| --- |
| license: apache-2.0 |
| base_model: |
| - microsoft/deberta-v3-large |
| language: en |
| tags: |
| - text-classification |
| - product-detection |
| - hazard-detection |
| datasets: |
| - your-dataset-name |
| library_name: transformers |
| pipeline_tag: text-classification |
| --- |
| 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 large) |
| 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] |
