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| from dataclasses import dataclass | |
| from enum import Enum | |
| class Task: | |
| benchmark: str | |
| metric: str | |
| col_name: str | |
| # Select your metrics here | |
| # --------------------------------------------------- | |
| # Each entry: first argument is the key inside "results" dict in the result JSON, | |
| # second is the metric key inside that sub-dict (we use "score" everywhere for uniformity), | |
| # third is the column name displayed in the leaderboard. | |
| class Tasks(Enum): | |
| bleu = Task("bleu", "score", "BLEU ⬆️") | |
| multimetric = Task("multimetric", "score", "Multimetric ⬆️") | |
| readability = Task("readability", "score", "Readability") | |
| relevance = Task("relevance", "score", "Relevance") | |
| explanation_clarity = Task("explanation_clarity", "score", "Explanation clarity") | |
| problem_identification = Task("problem_identification", "score", "Problem identification") | |
| actionability = Task("actionability", "score", "Actionability") | |
| completeness = Task("completeness", "score", "Completeness") | |
| specificity = Task("specificity", "score", "Specificity") | |
| contextual_adequacy = Task("contextual_adequacy", "score", "Contextual adequacy") | |
| consistency = Task("consistency", "score", "Consistency") | |
| brevity = Task("brevity", "score", "Brevity") | |
| pass_at_1 = Task("pass_at_1", "score", "Pass@1 ⬆️") | |
| pass_at_5 = Task("pass_at_5", "score", "Pass@5") | |
| pass_at_10 = Task("pass_at_10", "score", "Pass@10") | |
| NUM_FEWSHOT = 0 # Not applicable here but kept for compatibility | |
| # --------------------------------------------------- | |
| # Your leaderboard name | |
| TITLE = """<h1 align="center" id="space-title">Demo 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""" | |
| """ | |