from dataclasses import dataclass from enum import Enum @dataclass class Task: benchmark: str metric: str col_name: str # Relevant citation for the task cite: str = "" # Select your tasks here # --------------------------------------------------- class Tasks(Enum): # task_key in the json file, metric_key in the json file, name to display in the leaderboard task0 = Task("toxigen", "acc", "Toxicity (lower is better)", cite="_ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection._ Hartvigsen et al., ACL 2022.") task1 = Task("truthfulqa_gen", "bleurt_acc", "Truthful QA", cite="_TruthfulQA: Measuring How Models Mimic Human Falsehoods._ Lin et al., ACL 2022.") # https://aclanthology.org/2020.emnlp-main.154/ task2 = Task("crows_pairs_english", "pct_stereotype", "CrowS-Pairs English", cite="_CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models._ Nangia et al., EMNLP 2020.") #task2 = Task("anli_r1", "acc", "ANLI", cite="_Adversarial NLI: A New Benchmark for Natural Language Understanding._ Nie et al., ACL 2020.") #task3 = Task("logiqa", "acc_norm", "LogiQA", cite="_LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning_. Liu et al., IJCAI 2020.") NUM_FEWSHOT = 0 # Change with your few shot MEG NOTE: Not sure what that means. # --------------------------------------------------- # Leaderboard name TITLE = """

Toxicity Leaderboard

""" # What does your leaderboard evaluate? INTRODUCTION_TEXT = """

Evaluate the toxicity of open LLMs.

""" # Which evaluations are you running? how can people reproduce what you have? LLM_BENCHMARKS_TEXT = f""" ## How it works ## Reproducibility To reproduce the toxicity results, here is the command you can run using [this version](https://github.com/EleutherAI/lm-evaluation-harness/tree/b281b0921b636bc36ad05c0b0b0763bd6dd43463) of the EleutherAI LM Evaluation Harness: ```python main.py --model=hf-causal-experimental --model_args="pretrained=,use_accelerate=True" --tasks= --batch_size=1 --output_path=``` """ 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. See About tab for exact command. """ CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results" CITATION_BUTTON_TEXT = r""" @misc{open-llm-toxicity-leaderboard, author = {Margaret Mitchell and Edward Beeching and Clémentine Fourrier and Nathan Habib and Sheon Han and Nathan Lambert and Nazneen Rajani and Omar Sanseviero and Lewis Tunstall and Thomas Wolf}, title = {Open LLM Toxicity Leaderboard}, year = {2024}, publisher = {Hugging Face}, howpublished = "\url{https://huggingface.co/spaces/Bias-Leaderboard/leaderboard}" } """