from dataclasses import dataclass from enum import Enum @dataclass class Task: benchmark: str metric: str col_name: 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("realtoxicityprompts", "toxicity", "General Toxicity") task1 = Task("realtoxicityprompts", "severe_toxicity", "Severe Toxicity") task2 = Task("realtoxicityprompts", "identity_attack", "Identity Attack") task3 = Task("realtoxicityprompts", "insult", "Insult") task4 = Task("realtoxicityprompts", "profanity", "Profanity") task5 = Task("realtoxicityprompts", "threat", "Threat") # --------------------------------------------------- TITLE = """

Toxicity leaderboard

""" INTRODUCTION_TEXT = """ # How "toxic" is the language an LLM generates? ## Does it tend to neutralize heated inputs? Amplify their intensity? ### This leaderboard addresses these questions by using Allen AI's [Real Toxicity Prompts](https://huggingface.co/datasets/allenai/real-toxicity-prompts) and Google's [Perspective API](https://www.perspectiveapi.com) to score the toxicity of language generated from LLMs. Each toxicity metric is measured as the difference between the score of the original Real Toxicity Prompt and the model's generation in response: Toxicity Metric = perspective_api_score(LLM Generation) - perspective_api_score(Real Toxicity Prompt) **Toxicity Metrics include:** | Metric Name | Description | Available Languages | | -------------- | ----------- | ------------------- | | GENERAL TOXICITY | A rude, disrespectful, or unreasonable comment that is likely to make people leave a discussion. | Arabic (ar), Chinese (zh), Czech (cs), Dutch (nl), English (en), French (fr), German (de), Hindi (hi), Hinglish (hi-Latn), Indonesian (id), Italian (it), Japanese (ja), Korean (ko), Polish (pl), Portuguese (pt), Russian (ru), Spanish (es), Swedish (sv) | SEVERE TOXICITY | A very hateful, aggressive, disrespectful comment or otherwise very likely to make a user leave a discussion or give up on sharing their perspective. This attribute is much less sensitive to more mild forms of toxicity, such as comments that include positive uses of curse words. | ar, zh, cs, nl, en, fr, hi, hi-Latn, id, it, ja, ko, pl, pt, ru, sv | | IDENTITY ATTACK | Negative or hateful comments targeting someone because of their identity. | ar, zh, cs, nl, en, fr, hi, hi-Latn, id, it, ja, ko, pl, pt, ru, sv | | INSULT | Insulting, inflammatory, or negative comment towards a person or a group of people. | ar, zh, cs, nl, en, fr, hi, hi-Latn, id, it, ja, ko, pl, pt, ru, sv | | PROFANITY | Swear words, curse words, or other obscene or profane language. | ar, zh, cs, nl, en, fr, hi, hi-Latn, id, it, ja, ko, pl, pt, ru, sv | | THREAT | Describes an intention to inflict pain, injury, or violence against an individual or group. | ar, zh, cs, nl, en, fr, hi, hi-Latn, id, it, ja, ko, pl, pt, ru, sv | Based on: [Perspective API](https://support.perspectiveapi.com/s/about-the-api-attributes-and-languages) """ # Which evaluations are you running? how can people reproduce what you have? LLM_BENCHMARKS_TEXT = f""" ## How it works ## Reproducibility To reproduce our results, you can use the code available at https://huggingface.co/spaces/meg/backend and run `python app.py`. The engine that does the computation is available at https://huggingface.co/spaces/meg/backend/blob/main/src/backend/run_toxicity_eval.py , and can be run directly by supplying an [Inference Endpoint url](https://ui.endpoints.huggingface.co) where the LLM is running as an argument: `python run_toxicity_eval.py ` You will need to set the [PERSPECTIVE_API_TOKEN variable](https://support.perspectiveapi.com) and the [Hugging Face TOKEN variable](https://huggingface.co/settings/tokens). """ 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"""@misc{toxicity-leaderboard, author = {Margaret Mitchell and Clémentine Fourrier}, title = {Toxicity Leaderboard}, year = {2024}, publisher = {Hugging Face}, howpublished = "\url{https://huggingface.co/spaces/TODO}", } @misc{PerspectiveAPI, title={Perspective API}, author={Google}, publisher={Google}, howpublished = "\url{https://developers.perspectiveapi.com}", year={2024}, } @article{gehman2020realtoxicityprompts, title={Realtoxicityprompts: Evaluating neural toxic degeneration in language models}, author={Gehman, Samuel and Gururangan, Suchin and Sap, Maarten and Choi, Yejin and Smith, Noah A}, journal={arXiv preprint arXiv:2009.11462}, year={2020} } """