Transformers
Safetensors
English
t5
text2text-generation
department-routing
urgency-detection
intent-detection
multi-label
customer-support
business
complaints
text-generation-inference
Instructions to use Ataur77/ecommerce-customer-support with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ataur77/ecommerce-customer-support with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Ataur77/ecommerce-customer-support") model = AutoModelForSeq2SeqLM.from_pretrained("Ataur77/ecommerce-customer-support") - Notebooks
- Google Colab
- Kaggle
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README.md
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The model achieved:
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**Accuracy:** 92% for intent classification
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**Precision:** 90% for product category classification
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**Recall:** 88% for urgency level classification
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**F1-Score:** 89% overall across all tasks
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#### Summary
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The model shows strong performance in classifying customer queries across multiple categories and levels of urgency. However, there is room for improvement in handling edge cases or complex queries not covered in the training data.
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The model achieved:
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- **Accuracy:** 92% for intent classification
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- **Precision:** 90% for product category classification
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- **Recall:** 88% for urgency level classification
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- **F1-Score:** 89% overall across all tasks
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#### Summary
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The model shows strong performance in classifying customer queries across multiple categories and levels of urgency. However, there is room for improvement in handling edge cases or complex queries not covered in the training data.
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