src/about.py

from dataclasses import dataclass
from enum import Enum

@dataclass(frozen=True)
class Task:
    benchmark: str
    metric: str
    col_name: str
    type: str
    baseline: float = 0.0


# Select your tasks here
# ---------------------------------------------------
class Tasks(Enum):
    # task_key in the json file, metric_key in the json file, name to display in the leaderboard 
    # task2 = Task("belebele_pol_Latn", "acc,none", "belebele_pol_Latn", "multiple_choice", 0.279)
    task3 = Task("polemo2_in", "exact_match,score-first", "polemo2-in_g", "generate_until", 0.416)
    task4 = Task("polemo2_in_multiple_choice", "acc,none", "polemo2-in_mc", "multiple_choice", 0.416)
    task5 = Task("polemo2_out", "exact_match,score-first", "polemo2-out_g", "generate_until", 0.368)
    task6 = Task("polemo2_out_multiple_choice", "acc,none", "polemo2-out_mc", "multiple_choice", 0.368)
    task7 = Task("polish_8tags_multiple_choice", "acc,none", "8tags_mc", "multiple_choice", 0.143)
    task8 = Task("polish_8tags_regex", "exact_match,score-first", "8tags_g", "generate_until", 0.143)
    task9a = Task("polish_belebele_mc", "acc,none", "belebele_mc", "multiple_choice", 0.279)
    task9 = Task("polish_belebele_regex", "exact_match,score-first", "belebele_g", "generate_until", 0.279)
    task10 = Task("polish_dyk_multiple_choice", "f1,none", "dyk_mc", "multiple_choice", 0.289)
    task11 = Task("polish_dyk_regex", "f1,score-first", "dyk_g", "generate_until", 0.289)
    task12 = Task("polish_ppc_multiple_choice", "acc,none", "ppc_mc", "multiple_choice", 0.419)
    task13 = Task("polish_ppc_regex", "exact_match,score-first", "ppc_g", "generate_until", 0.419)
    task14 = Task("polish_psc_multiple_choice", "f1,none", "psc_mc", "multiple_choice", 0.466)
    task15 = Task("polish_psc_regex", "f1,score-first", "psc_g", "generate_until", 0.466)
    task16 = Task("polish_cbd_multiple_choice", "f1,none", "cbd_mc", "multiple_choice", 0.149)
    task17 = Task("polish_cbd_regex", "f1,score-first", "cbd_g", "generate_until", 0.149)
    task18 = Task("polish_klej_ner_multiple_choice", "acc,none", "klej_ner_mc", "multiple_choice", 0.343)
    task19 = Task("polish_klej_ner_regex", "exact_match,score-first", "klej_ner_g", "generate_until", 0.343)
    task21 = Task("polish_polqa_reranking_multiple_choice", "acc,none", "polqa_reranking_mc", "multiple_choice", 0.5335588952710677) # multiple_choice
    task22 = Task("polish_polqa_open_book", "levenshtein,none", "polqa_open_book_g", "generate_until", 0.0) # generate_until
    task23 = Task("polish_polqa_closed_book", "levenshtein,none", "polqa_closed_book_g", "generate_until", 0.0) # generate_until
    task24 = Task("polish_poquad_open_book", "levenshtein,none", "poquad_open_book", "generate_until", 0.0)
    task25 = Task("polish_eq_bench_first_turn", "first_eqbench,none", "eq_bench_first_turn", "generate_until", 0.0)
    task26 = Task("polish_eq_bench", "average_eqbench,none", "eq_bench", "other", 0.0)
    task20 = Task("polish_poleval2018_task3_test_10k", "word_perplexity,none", "poleval2018_task3_test_10k", "other")
    task27 = Task("polish_poquad_reranking", "acc,none", "poquad_reranking", "other", 0.0)
    task28 = Task("polish_abstractive_poquad_rag", "levenshtein,none", "abstractive_poquad_rag", "other", 0.0)
    task29 = Task("polish_abstractive_poquad_open_book", "levenshtein,none", "abstractive_poquad_open_book", "other", 0.0)
    task30 = Task("polish_pes", "exact_match,score-first", "pes", "other", 0.2)


g_tasks = [task.value.benchmark for task in Tasks if task.value.type == "generate_until"]
mc_tasks = [task.value.benchmark for task in Tasks if task.value.type == "multiple_choice"]
rag_tasks = ['polish_polqa_reranking_multiple_choice', 'polish_polqa_open_book', 'polish_poquad_open_book']
all_tasks = g_tasks + mc_tasks

