BERT Pre-Training Dataset Preparation | Explained in Tamil | Embedding Model | BERT | GenAI | Agents

AI with Akash · Beginner ·🤖 AI Agents & Automation ·4mo ago

Key Takeaways

The video explains the dataset preparation for BERT model, covering pre-training and fine-tuning processes, and discusses the importance of data quality and quantity in machine learning.

Full Transcript

Hi everyone. Token special tokens understand. And now let's get started. pre-inpcion and pre-trained modeling and pre-teen actual bird applic language model next practical implementation and next pre-trained model second step already download. So in the second stage of So phase one and the phase two modeling cycle data set and then once And then finesse. So set of inputs. And then final pre-training process data set data set. billion different sources almost a billion first resource and second billion.3 billion words. data set. So basically learning million.3 billion import. So computer science machine learning machine learning Full data. 15% Train and Training. Testing accuracy. Final validation full And then first step Extra spaces index space white Next sentence requirement. Just let me know in the comments. So, until then, cheers. Bye.

Original Description

In this video I have explained about the dataset preparation for BERT model. All Complete Tutorials for Beginners: RAG: https://www.youtube.com/watch?v=4Qp5D5hcE4A CrewAI Agents: https://www.youtube.com/watch?v=PgPo9WHQczw LangGraph Agents: https://www.youtube.com/watch?v=vVtzWXTv3vM MCP: https://www.youtube.com/watch?v=2wyaDf04n_I FastAPI: https://www.youtube.com/watch?v=DRPpaFNpS-8 Socials: 1:1 Mentorship : https://topmate.io/akash_balakrishnan/706031 LinkedIn: https://linkedin.com/in/akashb22 Instagram: https://instagram.com/ai.with.akash Tokenizer Playground: https://huggingface.co/spaces/Xenova/the-tokenizer-playground #aiintamil #aiwithakash #python #genai #mcp #langchain #agents #finetuning #llm #fastapi
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The video teaches how to prepare a dataset for the BERT model, including pre-training and fine-tuning processes, and highlights the importance of data quality and quantity in machine learning. The video is explained in Tamil and is intended for beginners. The speaker also provides additional resources for further learning.

Key Takeaways
  1. Download pre-trained BERT model
  2. Prepare dataset for pre-training
  3. Fine-tune the model for specific task
  4. Evaluate model performance using testing accuracy and validation
💡 The quality and quantity of the dataset are crucial for the performance of the BERT model, and pre-training and fine-tuning processes can significantly improve the model's accuracy.

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