Text Classification
Transformers
Safetensors
Portuguese
bert
moderation
portuguese
content-moderation
spam-detection
toxicity-detection
lightweight
text-embeddings-inference
Instructions to use Comunidade-Synapse-BR/Synapse-Guard-v1.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Comunidade-Synapse-BR/Synapse-Guard-v1.2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Comunidade-Synapse-BR/Synapse-Guard-v1.2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Comunidade-Synapse-BR/Synapse-Guard-v1.2") model = AutoModelForSequenceClassification.from_pretrained("Comunidade-Synapse-BR/Synapse-Guard-v1.2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Synapse-Guard-v1.2
Synapse-Guard-v1.2 is a lightweight Portuguese text classification model for content moderation.
This model does not generate harmful or adult content. It is a classifier designed to identify and categorize text.
Labels
- neutro – Normal and safe conversations.
- spam – Advertisements, scams, phishing and promotional messages.
- toxico – Offensive, abusive or insulting language.
- adulto – Adult or sexually explicit content.
Intended Uses
- AI assistants
- Chatbots
- Community moderation
- Forums
- Social networks
- Content filtering
- Spam detection
- Safety pipelines
Limitations
- Predictions may not always be correct.
- Performance depends on the domain.
- Should be used alongside other moderation systems.
Example
from transformers import pipeline
classifier = pipeline(
"text-classification",
model="Comunidade-Synapse-BR/Synapse-Guard-v1.2"
)
print(classifier("Você é um idiota."))
License
Apache-2.0
- Downloads last month
- 69
Model tree for Comunidade-Synapse-BR/Synapse-Guard-v1.2
Base model
Comunidade-Synapse-BR/Synapse-Guard-v1 Finetuned
Comunidade-Synapse-BR/Synapse-Guard-v1.1