What is the F1 score of DeepSeek's R1 model on various tasks?

DeepSeek’s R1 model does not have publicly disclosed F1 scores for specific tasks as of the latest available information. The F1 score—a metric balancing precision (correct positive predictions) and recall (coverage of actual positives)—is typically task-dependent and requires benchmarking on standardized datasets. While DeepSeek has highlighted R1’s general capabilities in areas like text generation and reasoning, detailed performance metrics for individual tasks (e.g., classification, named entity recognition) have not been formally released. Developers evaluating such models usually rely on published benchmarks or conduct their own tests, but neither is currently feasible for R1 without access to the model or its training data.

To estimate R1’s potential F1 performance, developers can consider its architecture and training approach. For example, models optimized for multi-task learning often achieve strong F1 scores on structured tasks like sentiment analysis or question answering by balancing specificity and sensitivity. If R1 uses techniques similar to BERT or GPT-3.5—such as attention mechanisms or fine-tuning on domain-specific data—its F1 scores on tasks like text classification could align with established benchmarks. For instance, BERT-based models often achieve F1 scores between 90-95% on the CoLA (Corpus of Linguistic Acceptability) dataset, while GPT-3.5 reaches ~85% on MMLU (Massive Multitask Language Understanding). If R1 employs advanced preprocessing or larger training datasets, it might exceed these ranges, but without concrete data, this remains speculative.

For developers interested in practical applications, the absence of published F1 scores means prioritizing direct experimentation. If granted access to R1, running it on task-specific benchmarks (e.g., GLUE for language understanding or Conll-2003 for NER) would provide actionable metrics. Alternatively, comparing R1’s outputs to open-source models like Llama-3 or Mistral on custom datasets could offer indirect insights. In scenarios where F1 is critical—such as medical text analysis or legal document processing—verifying performance through pilot testing is essential. Until DeepSeek releases detailed evaluations, developers should approach R1’s capabilities with caution, focusing on its documented strengths (e.g., code generation, logical reasoning) rather than assuming task-specific F1 performance.