Polish Emotional Intelligence
Evaluating language models on emotional intelligence in Polish: understanding emotional states, predicting emotional responses, and nuanced sentiment analysis.
Polish Emotional Intelligence is a key task in natural language processing. Below you will find the standard benchmarks used to evaluate models, along with current state-of-the-art results.
Benchmarks & SOTA
Related Tasks
Natural Language Inference
Determining entailment relationships between sentences (SNLI, MNLI).
Reading Comprehension
Understanding and answering questions about passages.
Text Ranking
Text ranking is the invisible backbone of every search engine and RAG pipeline. The field was transformed by ColBERT (2020) introducing late interaction, then by instruction-tuned embedding models like E5-Mistral and GTE-Qwen that turned general LLMs into retrieval engines. MS MARCO and BEIR remain the standard battlegrounds, but the real test is zero-shot transfer — can a model trained on web search generalize to legal documents, scientific papers, and code? The gap between supervised and zero-shot performance has shrunk from 15+ points to under 3 in two years.
Polish Text Understanding
Evaluating language models on understanding Polish text: sentiment, implicatures, phraseology, tricky questions, and hallucination resistance.
Get notified when these results update
New models drop weekly. We track them so you don't have to.
Something wrong or missing?
Help keep Polish Emotional Intelligence benchmarks accurate. Report outdated results, missing benchmarks, or errors.