src/about.py

from dataclasses import dataclass
from enum import Enum

@dataclass
class Task:
    benchmark: str
    metric: str
    col_name: str


# Select your tasks here
# ---------------------------------------------------
class Tasks(Enum):
    AVG = Task("scores", "AVG", "AVG")
    CG = Task("scores", "CG", "CG")
    EL = Task("scores", "EL", "EL")
    FA = Task("scores", "FA", "FA")
    HE = Task("scores", "HE", "HE")
    MC = Task("scores", "MC", "MC")
    MR = Task("scores", "MR", "MR")
    MT = Task("scores", "MT", "MT")
    NLI = Task("scores", "NLI", "NLI")
    QA = Task("scores", "QA", "QA")
    RC = Task("scores", "RC", "RC")
    SUM = Task("scores", "SUM", "SUM")
    alt_e_to_j_bert_score_ja_f1 = Task("scores", "alt-e-to-j_bert_score_ja_f1", "ALT E to J BERT Score")
    alt_e_to_j_bleu_ja = Task("scores", "alt-e-to-j_bleu_ja", "ALT E to J BLEU")
    alt_e_to_j_comet_wmt22 = Task("scores", "alt-e-to-j_comet_wmt22", "ALT E to J COMET WMT22")
    alt_j_to_e_bert_score_en_f1 = Task("scores", "alt-j-to-e_bert_score_en_f1", "ALT J to E BERT Score")
    alt_j_to_e_bleu_en = Task("scores", "alt-j-to-e_bleu_en", "ALT J to E BLEU")
    alt_j_to_e_comet_wmt22 = Task("scores", "alt-j-to-e_comet_wmt22", "ALT J to E COMET WMT22")
    chabsa_set_f1 = Task("scores", "chabsa_set_f1", "ChABSA")
    commonsensemoralja_exact_match = Task("scores", "commonsensemoralja_exact_match", "CommonSenseMoralJA")
    jamp_exact_match = Task("scores", "jamp_exact_match", "JAMP")
    janli_exact_match = Task("scores", "janli_exact_match", "JANLI")
    jcommonsenseqa_exact_match = Task("scores", "jcommonsenseqa_exact_match", "JCommonSenseQA")
    jemhopqa_char_f1 = Task("scores", "jemhopqa_char_f1", "JEMHopQA")
    jmmlu_exact_match = Task("scores", "jmmlu_exact_match", "JMMLU")
    jnli_exact_match = Task("scores", "jnli_exact_match", "JNLI")
    jsem_exact_match = Task("scores", "jsem_exact_match", "JSEM")
    jsick_exact_match = Task("scores", "jsick_exact_match", "JSICK")
    jsquad_char_f1 = Task("scores", "jsquad_char_f1", "JSquad")
    jsts_pearson = Task("scores", "jsts_pearson", "JSTS")
    jsts_spearman = Task("scores", "jsts_spearman", "JSTS")
    kuci_exact_match = Task("scores", "kuci_exact_match", "KUCI")
    mawps_exact_match = Task("scores", "mawps_exact_match", "MAWPS")
    mmlu_en_exact_match = Task("scores", "mmlu_en_exact_match", "MMLU")
    niilc_char_f1 = Task("scores", "niilc_char_f1", "NIILC")
    wiki_coreference_set_f1 = Task("scores", "wiki_coreference_set_f1", "Wiki Coreference")
    wiki_dependency_set_f1 = Task("scores", "wiki_dependency_set_f1", "Wiki Dependency")
    wiki_ner_set_f1 = Task("scores", "wiki_ner_set_f1", "Wiki NER")
    wiki_pas_set_f1 = Task("scores", "wiki_pas_set_f1", "Wiki PAS")
    wiki_reading_char_f1 = Task("scores", "wiki_reading_char_f1", "Wiki Reading")
    wikicorpus_e_to_j_bert_score_ja_f1 = Task("scores", "wikicorpus-e-to-j_bert_score_ja_f1", "WikiCorpus E to J BERT Score")
    wikicorpus_e_to_j_bleu_ja = Task("scores", "wikicorpus-e-to-j_bleu_ja", "WikiCorpus E to J BLEU")
    wikicorpus_e_to_j_comet_wmt22 = Task("scores", "wikicorpus-e-to-j_comet_wmt22", "WikiCorpus E to J COMET WMT22")
    wikicorpus_j_to_e_bert_score_en_f1 = Task("scores", "wikicorpus-j-to-e_bert_score_en_f1", "WikiCorpus J to E BERT Score")
    wikicorpus_j_to_e_bleu_en = Task("scores", "wikicorpus-j-to-e_bleu_en", "WikiCorpus J to E BLEU")
    wikicorpus_j_to_e_comet_wmt22 = Task("scores", "wikicorpus-j-to-e_comet_wmt22", "WikiCorpus J to E COMET WMT22")
    xlsum_ja_bert_score_ja_f1 = Task("scores", "xlsum_ja_bert_score_ja_f1", "XL-Sum JA BERT Score")
    xlsum_ja_bleu_ja = Task("scores", "xlsum_ja_bleu_ja", "XL-Sum JA BLEU")
    xlsum_ja_rouge1 = Task("scores", "xlsum_ja_rouge1", "XL-Sum ROUGE1")
    xlsum_ja_rouge2 = Task("scores", "xlsum_ja_rouge2", "XL-Sum ROUGE2")
    # xlsum_ja_rouge2_scaling = Task("scores", "xlsum_ja_rouge2_scaling", "XL-Sum JA ROUGE2 Scaling")
    xlsum_ja_rougeLsum = Task("scores", "xlsum_ja_rougeLsum", "XL-Sum ROUGE-Lsum")


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



# Your leaderboard name
TITLE = """<h1 align="center" id="space-title">Open Japanese LLM Leaderboard by LLM-Jp</h1>"""

# What does your leaderboard evaluate?
INTRODUCTION_TEXT = """
🇯🇵 The Open Japanese LLM Leaderboard 🌸 by [LLM-Jp](https://llm-jp.nii.ac.jp/en/) evaluates the performance of Japanese Large Language Models (LLMs).

