Text Classification
Transformers
Safetensors
English
distilbert
intent detection
distilBert
E-commerce
text-embeddings-inference
Instructions to use monish-sd-7/E-Commerce-Customer-Intent-Detection-Model-Finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use monish-sd-7/E-Commerce-Customer-Intent-Detection-Model-Finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="monish-sd-7/E-Commerce-Customer-Intent-Detection-Model-Finetuned")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("monish-sd-7/E-Commerce-Customer-Intent-Detection-Model-Finetuned") model = AutoModelForSequenceClassification.from_pretrained("monish-sd-7/E-Commerce-Customer-Intent-Detection-Model-Finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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base_model:
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- distilbert/distilbert-base-uncased
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pipeline_tag: text-classification
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---
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base_model:
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- distilbert/distilbert-base-uncased
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pipeline_tag: text-classification
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metrics:
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- accuracy
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library_name: transformers
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tags:
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- intent detection
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- distilBert
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- E-commerce
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