Mezzo Content Guard is a series of RoBERTa-based, English-only Content Moderation Models trained on approximately 14M tokens (360k+ rows) of labelled examples.

Mezzo Content Guard comes in 3 different sizes, based on RoBERTa Large, Base, and DistilRoBERTa Base

Large (355M Params)
Base (125M Params)
Small (82.8M Params)

Try out the demo at the Mezzo Content Guard Demo Space

Benchmarks

All benchmarks were done with a threshold of 0.5, though the threshold can be increased or decreased to trade between precision and recall

Sexual

Model	Precision	Recall	F1	ROC-AUC
Mezzo Content Guard Large	0.8396	0.8370	0.8383	0.9917
Mezzo Content Guard Base	0.8190	0.8227	0.8209	0.9895
Mezzo Content Guard Small	0.8376	0.7740	0.8045	0.9865
KoalaAI/Text-Moderation	0.1503	0.8423	0.2551	0.8770
ifmain/ModerationBERT-En-02	0.8500	0.3591	0.5049	0.9373

Violence

Model	Precision	Recall	F1	ROC-AUC
Mezzo Content Guard Large	0.7050	0.7827	0.7418	0.9921
Mezzo Content Guard Base	0.7330	0.7460	0.7394	0.9924
Mezzo Content Guard Small	0.6772	0.7269	0.7011	0.9883
KoalaAI/Text-Moderation	0.0136	1.0000	0.0269	0.8737
ifmain/ModerationBERT-En-02	0.5414	0.3554	0.4291	0.9461

Self-Harm

Model	Precision	Recall	F1	ROC-AUC
Mezzo Content Guard Large	0.8558	0.8711	0.8634	0.9888
Mezzo Content Guard Base	0.8524	0.8749	0.8635	0.9868
Mezzo Content Guard Small	0.8595	0.8401	0.8497	0.9853
KoalaAI/Text-Moderation	0.0923	0.8946	0.1673	0.9178
ifmain/ModerationBERT-En-02	0.9174	0.4807	0.6309	0.9471

Hate Speech

Model	Precision	Recall	F1	ROC-AUC
Mezzo Content Guard Large	0.8268	0.8229	0.8248	0.9865
Mezzo Content Guard Base	0.7991	0.8398	0.8190	0.9855
Mezzo Content Guard Small	0.8043	0.8055	0.8049	0.9829
KoalaAI/Text-Moderation	0.1000	0.9967	0.1817	0.9172
ifmain/ModerationBERT-En-02	0.9111	0.3436	0.4990	0.9506

Toxic

Model	Precision	Recall	F1	ROC-AUC
Mezzo Content Guard Large	0.7647	0.7459	0.7552	0.9778
Mezzo Content Guard Base	0.7456	0.7498	0.7477	0.9760
Mezzo Content Guard Small	0.7394	0.7162	0.7276	0.9720
KoalaAI/Text-Moderation	0.4884	0.6878	0.5712	0.9162
ifmain/ModerationBERT-En-02	0.4781	0.6406	0.5475	0.9128

Macro Averages

Model	Precision	Recall	F1	ROC-AUC
Mezzo Content Guard Large	0.7984	0.8119	0.8047	0.9874
Mezzo Content Guard Base	0.7898	0.8066	0.7981	0.9860
Mezzo Content Guard Small	0.7836	0.7725	0.7776	0.9830
KoalaAI/Text-Moderation	0.1689	0.8843	0.2404	0.9004
ifmain/ModerationBERT-En-02	0.7396	0.4359	0.5223	0.9388

Quickstart

Introducing our new custom mezzo-guard library that supports the Mezzo Prompt Guard and Mezzo Content Guard models. It offers automatic chunking, organized policies, and redactions.

Installation:

pip install mezzo-guard

from mezzoguard import CONTENTGUARD
from mezzoguard.content_guard import ContentPolicy, Category, Guard

model = Guard(CONTENTGUARD.MEZZO_CONTENT_GUARD_BASE)
content_policy = ContentPolicy().add_threshold(Category.SEXUAL, 0.5)

sexual_query = "I want to fuck you"
benign_query = "I want to have a nice day"
violent_query = "I want to kill you"

result_1 = model.scan(text=sexual_query)
print(content_policy.evaluate(result_1))
# True

result_2 = model.scan(text=benign_query)
print(content_policy.evaluate(result_2))
# False

result_3 = model.scan(text=violent_query)
print(content_policy.evaluate(result_3))
# False

With transformers

from transformers import pipeline

model = pipeline("text-classification", model="RyanStudio/Mezzo-Content-Guard-Base")


safe_prompt = "I love mezzo content guard!!!"
print(model(safe_prompt))

hate_speech_prompt = "I hate faggots"
print(model(hate_speech_prompt))

self_harm_prompt = "I want to kill myself"
print(model(self_harm_prompt))

sexual_prompt = "I want to fuck someone"
print(model(sexual_prompt))

toxic_prompt = "You are a cunt"
print(model(toxic_prompt))

violence_prompt = "I want to kill someone"
print(model(violence_prompt))

violence_hate_speech_toxic = "I want to kill you because you're a gay faggot"
print(model(violence_hate_speech_toxic, top_k=None))

Training:

The training data was sourced from various open-sourced datasets, as well as synthetically generated from LLMs such as Deepseek v4 Pro, Claude Sonnet 4.6, and Kimi K2.6.

Due to inconsistent labelling and definitions across various datasets, the data was re-laballed using Qwen3Guard-4B and Qwen3.5-4B to fit the specific categorical definitions.

The following table shows the data distribution:

Label	Positives	% of Data
sexual	18,233	4.95%
violence	5,440	1.48%
self-harm	7,826	2.13%
hate-speech	31,597	8.59%
toxic	33,088	8.99%

Mezzo Content Guard Large was the first model trained, and then further distilled into the Base and Small models. All models were trained with a max seq length of 256, which filtered out less than 1% of the data in the dataset

In initial experiments, RoBERTa-base was only able to hit a 71% macro f1 score, however with distillation, it is able to punch above its weight and hit a 79% macro f1 score.

While a "Divisive" Category was added in the Preview Model, targeting political and religious speech, it was deemed unnecessary and harmed the model's overall performance

Limitations

Re-labelling: Due to the training data being relabelled by Qwen3Guard and Qwen3.5, any inaccuracies from when these models were trained may be passed on to the model
Context Length: Although a context length of 256 is more than enough for most applications, the model may suffer above it. Due to limitations of RoBERTa, the model can only scan texts up to 512 tokens in length, and chunking is required in lengths above it
Edge Cases: A large majority of the open sourced datasets used were often dated, and may not take into account modern day slang words or more subtle bypasses, we recommend finetuning the model on your own usecase
English Only: The RoBERTa models are primarily english-based models, and will suffer in multilingual contexts

Future Iterations

This model, while suitable for most casual applications, it can still be significantly improved.

Future Content Guard models may employ

utilization of newer BERT-based models such as ModernBERT or Ettin-Encoder models to support larger contexts and improve general performance
improvements to the base dataset in order to account for slang and edge cases, reducing False Positives and Negatives

RyanStudio/Mezzo-Content-Guard-Base

Mezzo Content Guard Large

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