This leaderboard was built by [LLM-Jp](https://llm-jp.nii.ac.jp/en/), a cross-organizational project for the research and development of Japanese large language models (LLMs). Organized by the National Institute of Informatics, LLM-jp aims to develop open-source and strong Japanese LLMs, and as of this writing, more than 1,500 participants from academia and industry are working together for this purpose.

When you submit a model on the "Submit here!" page, it is automatically evaluated on a set of benchmarks.This Open Japanese LLM Leaderboard assesses language understanding, of Japanese LLMs with more than 52 benchmarks from classical to modern NLP tasks such as Natural language inference, Question Answering, Machine Translation, Code Generation, Mathematical reasoning, Summarization, etc.

For more information about benchmarks, and datasets, please consult the "About" page. For more details, please refer to the website of [LLM-Jp](https://llm-jp.nii.ac.jp/en/)

"""

# Which evaluations are you running? how can people reproduce what you have?
LLM_BENCHMARKS_TEXT = f"""
## How it works
📈 We evaluate Japanese Large Language Models on 52 key benchmarks leveraging our evaluation tool [llm-jp-eval](https://github.com/llm-jp/llm-jp-eval), a unified framework to evaluate Japanese LLMs on various evaluation tasks.

Benchmarks:
**NLI (Natural Language Inference)**

- `Jamp`  JAMP, a Japanese NLI benchmark focused on temporal inference [Source](https://github.com/tomo-ut/temporalNLI_dataset) | License CC BY-SA 4.0

### JaNLI

Source：https://github.com/verypluming/JaNLI
License：CC BY-SA 4.0

#### JNLI

Source：https://github.com/yahoojapan/JGLUE
License：CC BY-SA 4.0

###JSeM

Source：https://github.com/DaisukeBekki/JSeM
License：BSD 3-Clause

###JSICK

Source：https://github.com/verypluming/JSICK
License：CC BY-SA 4.0

QA (Question Answering)

###JEMHopQA

Source：https://github.com/aiishii/JEMHopQA
License：CC BY-SA 4.0

###NIILC

Source：https://github.com/mynlp/niilc-qa
License：CC BY-SA 4.0

###JAQKET (AIO)

Source：https://www.nlp.ecei.tohoku.ac.jp/projects/jaqket/
License：CC BY-SA 4.0（Other licenses are required for corporate usage）

RC (Reading Comprehension)

###JSQuAD

Source：https://github.com/yahoojapan/JGLUE
License：CC BY-SA 4.0

MC (Multiple Choice question answering)

###JCommonsenseMorality

Source：https://github.com/Language-Media-Lab/commonsense-moral-ja
License：MIT License

###JCommonsenseQA

Source：https://github.com/yahoojapan/JGLUE
License：CC BY-SA 4.0

###Kyoto University Commonsense Inference dataset (KUCI)

Source：https://github.com/ku-nlp/KUCI
License：CC BY-SA 4.0

EL (Entity Linking)

###chABSA

Source：https://github.com/chakki-works/chABSA-dataset
License：CC BY 4.0

FA (Fundamental Analysis)

###Wikipedia Annotated Corpus

Source：https://github.com/ku-nlp/WikipediaAnnotatedCorpus
License：CC BY-SA 4.0
List of tasks:

Reading Prediction
Named-entity recognition (NER)
Dependency Parsing
Predicate-argument structure analysis (PAS)
Coreference Resolution

MR (Mathematical Reasoning)

###MAWPS

Source：https://github.com/nlp-waseda/chain-of-thought-ja-dataset
License：Apache-2.0

###MGSM

Source：https://huggingface.co/datasets/juletxara/mgsm
License：MIT License

MT (Machine Translation)

###Asian Language Treebank (ALT) - Parallel Corpus

Source： https://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/index.html
License：CC BY 4.0

###WikiCorpus (Japanese-English Bilingual Corpus of Wikipedia's articles about the city of Kyoto)

Source： https://alaginrc.nict.go.jp/WikiCorpus/
License：CC BY-SA 3.0 deed

STS (Semantic Textual Similarity)

This task is supported by llm-jp-eval, but it is not included in the evaluation score average.

###JSTS

Source：https://github.com/yahoojapan/JGLUE
License：CC BY-SA 4.0

HE (Human Examination)

###MMLU

Source：https://github.com/hendrycks/test
License：MIT License

###JMMLU

Source：https://github.com/nlp-waseda/JMMLU
License：CC BY-SA 4.0（3 tasks under the CC BY-NC-ND 4.0 license）

CG (Code Generation)

###MBPP

Source：https://huggingface.co/datasets/llm-jp/mbpp-ja
License：CC-BY-4.0

SUM (Summarization)

###XL-Sum

Source：https://github.com/csebuetnlp/xl-sum
License：CC BY-NC-SA 4.0（Due to the non-commercial license, this dataset will not be used, unless you specifically agree to the license and terms of use）


## Reproducibility
To reproduce our results, here is the commands you can run:

"""

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"""
"""