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from dataclasses import dataclass |
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from enum import Enum |
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@dataclass |
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class Task: |
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benchmark: str |
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metric: str |
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col_name: str |
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higher_is_better: bool = True |
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scale_by_100: bool = True |
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class Tasks(Enum): |
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task0 = Task("arc_challenge_ita", "acc_norm,none", "ARC-C") |
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task1 = Task("ami_2020_aggressiveness", "f1,none", "AMI 2020 Agg") |
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task2 = Task("ami_2020_misogyny", "f1,none", "AMI 2020 Miso") |
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task3 = Task("gente_rephrasing", "acc,none", "GeNTE Neutralizing") |
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task4 = Task("belebele_ita", "acc_norm,none", "Belebele") |
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task5 = Task("hatecheck_ita", "f1,none", "HateCheck") |
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task6 = Task("honest_ita", "acc,none", "HONEST", higher_is_better=False) |
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task7 = Task("itacola", "mcc,none", "ItaCoLA", scale_by_100=False) |
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task8 = Task("news_sum", "bertscore,none", "News Sum") |
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task9 = Task("squad_it", "squad_f1,get-answer", "SQuAD it") |
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task10 = Task("truthfulqa_mc2_ita", "acc,none", "TruthfulQA") |
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task11 = Task("xcopa_it", "acc,none", "TruthfulQA") |
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NUM_FEWSHOT = 0 |
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TITLE = """<h1 align="center" id="space-title">ItaEval leaderboard</h1>""" |
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INTRODUCTION_TEXT = """ |
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This leaderboard evaluates language models on <b>ItaEval</b>, a new unified benchmark for Italian. |
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Some information: |
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- compared to other leaderboard you may found online, we do not support automatic evaluation for new model submissions |
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""" |
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ITA_EVAL_REPO = "https://github.com/g8a9/ita-eval" |
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LLM_BENCHMARKS_TEXT = f""" |
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## How it works |
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## Reproducibility |
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To reproduce our results, head to {ITA_EVAL_REPO} for all the instructions. |
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If all the setup goes smoothly, you can run 'MODEL' on ItaEval with: |
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```bash |
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MODEL="..." |
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lm_eval -mixed_precision=bf16 --model hf \ |
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--model_args pretrained=$MODEL,dtype=bfloat16 \ |
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--tasks ita_eval \ |
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--device cuda:0 \ |
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--batch_size "auto" \ |
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--log_samples \ |
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--output_path $FAST/ita_eval_v1/$MODEL \ |
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--use_cache $FAST/ita_eval_v1/$MODEL \ |
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--cache_requests "true" |
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``` |
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""" |
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EVALUATION_QUEUE_TEXT = """ |
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We do not plan to accept autonomous submissions, yet. |
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""" |
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CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results" |
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CITATION_BUTTON_TEXT = r""" |
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We are working on it! :) |
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""" |
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