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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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class Tasks(Enum): |
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task00 = Task("naive_judge", "score", "NaïveJudge") |
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task01 = Task("human_eval_solidity_pass_1", "score", "HumanEval for Solidity (pass@1)") |
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task02 = Task("human_eval_solidity_pass_3", "score", "HumanEval for Solidity (pass@3)") |
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task03 = Task("rouge1", "score", "ROUGE-unigrams") |
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task04 = Task("rouge2", "score", "ROUGE-bigrams") |
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task05 = Task("rougeL", "score", "ROUGE-Longest Common Subsequence") |
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task06 = Task("rougeLsum", "score", "ROUGE-Lsum") |
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task07 = Task("bleu", "score", "Bleu") |
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task08 = Task("brevity_penalty", "score", "Brevity Penalty") |
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TITLE = """<br><img src="file/images/solbench.svg" width="500" style="display: block; margin-left: auto; margin-right: auto;"> |
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<h2 align="center" id="space-title">IQ Code | Solidity Leaderboard</h2>""" |
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INTRODUCTION_TEXT = "" |
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LLM_BENCHMARKS_TEXT = """ |
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## How it works |
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## Reproducibility |
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To reproduce our results, here is the commands you can run: |
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""" |
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EVALUATION_REQUESTS_TEXT = """ |
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## Some good practices before submitting a model |
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### 1) Make sure you can load your model and tokenizer using AutoClasses: |
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```python |
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from transformers import AutoConfig, AutoModel, AutoTokenizer |
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config = AutoConfig.from_pretrained("your model name", revision=revision) |
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model = AutoModel.from_pretrained("your model name", revision=revision) |
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tokenizer = AutoTokenizer.from_pretrained("your model name", revision=revision) |
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``` |
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If this step fails, follow the error messages to debug your model before submitting it. It's likely your model has been improperly uploaded. |
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Note: make sure your model is public. |
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### 2) Fill up your model card |
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When we add extra information about models to the leaderboard, it will be automatically taken from the model card |
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""" |
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EVALUATION_SCRIPT = '' |
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CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results" |
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CITATION_BUTTON_TEXT = '' |
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