DaJulster/SuaveAI-Dectection-Multitask-Model-V1

SuaveAI Detection Multitask Model V1

This repository contains a custom PyTorch multitask model checkpoint and auxiliary files.

Files

• multitask_model.pth: model checkpoint weights
• label_encoder.pkl: label encoder used to map predictions to labels
• tok.txt: tokenizer/vocabulary artifact used during preprocessing

Important

This is a custom PyTorch checkpoint and is not a native Transformers AutoModel package. This repo now includes Hugging Face custom-code files so it can be loaded from Hub with trust_remote_code=True.

Load from Hugging Face Hub

import torch
from transformers import AutoModel, AutoTokenizer

repo_id = "DaJulster/SuaveAI-Dectection-Multitask-Model-V1"

tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModel.from_pretrained(repo_id, trust_remote_code=True)
model.eval()

text = "This is a sample input"
inputs = tokenizer(text, return_tensors="pt", truncation=True)
with torch.no_grad():
  outputs = model(**inputs)

binary_logits = outputs.logits_binary
multiclass_logits = outputs.logits_multiclass

Binary prediction uses logits_binary, and AI-model classification uses logits_multiclass.

Quick start

import torch
import pickle

# 1) Recreate your model class exactly as in training
# from model_def import MultiTaskModel
# model = MultiTaskModel(...)

model = ...  # instantiate your model architecture
state = torch.load("multitask_model.pth", map_location="cpu")
model.load_state_dict(state)
model.eval()

with open("label_encoder.pkl", "rb") as f:
    label_encoder = pickle.load(f)

with open("tok.txt", "r", encoding="utf-8") as f:
    tokenizer_artifact = f.read()

# Run your preprocessing + inference pipeline here

Intended use

• Multitask AI detection inference in your custom pipeline.

Limitations

• Requires matching model definition and preprocessing pipeline.
• Not plug-and-play with transformers.AutoModel.from_pretrained.