This study addresses multi-label emotion classification in Moroccan Arabic. We developeda lightweight computational approach to detect and categorize emotional content in sevendistinct categories: anger, fear, joy, disgust,sadness, surprise, and neutral. Our findings reveal that our efficient, subword-aware modelachieves 46.44% accuracy on the task, demonstrating the viability of lightweight approachesfor emotion recognition in under-resourcedlanguage variants. The model’s performance,while modest, establishes a baseline for emotion detection in Moroccan Arabic, highlighting both the potential and challenges of applying computationally efficient architectures to dialectal Arabic processing. Our analysis revealsparticular strengths in handling morphologicalvariations and out-of-vocabulary words, thoughchallenges persist in managing code-switchingand subtle emotional distinctions. These results offer valuable insights into the trade-offsbetween speed and accuracy in multilingualemotion detection systems, particularly for low-resource languages.

Text embeddings are commonly evaluated on a small set of datasets from a single task not covering their possible applications to other tasks. It is unclear whether state-of-the-art embeddings on semantic textual similarity (STS) can be equally well applied to other tasks like clustering or reranking. This makes progress in the field difficult to track, as various models are constantly being proposed without proper evaluation. To solve this problem, we introduce the Massive Text Embedding Benchmark (MTEB). MTEB spans 8 embedding tasks covering a total of 58 datasets and 112 languages. Through the benchmarking of 33 models on MTEB, we establish the most comprehensive benchmark of text embeddings todate. We find that no particular text embedding method dominates across all tasks. This suggests that the field has yet to converge on a universal text embedding method and scale it up sufficiently to provide state-of-theart results on all embedding tasks. MTEB comes with open-source code and a public leaderboard at https://github.com/embeddings-benchmark/mteb.

Large language models (LLMs) excel in many tasks in NLP and beyond, but most open models have very limited coverage of smaller languages and LLM work tends to focus on languages where nearly unlimited data is available for pretraining. In this work, we study the challenges of creating LLMs for Finnish, a language spoken by less than 0.1% of the world population. We compile an extensive dataset of Finnish combining web crawls, news, social media and eBooks. We pursue two approaches to pretrain models: 1) we train seven monolingual models from scratch (186M to 13B parameters) dubbed FinGPT, 2) we continue the pretraining of the multilingual BLOOM model on a mix of its original training data and Finnish, resulting in a 176 billion parameter model we call BLUUMI. For model evaluation, we introduce FIN-bench, a version of BIG-bench with Finnish tasks. We also assess other model qualities such as toxicity and bias. Our models and tools are openly available at https://turkunlp.org/gpt3-finnish.