Instructions to use Sarthak279/Intent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sarthak279/Intent with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sarthak279/Intent")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sarthak279/Intent") model = AutoModelForSequenceClassification.from_pretrained("Sarthak279/Intent", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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# INTENT
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It achieves the following results on the evaluation set:
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- Train Loss: 0.0084
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- Train Accuracy: 0.9987
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# INTENT
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This is intent classification for customer order service,
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Features such as placing, Tracking and managment of orders,
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Handles payment issues such as making and refund of payment
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Options for delivery , address for shipping and also account management like password change, update account and delete account
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Options for contacting human agent
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You can also sends complaints here
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model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.0084
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- Train Accuracy: 0.9987
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