The highlighted articles cover a variety of topics, including algorithmic thinking for data scientists, outlier detection in time-series data, route optimization for visiting NFL teams, minimum vertex coloring problem solution, high-cardinality features, multilingual RAG (Rapidly-explainable AI) system development, fine-tuning smaller transformer models, long-form visual understanding, multimodal image-text models, the theoretical underpinnings of learning, data science stress management, and reinforcement learning.
There’s a reason you’re confused
Each time you run the model, the results may vary a little bit. Overall, after 5 tries, I can conclude that SBERT has a bit better performance in terms of best f1 score while Data2vec used way less memory. The average f1 scores for both models are very close.
$$logloss(theta) = - {1 over m} sum_{i=1}^m (y_i ln(hat p(y_i=1)) + (1-y_i) ln(1-hat p(y_i=1)))$$
The Self-Learning Path To Becoming A Data Scientist, AI or ML Engineer
