--- language: - sr license: apache-2.0 task_categories: - question-answering dataset_info: features: - name: index dtype: int64 - name: questions dtype: string - name: options sequence: string - name: answer dtype: string - name: answer_index dtype: int64 splits: - name: test num_bytes: 200846 num_examples: 1003 download_size: 139630 dataset_size: 200846 configs: - config_name: default data_files: - split: test path: data/oz-eval-* --- # OZ Eval ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62d01cf580c9d4ceb20254bb/Ft9w9DNRN_1bzuQC9XNSG.png) ## Dataset Description OZ Eval (_sr._ Opšte Znanje Evaluacija) dataset was created for the purposes of evaluating General Knowledge of LLM models in Serbian language. Data consists of 1k+ high-quality questions and answers which were used as part of entry exams at the Faculty of Philosophy and Faculty of Organizational Sciences, University of Belgrade. The exams test the General Knowledge of students and were used in the enrollment periods from 2003 to 2024. This is a joint work with [@Stopwolf](https://huggingface.co/Stopwolf)! ## Evaluation process Models are evaluated by the following principle using HuggingFace's library `lighteval`. We supply the model with the following template: ``` Pitanje: {question} Ponuđeni odgovori: A. {option_a} B. {option_b} C. {option_c} D. {option_d} E. {option_e} Krajnji odgovor: ``` We then compare likelihoods of each letter (`A, B, C, D, E`) and calculate the final accuracy. All evaluations are ran in a 0-shot manner using a **chat template**. GPT-like models were evaluated by taking top 20 probabilities of the first output token, which were further filtered for letters `A` to `E`. Letter with the highest probability was taken as a final answer. Exact code for the task can be found [here](https://github.com/Stopwolf/lighteval/blob/oz-eval/community_tasks/oz_evals.py). You should run the evaluation with the following command (do not forget to add --use_chat_template): ``` accelerate launch lighteval/run_evals_accelerate.py \ --model_args "pretrained={MODEL_NAME},trust_remote_code=True" \ --use_chat_template \ --tasks "community|serbian_evals:oz_task|0|0" \ --custom_tasks "/content/lighteval/community_tasks/oz_evals.py" \ --output_dir "./evals" \ --override_batch_size 32 ``` ## Evaluation results | Model |Size|Accuracy| |Stderr| |-------|---:|-------:|--|-----:| |GPT-4-0125-preview|_???_|0.9199|±|0.002| |GPT-4o-2024-05-13|_???_|0.9196|±|0.0017| |GPT-3.5-turbo-0125|_???_|0.8245|±|0.0016| |[Llama3.1-70B-Instruct \[4bit\]](https://huggingface.co/unsloth/Meta-Llama-3.1-70B-bnb-4bit)|70B|0.8185|±| 0.0122| |GPT-4o-mini-2024-07-18|_???_|0.7971|±|0.0005| |[Mustra-7B-Instruct-v0.2](https://huggingface.co/Stopwolf/Mustra-7B-Instruct-v0.2)|7B|0.7388|± |0.0098| |[Tito-7B-slerp](https://huggingface.co/Stopwolf/Tito-7B-slerp)|7B|0.7099|±|0.0101| |[Yugo55A-GPT](https://huggingface.co/datatab/Yugo55A-GPT)|7B|0.6889|± |0.0103| |[Zamfir-7B-slerp](https://huggingface.co/Stopwolf/Zamfir-7B-slerp)|7B|0.6849|± |0.0104| |[Mistral-Nemo-Instruct-2407](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407)|12.2B|0.6839|± |0.0104| |[Llama-3.1-SauerkrautLM-8b-Instruct](https://huggingface.co/VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct)|8B|0.679|±|0.0147| [Qwen2-7B-instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct)|7B|0.6730|±|0.0105| |[Llama-3-SauerkrautLM-8b-Instruct](https://huggingface.co/VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct)|8B|0.661|± |0.0106| |[Yugo60-GPT](https://huggingface.co/datatab/Yugo60-GPT)|7B|0.6411|±|0.0107| |[DeepSeek-V2-Lite-Chat](https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite-Chat)|15.7B|0.6047|±|0.0109| |[Llama3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct)|8B|0.5972|±| 0.0155| |[Llama3-70B-Instruct \[4bit\]](https://huggingface.co/unsloth/llama-3-70b-Instruct-bnb-4bit)|70B|0.5942|±| 0.011| |[Hermes-3-Theta-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-8B)|8B|0.5932|±|0.0155| |[Hermes-2-Theta-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B)|8B|0.5852|±|0.011| |[Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3)|7B|0.5753|±| 0.011| |[openchat-3.6-8b-20240522](https://huggingface.co/openchat/openchat-3.6-8b-20240522)|8B|0.5513|±|0.0111| |[Llama3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)|8B|0.5274|±|0.0111| |[Starling-7B-beta](https://huggingface.co/Nexusflow/Starling-LM-7B-beta)|7B|0.5244|±|0.0112| |[Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)|7B|0.5145|±|0.0112| |[Qwen2-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2-1.5B-Instruct)|1.5B|0.4506|±|0.0111| |[Perucac-7B-slerp](https://huggingface.co/Stopwolf/Perucac-7B-slerp)|7B|0.4247|±|0.011| |[Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct)|3.8B|0.3719|±|0.0108| |[SambaLingo-Serbian-Chat](https://huggingface.co/sambanovasystems/SambaLingo-Serbian-Chat)|7B|0.2802|±|0.01| |[Gemma-2-9B-it](https://huggingface.co/google/gemma-2-9b-it)|9B|0.2193|±|0.0092| |[Gemma-2-2B-it](https://huggingface.co/google/gemma-2-2b-it)|2.6B|0.1715|±|0.0084| ### Citation ``` @article{oz-eval, author = "Stanivuk Siniša & Đorđević Milena", title = "OZ Eval: Measuring General Knowledge Skill at University Level of LLMs in Serbian Language", year = "2024" howpublished = {\url{https://huggingface.co/datasets/DjMel/oz-eval}}, } ```