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