Create README.md

README.md

@@ -1,3 +1,88 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+base_model:
+- microsoft/deberta-v3-large
+---
+Multi-Task Product and Hazard Classifier
+This model performs multi-task classification to predict both product categories and hazard categories from text descriptions. It's based on DeBERTa-v3 architecture and trained to identify product types and potential hazards simultaneously.
+Model Description
+
+Model Type: Multi-task classification (DeBERTa-v3 base)
+Languages: English
+Pipeline Tag: text-classification
+Max Sequence Length: 1024 tokens
+
+Usage
+pythonCopyfrom transformers import AutoTokenizer, AutoModel
+import torch
+from torch.nn import functional as F
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("your-username/model-name")
+model = AutoModel.from_pretrained("your-username/model-name")
+
+# Prepare your text
+text = "Your product description here"
+
+# Tokenize and prepare input
+inputs = tokenizer(
+    text,
+    padding=True,
+    truncation=True,
+    max_length=1024,
+    return_tensors="pt",
+    return_token_type_ids=False
+)
+
+# Run inference
+with torch.no_grad():
+    outputs = model(**inputs)
+    product_logits = outputs['product_logits']
+    hazard_logits = outputs['hazard_logits']
+    
+    product_probs = F.softmax(product_logits, dim=-1)
+    hazard_probs = F.softmax(hazard_logits, dim=-1)
+
+# Get predictions
+product_predictions = product_probs.cpu().numpy()
+hazard_predictions = hazard_probs.cpu().numpy()
+Prediction Labels
+Product Categories
+pythonCopyproduct_labels = {
+    '0': 'label_0',
+    '1': 'label_1',
+    # Add your product category labels here
+}
+Hazard Categories
+pythonCopyhazard_labels = {
+    '0': 'label_0',
+    '1': 'label_1',
+    # Add your hazard category labels here
+}
+Model Limitations
+
+The model is designed for English text only
+Maximum input length is 1024 tokens
+Performance may vary for texts significantly different from the training data
+
+Training Data
+The model was trained on a dataset containing product descriptions with their corresponding product categories and hazard classifications. The training data includes various product types and potential hazards commonly found in consumer products.
+Evaluation Results
+[Add your model's evaluation metrics here]
+Intended Uses & Limitations
+Intended Uses:
+
+Product categorization
+Hazard identification in product descriptions
+Safety analysis of product text
+
+Limitations:
+
+Should not be used as the sole decision maker for safety-critical applications
+Requires human verification for important safety decisions
+May not recognize new or unusual product types/hazards
+
+Citation
+[Add citation information if applicable]
+Contact
+[Your contact information or where to report issues]