EPYTHONLAB Telegram 1897
๐Ÿ“ข๐——๐—ฎ๐˜† ๐Ÿฎ๐Ÿญ/๐Ÿญ๐Ÿฌ๐Ÿฌ: ๐—ง๐—ฟ๐—ฎ๐—ถ๐—ป๐—ถ๐—ป๐—ด ๐—”๐—บ๐—ต๐—ฎ๐—ฟ๐—ถ๐—ฐ ๐—ก๐—˜๐—ฅ ๐— ๐—ผ๐—ฑ๐—ฒ๐—น๐˜€

I fine-tuned models on 27,989 labeled examples, optimizing key parameters:

- Learning rate: Experimented to find the sweet spot.

- Batch size: Limited to 16 to manage memory constraints.

- Metrics: Focused on precision, recall, and F1-score.



๐Ÿ’ก Finding: Smaller batches helped balance performance and computational efficiency.

๐Ÿ’ก Question: How do you optimize parameters for low-resource NLP tasks?

#AI #ModelTraining #Ethiopia #NLP



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๐Ÿ“ข๐——๐—ฎ๐˜† ๐Ÿฎ๐Ÿญ/๐Ÿญ๐Ÿฌ๐Ÿฌ: ๐—ง๐—ฟ๐—ฎ๐—ถ๐—ป๐—ถ๐—ป๐—ด ๐—”๐—บ๐—ต๐—ฎ๐—ฟ๐—ถ๐—ฐ ๐—ก๐—˜๐—ฅ ๐— ๐—ผ๐—ฑ๐—ฒ๐—น๐˜€

I fine-tuned models on 27,989 labeled examples, optimizing key parameters:

- Learning rate: Experimented to find the sweet spot.

- Batch size: Limited to 16 to manage memory constraints.

- Metrics: Focused on precision, recall, and F1-score.



๐Ÿ’ก Finding: Smaller batches helped balance performance and computational efficiency.

๐Ÿ’ก Question: How do you optimize parameters for low-resource NLP tasks?

#AI #ModelTraining #Ethiopia #NLP

BY Epython Lab




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