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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a0e66b1caf91648511f2da85a95b42515fd50e57a1d605bf1a48b5413a208e12
- Size of remote file:
- 5.2 kB
- SHA256:
- 14d876f7e04e3354083b14ad6ae065e1169b9da00bc2636d462ac29e970e2847
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