Text Preprocessing | Sentiment Analysis with BERT using huggingface, PyTorch and Python Tutorial

Learn how to preprocess raw text data using the huggingface BertTokenizer and create a PyTorch dataset. We'll look into adding special tokens, padding to fixed-length sequences and creating attention masks.

⭐️ Tutorial Contents ⭐️

(00:00) Introduction
(02:18) Notebook setup
(03:36) Data exploration
(11:07) Data preprocessing - tokenization, padding & attention mask
(26:55) Choosing maximum sequence length
(29:34) Create PyTorch dataset
(33:47) Splitting the data into train, validation, and test sets
(35:13) Creating data loaders

#BERT #Huggingface #PyTorch #SentimentAnalysis #TextPreprocessing #NLP #Tokenizer

[supervince]

Really appreciate your kindness to make this video.

[Daniel-hpoi]

Found this channel today, incredible videos and I love that there are timestamps for all the different subtopics.

[mamotivated]

This was so neatly laid out even at double speed. Nice work, keep it going. You are doing a great service and I will definitely keep your brand in mind when looking for consulting. Cheers

[nfox]

Thank you so much for taking the time to create and share this tutorial. I've been struggling to understand BERT tokens and using them for text classification and your video has helped me a lot. Thank you very much.

[leroychan]

The best tutorial about BERT I have ever watched! It would be better if there are subtitles. 😉

[BiranchiNarayanNayak]

Excellent video tutorial on BERT pre processing. Will wait for the next video series.

[usmanmalik-xkvi]

The explanation is great and the content itself is of very high quality. Thanks, Venelin.

[muggsy]

Thank you very much for these amazing series on BERT (Data Processing and Classification)!!! 😊 Explanations were crystal clear. Great job!!!! Hope you keep posting more NLP stuff👌🏻

[김기화-ru]

I'm so thankful for this video. I learned the basics of transformers by theory before, but had no idea how to apply it through pytorch. I'm looking forward to watch more of your tutorials. Genuine thanks to you again.

[madhavimourya]

Very helpful vedio tutorial for learning BERT. It's a saviour when I found it. Thanks a ton
Venelin Valkov

[christoben]

Great video. I have followed almost all your videos in pytorch and I have one word for you - "You are the BEST ". 1 will be glad if you will make a video on reinforcement learning. Thank you.

[sach]

26:33 it is not that 101 is for [CLS] is a token that will be used to predict whether or not Part B is a sentence that directly follows Part A.

[alextran]

love your video. Crisp instructions. Do you know why Colab would crash on Mac OS every time when running sequence length choosing (around 28:17)

[tacoblacho]

Love your explanations man! Clear-cut, to the point and very easy to follow. Pleease keep making videos!

[sohelshaikhh]

A nice and brief explanation of each concept, waiting for the next part(s)

[vpsfahad]

Very nice explanation. I hope you will teach us more in NLP.
Thank you for making such beautiful tutorials

[maryamaziz]

How to build and training model and upload it in huggingface like bert uncased model

[georgepetropoulos]

In 33:50, how could we use a stratified split in order to tackle the imbalance issue of the dataset?

[srivardhanchimmula]

Thank you so much for this easy to understand tutorial. Can someone please post the link to this playlist? I can't find the next video.

[stackologycentral]

Amazing tutorial, understanding was such a breeze . Thank you very much, your way of teaching is excellent.