A lightweight Transformer-based spam classifier designed for
Discord, live chat, streaming chats, gaming communities, and
other high-volume conversational environments.
Hermes is designed as a Layer-1 spam filtering model. Its purpose
is to process large volumes of incoming messages quickly and remove
obvious unwanted traffic before more expensive moderation systems
analyze the remaining messages.
It is not intended to be a universal spam, abuse, phishing, or
content-moderation model.
Model Overview
Model ID:hidan616/hermes_spam_filter
Task: Binary sequence classification
Classes:
0 → HAM
1 → SPAM
Architecture: Lightweight Transformer Encoder
Parameters:1,742,465
Model size: Approximately 7 MB
Language: English
Tokenizer: Custom BPE Tokenizer
Primary environments:
Discord
Live-stream chat
Gaming communities
Community servers
Real-time messaging
Chat applications
High-volume moderation pipelines
Architecture
Hermes uses a compact Transformer encoder architecture optimized for
low-latency binary spam classification.
Component
Configuration
Embedding dimension (d_model)
128
Attention heads
4
Encoder layers
2
Maximum sequence length
512
Tokenizer
Custom BPE Tokenizer
Parameters
1,742,465
Classification task
Binary
Architecture characteristics
Embedding dimension: 128
Attention heads: 4
Encoder layers: 2
Maximum sequence length: 512 tokens
Custom BPE tokenizer
Binary HAM/SPAM classification
The architecture deliberately uses a small embedding dimension and
only two Transformer encoder layers to keep the model computationally
compact.
Hermes does not attempt to model language at the scale of large
general-purpose Transformer models. It is specialized for its target
task:
Fast spam and unwanted-message filtering.
How to use
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
MODEL_ID = "hidan616/hermes_spam_filter"
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForSequenceClassification.from_pretrained(
MODEL_ID,
trust_remote_code=True
)
model.eval()
text = "Claim your exclusive special reserved reward by confirming."
cleaned_text = model.normalize_text(text)
inputs = tokenizer(cleaned_text, return_tensors="pt")
logits = model(**inputs).logits
score = torch.sigmoid(logits).squeeze().item()
label = "SPAM" if score >= 0.5 else "HAM"
print(f"Label: {label}")
print(f"Score: {score:.4f}")
Why Hermes Exists
Large Transformer models can provide strong language understanding,
but running a large model against every message can be unnecessarily
expensive in high-volume messaging environments.
A Discord server, live-streaming platform, gaming community, or chat
application may process thousands of messages continuously.
Most of those messages do not require expensive analysis.
Hermes is designed to handle the first filtering stage:
