
Please tell how should I resolve it
Who else is having issues installing pytorch? The error is shown below
ERROR: No matching distribution found for torch==1.8.2+cu111
If copied directly from the PT site, you should be good to go. Possibly another version of Python required? I used 3.7.3 in the video with no issues.
hey iam getting this error of -: tokenizer = PegasusTokenizer.from_pretrained("google/pegasus-xsum")
TypeError: 'NoneType' object is not callable
any idea whats the mistake ?
I really hope you reply to this. Thanks so much for.this project. It worked before.. now for the autotokenizer.from_pretrained(google/pegasus-xsum) .. it's giving an error that filenotfound
PegasusTokenizer.from_pretrained('google/pegasus-xsum') returns None. PegasusTokenizer also returns None for 'google/pegasus-large'