#BERT AND #GPT2 TEST
Quick test on earlier models of llm.
For more detailed taxonomy please refer to official documentation and papers.
#bert #gpt2 #gpt #test #python #colab #llm
[Comparing GPT-2 and BERT for Language Tasks]( • Transformers - BERT AN... )
In this video, the presenter compares *GPT-2* and *BERT* by running tests on their performance for various language tasks. Key highlights include:
- **Introduction to GPT-2 and BERT**: Overview of both models, their structures, and applications. GPT-2 is known for text generation, while BERT excels in understanding tasks like Q&A.
- *Testing in Google Colab**: The presenter demonstrates how to load and run both models, showing how BERT handles **question-answering* with context, and GPT-2's performance in **text generation**.
- *Interactive Interfaces**: A user-friendly interface with **Gradio* is used for testing, providing a better experience when querying the models.
- **Performance Insights**: While both models perform well, BERT shows better results for context-based queries, whereas GPT-2 repeats phrases and struggles with more specific tasks.
- **Technical Challenges**: The presenter discusses issues with loading larger models like GPT-J due to computational resource limits.
This comparison offers a look into how these models handle different tasks and their strengths in specific use cases.
1. Introduction to GPT2 and BERT models: 00:01
2. Overview of GPT2 model developed by OpenAI: 00:33
3. Introduction to BERT model developed by Google: 01:06
4. Model design and task comparison (GPT2, BERT): 01:40
5. Explanation of testing in Collab environment: 03:23
6. Running BERT with question + context input: 04:16
7. First test: Asking what is machine learning: 05:51
8. Expanding the example with Gladio: 06:21
9. Gladio test: Asking about linear regression: 07:12
10. Testing GPT2 with basic question input: 10:01
11. Understanding GPT2’s text generation pattern: 11:02
12. Comparison of GPT2 and BERT output: 12:04
13. Model refinement and documentation insights: 13:06
14. Running GPT-J model and RAM limitations: 16:14
15. Conclusion and final comparison of the models: 17:43
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