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 = """

LLM-JP leaderboard

""" # What does your leaderboard evaluate? INTRODUCTION_TEXT = """ This Leader-Board automatically evaluates large-scale Japanese language models across multiple datasets. Check here for supported evaluation methods. https://github.com/llm-jp/llm-jp-eval/blob/main/DATASET.md """ # Which evaluations are you running? how can people reproduce what you have? LLM_BENCHMARKS_TEXT = f""" ## How it works ## 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""" """