NUM_FEWSHOT = 0 # Change with your few shot
# ---------------------------------------------------



# Your leaderboard name
TITLE = """
<div style="display: flex; flex-wrap: wrap; justify-content: space-around;">
    <img src="https://speakleash.org/wp-content/uploads/2023/09/SpeakLeash_logo.svg">
    <div>
        <h1 align="center" id="space-title">Open PL LLM Leaderboard (0-shot and 5-shot)</h1>
        <h2 align="center" id="space-subtitle">Leaderboard was created as part of an open-science project SpeakLeash.org</h2>
    </div>
</div>
"""

# What does your leaderboard evaluate?
INTRODUCTION_TEXT = f"""
The leaderboard evaluates language models on a set of Polish tasks. The tasks are designed to test the models' ability to understand and generate Polish text. The leaderboard is designed to be a benchmark for the Polish language model community, and to help researchers and practitioners understand the capabilities of different models.
For now, models are tested without theirs templates.

Almost every task has two versions: regex and multiple choice.
* _g suffix means that a model needs to generate an answer (only suitable for instructions-based models)
* _mc suffix means that a model is scored against every possible class (suitable also for base models)

Average columns are normalized against scores by "Baseline (majority class)".

* `,chat` suffix means that a model is tested using chat templates
* `,chat,multiturn` suffix means that a model is tested using chat templates and fewshot examples are treated as a multi-turn conversation
* 🚧 prefix means that not all tasks were calculated and the average scores are not accurate

We gratefully acknowledge Polish high-performance computing infrastructure PLGrid (HPC Centers: ACK Cyfronet AGH) for providing computer facilities and support within computational grant no. PLG/2024/016951.
"""

# Which evaluations are you running? how can people reproduce what you have?
LLM_BENCHMARKS_TEXT = f"""
## Do you want to add your model to the leaderboard?

Contact with me: [LinkedIn](https://www.linkedin.com/in/wrobelkrzysztof/)

or join our [Discord SpeakLeash](https://discord.gg/FfYp4V6y3R)

## TODO

* fix long model names
* add inference time
* add more tasks
* fix scrolling on Firefox

## Tasks

Tasks taken into account while calculating averages:
* Average: {', '.join(all_tasks)}
* Avg g: {', '.join(g_tasks)}
* Avg mc: {', '.join(mc_tasks)}
* Avg RAG: {', '.join(rag_tasks)}

| Task                            | Dataset                               | Metric    | Type            |
|---------------------------------|---------------------------------------|-----------|-----------------|
| polemo2_in                      | allegro/klej-polemo2-in               | accuracy  | generate_until  |
| polemo2_in_mc      | allegro/klej-polemo2-in               | accuracy  | multiple_choice |
| polemo2_out                     | allegro/klej-polemo2-out              | accuracy  | generate_until  |
| polemo2_out_mc     | allegro/klej-polemo2-out              | accuracy  | multiple_choice |
| 8tags_mc    | sdadas/8tags                          | accuracy  | multiple_choice |
| 8tags_g              | sdadas/8tags                          | accuracy  | generate_until  |
| belebele_mc           | facebook/belebele                     | accuracy  | multiple_choice  |
| belebele_g           | facebook/belebele                     | accuracy  | generate_until  |
| dyk_mc      | allegro/klej-dyk                      | binary F1 | multiple_choice |
| dyk_g                | allegro/klej-dyk                      | binary F1 | generate_until  |
| ppc_mc      | sdadas/ppc                            | accuracy  | multiple_choice |
| ppc_g                | sdadas/ppc                            | accuracy  | generate_until  |
| psc_mc      | allegro/klej-psc                      | binary F1 | multiple_choice |
| psc_g                | allegro/klej-psc                      | binary F1 | generate_until  |
| cbd_mc      | ptaszynski/PolishCyberbullyingDataset | macro F1  | multiple_choice |
| cbd_g                | ptaszynski/PolishCyberbullyingDataset | macro F1  | generate_until  |
| klej_ner_mc | allegro/klej-nkjp-ner                 | accuracy  | multiple_choice |
| klej_ner_g           | allegro/klej-nkjp-ner                 | accuracy  | generate_until  |
| polqa_reranking_mc | ipipan/polqa   | accuracy | multiple_choice |
| polqa_open_book_g | ipipan/polqa   | levenshtein | generate_until |
| polqa_closed_book_g | ipipan/polqa   | levenshtein | generate_until |
| poleval2018_task3_test_10k | enelpol/poleval2018_task3_test_10k   | word perplexity | other |
| polish_poquad_open_book | enelpol/poleval2018_task3_test_10k   | levenshtein | generate_until |
| polish_eq_bench_first_turn | speakleash/EQ-Bench-PL   | eq_bench | generate_until |
| polish_eq_bench | speakleash/EQ-Bench-PL   | eq_bench | generate_until |

