Hugging Face: Fine-Tune NLP Pipeline for Question Answering | Transformers & Attention Mechanism

🔥 Hugging Face, a popular platform for Natural Language Processing offers pre-trained transformer models that can be fine-tuned to perform question-answering tasks with remarkable performance. However, to fully leverage the power of these models, it's essential to have a good understanding of how transformers and attention mechanisms work.

As NLP becomes increasingly important in many domains, the ability to accurately answer questions from large amounts of text is becoming a critical skill.

In this video, we will delve into the mechanics of transformers and attention mechanisms, and explore how to use them to fine-tune Hugging Face's pre-trained models for question answering. Through hands-on examples and interactive exercises, participants will gain a deep understanding of how to create custom datasets and how to pick pre-trained models based on the task. By the end of this session, participants will have the knowledge and skills necessary to develop highly accurate and effective question-answering systems and more importantly good intuition about how to use Hugging Face.

#deeplearning #huggingface #transformers #attentionmechanism

🔥 Who is this DataHour for?
- Students & Freshers who want to build a career in the Data-tech domain
- Working professionals who want to transition to the Data-tech domain
- Data science professionals who want to accelerate their career growth
- Prerequisites: Zeal for learning new technologies, & interest in data science

🔥 About the Speaker
Isuru Alagiyawanna is a highly skilled Data Scientist at Axiata Digital Labs (Pvt.) Ltd, with expertise in the field of Machine Learning. In addition to his professional work, he also serves as an Assistant Lecturer in Machine Learning at the Sri Lanka Institute of Information Technology, where he imparts his knowledge and experience to the next generation of data scientists.

With a passion for exploring the latest trends and techniques in the field of Data Science, Isuru is dedicated to delivering innovative solutions that drive business success. His commitment to excellence and strong work ethic make him a valuable asset to any team.

⭐Tags⭐
deep learning
hugging face
transformers
attention mechanism
question answering
NLP
pre-trained models
fine-tuning
data science
career development
machine learning
data scientist
AI
neural networks
self-attention
encoder-decoder architecture
BERT (Bidirectional Encoder Representations from Transformers)
GPT (Generative Pre-trained Transformer)
fine-tuning, tokenization
attention head
positional encoding
input embedding
output embedding
attention score
multi-head attention
self-attention mechanism
feed-forward neural network
gradient descent
backpropagation
learning rate
optimizer
loss function
masked language modeling
sequence classification
sequence generation
transformer architecture,
transfer learning
attention weights

[thekostekmarczak]

It isn't question answering. It is seq to seq. QA is question, context, answer

[veyselaytekin]

Thank you so much. could you share the link of dataset?☺