Update README.md

README.md

@@ -1,82 +1,84 @@
----
-language: en
-library_name: pytorch
-license: mit
-pipeline_tag: text-classification
-tags:
-  - pytorch
-  - multitask
-  - ai-detection
----
-
-# 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
-
-```python
-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
-
-```python
-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`.
+---
+language: en
+library_name: pytorch
+license: mit
+pipeline_tag: text-classification
+tags:
+  - pytorch
+  - multitask
+  - ai-detection
+---
+
+# SuaveAI Detection Multitask Model V1
+
+This repository contains a custom PyTorch multitask model checkpoint and auxiliary files.
+
+The notebook used to train this model is here: https://www.kaggle.com/code/julienserbanescu/suaveai
+
+## 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
+
+```python
+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
+
+```python
+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`.