## Reproducibility
To reproduce our results, you need to clone the repository:

```
git clone https://github.com/speakleash/lm-evaluation-harness.git -b polish3
cd lm-evaluation-harness
pip install -e .
```

and run benchmark for 0-shot and 5-shot:

```
lm_eval --model hf --model_args pretrained=speakleash/Bielik-7B-Instruct-v0.1 --tasks polish_generate --num_fewshot 0 --output_path results/ --log_samples
lm_eval --model hf --model_args pretrained=speakleash/Bielik-7B-Instruct-v0.1 --tasks polish_mc --num_fewshot 0 --output_path results/ --log_samples
lm_eval --model hf --model_args pretrained=speakleash/Bielik-7B-Instruct-v0.1 --tasks polish_generate_few --num_fewshot 5 --output_path results/ --log_samples
lm_eval --model hf --model_args pretrained=speakleash/Bielik-7B-Instruct-v0.1 --tasks polish_mc --num_fewshot 5 --output_path results/ --log_samples
```

With chat templates:
```
lm_eval --model hf --model_args pretrained=speakleash/Bielik-7B-Instruct-v0.1 --tasks polish_generate --num_fewshot 0 --output_path results/ --log_samples --apply_chat_template
lm_eval --model hf --model_args pretrained=speakleash/Bielik-7B-Instruct-v0.1 --tasks polish_mc --num_fewshot 0 --output_path results/ --log_samples --apply_chat_template
lm_eval --model hf --model_args pretrained=speakleash/Bielik-7B-Instruct-v0.1 --tasks polish_generate_few --num_fewshot 5 --output_path results/ --log_samples --apply_chat_template
lm_eval --model hf --model_args pretrained=speakleash/Bielik-7B-Instruct-v0.1 --tasks polish_mc --num_fewshot 5 --output_path results/ --log_samples --apply_chat_template
```

## List of Polish models

* speakleash/Bielik-7B-Instruct-v0.1
* speakleash/Bielik-7B-v0.1
* Azurro/APT3-1B-Base
* Azurro/APT3-1B-Instruct-v1
* Voicelab/trurl-2-7b
* Voicelab/trurl-2-13b-academic
* OPI-PG/Qra-1b
* OPI-PG/Qra-7b
* OPI-PG/Qra-13b
* szymonrucinski/Curie-7B-v1
* sdadas/polish-gpt2-xl

### List of multilingual models

* meta-llama/Llama-2-7b-chat-hf
* mistralai/Mistral-7B-Instruct-v0.1
* HuggingFaceH4/zephyr-7b-beta
* HuggingFaceH4/zephyr-7b-alpha
* internlm/internlm2-chat-7b-sft
* internlm/internlm2-chat-7b
* mistralai/Mistral-7B-Instruct-v0.2
* teknium/OpenHermes-2.5-Mistral-7B
* openchat/openchat-3.5-1210
* Nexusflow/Starling-LM-7B-beta
* openchat/openchat-3.5-0106
* berkeley-nest/Starling-LM-7B-alpha
* upstage/SOLAR-10.7B-Instruct-v1.0
* meta-llama/Llama-2-7b-hf
* internlm/internlm2-base-7b
* mistralai/Mistral-7B-v0.1
* internlm/internlm2-7b
* alpindale/Mistral-7B-v0.2-hf
* internlm/internlm2-1_8b



"""

EVALUATION_QUEUE_TEXT = """
## Some good practices before submitting a model

### 1) Make sure you can load your model and tokenizer using AutoClasses:
```python
from transformers import AutoConfig, AutoModel, AutoTokenizer
config = AutoConfig.from_pretrained("your model name", revision=revision)
model = AutoModel.from_pretrained("your model name", revision=revision)
tokenizer = AutoTokenizer.from_pretrained("your model name", revision=revision)
```
If this step fails, follow the error messages to debug your model before submitting it. It's likely your model has been improperly uploaded.

Note: make sure your model is public!
Note: if your model needs `use_remote_code=True`, we do not support this option yet but we are working on adding it, stay posted!

