chidamnat2002
/

content-multilabel-iab-classifier

@@ -1,199 +1,114 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
+base_model:
+- microsoft/MiniLM-L12-H384-uncased
+language:
+- en
 library_name: transformers
+license: apache-2.0
 ---
+# Fine-tuned LoRA Classifier on MiniLM for IAB Multi-Label Classification
+This is a fine-tuned LoRA (Low-Rank Adaptation) classifier based on MiniLM (microsoft/MiniLM-L12-H384-uncased), designed for multi-label content classification using the IAB content taxonomy. The model can assign one or more categories to input text — making it suitable for tasks such as content classification.
+🔍 Model Details
+Model Description
+This model is based on microsoft/MiniLM-L12-H384-uncased, a compact and efficient transformer model optimized for fast inference and low memory footprint. It has been fine-tuned using LoRA for multi-label classification over 20 IAB categories plus an "inconclusive" fallback class.
+The model predicts multiple applicable content labels from:
+    inconclusive
+    animals
+    arts
+    autos
+    business
+    career
+    education
+    fashion
+    finance
+    food
+    government
+    health
+    hobbies
+    home
+    news
+    realestate
+    society
+    sports
+    tech
+    travel
+Key Configuration:
+    Base Model: microsoft/MiniLM-L12-H384-uncased
+    Task: Multi-label content classification
+    Label Count: 21 (multi-hot vector)
+    Language: English
+    Fine-tuning Method: PEFT with LoRA
+    LoRA Config:
+        r=16
+        lora_alpha=16
+        lora_dropout=0.1
+        target_modules=["query", "key"]
+    Developed by: Mozilla
+    License: Apache-2.0
+📦 Model Sources:
+    Demo (optional): [Hugging Face Space](https://huggingface.co/spaces/chidamnat2002/iab_content_classifier)
+📥 Usage:
+```
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+model = AutoModelForSequenceClassification.from_pretrained("Mozilla/content-multilabel-iab-classifier")
+tokenizer = AutoTokenizer.from_pretrained("Mozilla/content-multilabel-iab-classifier")
+label_list = [
+    'inconclusive',
+    'animals',
+    'arts',
+    'autos',
+    'business',
+    'career',
+    'education',
+    'fashion',
+    'finance',
+    'food',
+    'government',
+    'health',
+    'hobbies',
+    'home',
+    'news',
+    'realestate',
+    'society',
+    'sports',
+    'tech',
+    'travel'
+]
+label2id = {label: idx for idx, label in enumerate(label_list)}
+id2label = {idx: label for label, idx in label2id.items()}
+text = "Discover the latest trends in AI and wearable technology."
+with torch.no_grad():
+    inputs = tokenizer(text, return_tensors="pt", truncation=True)
+    outputs = model(**inputs)
+    probs = torch.sigmoid(outputs.logits).squeeze().cpu().numpy()
+    predicted_labels = [(id2label[i], round(p, 3)) for i, p in enumerate(probs) if p >= 0.5]
+```
+📖 Citation
+If you use this model, please cite it as:
+```
+@misc{mozilla_iab_multilabel_lora,
+  title       = {Fine-tuned LoRA Classifier on MiniLM for IAB Multi-Label Classification},
+  author      = {Mozilla},
+  year        = {2025},
+  url         = {https://huggingface.co/mozilla/content-multilabel-iab-classifier},
+  license     = {Apache-2.0}
+}
+```