📢𝗗𝗮𝘆 𝟮𝟭/𝟭𝟬𝟬: 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗔𝗺𝗵𝗮𝗿𝗶𝗰 𝗡𝗘𝗥 𝗠𝗼𝗱𝗲𝗹𝘀
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.
💡 Finding: Smaller batches helped balance performance and computational efficiency.
💡 Question: How do you optimize parameters for low-resource NLP tasks?
#AI #ModelTraining #Ethiopia #NLP
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.
💡 Finding: Smaller batches helped balance performance and computational efficiency.
💡 Question: How do you optimize parameters for low-resource NLP tasks?
#AI #ModelTraining #Ethiopia #NLP