For a full list of supported vLLM configurations, please reference our vLLM integration and the vLLM documentation.
To use vllm, do `pip install lm_eval[vllm]`. For a full list of supported vLLM configurations, please reference our [vLLM integration](https://github.com/EleutherAI/lm-evaluation-harness/blob/e74ec966556253fbe3d8ecba9de675c77c075bce/lm_eval/models/vllm_causallms.py) and the vLLM documentation.
vLLM occasionally differs in output from Huggingface. We treat Huggingface as the reference implementation, and provide a [script](./scripts/model_comparator.py) for checking the validity of vllm results against HF.
-**use_prompt** (`str`, *optional*) — Name of prompt in promptsource to use. if defined, will overwrite doc_to_text, doc_to_target, and doc_to_choice.
-**doc_to_text** (`Union[Callable, str]`, *optional*) — Jinja2, f-string, or function to process a sample into the appropriate input for the model
-**doc_to_target** (`Union[Callable, str]`, *optional*) — Jinja2, f-string, or function to process a sample into the appropriate target output for the model. For multiple choice tasks, this should return an index into
-**doc_to_choice** (`Union[Callable, str]`, *optional*) — Jinja2, f-string, or function to process a sample into a list of possible string choices for `multiple_choice` tasks. Left undefined for `generate_until` tasks.
-**description** (`str`, *optional*) — An optional prepended Jinja2 template or string which will be prepended to the few-shot examples passed into the model, often describing the task or providing instructions to a model, such as `"The following are questions (with answers) about {{subject}}.\n\n"`. No delimiters or spacing are inserted between the description and the first few-shot example.
-**doc_to_text** (`Union[Callable, str]`, *optional*) — Jinja2 template, string, or function to process a sample into the appropriate input for the model
-**doc_to_target** (`Union[Callable, str]`, *optional*) — Jinja2 template, string, or function to process a sample into the appropriate target output for the model. For multiple choice tasks, this should return an index into
-**doc_to_choice** (`Union[Callable, str]`, *optional*) — Jinja2 template, string, or function to process a sample into a list of possible string choices for `multiple_choice` tasks. Left undefined for `generate_until` tasks.
-**fewshot_delimiter** (`str`, *optional*, defaults to "\n\n") — String to insert between few-shot examples.
-**target_delimiter** (`str`, *optional*, defaults to `" "`) — String to insert between input and target output for the datapoint being tested.
EXAMS: a resource specialized in multilingual high school exam questions.
The original paper [EXAMS](https://aclanthology.org/2020.emnlp-main.438/)
The Arabic EXAMS dataset includes five subjects
- Islamic studies
- Biology
- Physics
- Science
- Social
The original dataset [EXAMS-QA](https://github.com/mhardalov/exams-qa)
EXAMS is a benchmark dataset for cross-lingual and multilingual question answering for high school examinations.
With 24,000 high-quality high school exam questions in 16 languages, covering 8 language families and 24 school subjects from Natural Sciences and Social Sciences, among others.
EXAMS offers unique fine-grained evaluation framework across multiple languages and subjects
Homepage for Arabic EXAMS: [EXAMS Arabic Homepage](https://github.com/FreedomIntelligence/AceGPT/tree/main/eval/benchmark_eval/benchmarks/EXAMS_Arabic)
### Citation
### Groups and Tasks
#### Groups
-`EXAMS Arabic`: include IslamicStudies, Biology, Science, Physics, Social.
#### Tasks
The following tasks evaluate subjects in Arabic EXAMS dataset using loglikelihood-based multiple-choice scoring:
-`aexams_IslamicStudies`
-`aexams_Biology`
-`aexams_Science`
-`aexams_Physics`
-`aexams_Social`
### Checklist
* [x] Is the task an existing benchmark in the literature?
* [x] Have you referenced the original paper that introduced the task?
* [x] If yes, does the original paper provide a reference implementation?
* [x] Yes, original implementation contributed by author of the benchmark
If other tasks on this dataset are already supported:
* [x] Is the "Main" variant of this task clearly denoted?
* [x] Have you provided a short sentence in a README on what each new variant adds / evaluates?
* [x] Have you noted which, if any, published evaluation setups are matched by this variant?
