Text Classification
Transformers
Safetensors
English
deberta-v2
product-detection
hazard-detection
text-embeddings-inference
Instructions to use Quintu/deberta-multitask-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Quintu/deberta-multitask-v0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Quintu/deberta-multitask-v0")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Quintu/deberta-multitask-v0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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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
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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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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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