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from dataclasses import dataclass | |
from enum import Enum | |
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">LLM-JP leaderboard</h1>""" | |
# What does your leaderboard evaluate? | |
INTRODUCTION_TEXT = """ | |
Intro text | |
""" | |
# 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""" | |
""" | |