### 2) Convert your model weights to [safetensors](https://huggingface.co/docs/safetensors/index)
It's a new format for storing weights which is safer and faster to load and use. It will also allow us to add the number of parameters of your model to the `Extended Viewer`!

### 3) Make sure your model has an open license!
This is a leaderboard for Open LLMs, and we'd love for as many people as possible to know they can use your model 🤗

### 4) Fill up your model card
When we add extra information about models to the leaderboard, it will be automatically taken from the model card

## In case of model failure
If your model is displayed in the `FAILED` category, its execution stopped.
Make sure you have followed the above steps first.
If everything is done, check you can launch the EleutherAIHarness on your model locally, using the above command without modifications (you can add `--limit` to limit the number of examples per task).
"""

CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
CITATION_BUTTON_TEXT = r"""
@misc{open-pl-llm-leaderboard,
	title        = {Open PL LLM Leaderboard},
	author       = {Wróbel, Krzysztof and {SpeakLeash Team} and {Cyfronet Team}},
	year         = 2024,
	publisher    = {Hugging Face},
	howpublished = "\url{https://huggingface.co/spaces/speakleash/open_pl_llm_leaderboard}"
}

@misc{eval-harness,
  author       = {Gao, Leo and Tow, Jonathan and Abbasi, Baber and Biderman, Stella and Black, Sid and DiPofi, Anthony and Foster, Charles and Golding, Laurence and Hsu, Jeffrey and Le Noac'h, Alain and Li, Haonan and McDonell, Kyle and Muennighoff, Niklas and Ociepa, Chris and Phang, Jason and Reynolds, Laria and Schoelkopf, Hailey and Skowron, Aviya and Sutawika, Lintang and Tang, Eric and Thite, Anish and Wang, Ben and Wang, Kevin and Zou, Andy},
  title        = {A framework for few-shot language model evaluation},
  month        = 07,
  year         = 2024,
  publisher    = {Zenodo},
  version      = {v0.4.3},
  doi          = {10.5281/zenodo.12608602},
  url          = {https://zenodo.org/records/12608602}
}

@book{przepiorkowski2012narodowy,
title={Narodowy korpus j{\k{e}}zyka polskiego},
author={Przepi{\'o}rkowski, Adam},
year={2012},
publisher={Naukowe PWN}
}

@inproceedings{kocon-etal-2019-multi,
title = "Multi-Level Sentiment Analysis of {P}ol{E}mo 2.0: Extended Corpus of Multi-Domain Consumer Reviews",
author = "Koco{\'n}, Jan and
Mi{\l}kowski, Piotr and
Za{\'s}ko-Zieli{\'n}ska, Monika",
booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/K19-1092",
doi = "10.18653/v1/K19-1092",
pages = "980--991",
}

@inproceedings{marcinczuk2013open,
title={Open dataset for development of Polish Question Answering systems},
author={Marcinczuk, Micha{\l} and Ptak, Marcin and Radziszewski, Adam and Piasecki, Maciej},
booktitle={Proceedings of the 6th Language \& Technology Conference: Human Language Technologies as a Challenge for Computer Science and Linguistics, Wydawnictwo Poznanskie, Fundacja Uniwersytetu im. Adama Mickiewicza},
year={2013}
}

@inproceedings{ogro:kop:14:lrec,
title={The {P}olish {S}ummaries {C}orpus},
author={Ogrodniczuk, Maciej and Kope{\'c}, Mateusz},
booktitle = "Proceedings of the Ninth International {C}onference on {L}anguage {R}esources and {E}valuation, {LREC}~2014",
year = "2014",
}

@inproceedings{dadas-etal-2020-evaluation,
  title = "Evaluation of Sentence Representations in {P}olish",
  author = "Dadas, Slawomir  and Pere{\l}kiewicz, Micha{\l} and Po{\'s}wiata, Rafa{\l}",
  booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
  month = may,
  year = "2020",
  address = "Marseille, France",
  publisher = "European Language Resources Association",
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  ISBN = "979-10-95546-34-4",
}

@inproceedings{9945218,
  author={Dadas, S{\l}awomir},
  booktitle={2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)}, 
  title={Training Effective Neural Sentence Encoders from Automatically Mined Paraphrases}, 
  year={2022},
  volume={},
  number={},
  pages={371-378},
  doi={10.1109/SMC53654.2022.9945218}
}

