README.md

metadata

license: bigscience-bloom-rail-1.0
language:
  - fr
  - en
pipeline_tag: text-classification

Bloomz-3b-guardrail

We introduce the Bloomz-3b-guardrail model, which is a fine-tuning of the Bloomz-3b-sft-chat model. This model is designed to detect the toxicity of a text in five modes:

• Obscene: Content that is offensive, indecent, or morally inappropriate, especially in relation to social norms or standards of decency.
• Sexual explicit: Content that presents explicit sexual aspects in a clear and detailed manner.
• Identity attack: Content that aims to attack, denigrate, or harass someone based on their identity, especially related to characteristics such as race, gender, sexual orientation, religion, ethnic origin, or other personal aspects.
• Insult: Offensive, disrespectful, or hurtful content used to attack or denigrate a person.
• Threat: Content that presents a direct threat to an individual.

Training

The training dataset consists of 500k examples of comments in English and 500k comments in French (translated by Google Translate), each annotated with a toxicity severity gradient. The dataset used is provided by Jigsaw as part of a Kaggle competition : Jigsaw Unintended Bias in Toxicity Classification. Since the scores represent severity gradients, regression was preferred using the following loss function: $$loss=l_{\mathrm{o}\mathrm{b}\mathrm{s}\mathrm{c}\mathrm{e}\mathrm{n}\mathrm{e}}+l_{\mathrm{s}\mathrm{e}\mathrm{x}\mathrm{u}\mathrm{a}\mathrm{l}\mathrm{\_}\mathrm{e}\mathrm{x}\mathrm{p}\mathrm{l}\mathrm{i}\mathrm{c}\mathrm{i}\mathrm{t}}+l_{\mathrm{i}\mathrm{d}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{i}\mathrm{t}\mathrm{y}\mathrm{\_}\mathrm{a}\mathrm{t}\mathrm{t}\mathrm{a}\mathrm{c}\mathrm{k}}+l_{\mathrm{i}\mathrm{n}\mathrm{s}\mathrm{u}\mathrm{l}\mathrm{t}}+l_{\mathrm{t}\mathrm{h}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{t}}loss=l\_\{\backslash mathrm\{obscene\}\}+l\_\{\backslash mathrm\{sexual\backslash \_explicit\}\}+l\_\{\backslash mathrm\{identity\backslash \_attack\}\}+l\_\{\backslash mathrm\{insult\}\}+l\_\{\backslash mathrm\{threat\}\}$$ with $$l_i=\frac{1}{\mathrm{\mid }\mathcal{O}\mathrm{\mid }}\sum _{o\in \mathcal{O}}\mathrm{\mid }{\mathrm{s}\mathrm{c}\mathrm{o}\mathrm{r}\mathrm{e}}_{i,o}-\sigma ({\mathrm{l}\mathrm{o}\mathrm{g}\mathrm{i}\mathrm{t}}_{i,o})\mathrm{\mid }l\_i=\backslash frac\{1\}\{\backslash vert\backslash mathcal\{O\}\backslash vert\}\backslash sum\_\{o\backslash in\backslash mathcal\{O\}\}\backslash vert\backslash mathrm\{score\}\_\{i,o\}-\backslash sigma(\backslash mathrm\{logit\}\_\{i,o\})\backslash vert$$ Where sigma is the sigmoid function and O represents the set of learning observations.

Benchmark

As the scores range from 0 to 1, a performance measure such as MAE or RMSE may be challenging to interpret. Therefore, Pearson's inter-correlation was chosen as a measure. Pearson's inter-correlation is a measure ranging from -1 to 1, where 0 represents no correlation, -1 represents perfect negative correlation, and 1 represents perfect positive correlation. The goal is to quantitatively measure the correlation between the model's scores and the scores assigned by judges for 750 comments not seen during training.

Model	Language	Obsecene (x100)	Sexual explicit (x100)	Identity attack (x100)	Insult (x100)	Threat (x100)	Mean
Bloomz-560m-guardrail	French	62	73	73	68	61	67
Bloomz-560m-guardrail	English	63	61	63	67	55	62
Bloomz-3b-guardrail	French	72	82	80	78	77	78
Bloomz-3b-guardrail	English	76	78	77	75	79	77

With a correlation of approximately 60 for the 560m model and approximately 80 for the 3b model, the output is highly correlated with the judges' scores.

How to Use Blommz-3b-guardrail

The following example utilizes the API Pipeline of the Transformers library.

from transformers import pipeline

guardrail = pipeline("text-classification", "cmarkea/bloomz-3b-guardrail")

list_text = [...]
result = guardrail(
    list_text,
    return_all_scores=True, # Crucial for assessing all modalities of toxicity!
    function_to_apply='sigmoid' # To ensure obtaining a score between 0 and 1!
)

Citation

@online{DeBloomzGuard,
  AUTHOR = {Cyrile Delestre},
  ORGANIZATION = {Cr{\'e}dit Mutuel Ark{\'e}a},
  URL = {https://huggingface.co/cmarkea/bloomz-3b-guardrail},
  YEAR = {2023},
  KEYWORDS = {NLP ; Transformers ; LLM ; Bloomz},
}