@article{ptaszynski2023expert,
  title={Expert-Annotated Dataset to Study Cyberbullying in Polish Language},
  author={Ptaszynski, Michal and Pieciukiewicz, Agata and Dybala, Pawel and Skrzek, Pawel and Soliwoda, Kamil and Fortuna, Marcin and Leliwa, Gniewosz and Wroczynski, Michal},
  journal={Data},
  volume={9},
  number={1},
  pages={1},
  year={2023},
  publisher={MDPI}
}

@inproceedings{rybak-etal-2024-polqa-polish,
    title = "{P}ol{QA}: {P}olish Question Answering Dataset",
    author = "Rybak, Piotr  and
      Przyby{\l}a, Piotr  and
      Ogrodniczuk, Maciej",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1125",
    pages = "12846--12855",
    abstract = "Recently proposed systems for open-domain question answering (OpenQA) require large amounts of training data to achieve state-of-the-art performance. However, data annotation is known to be time-consuming and therefore expensive to acquire. As a result, the appropriate datasets are available only for a handful of languages (mainly English and Chinese). In this work, we introduce and publicly release PolQA, the first Polish dataset for OpenQA. It consists of 7,000 questions, 87,525 manually labeled evidence passages, and a corpus of over 7,097,322 candidate passages. Each question is classified according to its formulation, type, as well as entity type of the answer. This resource allows us to evaluate the impact of different annotation choices on the performance of the QA system and propose an efficient annotation strategy that increases the passage retrieval accuracy@10 by 10.55 p.p. while reducing the annotation cost by 82{\%}.",
}

@inproceedings{tuora2023poquad,
    title={PoQuAD-The Polish Question Answering Dataset-Description and Analysis},
    author={Tuora, Ryszard and Zwierzchowska, Aleksandra and Zawadzka-Paluektau, Natalia and Klamra, Cezary and Kobyli{\'n}ski, {\L}ukasz},
    booktitle={Proceedings of the 12th Knowledge Capture Conference 2023},
    pages={105--113},
    year={2023}
}

@inproceedings{bandarkar-etal-2024-belebele,
    title = "The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants",
    author = "Bandarkar, Lucas  and
      Liang, Davis  and
      Muller, Benjamin  and
      Artetxe, Mikel  and
      Shukla, Satya Narayan  and
      Husa, Donald  and
      Goyal, Naman  and
      Krishnan, Abhinandan  and
      Zettlemoyer, Luke  and
      Khabsa, Madian",
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand and virtual meeting",
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    url = "https://aclanthology.org/2024.acl-long.44",
    pages = "749--775",
}

@misc{paech2024eqbenchemotionalintelligencebenchmark,
      title={EQ-Bench: An Emotional Intelligence Benchmark for Large Language Models}, 
      author={Samuel J. Paech},
      year={2024},
      eprint={2312.06281},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2312.06281}, 
}

@misc{fwpes2024,
  title={speakleash/PES-2018-2022},
  author={Maria, Filipkowska and Krzyszof, Wróbel},
  year={2024},
  howpublished={\url{https://huggingface.co/datasets/speakleash/PES-2018-2022}},
}

@misc{pkgpes2024,
  title={amu-cai/PES-2018-2022},
  author={Jakub Pokrywka and Jeremi Kaczmarek and Edward Gorzelańczyk},
  year={2024},
  howpublished={\url{https://huggingface.co/datasets/amu-cai/PES-2018-2022}},
}

@misc{pokrywka2024gpt4,
      title={GPT-4 passes most of the 297 written Polish Board Certification Examinations}, 
      author={Jakub Pokrywka and Jeremi Kaczmarek and Edward Gorzelańczyk},
      year={2024},
      eprint={2405.01589},
      archivePrefix={arXiv},
      primaryClass={id='cs.CL' full_name='Computation and Language' is_active=True alt_name='cmp-lg' in_archive='cs' is_general=False description='Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.'}
}

@inproceedings{rybak-etal-2020-klej,
title = "{KLEJ}: Comprehensive Benchmark for Polish Language Understanding",
author = "Rybak, Piotr and Mroczkowski, Robert and Tracz, Janusz and Gawlik, Ireneusz",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.111",
pages = "1191--1201",
}

@misc{open-llm-leaderboard-v1,
  author = {Edward Beeching and Clémentine Fourrier and Nathan Habib and Sheon Han and Nathan Lambert and Nazneen Rajani and Omar Sanseviero and Lewis Tunstall and Thomas Wolf},
  title = {Open LLM Leaderboard (2023-2024)},
  year = {2023},
  publisher = {Hugging Face},
  howpublished = "\url{https://huggingface.co/spaces/open-llm-leaderboard-old/open_llm_leaderboard}"
}
"""